LexEmbed-Contracts
2
—
by
yasserrmd
Embedding Model
OTHER
New
0 downloads
Early-stage
Edge AI:
Mobile
Laptop
Server
Unknown
Mobile
Laptop
Server
Quick Summary
AI model with specialized capabilities.
Code Examples
Usagebash
pip install -U sentence-transformersUsagebash
pip install -U sentence-transformersUsagebash
pip install -U sentence-transformersUsagebash
pip install -U sentence-transformersUsagebash
pip install -U sentence-transformersUsagebash
pip install -U sentence-transformersUsagebash
pip install -U sentence-transformersUsagebash
pip install -U sentence-transformersUsagebash
pip install -U sentence-transformersUsagebash
pip install -U sentence-transformersUsagebash
pip install -U sentence-transformersUsagebash
pip install -U sentence-transformersUsagebash
pip install -U sentence-transformersUsagebash
pip install -U sentence-transformersUsagebash
pip install -U sentence-transformersUsagebash
pip install -U sentence-transformersUsagebash
pip install -U sentence-transformersUsagebash
pip install -U sentence-transformersUsagebash
pip install -U sentence-transformersUsagebash
pip install -U sentence-transformersUsagebash
pip install -U sentence-transformersUsagebash
pip install -U sentence-transformersUsagebash
pip install -U sentence-transformersUsagebash
pip install -U sentence-transformersUsagebash
pip install -U sentence-transformersUsagebash
pip install -U sentence-transformersUsagebash
pip install -U sentence-transformersUsagebash
pip install -U sentence-transformersUsagebash
pip install -U sentence-transformersUsagebash
pip install -U sentence-transformersUsagebash
pip install -U sentence-transformersUsagebash
pip install -U sentence-transformersUsagebash
pip install -U sentence-transformersUsagebash
pip install -U sentence-transformersUsagebash
pip install -U sentence-transformersUsagebash
pip install -U sentence-transformersUsagebash
pip install -U sentence-transformersUsagebash
pip install -U sentence-transformersUsagebash
pip install -U sentence-transformersUsagebash
pip install -U sentence-transformersUsagebash
pip install -U sentence-transformersUsagebash
pip install -U sentence-transformersUsagebash
pip install -U sentence-transformersUsagebash
pip install -U sentence-transformersUsagebash
pip install -U sentence-transformersUsagebash
pip install -U sentence-transformersUsagebash
pip install -U sentence-transformersUsagebash
pip install -U sentence-transformersUsagebash
pip install -U sentence-transformersUsagebash
pip install -U sentence-transformersUsagebash
pip install -U sentence-transformersUsagebash
pip install -U sentence-transformersUsagebash
pip install -U sentence-transformersUsagebash
pip install -U sentence-transformersUsagebash
pip install -U sentence-transformersUsagebash
pip install -U sentence-transformersUsagebash
pip install -U sentence-transformersUsagebash
pip install -U sentence-transformersUsagebash
pip install -U sentence-transformersUsagebash
pip install -U sentence-transformersUsagebash
pip install -U sentence-transformersUsagebash
pip install -U sentence-transformersUsagebash
pip install -U sentence-transformersUsagebash
pip install -U sentence-transformersUsagebash
pip install -U sentence-transformersUsagebash
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pip install -U sentence-transformersUsagebash
pip install -U sentence-transformersUsagebash
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pip install -U sentence-transformersUsagebash
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pip install -U sentence-transformersUsagebash
pip install -U sentence-transformersUsagebash
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pip install -U sentence-transformersUsagebash
pip install -U sentence-transformersUsagebash
pip install -U sentence-transformersUsagebash
pip install -U sentence-transformersUsagebash
pip install -U sentence-transformersUsagebash
pip install -U sentence-transformersUsagebash
pip install -U sentence-transformersUsagebash
pip install -U sentence-transformersUsagebash
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pip install -U sentence-transformersUsagebash
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pip install -U sentence-transformersUsagebash
pip install -U sentence-transformersUsagebash
pip install -U sentence-transformersUsagebash
pip install -U sentence-transformersUsagebash
pip install -U sentence-transformersUsagebash
pip install -U sentence-transformersUsagebash
pip install -U sentence-transformersUsagebash
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pip install -U sentence-transformersUsagebash
pip install -U sentence-transformersUsagebash
pip install -U sentence-transformersUsagebash
pip install -U sentence-transformersUsagebash
pip install -U sentence-transformersUsagebash
pip install -U sentence-transformersUsagebash
pip install -U sentence-transformersUsagebash
pip install -U sentence-transformersUsagebash
pip install -U sentence-transformersUsagebash
pip install -U sentence-transformersUsagebash
pip install -U sentence-transformersUsagebash
pip install -U sentence-transformersUsagebash
pip install -U sentence-transformersUsagebash
pip install -U sentence-transformersUsagebash
pip install -U sentence-transformersUsagebash
pip install -U sentence-transformersUsagebash
pip install -U sentence-transformersUsagebash
pip install -U sentence-transformersUsagebash
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pip install -U sentence-transformersUsagebash
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pip install -U sentence-transformersUsagebash
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pip install -U sentence-transformersUsagebash
pip install -U sentence-transformersUsagebash
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pip install -U sentence-transformersUsagebash
pip install -U sentence-transformersUsagebash
pip install -U sentence-transformersUsagebash
pip install -U sentence-transformersUsagebash
pip install -U sentence-transformersUsagebash
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pip install -U sentence-transformersUsagebash
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pip install -U sentence-transformersUsagebash
pip install -U sentence-transformersUsagebash
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pip install -U sentence-transformersUsagebash
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pip install -U sentence-transformersUsagebash
pip install -U sentence-transformersUsagebash
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pip install -U sentence-transformersUsagebash
pip install -U sentence-transformersUsagebash
pip install -U sentence-transformersUsagebash
pip install -U sentence-transformersUsagebash
pip install -U sentence-transformersUsagebash
pip install -U sentence-transformersUsagebash
pip install -U sentence-transformersUsagebash
pip install -U sentence-transformersUsagebash
pip install -U sentence-transformersUsagebash
pip install -U sentence-transformersUsagebash
pip install -U sentence-transformersUsagebash
pip install -U sentence-transformersUsagebash
pip install -U sentence-transformersUsagebash
pip install -U sentence-transformersUsagebash
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pip install -U sentence-transformersUsagebash
pip install -U sentence-transformersUsagebash
pip install -U sentence-transformersUsagebash
pip install -U sentence-transformersUsagebash
pip install -U sentence-transformersUsagebash
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pip install -U sentence-transformersUsagebash
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pip install -U sentence-transformersUsagebash
pip install -U sentence-transformersUsagebash
pip install -U sentence-transformersUsagebash
pip install -U sentence-transformersUsagebash
pip install -U sentence-transformersUsagebash
pip install -U sentence-transformersUsagebash
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pip install -U sentence-transformersUsagebash
pip install -U sentence-transformersUsagebash
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pip install -U sentence-transformersUsagebash
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pip install -U sentence-transformersUsagebash
pip install -U sentence-transformersUsagebash
pip install -U sentence-transformersUsagebash
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pip install -U sentence-transformersUsagebash
pip install -U sentence-transformersUsagebash
pip install -U sentence-transformersUsagebash
pip install -U sentence-transformersUsagebash
pip install -U sentence-transformersUsagebash
pip install -U sentence-transformersUsagebash
pip install -U sentence-transformersUsagebash
pip install -U sentence-transformersUsagebash
pip install -U sentence-transformersUsagebash
pip install -U sentence-transformersUsagebash
pip install -U sentence-transformersUsagebash
pip install -U sentence-transformersUsagebash
pip install -U sentence-transformersUsagebash
pip install -U sentence-transformersUsagebash
pip install -U sentence-transformersUsagebash
pip install -U sentence-transformersUsagebash
pip install -U sentence-transformersUsagebash
pip install -U sentence-transformersUsagebash
pip install -U sentence-transformersUsagebash
pip install -U sentence-transformersUsagebash
pip install -U sentence-transformersUsagepython
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
'License Grant',
"In the event FCE notifies ExxonMobil that it has formally decided not to pursue Generation 2 Technology for Power Applications, then upon ExxonMobil's written request, FCE agrees to negotiate a grant to ExxonMobil and its Affiliates, under commercially reasonable terms to be determined in good faith, a worldwide, royalty-bearing (with the royalty to be negotiated), non-exclusive, sub-licensable right and license to practice FCE Background Information and FCE Background Patents for Generation 2 Technology in any application outside of Carbon Capture Applications and Hydrogen Applications.",
'Aucta should continue to receive 15% of Net Sales Royalty for as long as ETON is selling the Product(s) in the Territory, unless otherwise agreed to under this Agreement.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.7920, 0.3253],
# [0.7920, 1.0000, 0.4614],
# [0.3253, 0.4614, 1.0000]])Usagepython
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
'License Grant',
"In the event FCE notifies ExxonMobil that it has formally decided not to pursue Generation 2 Technology for Power Applications, then upon ExxonMobil's written request, FCE agrees to negotiate a grant to ExxonMobil and its Affiliates, under commercially reasonable terms to be determined in good faith, a worldwide, royalty-bearing (with the royalty to be negotiated), non-exclusive, sub-licensable right and license to practice FCE Background Information and FCE Background Patents for Generation 2 Technology in any application outside of Carbon Capture Applications and Hydrogen Applications.",
'Aucta should continue to receive 15% of Net Sales Royalty for as long as ETON is selling the Product(s) in the Territory, unless otherwise agreed to under this Agreement.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.7920, 0.3253],
# [0.7920, 1.0000, 0.4614],
# [0.3253, 0.4614, 1.0000]])Usagepython
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
'License Grant',
"In the event FCE notifies ExxonMobil that it has formally decided not to pursue Generation 2 Technology for Power Applications, then upon ExxonMobil's written request, FCE agrees to negotiate a grant to ExxonMobil and its Affiliates, under commercially reasonable terms to be determined in good faith, a worldwide, royalty-bearing (with the royalty to be negotiated), non-exclusive, sub-licensable right and license to practice FCE Background Information and FCE Background Patents for Generation 2 Technology in any application outside of Carbon Capture Applications and Hydrogen Applications.",
'Aucta should continue to receive 15% of Net Sales Royalty for as long as ETON is selling the Product(s) in the Territory, unless otherwise agreed to under this Agreement.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.7920, 0.3253],
# [0.7920, 1.0000, 0.4614],
# [0.3253, 0.4614, 1.0000]])Usagepython
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
'License Grant',
"In the event FCE notifies ExxonMobil that it has formally decided not to pursue Generation 2 Technology for Power Applications, then upon ExxonMobil's written request, FCE agrees to negotiate a grant to ExxonMobil and its Affiliates, under commercially reasonable terms to be determined in good faith, a worldwide, royalty-bearing (with the royalty to be negotiated), non-exclusive, sub-licensable right and license to practice FCE Background Information and FCE Background Patents for Generation 2 Technology in any application outside of Carbon Capture Applications and Hydrogen Applications.",
'Aucta should continue to receive 15% of Net Sales Royalty for as long as ETON is selling the Product(s) in the Territory, unless otherwise agreed to under this Agreement.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.7920, 0.3253],
# [0.7920, 1.0000, 0.4614],
# [0.3253, 0.4614, 1.0000]])Usagepython
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
'License Grant',
"In the event FCE notifies ExxonMobil that it has formally decided not to pursue Generation 2 Technology for Power Applications, then upon ExxonMobil's written request, FCE agrees to negotiate a grant to ExxonMobil and its Affiliates, under commercially reasonable terms to be determined in good faith, a worldwide, royalty-bearing (with the royalty to be negotiated), non-exclusive, sub-licensable right and license to practice FCE Background Information and FCE Background Patents for Generation 2 Technology in any application outside of Carbon Capture Applications and Hydrogen Applications.",
'Aucta should continue to receive 15% of Net Sales Royalty for as long as ETON is selling the Product(s) in the Territory, unless otherwise agreed to under this Agreement.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.7920, 0.3253],
# [0.7920, 1.0000, 0.4614],
# [0.3253, 0.4614, 1.0000]])Usagepython
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
'License Grant',
"In the event FCE notifies ExxonMobil that it has formally decided not to pursue Generation 2 Technology for Power Applications, then upon ExxonMobil's written request, FCE agrees to negotiate a grant to ExxonMobil and its Affiliates, under commercially reasonable terms to be determined in good faith, a worldwide, royalty-bearing (with the royalty to be negotiated), non-exclusive, sub-licensable right and license to practice FCE Background Information and FCE Background Patents for Generation 2 Technology in any application outside of Carbon Capture Applications and Hydrogen Applications.",
'Aucta should continue to receive 15% of Net Sales Royalty for as long as ETON is selling the Product(s) in the Territory, unless otherwise agreed to under this Agreement.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.7920, 0.3253],
# [0.7920, 1.0000, 0.4614],
# [0.3253, 0.4614, 1.0000]])Usagepython
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
'License Grant',
"In the event FCE notifies ExxonMobil that it has formally decided not to pursue Generation 2 Technology for Power Applications, then upon ExxonMobil's written request, FCE agrees to negotiate a grant to ExxonMobil and its Affiliates, under commercially reasonable terms to be determined in good faith, a worldwide, royalty-bearing (with the royalty to be negotiated), non-exclusive, sub-licensable right and license to practice FCE Background Information and FCE Background Patents for Generation 2 Technology in any application outside of Carbon Capture Applications and Hydrogen Applications.",
'Aucta should continue to receive 15% of Net Sales Royalty for as long as ETON is selling the Product(s) in the Territory, unless otherwise agreed to under this Agreement.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.7920, 0.3253],
# [0.7920, 1.0000, 0.4614],
# [0.3253, 0.4614, 1.0000]])Usagepython
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
'License Grant',
"In the event FCE notifies ExxonMobil that it has formally decided not to pursue Generation 2 Technology for Power Applications, then upon ExxonMobil's written request, FCE agrees to negotiate a grant to ExxonMobil and its Affiliates, under commercially reasonable terms to be determined in good faith, a worldwide, royalty-bearing (with the royalty to be negotiated), non-exclusive, sub-licensable right and license to practice FCE Background Information and FCE Background Patents for Generation 2 Technology in any application outside of Carbon Capture Applications and Hydrogen Applications.",
'Aucta should continue to receive 15% of Net Sales Royalty for as long as ETON is selling the Product(s) in the Territory, unless otherwise agreed to under this Agreement.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.7920, 0.3253],
# [0.7920, 1.0000, 0.4614],
# [0.3253, 0.4614, 1.0000]])Usagepython
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
'License Grant',
"In the event FCE notifies ExxonMobil that it has formally decided not to pursue Generation 2 Technology for Power Applications, then upon ExxonMobil's written request, FCE agrees to negotiate a grant to ExxonMobil and its Affiliates, under commercially reasonable terms to be determined in good faith, a worldwide, royalty-bearing (with the royalty to be negotiated), non-exclusive, sub-licensable right and license to practice FCE Background Information and FCE Background Patents for Generation 2 Technology in any application outside of Carbon Capture Applications and Hydrogen Applications.",
'Aucta should continue to receive 15% of Net Sales Royalty for as long as ETON is selling the Product(s) in the Territory, unless otherwise agreed to under this Agreement.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.7920, 0.3253],
# [0.7920, 1.0000, 0.4614],
# [0.3253, 0.4614, 1.0000]])Usagepython
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
'License Grant',
"In the event FCE notifies ExxonMobil that it has formally decided not to pursue Generation 2 Technology for Power Applications, then upon ExxonMobil's written request, FCE agrees to negotiate a grant to ExxonMobil and its Affiliates, under commercially reasonable terms to be determined in good faith, a worldwide, royalty-bearing (with the royalty to be negotiated), non-exclusive, sub-licensable right and license to practice FCE Background Information and FCE Background Patents for Generation 2 Technology in any application outside of Carbon Capture Applications and Hydrogen Applications.",
'Aucta should continue to receive 15% of Net Sales Royalty for as long as ETON is selling the Product(s) in the Territory, unless otherwise agreed to under this Agreement.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.7920, 0.3253],
# [0.7920, 1.0000, 0.4614],
# [0.3253, 0.4614, 1.0000]])Usagepython
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
'License Grant',
"In the event FCE notifies ExxonMobil that it has formally decided not to pursue Generation 2 Technology for Power Applications, then upon ExxonMobil's written request, FCE agrees to negotiate a grant to ExxonMobil and its Affiliates, under commercially reasonable terms to be determined in good faith, a worldwide, royalty-bearing (with the royalty to be negotiated), non-exclusive, sub-licensable right and license to practice FCE Background Information and FCE Background Patents for Generation 2 Technology in any application outside of Carbon Capture Applications and Hydrogen Applications.",
'Aucta should continue to receive 15% of Net Sales Royalty for as long as ETON is selling the Product(s) in the Territory, unless otherwise agreed to under this Agreement.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.7920, 0.3253],
# [0.7920, 1.0000, 0.4614],
# [0.3253, 0.4614, 1.0000]])Usagepython
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
'License Grant',
"In the event FCE notifies ExxonMobil that it has formally decided not to pursue Generation 2 Technology for Power Applications, then upon ExxonMobil's written request, FCE agrees to negotiate a grant to ExxonMobil and its Affiliates, under commercially reasonable terms to be determined in good faith, a worldwide, royalty-bearing (with the royalty to be negotiated), non-exclusive, sub-licensable right and license to practice FCE Background Information and FCE Background Patents for Generation 2 Technology in any application outside of Carbon Capture Applications and Hydrogen Applications.",
'Aucta should continue to receive 15% of Net Sales Royalty for as long as ETON is selling the Product(s) in the Territory, unless otherwise agreed to under this Agreement.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.7920, 0.3253],
# [0.7920, 1.0000, 0.4614],
# [0.3253, 0.4614, 1.0000]])Usagepython
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
'License Grant',
"In the event FCE notifies ExxonMobil that it has formally decided not to pursue Generation 2 Technology for Power Applications, then upon ExxonMobil's written request, FCE agrees to negotiate a grant to ExxonMobil and its Affiliates, under commercially reasonable terms to be determined in good faith, a worldwide, royalty-bearing (with the royalty to be negotiated), non-exclusive, sub-licensable right and license to practice FCE Background Information and FCE Background Patents for Generation 2 Technology in any application outside of Carbon Capture Applications and Hydrogen Applications.",
'Aucta should continue to receive 15% of Net Sales Royalty for as long as ETON is selling the Product(s) in the Territory, unless otherwise agreed to under this Agreement.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.7920, 0.3253],
# [0.7920, 1.0000, 0.4614],
# [0.3253, 0.4614, 1.0000]])Usagepython
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
'License Grant',
"In the event FCE notifies ExxonMobil that it has formally decided not to pursue Generation 2 Technology for Power Applications, then upon ExxonMobil's written request, FCE agrees to negotiate a grant to ExxonMobil and its Affiliates, under commercially reasonable terms to be determined in good faith, a worldwide, royalty-bearing (with the royalty to be negotiated), non-exclusive, sub-licensable right and license to practice FCE Background Information and FCE Background Patents for Generation 2 Technology in any application outside of Carbon Capture Applications and Hydrogen Applications.",
'Aucta should continue to receive 15% of Net Sales Royalty for as long as ETON is selling the Product(s) in the Territory, unless otherwise agreed to under this Agreement.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.7920, 0.3253],
# [0.7920, 1.0000, 0.4614],
# [0.3253, 0.4614, 1.0000]])Usagepython
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
'License Grant',
"In the event FCE notifies ExxonMobil that it has formally decided not to pursue Generation 2 Technology for Power Applications, then upon ExxonMobil's written request, FCE agrees to negotiate a grant to ExxonMobil and its Affiliates, under commercially reasonable terms to be determined in good faith, a worldwide, royalty-bearing (with the royalty to be negotiated), non-exclusive, sub-licensable right and license to practice FCE Background Information and FCE Background Patents for Generation 2 Technology in any application outside of Carbon Capture Applications and Hydrogen Applications.",
'Aucta should continue to receive 15% of Net Sales Royalty for as long as ETON is selling the Product(s) in the Territory, unless otherwise agreed to under this Agreement.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.7920, 0.3253],
# [0.7920, 1.0000, 0.4614],
# [0.3253, 0.4614, 1.0000]])Usagepython
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
'License Grant',
"In the event FCE notifies ExxonMobil that it has formally decided not to pursue Generation 2 Technology for Power Applications, then upon ExxonMobil's written request, FCE agrees to negotiate a grant to ExxonMobil and its Affiliates, under commercially reasonable terms to be determined in good faith, a worldwide, royalty-bearing (with the royalty to be negotiated), non-exclusive, sub-licensable right and license to practice FCE Background Information and FCE Background Patents for Generation 2 Technology in any application outside of Carbon Capture Applications and Hydrogen Applications.",
'Aucta should continue to receive 15% of Net Sales Royalty for as long as ETON is selling the Product(s) in the Territory, unless otherwise agreed to under this Agreement.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.7920, 0.3253],
# [0.7920, 1.0000, 0.4614],
# [0.3253, 0.4614, 1.0000]])Usagepython
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
'License Grant',
"In the event FCE notifies ExxonMobil that it has formally decided not to pursue Generation 2 Technology for Power Applications, then upon ExxonMobil's written request, FCE agrees to negotiate a grant to ExxonMobil and its Affiliates, under commercially reasonable terms to be determined in good faith, a worldwide, royalty-bearing (with the royalty to be negotiated), non-exclusive, sub-licensable right and license to practice FCE Background Information and FCE Background Patents for Generation 2 Technology in any application outside of Carbon Capture Applications and Hydrogen Applications.",
'Aucta should continue to receive 15% of Net Sales Royalty for as long as ETON is selling the Product(s) in the Territory, unless otherwise agreed to under this Agreement.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.7920, 0.3253],
# [0.7920, 1.0000, 0.4614],
# [0.3253, 0.4614, 1.0000]])Usagepython
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
'License Grant',
"In the event FCE notifies ExxonMobil that it has formally decided not to pursue Generation 2 Technology for Power Applications, then upon ExxonMobil's written request, FCE agrees to negotiate a grant to ExxonMobil and its Affiliates, under commercially reasonable terms to be determined in good faith, a worldwide, royalty-bearing (with the royalty to be negotiated), non-exclusive, sub-licensable right and license to practice FCE Background Information and FCE Background Patents for Generation 2 Technology in any application outside of Carbon Capture Applications and Hydrogen Applications.",
'Aucta should continue to receive 15% of Net Sales Royalty for as long as ETON is selling the Product(s) in the Territory, unless otherwise agreed to under this Agreement.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.7920, 0.3253],
# [0.7920, 1.0000, 0.4614],
# [0.3253, 0.4614, 1.0000]])Usagepython
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
'License Grant',
"In the event FCE notifies ExxonMobil that it has formally decided not to pursue Generation 2 Technology for Power Applications, then upon ExxonMobil's written request, FCE agrees to negotiate a grant to ExxonMobil and its Affiliates, under commercially reasonable terms to be determined in good faith, a worldwide, royalty-bearing (with the royalty to be negotiated), non-exclusive, sub-licensable right and license to practice FCE Background Information and FCE Background Patents for Generation 2 Technology in any application outside of Carbon Capture Applications and Hydrogen Applications.",
'Aucta should continue to receive 15% of Net Sales Royalty for as long as ETON is selling the Product(s) in the Territory, unless otherwise agreed to under this Agreement.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.7920, 0.3253],
# [0.7920, 1.0000, 0.4614],
# [0.3253, 0.4614, 1.0000]])Usagepython
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
'License Grant',
"In the event FCE notifies ExxonMobil that it has formally decided not to pursue Generation 2 Technology for Power Applications, then upon ExxonMobil's written request, FCE agrees to negotiate a grant to ExxonMobil and its Affiliates, under commercially reasonable terms to be determined in good faith, a worldwide, royalty-bearing (with the royalty to be negotiated), non-exclusive, sub-licensable right and license to practice FCE Background Information and FCE Background Patents for Generation 2 Technology in any application outside of Carbon Capture Applications and Hydrogen Applications.",
'Aucta should continue to receive 15% of Net Sales Royalty for as long as ETON is selling the Product(s) in the Territory, unless otherwise agreed to under this Agreement.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.7920, 0.3253],
# [0.7920, 1.0000, 0.4614],
# [0.3253, 0.4614, 1.0000]])Usagepython
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
'License Grant',
"In the event FCE notifies ExxonMobil that it has formally decided not to pursue Generation 2 Technology for Power Applications, then upon ExxonMobil's written request, FCE agrees to negotiate a grant to ExxonMobil and its Affiliates, under commercially reasonable terms to be determined in good faith, a worldwide, royalty-bearing (with the royalty to be negotiated), non-exclusive, sub-licensable right and license to practice FCE Background Information and FCE Background Patents for Generation 2 Technology in any application outside of Carbon Capture Applications and Hydrogen Applications.",
'Aucta should continue to receive 15% of Net Sales Royalty for as long as ETON is selling the Product(s) in the Territory, unless otherwise agreed to under this Agreement.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.7920, 0.3253],
# [0.7920, 1.0000, 0.4614],
# [0.3253, 0.4614, 1.0000]])Usagepython
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
'License Grant',
"In the event FCE notifies ExxonMobil that it has formally decided not to pursue Generation 2 Technology for Power Applications, then upon ExxonMobil's written request, FCE agrees to negotiate a grant to ExxonMobil and its Affiliates, under commercially reasonable terms to be determined in good faith, a worldwide, royalty-bearing (with the royalty to be negotiated), non-exclusive, sub-licensable right and license to practice FCE Background Information and FCE Background Patents for Generation 2 Technology in any application outside of Carbon Capture Applications and Hydrogen Applications.",
'Aucta should continue to receive 15% of Net Sales Royalty for as long as ETON is selling the Product(s) in the Territory, unless otherwise agreed to under this Agreement.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.7920, 0.3253],
# [0.7920, 1.0000, 0.4614],
# [0.3253, 0.4614, 1.0000]])Usagepython
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
'License Grant',
"In the event FCE notifies ExxonMobil that it has formally decided not to pursue Generation 2 Technology for Power Applications, then upon ExxonMobil's written request, FCE agrees to negotiate a grant to ExxonMobil and its Affiliates, under commercially reasonable terms to be determined in good faith, a worldwide, royalty-bearing (with the royalty to be negotiated), non-exclusive, sub-licensable right and license to practice FCE Background Information and FCE Background Patents for Generation 2 Technology in any application outside of Carbon Capture Applications and Hydrogen Applications.",
'Aucta should continue to receive 15% of Net Sales Royalty for as long as ETON is selling the Product(s) in the Territory, unless otherwise agreed to under this Agreement.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.7920, 0.3253],
# [0.7920, 1.0000, 0.4614],
# [0.3253, 0.4614, 1.0000]])Usagepython
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
'License Grant',
"In the event FCE notifies ExxonMobil that it has formally decided not to pursue Generation 2 Technology for Power Applications, then upon ExxonMobil's written request, FCE agrees to negotiate a grant to ExxonMobil and its Affiliates, under commercially reasonable terms to be determined in good faith, a worldwide, royalty-bearing (with the royalty to be negotiated), non-exclusive, sub-licensable right and license to practice FCE Background Information and FCE Background Patents for Generation 2 Technology in any application outside of Carbon Capture Applications and Hydrogen Applications.",
'Aucta should continue to receive 15% of Net Sales Royalty for as long as ETON is selling the Product(s) in the Territory, unless otherwise agreed to under this Agreement.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.7920, 0.3253],
# [0.7920, 1.0000, 0.4614],
# [0.3253, 0.4614, 1.0000]])Usagepython
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
'License Grant',
"In the event FCE notifies ExxonMobil that it has formally decided not to pursue Generation 2 Technology for Power Applications, then upon ExxonMobil's written request, FCE agrees to negotiate a grant to ExxonMobil and its Affiliates, under commercially reasonable terms to be determined in good faith, a worldwide, royalty-bearing (with the royalty to be negotiated), non-exclusive, sub-licensable right and license to practice FCE Background Information and FCE Background Patents for Generation 2 Technology in any application outside of Carbon Capture Applications and Hydrogen Applications.",
'Aucta should continue to receive 15% of Net Sales Royalty for as long as ETON is selling the Product(s) in the Territory, unless otherwise agreed to under this Agreement.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.7920, 0.3253],
# [0.7920, 1.0000, 0.4614],
# [0.3253, 0.4614, 1.0000]])Usagepython
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
'License Grant',
"In the event FCE notifies ExxonMobil that it has formally decided not to pursue Generation 2 Technology for Power Applications, then upon ExxonMobil's written request, FCE agrees to negotiate a grant to ExxonMobil and its Affiliates, under commercially reasonable terms to be determined in good faith, a worldwide, royalty-bearing (with the royalty to be negotiated), non-exclusive, sub-licensable right and license to practice FCE Background Information and FCE Background Patents for Generation 2 Technology in any application outside of Carbon Capture Applications and Hydrogen Applications.",
'Aucta should continue to receive 15% of Net Sales Royalty for as long as ETON is selling the Product(s) in the Territory, unless otherwise agreed to under this Agreement.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.7920, 0.3253],
# [0.7920, 1.0000, 0.4614],
# [0.3253, 0.4614, 1.0000]])Usagepython
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
'License Grant',
"In the event FCE notifies ExxonMobil that it has formally decided not to pursue Generation 2 Technology for Power Applications, then upon ExxonMobil's written request, FCE agrees to negotiate a grant to ExxonMobil and its Affiliates, under commercially reasonable terms to be determined in good faith, a worldwide, royalty-bearing (with the royalty to be negotiated), non-exclusive, sub-licensable right and license to practice FCE Background Information and FCE Background Patents for Generation 2 Technology in any application outside of Carbon Capture Applications and Hydrogen Applications.",
'Aucta should continue to receive 15% of Net Sales Royalty for as long as ETON is selling the Product(s) in the Territory, unless otherwise agreed to under this Agreement.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.7920, 0.3253],
# [0.7920, 1.0000, 0.4614],
# [0.3253, 0.4614, 1.0000]])Usagepython
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
'License Grant',
"In the event FCE notifies ExxonMobil that it has formally decided not to pursue Generation 2 Technology for Power Applications, then upon ExxonMobil's written request, FCE agrees to negotiate a grant to ExxonMobil and its Affiliates, under commercially reasonable terms to be determined in good faith, a worldwide, royalty-bearing (with the royalty to be negotiated), non-exclusive, sub-licensable right and license to practice FCE Background Information and FCE Background Patents for Generation 2 Technology in any application outside of Carbon Capture Applications and Hydrogen Applications.",
'Aucta should continue to receive 15% of Net Sales Royalty for as long as ETON is selling the Product(s) in the Territory, unless otherwise agreed to under this Agreement.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.7920, 0.3253],
# [0.7920, 1.0000, 0.4614],
# [0.3253, 0.4614, 1.0000]])Usagepython
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
'License Grant',
"In the event FCE notifies ExxonMobil that it has formally decided not to pursue Generation 2 Technology for Power Applications, then upon ExxonMobil's written request, FCE agrees to negotiate a grant to ExxonMobil and its Affiliates, under commercially reasonable terms to be determined in good faith, a worldwide, royalty-bearing (with the royalty to be negotiated), non-exclusive, sub-licensable right and license to practice FCE Background Information and FCE Background Patents for Generation 2 Technology in any application outside of Carbon Capture Applications and Hydrogen Applications.",
'Aucta should continue to receive 15% of Net Sales Royalty for as long as ETON is selling the Product(s) in the Territory, unless otherwise agreed to under this Agreement.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.7920, 0.3253],
# [0.7920, 1.0000, 0.4614],
# [0.3253, 0.4614, 1.0000]])Usagepython
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
'License Grant',
"In the event FCE notifies ExxonMobil that it has formally decided not to pursue Generation 2 Technology for Power Applications, then upon ExxonMobil's written request, FCE agrees to negotiate a grant to ExxonMobil and its Affiliates, under commercially reasonable terms to be determined in good faith, a worldwide, royalty-bearing (with the royalty to be negotiated), non-exclusive, sub-licensable right and license to practice FCE Background Information and FCE Background Patents for Generation 2 Technology in any application outside of Carbon Capture Applications and Hydrogen Applications.",
'Aucta should continue to receive 15% of Net Sales Royalty for as long as ETON is selling the Product(s) in the Territory, unless otherwise agreed to under this Agreement.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.7920, 0.3253],
# [0.7920, 1.0000, 0.4614],
# [0.3253, 0.4614, 1.0000]])Usagepython
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
'License Grant',
"In the event FCE notifies ExxonMobil that it has formally decided not to pursue Generation 2 Technology for Power Applications, then upon ExxonMobil's written request, FCE agrees to negotiate a grant to ExxonMobil and its Affiliates, under commercially reasonable terms to be determined in good faith, a worldwide, royalty-bearing (with the royalty to be negotiated), non-exclusive, sub-licensable right and license to practice FCE Background Information and FCE Background Patents for Generation 2 Technology in any application outside of Carbon Capture Applications and Hydrogen Applications.",
'Aucta should continue to receive 15% of Net Sales Royalty for as long as ETON is selling the Product(s) in the Territory, unless otherwise agreed to under this Agreement.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.7920, 0.3253],
# [0.7920, 1.0000, 0.4614],
# [0.3253, 0.4614, 1.0000]])Usagepython
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
'License Grant',
"In the event FCE notifies ExxonMobil that it has formally decided not to pursue Generation 2 Technology for Power Applications, then upon ExxonMobil's written request, FCE agrees to negotiate a grant to ExxonMobil and its Affiliates, under commercially reasonable terms to be determined in good faith, a worldwide, royalty-bearing (with the royalty to be negotiated), non-exclusive, sub-licensable right and license to practice FCE Background Information and FCE Background Patents for Generation 2 Technology in any application outside of Carbon Capture Applications and Hydrogen Applications.",
'Aucta should continue to receive 15% of Net Sales Royalty for as long as ETON is selling the Product(s) in the Territory, unless otherwise agreed to under this Agreement.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.7920, 0.3253],
# [0.7920, 1.0000, 0.4614],
# [0.3253, 0.4614, 1.0000]])Usagepython
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
'License Grant',
"In the event FCE notifies ExxonMobil that it has formally decided not to pursue Generation 2 Technology for Power Applications, then upon ExxonMobil's written request, FCE agrees to negotiate a grant to ExxonMobil and its Affiliates, under commercially reasonable terms to be determined in good faith, a worldwide, royalty-bearing (with the royalty to be negotiated), non-exclusive, sub-licensable right and license to practice FCE Background Information and FCE Background Patents for Generation 2 Technology in any application outside of Carbon Capture Applications and Hydrogen Applications.",
'Aucta should continue to receive 15% of Net Sales Royalty for as long as ETON is selling the Product(s) in the Territory, unless otherwise agreed to under this Agreement.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.7920, 0.3253],
# [0.7920, 1.0000, 0.4614],
# [0.3253, 0.4614, 1.0000]])Usagepython
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
'License Grant',
"In the event FCE notifies ExxonMobil that it has formally decided not to pursue Generation 2 Technology for Power Applications, then upon ExxonMobil's written request, FCE agrees to negotiate a grant to ExxonMobil and its Affiliates, under commercially reasonable terms to be determined in good faith, a worldwide, royalty-bearing (with the royalty to be negotiated), non-exclusive, sub-licensable right and license to practice FCE Background Information and FCE Background Patents for Generation 2 Technology in any application outside of Carbon Capture Applications and Hydrogen Applications.",
'Aucta should continue to receive 15% of Net Sales Royalty for as long as ETON is selling the Product(s) in the Territory, unless otherwise agreed to under this Agreement.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.7920, 0.3253],
# [0.7920, 1.0000, 0.4614],
# [0.3253, 0.4614, 1.0000]])Usagepython
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
'License Grant',
"In the event FCE notifies ExxonMobil that it has formally decided not to pursue Generation 2 Technology for Power Applications, then upon ExxonMobil's written request, FCE agrees to negotiate a grant to ExxonMobil and its Affiliates, under commercially reasonable terms to be determined in good faith, a worldwide, royalty-bearing (with the royalty to be negotiated), non-exclusive, sub-licensable right and license to practice FCE Background Information and FCE Background Patents for Generation 2 Technology in any application outside of Carbon Capture Applications and Hydrogen Applications.",
'Aucta should continue to receive 15% of Net Sales Royalty for as long as ETON is selling the Product(s) in the Territory, unless otherwise agreed to under this Agreement.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.7920, 0.3253],
# [0.7920, 1.0000, 0.4614],
# [0.3253, 0.4614, 1.0000]])Usagepython
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
'License Grant',
"In the event FCE notifies ExxonMobil that it has formally decided not to pursue Generation 2 Technology for Power Applications, then upon ExxonMobil's written request, FCE agrees to negotiate a grant to ExxonMobil and its Affiliates, under commercially reasonable terms to be determined in good faith, a worldwide, royalty-bearing (with the royalty to be negotiated), non-exclusive, sub-licensable right and license to practice FCE Background Information and FCE Background Patents for Generation 2 Technology in any application outside of Carbon Capture Applications and Hydrogen Applications.",
'Aucta should continue to receive 15% of Net Sales Royalty for as long as ETON is selling the Product(s) in the Territory, unless otherwise agreed to under this Agreement.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.7920, 0.3253],
# [0.7920, 1.0000, 0.4614],
# [0.3253, 0.4614, 1.0000]])Usagepython
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
'License Grant',
"In the event FCE notifies ExxonMobil that it has formally decided not to pursue Generation 2 Technology for Power Applications, then upon ExxonMobil's written request, FCE agrees to negotiate a grant to ExxonMobil and its Affiliates, under commercially reasonable terms to be determined in good faith, a worldwide, royalty-bearing (with the royalty to be negotiated), non-exclusive, sub-licensable right and license to practice FCE Background Information and FCE Background Patents for Generation 2 Technology in any application outside of Carbon Capture Applications and Hydrogen Applications.",
'Aucta should continue to receive 15% of Net Sales Royalty for as long as ETON is selling the Product(s) in the Territory, unless otherwise agreed to under this Agreement.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.7920, 0.3253],
# [0.7920, 1.0000, 0.4614],
# [0.3253, 0.4614, 1.0000]])Usagepython
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
'License Grant',
"In the event FCE notifies ExxonMobil that it has formally decided not to pursue Generation 2 Technology for Power Applications, then upon ExxonMobil's written request, FCE agrees to negotiate a grant to ExxonMobil and its Affiliates, under commercially reasonable terms to be determined in good faith, a worldwide, royalty-bearing (with the royalty to be negotiated), non-exclusive, sub-licensable right and license to practice FCE Background Information and FCE Background Patents for Generation 2 Technology in any application outside of Carbon Capture Applications and Hydrogen Applications.",
'Aucta should continue to receive 15% of Net Sales Royalty for as long as ETON is selling the Product(s) in the Territory, unless otherwise agreed to under this Agreement.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.7920, 0.3253],
# [0.7920, 1.0000, 0.4614],
# [0.3253, 0.4614, 1.0000]])Usagepython
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
'License Grant',
"In the event FCE notifies ExxonMobil that it has formally decided not to pursue Generation 2 Technology for Power Applications, then upon ExxonMobil's written request, FCE agrees to negotiate a grant to ExxonMobil and its Affiliates, under commercially reasonable terms to be determined in good faith, a worldwide, royalty-bearing (with the royalty to be negotiated), non-exclusive, sub-licensable right and license to practice FCE Background Information and FCE Background Patents for Generation 2 Technology in any application outside of Carbon Capture Applications and Hydrogen Applications.",
'Aucta should continue to receive 15% of Net Sales Royalty for as long as ETON is selling the Product(s) in the Territory, unless otherwise agreed to under this Agreement.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.7920, 0.3253],
# [0.7920, 1.0000, 0.4614],
# [0.3253, 0.4614, 1.0000]])Usagepython
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
'License Grant',
"In the event FCE notifies ExxonMobil that it has formally decided not to pursue Generation 2 Technology for Power Applications, then upon ExxonMobil's written request, FCE agrees to negotiate a grant to ExxonMobil and its Affiliates, under commercially reasonable terms to be determined in good faith, a worldwide, royalty-bearing (with the royalty to be negotiated), non-exclusive, sub-licensable right and license to practice FCE Background Information and FCE Background Patents for Generation 2 Technology in any application outside of Carbon Capture Applications and Hydrogen Applications.",
'Aucta should continue to receive 15% of Net Sales Royalty for as long as ETON is selling the Product(s) in the Territory, unless otherwise agreed to under this Agreement.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.7920, 0.3253],
# [0.7920, 1.0000, 0.4614],
# [0.3253, 0.4614, 1.0000]])Usagepython
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
'License Grant',
"In the event FCE notifies ExxonMobil that it has formally decided not to pursue Generation 2 Technology for Power Applications, then upon ExxonMobil's written request, FCE agrees to negotiate a grant to ExxonMobil and its Affiliates, under commercially reasonable terms to be determined in good faith, a worldwide, royalty-bearing (with the royalty to be negotiated), non-exclusive, sub-licensable right and license to practice FCE Background Information and FCE Background Patents for Generation 2 Technology in any application outside of Carbon Capture Applications and Hydrogen Applications.",
'Aucta should continue to receive 15% of Net Sales Royalty for as long as ETON is selling the Product(s) in the Territory, unless otherwise agreed to under this Agreement.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.7920, 0.3253],
# [0.7920, 1.0000, 0.4614],
# [0.3253, 0.4614, 1.0000]])Usagepython
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
'License Grant',
"In the event FCE notifies ExxonMobil that it has formally decided not to pursue Generation 2 Technology for Power Applications, then upon ExxonMobil's written request, FCE agrees to negotiate a grant to ExxonMobil and its Affiliates, under commercially reasonable terms to be determined in good faith, a worldwide, royalty-bearing (with the royalty to be negotiated), non-exclusive, sub-licensable right and license to practice FCE Background Information and FCE Background Patents for Generation 2 Technology in any application outside of Carbon Capture Applications and Hydrogen Applications.",
'Aucta should continue to receive 15% of Net Sales Royalty for as long as ETON is selling the Product(s) in the Territory, unless otherwise agreed to under this Agreement.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.7920, 0.3253],
# [0.7920, 1.0000, 0.4614],
# [0.3253, 0.4614, 1.0000]])Usagepython
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
'License Grant',
"In the event FCE notifies ExxonMobil that it has formally decided not to pursue Generation 2 Technology for Power Applications, then upon ExxonMobil's written request, FCE agrees to negotiate a grant to ExxonMobil and its Affiliates, under commercially reasonable terms to be determined in good faith, a worldwide, royalty-bearing (with the royalty to be negotiated), non-exclusive, sub-licensable right and license to practice FCE Background Information and FCE Background Patents for Generation 2 Technology in any application outside of Carbon Capture Applications and Hydrogen Applications.",
'Aucta should continue to receive 15% of Net Sales Royalty for as long as ETON is selling the Product(s) in the Territory, unless otherwise agreed to under this Agreement.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.7920, 0.3253],
# [0.7920, 1.0000, 0.4614],
# [0.3253, 0.4614, 1.0000]])Usagepython
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
'License Grant',
"In the event FCE notifies ExxonMobil that it has formally decided not to pursue Generation 2 Technology for Power Applications, then upon ExxonMobil's written request, FCE agrees to negotiate a grant to ExxonMobil and its Affiliates, under commercially reasonable terms to be determined in good faith, a worldwide, royalty-bearing (with the royalty to be negotiated), non-exclusive, sub-licensable right and license to practice FCE Background Information and FCE Background Patents for Generation 2 Technology in any application outside of Carbon Capture Applications and Hydrogen Applications.",
'Aucta should continue to receive 15% of Net Sales Royalty for as long as ETON is selling the Product(s) in the Territory, unless otherwise agreed to under this Agreement.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.7920, 0.3253],
# [0.7920, 1.0000, 0.4614],
# [0.3253, 0.4614, 1.0000]])Usagepython
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
'License Grant',
"In the event FCE notifies ExxonMobil that it has formally decided not to pursue Generation 2 Technology for Power Applications, then upon ExxonMobil's written request, FCE agrees to negotiate a grant to ExxonMobil and its Affiliates, under commercially reasonable terms to be determined in good faith, a worldwide, royalty-bearing (with the royalty to be negotiated), non-exclusive, sub-licensable right and license to practice FCE Background Information and FCE Background Patents for Generation 2 Technology in any application outside of Carbon Capture Applications and Hydrogen Applications.",
'Aucta should continue to receive 15% of Net Sales Royalty for as long as ETON is selling the Product(s) in the Territory, unless otherwise agreed to under this Agreement.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.7920, 0.3253],
# [0.7920, 1.0000, 0.4614],
# [0.3253, 0.4614, 1.0000]])Usagepython
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
'License Grant',
"In the event FCE notifies ExxonMobil that it has formally decided not to pursue Generation 2 Technology for Power Applications, then upon ExxonMobil's written request, FCE agrees to negotiate a grant to ExxonMobil and its Affiliates, under commercially reasonable terms to be determined in good faith, a worldwide, royalty-bearing (with the royalty to be negotiated), non-exclusive, sub-licensable right and license to practice FCE Background Information and FCE Background Patents for Generation 2 Technology in any application outside of Carbon Capture Applications and Hydrogen Applications.",
'Aucta should continue to receive 15% of Net Sales Royalty for as long as ETON is selling the Product(s) in the Territory, unless otherwise agreed to under this Agreement.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.7920, 0.3253],
# [0.7920, 1.0000, 0.4614],
# [0.3253, 0.4614, 1.0000]])Usagepython
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
'License Grant',
"In the event FCE notifies ExxonMobil that it has formally decided not to pursue Generation 2 Technology for Power Applications, then upon ExxonMobil's written request, FCE agrees to negotiate a grant to ExxonMobil and its Affiliates, under commercially reasonable terms to be determined in good faith, a worldwide, royalty-bearing (with the royalty to be negotiated), non-exclusive, sub-licensable right and license to practice FCE Background Information and FCE Background Patents for Generation 2 Technology in any application outside of Carbon Capture Applications and Hydrogen Applications.",
'Aucta should continue to receive 15% of Net Sales Royalty for as long as ETON is selling the Product(s) in the Territory, unless otherwise agreed to under this Agreement.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.7920, 0.3253],
# [0.7920, 1.0000, 0.4614],
# [0.3253, 0.4614, 1.0000]])Usagepython
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
'License Grant',
"In the event FCE notifies ExxonMobil that it has formally decided not to pursue Generation 2 Technology for Power Applications, then upon ExxonMobil's written request, FCE agrees to negotiate a grant to ExxonMobil and its Affiliates, under commercially reasonable terms to be determined in good faith, a worldwide, royalty-bearing (with the royalty to be negotiated), non-exclusive, sub-licensable right and license to practice FCE Background Information and FCE Background Patents for Generation 2 Technology in any application outside of Carbon Capture Applications and Hydrogen Applications.",
'Aucta should continue to receive 15% of Net Sales Royalty for as long as ETON is selling the Product(s) in the Territory, unless otherwise agreed to under this Agreement.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.7920, 0.3253],
# [0.7920, 1.0000, 0.4614],
# [0.3253, 0.4614, 1.0000]])Usagepython
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
'License Grant',
"In the event FCE notifies ExxonMobil that it has formally decided not to pursue Generation 2 Technology for Power Applications, then upon ExxonMobil's written request, FCE agrees to negotiate a grant to ExxonMobil and its Affiliates, under commercially reasonable terms to be determined in good faith, a worldwide, royalty-bearing (with the royalty to be negotiated), non-exclusive, sub-licensable right and license to practice FCE Background Information and FCE Background Patents for Generation 2 Technology in any application outside of Carbon Capture Applications and Hydrogen Applications.",
'Aucta should continue to receive 15% of Net Sales Royalty for as long as ETON is selling the Product(s) in the Territory, unless otherwise agreed to under this Agreement.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.7920, 0.3253],
# [0.7920, 1.0000, 0.4614],
# [0.3253, 0.4614, 1.0000]])Usagepython
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
'License Grant',
"In the event FCE notifies ExxonMobil that it has formally decided not to pursue Generation 2 Technology for Power Applications, then upon ExxonMobil's written request, FCE agrees to negotiate a grant to ExxonMobil and its Affiliates, under commercially reasonable terms to be determined in good faith, a worldwide, royalty-bearing (with the royalty to be negotiated), non-exclusive, sub-licensable right and license to practice FCE Background Information and FCE Background Patents for Generation 2 Technology in any application outside of Carbon Capture Applications and Hydrogen Applications.",
'Aucta should continue to receive 15% of Net Sales Royalty for as long as ETON is selling the Product(s) in the Territory, unless otherwise agreed to under this Agreement.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.7920, 0.3253],
# [0.7920, 1.0000, 0.4614],
# [0.3253, 0.4614, 1.0000]])Usagepython
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
'License Grant',
"In the event FCE notifies ExxonMobil that it has formally decided not to pursue Generation 2 Technology for Power Applications, then upon ExxonMobil's written request, FCE agrees to negotiate a grant to ExxonMobil and its Affiliates, under commercially reasonable terms to be determined in good faith, a worldwide, royalty-bearing (with the royalty to be negotiated), non-exclusive, sub-licensable right and license to practice FCE Background Information and FCE Background Patents for Generation 2 Technology in any application outside of Carbon Capture Applications and Hydrogen Applications.",
'Aucta should continue to receive 15% of Net Sales Royalty for as long as ETON is selling the Product(s) in the Territory, unless otherwise agreed to under this Agreement.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.7920, 0.3253],
# [0.7920, 1.0000, 0.4614],
# [0.3253, 0.4614, 1.0000]])Usagepython
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
'License Grant',
"In the event FCE notifies ExxonMobil that it has formally decided not to pursue Generation 2 Technology for Power Applications, then upon ExxonMobil's written request, FCE agrees to negotiate a grant to ExxonMobil and its Affiliates, under commercially reasonable terms to be determined in good faith, a worldwide, royalty-bearing (with the royalty to be negotiated), non-exclusive, sub-licensable right and license to practice FCE Background Information and FCE Background Patents for Generation 2 Technology in any application outside of Carbon Capture Applications and Hydrogen Applications.",
'Aucta should continue to receive 15% of Net Sales Royalty for as long as ETON is selling the Product(s) in the Territory, unless otherwise agreed to under this Agreement.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.7920, 0.3253],
# [0.7920, 1.0000, 0.4614],
# [0.3253, 0.4614, 1.0000]])Usagepython
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
'License Grant',
"In the event FCE notifies ExxonMobil that it has formally decided not to pursue Generation 2 Technology for Power Applications, then upon ExxonMobil's written request, FCE agrees to negotiate a grant to ExxonMobil and its Affiliates, under commercially reasonable terms to be determined in good faith, a worldwide, royalty-bearing (with the royalty to be negotiated), non-exclusive, sub-licensable right and license to practice FCE Background Information and FCE Background Patents for Generation 2 Technology in any application outside of Carbon Capture Applications and Hydrogen Applications.",
'Aucta should continue to receive 15% of Net Sales Royalty for as long as ETON is selling the Product(s) in the Territory, unless otherwise agreed to under this Agreement.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.7920, 0.3253],
# [0.7920, 1.0000, 0.4614],
# [0.3253, 0.4614, 1.0000]])Usagepython
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
'License Grant',
"In the event FCE notifies ExxonMobil that it has formally decided not to pursue Generation 2 Technology for Power Applications, then upon ExxonMobil's written request, FCE agrees to negotiate a grant to ExxonMobil and its Affiliates, under commercially reasonable terms to be determined in good faith, a worldwide, royalty-bearing (with the royalty to be negotiated), non-exclusive, sub-licensable right and license to practice FCE Background Information and FCE Background Patents for Generation 2 Technology in any application outside of Carbon Capture Applications and Hydrogen Applications.",
'Aucta should continue to receive 15% of Net Sales Royalty for as long as ETON is selling the Product(s) in the Territory, unless otherwise agreed to under this Agreement.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.7920, 0.3253],
# [0.7920, 1.0000, 0.4614],
# [0.3253, 0.4614, 1.0000]])Usagepython
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
'License Grant',
"In the event FCE notifies ExxonMobil that it has formally decided not to pursue Generation 2 Technology for Power Applications, then upon ExxonMobil's written request, FCE agrees to negotiate a grant to ExxonMobil and its Affiliates, under commercially reasonable terms to be determined in good faith, a worldwide, royalty-bearing (with the royalty to be negotiated), non-exclusive, sub-licensable right and license to practice FCE Background Information and FCE Background Patents for Generation 2 Technology in any application outside of Carbon Capture Applications and Hydrogen Applications.",
'Aucta should continue to receive 15% of Net Sales Royalty for as long as ETON is selling the Product(s) in the Territory, unless otherwise agreed to under this Agreement.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.7920, 0.3253],
# [0.7920, 1.0000, 0.4614],
# [0.3253, 0.4614, 1.0000]])Usagepython
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
'License Grant',
"In the event FCE notifies ExxonMobil that it has formally decided not to pursue Generation 2 Technology for Power Applications, then upon ExxonMobil's written request, FCE agrees to negotiate a grant to ExxonMobil and its Affiliates, under commercially reasonable terms to be determined in good faith, a worldwide, royalty-bearing (with the royalty to be negotiated), non-exclusive, sub-licensable right and license to practice FCE Background Information and FCE Background Patents for Generation 2 Technology in any application outside of Carbon Capture Applications and Hydrogen Applications.",
'Aucta should continue to receive 15% of Net Sales Royalty for as long as ETON is selling the Product(s) in the Territory, unless otherwise agreed to under this Agreement.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.7920, 0.3253],
# [0.7920, 1.0000, 0.4614],
# [0.3253, 0.4614, 1.0000]])Usagepython
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
'License Grant',
"In the event FCE notifies ExxonMobil that it has formally decided not to pursue Generation 2 Technology for Power Applications, then upon ExxonMobil's written request, FCE agrees to negotiate a grant to ExxonMobil and its Affiliates, under commercially reasonable terms to be determined in good faith, a worldwide, royalty-bearing (with the royalty to be negotiated), non-exclusive, sub-licensable right and license to practice FCE Background Information and FCE Background Patents for Generation 2 Technology in any application outside of Carbon Capture Applications and Hydrogen Applications.",
'Aucta should continue to receive 15% of Net Sales Royalty for as long as ETON is selling the Product(s) in the Territory, unless otherwise agreed to under this Agreement.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.7920, 0.3253],
# [0.7920, 1.0000, 0.4614],
# [0.3253, 0.4614, 1.0000]])Usagepython
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
'License Grant',
"In the event FCE notifies ExxonMobil that it has formally decided not to pursue Generation 2 Technology for Power Applications, then upon ExxonMobil's written request, FCE agrees to negotiate a grant to ExxonMobil and its Affiliates, under commercially reasonable terms to be determined in good faith, a worldwide, royalty-bearing (with the royalty to be negotiated), non-exclusive, sub-licensable right and license to practice FCE Background Information and FCE Background Patents for Generation 2 Technology in any application outside of Carbon Capture Applications and Hydrogen Applications.",
'Aucta should continue to receive 15% of Net Sales Royalty for as long as ETON is selling the Product(s) in the Territory, unless otherwise agreed to under this Agreement.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.7920, 0.3253],
# [0.7920, 1.0000, 0.4614],
# [0.3253, 0.4614, 1.0000]])Usagepython
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
'License Grant',
"In the event FCE notifies ExxonMobil that it has formally decided not to pursue Generation 2 Technology for Power Applications, then upon ExxonMobil's written request, FCE agrees to negotiate a grant to ExxonMobil and its Affiliates, under commercially reasonable terms to be determined in good faith, a worldwide, royalty-bearing (with the royalty to be negotiated), non-exclusive, sub-licensable right and license to practice FCE Background Information and FCE Background Patents for Generation 2 Technology in any application outside of Carbon Capture Applications and Hydrogen Applications.",
'Aucta should continue to receive 15% of Net Sales Royalty for as long as ETON is selling the Product(s) in the Territory, unless otherwise agreed to under this Agreement.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.7920, 0.3253],
# [0.7920, 1.0000, 0.4614],
# [0.3253, 0.4614, 1.0000]])Usagepython
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
'License Grant',
"In the event FCE notifies ExxonMobil that it has formally decided not to pursue Generation 2 Technology for Power Applications, then upon ExxonMobil's written request, FCE agrees to negotiate a grant to ExxonMobil and its Affiliates, under commercially reasonable terms to be determined in good faith, a worldwide, royalty-bearing (with the royalty to be negotiated), non-exclusive, sub-licensable right and license to practice FCE Background Information and FCE Background Patents for Generation 2 Technology in any application outside of Carbon Capture Applications and Hydrogen Applications.",
'Aucta should continue to receive 15% of Net Sales Royalty for as long as ETON is selling the Product(s) in the Territory, unless otherwise agreed to under this Agreement.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.7920, 0.3253],
# [0.7920, 1.0000, 0.4614],
# [0.3253, 0.4614, 1.0000]])Usagepython
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
'License Grant',
"In the event FCE notifies ExxonMobil that it has formally decided not to pursue Generation 2 Technology for Power Applications, then upon ExxonMobil's written request, FCE agrees to negotiate a grant to ExxonMobil and its Affiliates, under commercially reasonable terms to be determined in good faith, a worldwide, royalty-bearing (with the royalty to be negotiated), non-exclusive, sub-licensable right and license to practice FCE Background Information and FCE Background Patents for Generation 2 Technology in any application outside of Carbon Capture Applications and Hydrogen Applications.",
'Aucta should continue to receive 15% of Net Sales Royalty for as long as ETON is selling the Product(s) in the Territory, unless otherwise agreed to under this Agreement.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.7920, 0.3253],
# [0.7920, 1.0000, 0.4614],
# [0.3253, 0.4614, 1.0000]])Usagepython
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
'License Grant',
"In the event FCE notifies ExxonMobil that it has formally decided not to pursue Generation 2 Technology for Power Applications, then upon ExxonMobil's written request, FCE agrees to negotiate a grant to ExxonMobil and its Affiliates, under commercially reasonable terms to be determined in good faith, a worldwide, royalty-bearing (with the royalty to be negotiated), non-exclusive, sub-licensable right and license to practice FCE Background Information and FCE Background Patents for Generation 2 Technology in any application outside of Carbon Capture Applications and Hydrogen Applications.",
'Aucta should continue to receive 15% of Net Sales Royalty for as long as ETON is selling the Product(s) in the Territory, unless otherwise agreed to under this Agreement.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.7920, 0.3253],
# [0.7920, 1.0000, 0.4614],
# [0.3253, 0.4614, 1.0000]])Usagepython
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
'License Grant',
"In the event FCE notifies ExxonMobil that it has formally decided not to pursue Generation 2 Technology for Power Applications, then upon ExxonMobil's written request, FCE agrees to negotiate a grant to ExxonMobil and its Affiliates, under commercially reasonable terms to be determined in good faith, a worldwide, royalty-bearing (with the royalty to be negotiated), non-exclusive, sub-licensable right and license to practice FCE Background Information and FCE Background Patents for Generation 2 Technology in any application outside of Carbon Capture Applications and Hydrogen Applications.",
'Aucta should continue to receive 15% of Net Sales Royalty for as long as ETON is selling the Product(s) in the Territory, unless otherwise agreed to under this Agreement.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.7920, 0.3253],
# [0.7920, 1.0000, 0.4614],
# [0.3253, 0.4614, 1.0000]])Usagepython
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
'License Grant',
"In the event FCE notifies ExxonMobil that it has formally decided not to pursue Generation 2 Technology for Power Applications, then upon ExxonMobil's written request, FCE agrees to negotiate a grant to ExxonMobil and its Affiliates, under commercially reasonable terms to be determined in good faith, a worldwide, royalty-bearing (with the royalty to be negotiated), non-exclusive, sub-licensable right and license to practice FCE Background Information and FCE Background Patents for Generation 2 Technology in any application outside of Carbon Capture Applications and Hydrogen Applications.",
'Aucta should continue to receive 15% of Net Sales Royalty for as long as ETON is selling the Product(s) in the Territory, unless otherwise agreed to under this Agreement.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.7920, 0.3253],
# [0.7920, 1.0000, 0.4614],
# [0.3253, 0.4614, 1.0000]])Usagepython
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
'License Grant',
"In the event FCE notifies ExxonMobil that it has formally decided not to pursue Generation 2 Technology for Power Applications, then upon ExxonMobil's written request, FCE agrees to negotiate a grant to ExxonMobil and its Affiliates, under commercially reasonable terms to be determined in good faith, a worldwide, royalty-bearing (with the royalty to be negotiated), non-exclusive, sub-licensable right and license to practice FCE Background Information and FCE Background Patents for Generation 2 Technology in any application outside of Carbon Capture Applications and Hydrogen Applications.",
'Aucta should continue to receive 15% of Net Sales Royalty for as long as ETON is selling the Product(s) in the Territory, unless otherwise agreed to under this Agreement.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.7920, 0.3253],
# [0.7920, 1.0000, 0.4614],
# [0.3253, 0.4614, 1.0000]])Usagepython
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
'License Grant',
"In the event FCE notifies ExxonMobil that it has formally decided not to pursue Generation 2 Technology for Power Applications, then upon ExxonMobil's written request, FCE agrees to negotiate a grant to ExxonMobil and its Affiliates, under commercially reasonable terms to be determined in good faith, a worldwide, royalty-bearing (with the royalty to be negotiated), non-exclusive, sub-licensable right and license to practice FCE Background Information and FCE Background Patents for Generation 2 Technology in any application outside of Carbon Capture Applications and Hydrogen Applications.",
'Aucta should continue to receive 15% of Net Sales Royalty for as long as ETON is selling the Product(s) in the Territory, unless otherwise agreed to under this Agreement.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.7920, 0.3253],
# [0.7920, 1.0000, 0.4614],
# [0.3253, 0.4614, 1.0000]])Usagepython
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
'License Grant',
"In the event FCE notifies ExxonMobil that it has formally decided not to pursue Generation 2 Technology for Power Applications, then upon ExxonMobil's written request, FCE agrees to negotiate a grant to ExxonMobil and its Affiliates, under commercially reasonable terms to be determined in good faith, a worldwide, royalty-bearing (with the royalty to be negotiated), non-exclusive, sub-licensable right and license to practice FCE Background Information and FCE Background Patents for Generation 2 Technology in any application outside of Carbon Capture Applications and Hydrogen Applications.",
'Aucta should continue to receive 15% of Net Sales Royalty for as long as ETON is selling the Product(s) in the Territory, unless otherwise agreed to under this Agreement.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.7920, 0.3253],
# [0.7920, 1.0000, 0.4614],
# [0.3253, 0.4614, 1.0000]])Usagepython
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
'License Grant',
"In the event FCE notifies ExxonMobil that it has formally decided not to pursue Generation 2 Technology for Power Applications, then upon ExxonMobil's written request, FCE agrees to negotiate a grant to ExxonMobil and its Affiliates, under commercially reasonable terms to be determined in good faith, a worldwide, royalty-bearing (with the royalty to be negotiated), non-exclusive, sub-licensable right and license to practice FCE Background Information and FCE Background Patents for Generation 2 Technology in any application outside of Carbon Capture Applications and Hydrogen Applications.",
'Aucta should continue to receive 15% of Net Sales Royalty for as long as ETON is selling the Product(s) in the Territory, unless otherwise agreed to under this Agreement.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.7920, 0.3253],
# [0.7920, 1.0000, 0.4614],
# [0.3253, 0.4614, 1.0000]])Usagepython
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
'License Grant',
"In the event FCE notifies ExxonMobil that it has formally decided not to pursue Generation 2 Technology for Power Applications, then upon ExxonMobil's written request, FCE agrees to negotiate a grant to ExxonMobil and its Affiliates, under commercially reasonable terms to be determined in good faith, a worldwide, royalty-bearing (with the royalty to be negotiated), non-exclusive, sub-licensable right and license to practice FCE Background Information and FCE Background Patents for Generation 2 Technology in any application outside of Carbon Capture Applications and Hydrogen Applications.",
'Aucta should continue to receive 15% of Net Sales Royalty for as long as ETON is selling the Product(s) in the Territory, unless otherwise agreed to under this Agreement.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.7920, 0.3253],
# [0.7920, 1.0000, 0.4614],
# [0.3253, 0.4614, 1.0000]])Usagepython
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
'License Grant',
"In the event FCE notifies ExxonMobil that it has formally decided not to pursue Generation 2 Technology for Power Applications, then upon ExxonMobil's written request, FCE agrees to negotiate a grant to ExxonMobil and its Affiliates, under commercially reasonable terms to be determined in good faith, a worldwide, royalty-bearing (with the royalty to be negotiated), non-exclusive, sub-licensable right and license to practice FCE Background Information and FCE Background Patents for Generation 2 Technology in any application outside of Carbon Capture Applications and Hydrogen Applications.",
'Aucta should continue to receive 15% of Net Sales Royalty for as long as ETON is selling the Product(s) in the Territory, unless otherwise agreed to under this Agreement.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.7920, 0.3253],
# [0.7920, 1.0000, 0.4614],
# [0.3253, 0.4614, 1.0000]])Usagepython
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
'License Grant',
"In the event FCE notifies ExxonMobil that it has formally decided not to pursue Generation 2 Technology for Power Applications, then upon ExxonMobil's written request, FCE agrees to negotiate a grant to ExxonMobil and its Affiliates, under commercially reasonable terms to be determined in good faith, a worldwide, royalty-bearing (with the royalty to be negotiated), non-exclusive, sub-licensable right and license to practice FCE Background Information and FCE Background Patents for Generation 2 Technology in any application outside of Carbon Capture Applications and Hydrogen Applications.",
'Aucta should continue to receive 15% of Net Sales Royalty for as long as ETON is selling the Product(s) in the Territory, unless otherwise agreed to under this Agreement.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.7920, 0.3253],
# [0.7920, 1.0000, 0.4614],
# [0.3253, 0.4614, 1.0000]])Usagepython
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
'License Grant',
"In the event FCE notifies ExxonMobil that it has formally decided not to pursue Generation 2 Technology for Power Applications, then upon ExxonMobil's written request, FCE agrees to negotiate a grant to ExxonMobil and its Affiliates, under commercially reasonable terms to be determined in good faith, a worldwide, royalty-bearing (with the royalty to be negotiated), non-exclusive, sub-licensable right and license to practice FCE Background Information and FCE Background Patents for Generation 2 Technology in any application outside of Carbon Capture Applications and Hydrogen Applications.",
'Aucta should continue to receive 15% of Net Sales Royalty for as long as ETON is selling the Product(s) in the Territory, unless otherwise agreed to under this Agreement.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.7920, 0.3253],
# [0.7920, 1.0000, 0.4614],
# [0.3253, 0.4614, 1.0000]])Usagepython
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
'License Grant',
"In the event FCE notifies ExxonMobil that it has formally decided not to pursue Generation 2 Technology for Power Applications, then upon ExxonMobil's written request, FCE agrees to negotiate a grant to ExxonMobil and its Affiliates, under commercially reasonable terms to be determined in good faith, a worldwide, royalty-bearing (with the royalty to be negotiated), non-exclusive, sub-licensable right and license to practice FCE Background Information and FCE Background Patents for Generation 2 Technology in any application outside of Carbon Capture Applications and Hydrogen Applications.",
'Aucta should continue to receive 15% of Net Sales Royalty for as long as ETON is selling the Product(s) in the Territory, unless otherwise agreed to under this Agreement.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.7920, 0.3253],
# [0.7920, 1.0000, 0.4614],
# [0.3253, 0.4614, 1.0000]])Usagepython
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
'License Grant',
"In the event FCE notifies ExxonMobil that it has formally decided not to pursue Generation 2 Technology for Power Applications, then upon ExxonMobil's written request, FCE agrees to negotiate a grant to ExxonMobil and its Affiliates, under commercially reasonable terms to be determined in good faith, a worldwide, royalty-bearing (with the royalty to be negotiated), non-exclusive, sub-licensable right and license to practice FCE Background Information and FCE Background Patents for Generation 2 Technology in any application outside of Carbon Capture Applications and Hydrogen Applications.",
'Aucta should continue to receive 15% of Net Sales Royalty for as long as ETON is selling the Product(s) in the Territory, unless otherwise agreed to under this Agreement.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.7920, 0.3253],
# [0.7920, 1.0000, 0.4614],
# [0.3253, 0.4614, 1.0000]])Usagepython
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
'License Grant',
"In the event FCE notifies ExxonMobil that it has formally decided not to pursue Generation 2 Technology for Power Applications, then upon ExxonMobil's written request, FCE agrees to negotiate a grant to ExxonMobil and its Affiliates, under commercially reasonable terms to be determined in good faith, a worldwide, royalty-bearing (with the royalty to be negotiated), non-exclusive, sub-licensable right and license to practice FCE Background Information and FCE Background Patents for Generation 2 Technology in any application outside of Carbon Capture Applications and Hydrogen Applications.",
'Aucta should continue to receive 15% of Net Sales Royalty for as long as ETON is selling the Product(s) in the Territory, unless otherwise agreed to under this Agreement.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.7920, 0.3253],
# [0.7920, 1.0000, 0.4614],
# [0.3253, 0.4614, 1.0000]])Usagepython
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
'License Grant',
"In the event FCE notifies ExxonMobil that it has formally decided not to pursue Generation 2 Technology for Power Applications, then upon ExxonMobil's written request, FCE agrees to negotiate a grant to ExxonMobil and its Affiliates, under commercially reasonable terms to be determined in good faith, a worldwide, royalty-bearing (with the royalty to be negotiated), non-exclusive, sub-licensable right and license to practice FCE Background Information and FCE Background Patents for Generation 2 Technology in any application outside of Carbon Capture Applications and Hydrogen Applications.",
'Aucta should continue to receive 15% of Net Sales Royalty for as long as ETON is selling the Product(s) in the Territory, unless otherwise agreed to under this Agreement.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.7920, 0.3253],
# [0.7920, 1.0000, 0.4614],
# [0.3253, 0.4614, 1.0000]])Usagepython
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
'License Grant',
"In the event FCE notifies ExxonMobil that it has formally decided not to pursue Generation 2 Technology for Power Applications, then upon ExxonMobil's written request, FCE agrees to negotiate a grant to ExxonMobil and its Affiliates, under commercially reasonable terms to be determined in good faith, a worldwide, royalty-bearing (with the royalty to be negotiated), non-exclusive, sub-licensable right and license to practice FCE Background Information and FCE Background Patents for Generation 2 Technology in any application outside of Carbon Capture Applications and Hydrogen Applications.",
'Aucta should continue to receive 15% of Net Sales Royalty for as long as ETON is selling the Product(s) in the Territory, unless otherwise agreed to under this Agreement.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.7920, 0.3253],
# [0.7920, 1.0000, 0.4614],
# [0.3253, 0.4614, 1.0000]])Usagepython
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
'License Grant',
"In the event FCE notifies ExxonMobil that it has formally decided not to pursue Generation 2 Technology for Power Applications, then upon ExxonMobil's written request, FCE agrees to negotiate a grant to ExxonMobil and its Affiliates, under commercially reasonable terms to be determined in good faith, a worldwide, royalty-bearing (with the royalty to be negotiated), non-exclusive, sub-licensable right and license to practice FCE Background Information and FCE Background Patents for Generation 2 Technology in any application outside of Carbon Capture Applications and Hydrogen Applications.",
'Aucta should continue to receive 15% of Net Sales Royalty for as long as ETON is selling the Product(s) in the Territory, unless otherwise agreed to under this Agreement.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.7920, 0.3253],
# [0.7920, 1.0000, 0.4614],
# [0.3253, 0.4614, 1.0000]])Usagepython
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
'License Grant',
"In the event FCE notifies ExxonMobil that it has formally decided not to pursue Generation 2 Technology for Power Applications, then upon ExxonMobil's written request, FCE agrees to negotiate a grant to ExxonMobil and its Affiliates, under commercially reasonable terms to be determined in good faith, a worldwide, royalty-bearing (with the royalty to be negotiated), non-exclusive, sub-licensable right and license to practice FCE Background Information and FCE Background Patents for Generation 2 Technology in any application outside of Carbon Capture Applications and Hydrogen Applications.",
'Aucta should continue to receive 15% of Net Sales Royalty for as long as ETON is selling the Product(s) in the Territory, unless otherwise agreed to under this Agreement.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.7920, 0.3253],
# [0.7920, 1.0000, 0.4614],
# [0.3253, 0.4614, 1.0000]])Usagepython
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
'License Grant',
"In the event FCE notifies ExxonMobil that it has formally decided not to pursue Generation 2 Technology for Power Applications, then upon ExxonMobil's written request, FCE agrees to negotiate a grant to ExxonMobil and its Affiliates, under commercially reasonable terms to be determined in good faith, a worldwide, royalty-bearing (with the royalty to be negotiated), non-exclusive, sub-licensable right and license to practice FCE Background Information and FCE Background Patents for Generation 2 Technology in any application outside of Carbon Capture Applications and Hydrogen Applications.",
'Aucta should continue to receive 15% of Net Sales Royalty for as long as ETON is selling the Product(s) in the Territory, unless otherwise agreed to under this Agreement.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.7920, 0.3253],
# [0.7920, 1.0000, 0.4614],
# [0.3253, 0.4614, 1.0000]])Usagepython
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
'License Grant',
"In the event FCE notifies ExxonMobil that it has formally decided not to pursue Generation 2 Technology for Power Applications, then upon ExxonMobil's written request, FCE agrees to negotiate a grant to ExxonMobil and its Affiliates, under commercially reasonable terms to be determined in good faith, a worldwide, royalty-bearing (with the royalty to be negotiated), non-exclusive, sub-licensable right and license to practice FCE Background Information and FCE Background Patents for Generation 2 Technology in any application outside of Carbon Capture Applications and Hydrogen Applications.",
'Aucta should continue to receive 15% of Net Sales Royalty for as long as ETON is selling the Product(s) in the Territory, unless otherwise agreed to under this Agreement.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.7920, 0.3253],
# [0.7920, 1.0000, 0.4614],
# [0.3253, 0.4614, 1.0000]])Usagepython
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
'License Grant',
"In the event FCE notifies ExxonMobil that it has formally decided not to pursue Generation 2 Technology for Power Applications, then upon ExxonMobil's written request, FCE agrees to negotiate a grant to ExxonMobil and its Affiliates, under commercially reasonable terms to be determined in good faith, a worldwide, royalty-bearing (with the royalty to be negotiated), non-exclusive, sub-licensable right and license to practice FCE Background Information and FCE Background Patents for Generation 2 Technology in any application outside of Carbon Capture Applications and Hydrogen Applications.",
'Aucta should continue to receive 15% of Net Sales Royalty for as long as ETON is selling the Product(s) in the Territory, unless otherwise agreed to under this Agreement.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.7920, 0.3253],
# [0.7920, 1.0000, 0.4614],
# [0.3253, 0.4614, 1.0000]])Usagepython
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
'License Grant',
"In the event FCE notifies ExxonMobil that it has formally decided not to pursue Generation 2 Technology for Power Applications, then upon ExxonMobil's written request, FCE agrees to negotiate a grant to ExxonMobil and its Affiliates, under commercially reasonable terms to be determined in good faith, a worldwide, royalty-bearing (with the royalty to be negotiated), non-exclusive, sub-licensable right and license to practice FCE Background Information and FCE Background Patents for Generation 2 Technology in any application outside of Carbon Capture Applications and Hydrogen Applications.",
'Aucta should continue to receive 15% of Net Sales Royalty for as long as ETON is selling the Product(s) in the Territory, unless otherwise agreed to under this Agreement.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.7920, 0.3253],
# [0.7920, 1.0000, 0.4614],
# [0.3253, 0.4614, 1.0000]])Usagepython
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
'License Grant',
"In the event FCE notifies ExxonMobil that it has formally decided not to pursue Generation 2 Technology for Power Applications, then upon ExxonMobil's written request, FCE agrees to negotiate a grant to ExxonMobil and its Affiliates, under commercially reasonable terms to be determined in good faith, a worldwide, royalty-bearing (with the royalty to be negotiated), non-exclusive, sub-licensable right and license to practice FCE Background Information and FCE Background Patents for Generation 2 Technology in any application outside of Carbon Capture Applications and Hydrogen Applications.",
'Aucta should continue to receive 15% of Net Sales Royalty for as long as ETON is selling the Product(s) in the Territory, unless otherwise agreed to under this Agreement.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.7920, 0.3253],
# [0.7920, 1.0000, 0.4614],
# [0.3253, 0.4614, 1.0000]])Usagepython
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
'License Grant',
"In the event FCE notifies ExxonMobil that it has formally decided not to pursue Generation 2 Technology for Power Applications, then upon ExxonMobil's written request, FCE agrees to negotiate a grant to ExxonMobil and its Affiliates, under commercially reasonable terms to be determined in good faith, a worldwide, royalty-bearing (with the royalty to be negotiated), non-exclusive, sub-licensable right and license to practice FCE Background Information and FCE Background Patents for Generation 2 Technology in any application outside of Carbon Capture Applications and Hydrogen Applications.",
'Aucta should continue to receive 15% of Net Sales Royalty for as long as ETON is selling the Product(s) in the Territory, unless otherwise agreed to under this Agreement.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.7920, 0.3253],
# [0.7920, 1.0000, 0.4614],
# [0.3253, 0.4614, 1.0000]])Usagepython
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
'License Grant',
"In the event FCE notifies ExxonMobil that it has formally decided not to pursue Generation 2 Technology for Power Applications, then upon ExxonMobil's written request, FCE agrees to negotiate a grant to ExxonMobil and its Affiliates, under commercially reasonable terms to be determined in good faith, a worldwide, royalty-bearing (with the royalty to be negotiated), non-exclusive, sub-licensable right and license to practice FCE Background Information and FCE Background Patents for Generation 2 Technology in any application outside of Carbon Capture Applications and Hydrogen Applications.",
'Aucta should continue to receive 15% of Net Sales Royalty for as long as ETON is selling the Product(s) in the Territory, unless otherwise agreed to under this Agreement.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.7920, 0.3253],
# [0.7920, 1.0000, 0.4614],
# [0.3253, 0.4614, 1.0000]])Usagepython
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
'License Grant',
"In the event FCE notifies ExxonMobil that it has formally decided not to pursue Generation 2 Technology for Power Applications, then upon ExxonMobil's written request, FCE agrees to negotiate a grant to ExxonMobil and its Affiliates, under commercially reasonable terms to be determined in good faith, a worldwide, royalty-bearing (with the royalty to be negotiated), non-exclusive, sub-licensable right and license to practice FCE Background Information and FCE Background Patents for Generation 2 Technology in any application outside of Carbon Capture Applications and Hydrogen Applications.",
'Aucta should continue to receive 15% of Net Sales Royalty for as long as ETON is selling the Product(s) in the Territory, unless otherwise agreed to under this Agreement.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.7920, 0.3253],
# [0.7920, 1.0000, 0.4614],
# [0.3253, 0.4614, 1.0000]])Usagepython
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
'License Grant',
"In the event FCE notifies ExxonMobil that it has formally decided not to pursue Generation 2 Technology for Power Applications, then upon ExxonMobil's written request, FCE agrees to negotiate a grant to ExxonMobil and its Affiliates, under commercially reasonable terms to be determined in good faith, a worldwide, royalty-bearing (with the royalty to be negotiated), non-exclusive, sub-licensable right and license to practice FCE Background Information and FCE Background Patents for Generation 2 Technology in any application outside of Carbon Capture Applications and Hydrogen Applications.",
'Aucta should continue to receive 15% of Net Sales Royalty for as long as ETON is selling the Product(s) in the Territory, unless otherwise agreed to under this Agreement.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.7920, 0.3253],
# [0.7920, 1.0000, 0.4614],
# [0.3253, 0.4614, 1.0000]])Usagepython
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
'License Grant',
"In the event FCE notifies ExxonMobil that it has formally decided not to pursue Generation 2 Technology for Power Applications, then upon ExxonMobil's written request, FCE agrees to negotiate a grant to ExxonMobil and its Affiliates, under commercially reasonable terms to be determined in good faith, a worldwide, royalty-bearing (with the royalty to be negotiated), non-exclusive, sub-licensable right and license to practice FCE Background Information and FCE Background Patents for Generation 2 Technology in any application outside of Carbon Capture Applications and Hydrogen Applications.",
'Aucta should continue to receive 15% of Net Sales Royalty for as long as ETON is selling the Product(s) in the Territory, unless otherwise agreed to under this Agreement.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.7920, 0.3253],
# [0.7920, 1.0000, 0.4614],
# [0.3253, 0.4614, 1.0000]])Usagepython
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
'License Grant',
"In the event FCE notifies ExxonMobil that it has formally decided not to pursue Generation 2 Technology for Power Applications, then upon ExxonMobil's written request, FCE agrees to negotiate a grant to ExxonMobil and its Affiliates, under commercially reasonable terms to be determined in good faith, a worldwide, royalty-bearing (with the royalty to be negotiated), non-exclusive, sub-licensable right and license to practice FCE Background Information and FCE Background Patents for Generation 2 Technology in any application outside of Carbon Capture Applications and Hydrogen Applications.",
'Aucta should continue to receive 15% of Net Sales Royalty for as long as ETON is selling the Product(s) in the Territory, unless otherwise agreed to under this Agreement.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.7920, 0.3253],
# [0.7920, 1.0000, 0.4614],
# [0.3253, 0.4614, 1.0000]])Usagepython
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
'License Grant',
"In the event FCE notifies ExxonMobil that it has formally decided not to pursue Generation 2 Technology for Power Applications, then upon ExxonMobil's written request, FCE agrees to negotiate a grant to ExxonMobil and its Affiliates, under commercially reasonable terms to be determined in good faith, a worldwide, royalty-bearing (with the royalty to be negotiated), non-exclusive, sub-licensable right and license to practice FCE Background Information and FCE Background Patents for Generation 2 Technology in any application outside of Carbon Capture Applications and Hydrogen Applications.",
'Aucta should continue to receive 15% of Net Sales Royalty for as long as ETON is selling the Product(s) in the Territory, unless otherwise agreed to under this Agreement.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.7920, 0.3253],
# [0.7920, 1.0000, 0.4614],
# [0.3253, 0.4614, 1.0000]])Usagepython
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
'License Grant',
"In the event FCE notifies ExxonMobil that it has formally decided not to pursue Generation 2 Technology for Power Applications, then upon ExxonMobil's written request, FCE agrees to negotiate a grant to ExxonMobil and its Affiliates, under commercially reasonable terms to be determined in good faith, a worldwide, royalty-bearing (with the royalty to be negotiated), non-exclusive, sub-licensable right and license to practice FCE Background Information and FCE Background Patents for Generation 2 Technology in any application outside of Carbon Capture Applications and Hydrogen Applications.",
'Aucta should continue to receive 15% of Net Sales Royalty for as long as ETON is selling the Product(s) in the Territory, unless otherwise agreed to under this Agreement.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.7920, 0.3253],
# [0.7920, 1.0000, 0.4614],
# [0.3253, 0.4614, 1.0000]])Usagepython
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
'License Grant',
"In the event FCE notifies ExxonMobil that it has formally decided not to pursue Generation 2 Technology for Power Applications, then upon ExxonMobil's written request, FCE agrees to negotiate a grant to ExxonMobil and its Affiliates, under commercially reasonable terms to be determined in good faith, a worldwide, royalty-bearing (with the royalty to be negotiated), non-exclusive, sub-licensable right and license to practice FCE Background Information and FCE Background Patents for Generation 2 Technology in any application outside of Carbon Capture Applications and Hydrogen Applications.",
'Aucta should continue to receive 15% of Net Sales Royalty for as long as ETON is selling the Product(s) in the Territory, unless otherwise agreed to under this Agreement.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.7920, 0.3253],
# [0.7920, 1.0000, 0.4614],
# [0.3253, 0.4614, 1.0000]])Usagepython
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
'License Grant',
"In the event FCE notifies ExxonMobil that it has formally decided not to pursue Generation 2 Technology for Power Applications, then upon ExxonMobil's written request, FCE agrees to negotiate a grant to ExxonMobil and its Affiliates, under commercially reasonable terms to be determined in good faith, a worldwide, royalty-bearing (with the royalty to be negotiated), non-exclusive, sub-licensable right and license to practice FCE Background Information and FCE Background Patents for Generation 2 Technology in any application outside of Carbon Capture Applications and Hydrogen Applications.",
'Aucta should continue to receive 15% of Net Sales Royalty for as long as ETON is selling the Product(s) in the Territory, unless otherwise agreed to under this Agreement.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.7920, 0.3253],
# [0.7920, 1.0000, 0.4614],
# [0.3253, 0.4614, 1.0000]])Usagepython
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
'License Grant',
"In the event FCE notifies ExxonMobil that it has formally decided not to pursue Generation 2 Technology for Power Applications, then upon ExxonMobil's written request, FCE agrees to negotiate a grant to ExxonMobil and its Affiliates, under commercially reasonable terms to be determined in good faith, a worldwide, royalty-bearing (with the royalty to be negotiated), non-exclusive, sub-licensable right and license to practice FCE Background Information and FCE Background Patents for Generation 2 Technology in any application outside of Carbon Capture Applications and Hydrogen Applications.",
'Aucta should continue to receive 15% of Net Sales Royalty for as long as ETON is selling the Product(s) in the Territory, unless otherwise agreed to under this Agreement.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.7920, 0.3253],
# [0.7920, 1.0000, 0.4614],
# [0.3253, 0.4614, 1.0000]])Usagepython
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
'License Grant',
"In the event FCE notifies ExxonMobil that it has formally decided not to pursue Generation 2 Technology for Power Applications, then upon ExxonMobil's written request, FCE agrees to negotiate a grant to ExxonMobil and its Affiliates, under commercially reasonable terms to be determined in good faith, a worldwide, royalty-bearing (with the royalty to be negotiated), non-exclusive, sub-licensable right and license to practice FCE Background Information and FCE Background Patents for Generation 2 Technology in any application outside of Carbon Capture Applications and Hydrogen Applications.",
'Aucta should continue to receive 15% of Net Sales Royalty for as long as ETON is selling the Product(s) in the Territory, unless otherwise agreed to under this Agreement.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.7920, 0.3253],
# [0.7920, 1.0000, 0.4614],
# [0.3253, 0.4614, 1.0000]])Usagepython
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
'License Grant',
"In the event FCE notifies ExxonMobil that it has formally decided not to pursue Generation 2 Technology for Power Applications, then upon ExxonMobil's written request, FCE agrees to negotiate a grant to ExxonMobil and its Affiliates, under commercially reasonable terms to be determined in good faith, a worldwide, royalty-bearing (with the royalty to be negotiated), non-exclusive, sub-licensable right and license to practice FCE Background Information and FCE Background Patents for Generation 2 Technology in any application outside of Carbon Capture Applications and Hydrogen Applications.",
'Aucta should continue to receive 15% of Net Sales Royalty for as long as ETON is selling the Product(s) in the Territory, unless otherwise agreed to under this Agreement.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.7920, 0.3253],
# [0.7920, 1.0000, 0.4614],
# [0.3253, 0.4614, 1.0000]])Usagepython
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
'License Grant',
"In the event FCE notifies ExxonMobil that it has formally decided not to pursue Generation 2 Technology for Power Applications, then upon ExxonMobil's written request, FCE agrees to negotiate a grant to ExxonMobil and its Affiliates, under commercially reasonable terms to be determined in good faith, a worldwide, royalty-bearing (with the royalty to be negotiated), non-exclusive, sub-licensable right and license to practice FCE Background Information and FCE Background Patents for Generation 2 Technology in any application outside of Carbon Capture Applications and Hydrogen Applications.",
'Aucta should continue to receive 15% of Net Sales Royalty for as long as ETON is selling the Product(s) in the Territory, unless otherwise agreed to under this Agreement.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.7920, 0.3253],
# [0.7920, 1.0000, 0.4614],
# [0.3253, 0.4614, 1.0000]])Usagepython
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
'License Grant',
"In the event FCE notifies ExxonMobil that it has formally decided not to pursue Generation 2 Technology for Power Applications, then upon ExxonMobil's written request, FCE agrees to negotiate a grant to ExxonMobil and its Affiliates, under commercially reasonable terms to be determined in good faith, a worldwide, royalty-bearing (with the royalty to be negotiated), non-exclusive, sub-licensable right and license to practice FCE Background Information and FCE Background Patents for Generation 2 Technology in any application outside of Carbon Capture Applications and Hydrogen Applications.",
'Aucta should continue to receive 15% of Net Sales Royalty for as long as ETON is selling the Product(s) in the Territory, unless otherwise agreed to under this Agreement.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.7920, 0.3253],
# [0.7920, 1.0000, 0.4614],
# [0.3253, 0.4614, 1.0000]])Usagepython
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
'License Grant',
"In the event FCE notifies ExxonMobil that it has formally decided not to pursue Generation 2 Technology for Power Applications, then upon ExxonMobil's written request, FCE agrees to negotiate a grant to ExxonMobil and its Affiliates, under commercially reasonable terms to be determined in good faith, a worldwide, royalty-bearing (with the royalty to be negotiated), non-exclusive, sub-licensable right and license to practice FCE Background Information and FCE Background Patents for Generation 2 Technology in any application outside of Carbon Capture Applications and Hydrogen Applications.",
'Aucta should continue to receive 15% of Net Sales Royalty for as long as ETON is selling the Product(s) in the Territory, unless otherwise agreed to under this Agreement.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.7920, 0.3253],
# [0.7920, 1.0000, 0.4614],
# [0.3253, 0.4614, 1.0000]])Usagepython
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
'License Grant',
"In the event FCE notifies ExxonMobil that it has formally decided not to pursue Generation 2 Technology for Power Applications, then upon ExxonMobil's written request, FCE agrees to negotiate a grant to ExxonMobil and its Affiliates, under commercially reasonable terms to be determined in good faith, a worldwide, royalty-bearing (with the royalty to be negotiated), non-exclusive, sub-licensable right and license to practice FCE Background Information and FCE Background Patents for Generation 2 Technology in any application outside of Carbon Capture Applications and Hydrogen Applications.",
'Aucta should continue to receive 15% of Net Sales Royalty for as long as ETON is selling the Product(s) in the Territory, unless otherwise agreed to under this Agreement.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.7920, 0.3253],
# [0.7920, 1.0000, 0.4614],
# [0.3253, 0.4614, 1.0000]])Usagepython
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
'License Grant',
"In the event FCE notifies ExxonMobil that it has formally decided not to pursue Generation 2 Technology for Power Applications, then upon ExxonMobil's written request, FCE agrees to negotiate a grant to ExxonMobil and its Affiliates, under commercially reasonable terms to be determined in good faith, a worldwide, royalty-bearing (with the royalty to be negotiated), non-exclusive, sub-licensable right and license to practice FCE Background Information and FCE Background Patents for Generation 2 Technology in any application outside of Carbon Capture Applications and Hydrogen Applications.",
'Aucta should continue to receive 15% of Net Sales Royalty for as long as ETON is selling the Product(s) in the Territory, unless otherwise agreed to under this Agreement.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.7920, 0.3253],
# [0.7920, 1.0000, 0.4614],
# [0.3253, 0.4614, 1.0000]])Usagepython
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
'License Grant',
"In the event FCE notifies ExxonMobil that it has formally decided not to pursue Generation 2 Technology for Power Applications, then upon ExxonMobil's written request, FCE agrees to negotiate a grant to ExxonMobil and its Affiliates, under commercially reasonable terms to be determined in good faith, a worldwide, royalty-bearing (with the royalty to be negotiated), non-exclusive, sub-licensable right and license to practice FCE Background Information and FCE Background Patents for Generation 2 Technology in any application outside of Carbon Capture Applications and Hydrogen Applications.",
'Aucta should continue to receive 15% of Net Sales Royalty for as long as ETON is selling the Product(s) in the Territory, unless otherwise agreed to under this Agreement.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.7920, 0.3253],
# [0.7920, 1.0000, 0.4614],
# [0.3253, 0.4614, 1.0000]])Usagepython
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
'License Grant',
"In the event FCE notifies ExxonMobil that it has formally decided not to pursue Generation 2 Technology for Power Applications, then upon ExxonMobil's written request, FCE agrees to negotiate a grant to ExxonMobil and its Affiliates, under commercially reasonable terms to be determined in good faith, a worldwide, royalty-bearing (with the royalty to be negotiated), non-exclusive, sub-licensable right and license to practice FCE Background Information and FCE Background Patents for Generation 2 Technology in any application outside of Carbon Capture Applications and Hydrogen Applications.",
'Aucta should continue to receive 15% of Net Sales Royalty for as long as ETON is selling the Product(s) in the Territory, unless otherwise agreed to under this Agreement.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.7920, 0.3253],
# [0.7920, 1.0000, 0.4614],
# [0.3253, 0.4614, 1.0000]])Usagepython
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
'License Grant',
"In the event FCE notifies ExxonMobil that it has formally decided not to pursue Generation 2 Technology for Power Applications, then upon ExxonMobil's written request, FCE agrees to negotiate a grant to ExxonMobil and its Affiliates, under commercially reasonable terms to be determined in good faith, a worldwide, royalty-bearing (with the royalty to be negotiated), non-exclusive, sub-licensable right and license to practice FCE Background Information and FCE Background Patents for Generation 2 Technology in any application outside of Carbon Capture Applications and Hydrogen Applications.",
'Aucta should continue to receive 15% of Net Sales Royalty for as long as ETON is selling the Product(s) in the Territory, unless otherwise agreed to under this Agreement.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.7920, 0.3253],
# [0.7920, 1.0000, 0.4614],
# [0.3253, 0.4614, 1.0000]])Usagepython
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
'License Grant',
"In the event FCE notifies ExxonMobil that it has formally decided not to pursue Generation 2 Technology for Power Applications, then upon ExxonMobil's written request, FCE agrees to negotiate a grant to ExxonMobil and its Affiliates, under commercially reasonable terms to be determined in good faith, a worldwide, royalty-bearing (with the royalty to be negotiated), non-exclusive, sub-licensable right and license to practice FCE Background Information and FCE Background Patents for Generation 2 Technology in any application outside of Carbon Capture Applications and Hydrogen Applications.",
'Aucta should continue to receive 15% of Net Sales Royalty for as long as ETON is selling the Product(s) in the Territory, unless otherwise agreed to under this Agreement.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.7920, 0.3253],
# [0.7920, 1.0000, 0.4614],
# [0.3253, 0.4614, 1.0000]])Usagepython
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
'License Grant',
"In the event FCE notifies ExxonMobil that it has formally decided not to pursue Generation 2 Technology for Power Applications, then upon ExxonMobil's written request, FCE agrees to negotiate a grant to ExxonMobil and its Affiliates, under commercially reasonable terms to be determined in good faith, a worldwide, royalty-bearing (with the royalty to be negotiated), non-exclusive, sub-licensable right and license to practice FCE Background Information and FCE Background Patents for Generation 2 Technology in any application outside of Carbon Capture Applications and Hydrogen Applications.",
'Aucta should continue to receive 15% of Net Sales Royalty for as long as ETON is selling the Product(s) in the Territory, unless otherwise agreed to under this Agreement.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.7920, 0.3253],
# [0.7920, 1.0000, 0.4614],
# [0.3253, 0.4614, 1.0000]])Usagepython
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
'License Grant',
"In the event FCE notifies ExxonMobil that it has formally decided not to pursue Generation 2 Technology for Power Applications, then upon ExxonMobil's written request, FCE agrees to negotiate a grant to ExxonMobil and its Affiliates, under commercially reasonable terms to be determined in good faith, a worldwide, royalty-bearing (with the royalty to be negotiated), non-exclusive, sub-licensable right and license to practice FCE Background Information and FCE Background Patents for Generation 2 Technology in any application outside of Carbon Capture Applications and Hydrogen Applications.",
'Aucta should continue to receive 15% of Net Sales Royalty for as long as ETON is selling the Product(s) in the Territory, unless otherwise agreed to under this Agreement.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.7920, 0.3253],
# [0.7920, 1.0000, 0.4614],
# [0.3253, 0.4614, 1.0000]])Usagepython
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
'License Grant',
"In the event FCE notifies ExxonMobil that it has formally decided not to pursue Generation 2 Technology for Power Applications, then upon ExxonMobil's written request, FCE agrees to negotiate a grant to ExxonMobil and its Affiliates, under commercially reasonable terms to be determined in good faith, a worldwide, royalty-bearing (with the royalty to be negotiated), non-exclusive, sub-licensable right and license to practice FCE Background Information and FCE Background Patents for Generation 2 Technology in any application outside of Carbon Capture Applications and Hydrogen Applications.",
'Aucta should continue to receive 15% of Net Sales Royalty for as long as ETON is selling the Product(s) in the Territory, unless otherwise agreed to under this Agreement.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.7920, 0.3253],
# [0.7920, 1.0000, 0.4614],
# [0.3253, 0.4614, 1.0000]])Usagepython
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
'License Grant',
"In the event FCE notifies ExxonMobil that it has formally decided not to pursue Generation 2 Technology for Power Applications, then upon ExxonMobil's written request, FCE agrees to negotiate a grant to ExxonMobil and its Affiliates, under commercially reasonable terms to be determined in good faith, a worldwide, royalty-bearing (with the royalty to be negotiated), non-exclusive, sub-licensable right and license to practice FCE Background Information and FCE Background Patents for Generation 2 Technology in any application outside of Carbon Capture Applications and Hydrogen Applications.",
'Aucta should continue to receive 15% of Net Sales Royalty for as long as ETON is selling the Product(s) in the Territory, unless otherwise agreed to under this Agreement.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.7920, 0.3253],
# [0.7920, 1.0000, 0.4614],
# [0.3253, 0.4614, 1.0000]])Usagepython
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
'License Grant',
"In the event FCE notifies ExxonMobil that it has formally decided not to pursue Generation 2 Technology for Power Applications, then upon ExxonMobil's written request, FCE agrees to negotiate a grant to ExxonMobil and its Affiliates, under commercially reasonable terms to be determined in good faith, a worldwide, royalty-bearing (with the royalty to be negotiated), non-exclusive, sub-licensable right and license to practice FCE Background Information and FCE Background Patents for Generation 2 Technology in any application outside of Carbon Capture Applications and Hydrogen Applications.",
'Aucta should continue to receive 15% of Net Sales Royalty for as long as ETON is selling the Product(s) in the Territory, unless otherwise agreed to under this Agreement.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.7920, 0.3253],
# [0.7920, 1.0000, 0.4614],
# [0.3253, 0.4614, 1.0000]])Usagepython
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
'License Grant',
"In the event FCE notifies ExxonMobil that it has formally decided not to pursue Generation 2 Technology for Power Applications, then upon ExxonMobil's written request, FCE agrees to negotiate a grant to ExxonMobil and its Affiliates, under commercially reasonable terms to be determined in good faith, a worldwide, royalty-bearing (with the royalty to be negotiated), non-exclusive, sub-licensable right and license to practice FCE Background Information and FCE Background Patents for Generation 2 Technology in any application outside of Carbon Capture Applications and Hydrogen Applications.",
'Aucta should continue to receive 15% of Net Sales Royalty for as long as ETON is selling the Product(s) in the Territory, unless otherwise agreed to under this Agreement.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.7920, 0.3253],
# [0.7920, 1.0000, 0.4614],
# [0.3253, 0.4614, 1.0000]])Usagepython
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
'License Grant',
"In the event FCE notifies ExxonMobil that it has formally decided not to pursue Generation 2 Technology for Power Applications, then upon ExxonMobil's written request, FCE agrees to negotiate a grant to ExxonMobil and its Affiliates, under commercially reasonable terms to be determined in good faith, a worldwide, royalty-bearing (with the royalty to be negotiated), non-exclusive, sub-licensable right and license to practice FCE Background Information and FCE Background Patents for Generation 2 Technology in any application outside of Carbon Capture Applications and Hydrogen Applications.",
'Aucta should continue to receive 15% of Net Sales Royalty for as long as ETON is selling the Product(s) in the Territory, unless otherwise agreed to under this Agreement.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.7920, 0.3253],
# [0.7920, 1.0000, 0.4614],
# [0.3253, 0.4614, 1.0000]])Usagepython
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
'License Grant',
"In the event FCE notifies ExxonMobil that it has formally decided not to pursue Generation 2 Technology for Power Applications, then upon ExxonMobil's written request, FCE agrees to negotiate a grant to ExxonMobil and its Affiliates, under commercially reasonable terms to be determined in good faith, a worldwide, royalty-bearing (with the royalty to be negotiated), non-exclusive, sub-licensable right and license to practice FCE Background Information and FCE Background Patents for Generation 2 Technology in any application outside of Carbon Capture Applications and Hydrogen Applications.",
'Aucta should continue to receive 15% of Net Sales Royalty for as long as ETON is selling the Product(s) in the Territory, unless otherwise agreed to under this Agreement.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.7920, 0.3253],
# [0.7920, 1.0000, 0.4614],
# [0.3253, 0.4614, 1.0000]])Usagepython
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
'License Grant',
"In the event FCE notifies ExxonMobil that it has formally decided not to pursue Generation 2 Technology for Power Applications, then upon ExxonMobil's written request, FCE agrees to negotiate a grant to ExxonMobil and its Affiliates, under commercially reasonable terms to be determined in good faith, a worldwide, royalty-bearing (with the royalty to be negotiated), non-exclusive, sub-licensable right and license to practice FCE Background Information and FCE Background Patents for Generation 2 Technology in any application outside of Carbon Capture Applications and Hydrogen Applications.",
'Aucta should continue to receive 15% of Net Sales Royalty for as long as ETON is selling the Product(s) in the Territory, unless otherwise agreed to under this Agreement.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.7920, 0.3253],
# [0.7920, 1.0000, 0.4614],
# [0.3253, 0.4614, 1.0000]])Usagepython
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
'License Grant',
"In the event FCE notifies ExxonMobil that it has formally decided not to pursue Generation 2 Technology for Power Applications, then upon ExxonMobil's written request, FCE agrees to negotiate a grant to ExxonMobil and its Affiliates, under commercially reasonable terms to be determined in good faith, a worldwide, royalty-bearing (with the royalty to be negotiated), non-exclusive, sub-licensable right and license to practice FCE Background Information and FCE Background Patents for Generation 2 Technology in any application outside of Carbon Capture Applications and Hydrogen Applications.",
'Aucta should continue to receive 15% of Net Sales Royalty for as long as ETON is selling the Product(s) in the Territory, unless otherwise agreed to under this Agreement.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.7920, 0.3253],
# [0.7920, 1.0000, 0.4614],
# [0.3253, 0.4614, 1.0000]])Usagepython
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
'License Grant',
"In the event FCE notifies ExxonMobil that it has formally decided not to pursue Generation 2 Technology for Power Applications, then upon ExxonMobil's written request, FCE agrees to negotiate a grant to ExxonMobil and its Affiliates, under commercially reasonable terms to be determined in good faith, a worldwide, royalty-bearing (with the royalty to be negotiated), non-exclusive, sub-licensable right and license to practice FCE Background Information and FCE Background Patents for Generation 2 Technology in any application outside of Carbon Capture Applications and Hydrogen Applications.",
'Aucta should continue to receive 15% of Net Sales Royalty for as long as ETON is selling the Product(s) in the Territory, unless otherwise agreed to under this Agreement.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.7920, 0.3253],
# [0.7920, 1.0000, 0.4614],
# [0.3253, 0.4614, 1.0000]])Usagepython
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
'License Grant',
"In the event FCE notifies ExxonMobil that it has formally decided not to pursue Generation 2 Technology for Power Applications, then upon ExxonMobil's written request, FCE agrees to negotiate a grant to ExxonMobil and its Affiliates, under commercially reasonable terms to be determined in good faith, a worldwide, royalty-bearing (with the royalty to be negotiated), non-exclusive, sub-licensable right and license to practice FCE Background Information and FCE Background Patents for Generation 2 Technology in any application outside of Carbon Capture Applications and Hydrogen Applications.",
'Aucta should continue to receive 15% of Net Sales Royalty for as long as ETON is selling the Product(s) in the Territory, unless otherwise agreed to under this Agreement.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.7920, 0.3253],
# [0.7920, 1.0000, 0.4614],
# [0.3253, 0.4614, 1.0000]])Usagepython
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
'License Grant',
"In the event FCE notifies ExxonMobil that it has formally decided not to pursue Generation 2 Technology for Power Applications, then upon ExxonMobil's written request, FCE agrees to negotiate a grant to ExxonMobil and its Affiliates, under commercially reasonable terms to be determined in good faith, a worldwide, royalty-bearing (with the royalty to be negotiated), non-exclusive, sub-licensable right and license to practice FCE Background Information and FCE Background Patents for Generation 2 Technology in any application outside of Carbon Capture Applications and Hydrogen Applications.",
'Aucta should continue to receive 15% of Net Sales Royalty for as long as ETON is selling the Product(s) in the Territory, unless otherwise agreed to under this Agreement.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.7920, 0.3253],
# [0.7920, 1.0000, 0.4614],
# [0.3253, 0.4614, 1.0000]])Usagepython
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
'License Grant',
"In the event FCE notifies ExxonMobil that it has formally decided not to pursue Generation 2 Technology for Power Applications, then upon ExxonMobil's written request, FCE agrees to negotiate a grant to ExxonMobil and its Affiliates, under commercially reasonable terms to be determined in good faith, a worldwide, royalty-bearing (with the royalty to be negotiated), non-exclusive, sub-licensable right and license to practice FCE Background Information and FCE Background Patents for Generation 2 Technology in any application outside of Carbon Capture Applications and Hydrogen Applications.",
'Aucta should continue to receive 15% of Net Sales Royalty for as long as ETON is selling the Product(s) in the Territory, unless otherwise agreed to under this Agreement.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.7920, 0.3253],
# [0.7920, 1.0000, 0.4614],
# [0.3253, 0.4614, 1.0000]])Usagepython
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
'License Grant',
"In the event FCE notifies ExxonMobil that it has formally decided not to pursue Generation 2 Technology for Power Applications, then upon ExxonMobil's written request, FCE agrees to negotiate a grant to ExxonMobil and its Affiliates, under commercially reasonable terms to be determined in good faith, a worldwide, royalty-bearing (with the royalty to be negotiated), non-exclusive, sub-licensable right and license to practice FCE Background Information and FCE Background Patents for Generation 2 Technology in any application outside of Carbon Capture Applications and Hydrogen Applications.",
'Aucta should continue to receive 15% of Net Sales Royalty for as long as ETON is selling the Product(s) in the Territory, unless otherwise agreed to under this Agreement.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.7920, 0.3253],
# [0.7920, 1.0000, 0.4614],
# [0.3253, 0.4614, 1.0000]])Usagepython
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
'License Grant',
"In the event FCE notifies ExxonMobil that it has formally decided not to pursue Generation 2 Technology for Power Applications, then upon ExxonMobil's written request, FCE agrees to negotiate a grant to ExxonMobil and its Affiliates, under commercially reasonable terms to be determined in good faith, a worldwide, royalty-bearing (with the royalty to be negotiated), non-exclusive, sub-licensable right and license to practice FCE Background Information and FCE Background Patents for Generation 2 Technology in any application outside of Carbon Capture Applications and Hydrogen Applications.",
'Aucta should continue to receive 15% of Net Sales Royalty for as long as ETON is selling the Product(s) in the Territory, unless otherwise agreed to under this Agreement.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.7920, 0.3253],
# [0.7920, 1.0000, 0.4614],
# [0.3253, 0.4614, 1.0000]])Usagepython
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
'License Grant',
"In the event FCE notifies ExxonMobil that it has formally decided not to pursue Generation 2 Technology for Power Applications, then upon ExxonMobil's written request, FCE agrees to negotiate a grant to ExxonMobil and its Affiliates, under commercially reasonable terms to be determined in good faith, a worldwide, royalty-bearing (with the royalty to be negotiated), non-exclusive, sub-licensable right and license to practice FCE Background Information and FCE Background Patents for Generation 2 Technology in any application outside of Carbon Capture Applications and Hydrogen Applications.",
'Aucta should continue to receive 15% of Net Sales Royalty for as long as ETON is selling the Product(s) in the Territory, unless otherwise agreed to under this Agreement.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.7920, 0.3253],
# [0.7920, 1.0000, 0.4614],
# [0.3253, 0.4614, 1.0000]])Usagepython
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
'License Grant',
"In the event FCE notifies ExxonMobil that it has formally decided not to pursue Generation 2 Technology for Power Applications, then upon ExxonMobil's written request, FCE agrees to negotiate a grant to ExxonMobil and its Affiliates, under commercially reasonable terms to be determined in good faith, a worldwide, royalty-bearing (with the royalty to be negotiated), non-exclusive, sub-licensable right and license to practice FCE Background Information and FCE Background Patents for Generation 2 Technology in any application outside of Carbon Capture Applications and Hydrogen Applications.",
'Aucta should continue to receive 15% of Net Sales Royalty for as long as ETON is selling the Product(s) in the Territory, unless otherwise agreed to under this Agreement.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.7920, 0.3253],
# [0.7920, 1.0000, 0.4614],
# [0.3253, 0.4614, 1.0000]])Usagepython
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
'License Grant',
"In the event FCE notifies ExxonMobil that it has formally decided not to pursue Generation 2 Technology for Power Applications, then upon ExxonMobil's written request, FCE agrees to negotiate a grant to ExxonMobil and its Affiliates, under commercially reasonable terms to be determined in good faith, a worldwide, royalty-bearing (with the royalty to be negotiated), non-exclusive, sub-licensable right and license to practice FCE Background Information and FCE Background Patents for Generation 2 Technology in any application outside of Carbon Capture Applications and Hydrogen Applications.",
'Aucta should continue to receive 15% of Net Sales Royalty for as long as ETON is selling the Product(s) in the Territory, unless otherwise agreed to under this Agreement.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.7920, 0.3253],
# [0.7920, 1.0000, 0.4614],
# [0.3253, 0.4614, 1.0000]])Usagepython
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
'License Grant',
"In the event FCE notifies ExxonMobil that it has formally decided not to pursue Generation 2 Technology for Power Applications, then upon ExxonMobil's written request, FCE agrees to negotiate a grant to ExxonMobil and its Affiliates, under commercially reasonable terms to be determined in good faith, a worldwide, royalty-bearing (with the royalty to be negotiated), non-exclusive, sub-licensable right and license to practice FCE Background Information and FCE Background Patents for Generation 2 Technology in any application outside of Carbon Capture Applications and Hydrogen Applications.",
'Aucta should continue to receive 15% of Net Sales Royalty for as long as ETON is selling the Product(s) in the Territory, unless otherwise agreed to under this Agreement.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.7920, 0.3253],
# [0.7920, 1.0000, 0.4614],
# [0.3253, 0.4614, 1.0000]])Usagepython
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
'License Grant',
"In the event FCE notifies ExxonMobil that it has formally decided not to pursue Generation 2 Technology for Power Applications, then upon ExxonMobil's written request, FCE agrees to negotiate a grant to ExxonMobil and its Affiliates, under commercially reasonable terms to be determined in good faith, a worldwide, royalty-bearing (with the royalty to be negotiated), non-exclusive, sub-licensable right and license to practice FCE Background Information and FCE Background Patents for Generation 2 Technology in any application outside of Carbon Capture Applications and Hydrogen Applications.",
'Aucta should continue to receive 15% of Net Sales Royalty for as long as ETON is selling the Product(s) in the Territory, unless otherwise agreed to under this Agreement.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.7920, 0.3253],
# [0.7920, 1.0000, 0.4614],
# [0.3253, 0.4614, 1.0000]])Usagepython
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
'License Grant',
"In the event FCE notifies ExxonMobil that it has formally decided not to pursue Generation 2 Technology for Power Applications, then upon ExxonMobil's written request, FCE agrees to negotiate a grant to ExxonMobil and its Affiliates, under commercially reasonable terms to be determined in good faith, a worldwide, royalty-bearing (with the royalty to be negotiated), non-exclusive, sub-licensable right and license to practice FCE Background Information and FCE Background Patents for Generation 2 Technology in any application outside of Carbon Capture Applications and Hydrogen Applications.",
'Aucta should continue to receive 15% of Net Sales Royalty for as long as ETON is selling the Product(s) in the Territory, unless otherwise agreed to under this Agreement.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.7920, 0.3253],
# [0.7920, 1.0000, 0.4614],
# [0.3253, 0.4614, 1.0000]])Usagepython
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
'License Grant',
"In the event FCE notifies ExxonMobil that it has formally decided not to pursue Generation 2 Technology for Power Applications, then upon ExxonMobil's written request, FCE agrees to negotiate a grant to ExxonMobil and its Affiliates, under commercially reasonable terms to be determined in good faith, a worldwide, royalty-bearing (with the royalty to be negotiated), non-exclusive, sub-licensable right and license to practice FCE Background Information and FCE Background Patents for Generation 2 Technology in any application outside of Carbon Capture Applications and Hydrogen Applications.",
'Aucta should continue to receive 15% of Net Sales Royalty for as long as ETON is selling the Product(s) in the Territory, unless otherwise agreed to under this Agreement.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.7920, 0.3253],
# [0.7920, 1.0000, 0.4614],
# [0.3253, 0.4614, 1.0000]])Usagepython
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
'License Grant',
"In the event FCE notifies ExxonMobil that it has formally decided not to pursue Generation 2 Technology for Power Applications, then upon ExxonMobil's written request, FCE agrees to negotiate a grant to ExxonMobil and its Affiliates, under commercially reasonable terms to be determined in good faith, a worldwide, royalty-bearing (with the royalty to be negotiated), non-exclusive, sub-licensable right and license to practice FCE Background Information and FCE Background Patents for Generation 2 Technology in any application outside of Carbon Capture Applications and Hydrogen Applications.",
'Aucta should continue to receive 15% of Net Sales Royalty for as long as ETON is selling the Product(s) in the Territory, unless otherwise agreed to under this Agreement.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.7920, 0.3253],
# [0.7920, 1.0000, 0.4614],
# [0.3253, 0.4614, 1.0000]])Usagepython
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
'License Grant',
"In the event FCE notifies ExxonMobil that it has formally decided not to pursue Generation 2 Technology for Power Applications, then upon ExxonMobil's written request, FCE agrees to negotiate a grant to ExxonMobil and its Affiliates, under commercially reasonable terms to be determined in good faith, a worldwide, royalty-bearing (with the royalty to be negotiated), non-exclusive, sub-licensable right and license to practice FCE Background Information and FCE Background Patents for Generation 2 Technology in any application outside of Carbon Capture Applications and Hydrogen Applications.",
'Aucta should continue to receive 15% of Net Sales Royalty for as long as ETON is selling the Product(s) in the Territory, unless otherwise agreed to under this Agreement.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.7920, 0.3253],
# [0.7920, 1.0000, 0.4614],
# [0.3253, 0.4614, 1.0000]])Usagepython
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
'License Grant',
"In the event FCE notifies ExxonMobil that it has formally decided not to pursue Generation 2 Technology for Power Applications, then upon ExxonMobil's written request, FCE agrees to negotiate a grant to ExxonMobil and its Affiliates, under commercially reasonable terms to be determined in good faith, a worldwide, royalty-bearing (with the royalty to be negotiated), non-exclusive, sub-licensable right and license to practice FCE Background Information and FCE Background Patents for Generation 2 Technology in any application outside of Carbon Capture Applications and Hydrogen Applications.",
'Aucta should continue to receive 15% of Net Sales Royalty for as long as ETON is selling the Product(s) in the Territory, unless otherwise agreed to under this Agreement.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.7920, 0.3253],
# [0.7920, 1.0000, 0.4614],
# [0.3253, 0.4614, 1.0000]])Usagepython
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
'License Grant',
"In the event FCE notifies ExxonMobil that it has formally decided not to pursue Generation 2 Technology for Power Applications, then upon ExxonMobil's written request, FCE agrees to negotiate a grant to ExxonMobil and its Affiliates, under commercially reasonable terms to be determined in good faith, a worldwide, royalty-bearing (with the royalty to be negotiated), non-exclusive, sub-licensable right and license to practice FCE Background Information and FCE Background Patents for Generation 2 Technology in any application outside of Carbon Capture Applications and Hydrogen Applications.",
'Aucta should continue to receive 15% of Net Sales Royalty for as long as ETON is selling the Product(s) in the Territory, unless otherwise agreed to under this Agreement.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.7920, 0.3253],
# [0.7920, 1.0000, 0.4614],
# [0.3253, 0.4614, 1.0000]])Usagepython
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
'License Grant',
"In the event FCE notifies ExxonMobil that it has formally decided not to pursue Generation 2 Technology for Power Applications, then upon ExxonMobil's written request, FCE agrees to negotiate a grant to ExxonMobil and its Affiliates, under commercially reasonable terms to be determined in good faith, a worldwide, royalty-bearing (with the royalty to be negotiated), non-exclusive, sub-licensable right and license to practice FCE Background Information and FCE Background Patents for Generation 2 Technology in any application outside of Carbon Capture Applications and Hydrogen Applications.",
'Aucta should continue to receive 15% of Net Sales Royalty for as long as ETON is selling the Product(s) in the Territory, unless otherwise agreed to under this Agreement.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.7920, 0.3253],
# [0.7920, 1.0000, 0.4614],
# [0.3253, 0.4614, 1.0000]])Usagepython
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
'License Grant',
"In the event FCE notifies ExxonMobil that it has formally decided not to pursue Generation 2 Technology for Power Applications, then upon ExxonMobil's written request, FCE agrees to negotiate a grant to ExxonMobil and its Affiliates, under commercially reasonable terms to be determined in good faith, a worldwide, royalty-bearing (with the royalty to be negotiated), non-exclusive, sub-licensable right and license to practice FCE Background Information and FCE Background Patents for Generation 2 Technology in any application outside of Carbon Capture Applications and Hydrogen Applications.",
'Aucta should continue to receive 15% of Net Sales Royalty for as long as ETON is selling the Product(s) in the Territory, unless otherwise agreed to under this Agreement.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.7920, 0.3253],
# [0.7920, 1.0000, 0.4614],
# [0.3253, 0.4614, 1.0000]])Usagepython
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
'License Grant',
"In the event FCE notifies ExxonMobil that it has formally decided not to pursue Generation 2 Technology for Power Applications, then upon ExxonMobil's written request, FCE agrees to negotiate a grant to ExxonMobil and its Affiliates, under commercially reasonable terms to be determined in good faith, a worldwide, royalty-bearing (with the royalty to be negotiated), non-exclusive, sub-licensable right and license to practice FCE Background Information and FCE Background Patents for Generation 2 Technology in any application outside of Carbon Capture Applications and Hydrogen Applications.",
'Aucta should continue to receive 15% of Net Sales Royalty for as long as ETON is selling the Product(s) in the Territory, unless otherwise agreed to under this Agreement.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.7920, 0.3253],
# [0.7920, 1.0000, 0.4614],
# [0.3253, 0.4614, 1.0000]])Usagepython
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
'License Grant',
"In the event FCE notifies ExxonMobil that it has formally decided not to pursue Generation 2 Technology for Power Applications, then upon ExxonMobil's written request, FCE agrees to negotiate a grant to ExxonMobil and its Affiliates, under commercially reasonable terms to be determined in good faith, a worldwide, royalty-bearing (with the royalty to be negotiated), non-exclusive, sub-licensable right and license to practice FCE Background Information and FCE Background Patents for Generation 2 Technology in any application outside of Carbon Capture Applications and Hydrogen Applications.",
'Aucta should continue to receive 15% of Net Sales Royalty for as long as ETON is selling the Product(s) in the Territory, unless otherwise agreed to under this Agreement.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.7920, 0.3253],
# [0.7920, 1.0000, 0.4614],
# [0.3253, 0.4614, 1.0000]])Usagepython
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
'License Grant',
"In the event FCE notifies ExxonMobil that it has formally decided not to pursue Generation 2 Technology for Power Applications, then upon ExxonMobil's written request, FCE agrees to negotiate a grant to ExxonMobil and its Affiliates, under commercially reasonable terms to be determined in good faith, a worldwide, royalty-bearing (with the royalty to be negotiated), non-exclusive, sub-licensable right and license to practice FCE Background Information and FCE Background Patents for Generation 2 Technology in any application outside of Carbon Capture Applications and Hydrogen Applications.",
'Aucta should continue to receive 15% of Net Sales Royalty for as long as ETON is selling the Product(s) in the Territory, unless otherwise agreed to under this Agreement.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.7920, 0.3253],
# [0.7920, 1.0000, 0.4614],
# [0.3253, 0.4614, 1.0000]])Usagepython
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
'License Grant',
"In the event FCE notifies ExxonMobil that it has formally decided not to pursue Generation 2 Technology for Power Applications, then upon ExxonMobil's written request, FCE agrees to negotiate a grant to ExxonMobil and its Affiliates, under commercially reasonable terms to be determined in good faith, a worldwide, royalty-bearing (with the royalty to be negotiated), non-exclusive, sub-licensable right and license to practice FCE Background Information and FCE Background Patents for Generation 2 Technology in any application outside of Carbon Capture Applications and Hydrogen Applications.",
'Aucta should continue to receive 15% of Net Sales Royalty for as long as ETON is selling the Product(s) in the Territory, unless otherwise agreed to under this Agreement.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.7920, 0.3253],
# [0.7920, 1.0000, 0.4614],
# [0.3253, 0.4614, 1.0000]])Usagepython
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
'License Grant',
"In the event FCE notifies ExxonMobil that it has formally decided not to pursue Generation 2 Technology for Power Applications, then upon ExxonMobil's written request, FCE agrees to negotiate a grant to ExxonMobil and its Affiliates, under commercially reasonable terms to be determined in good faith, a worldwide, royalty-bearing (with the royalty to be negotiated), non-exclusive, sub-licensable right and license to practice FCE Background Information and FCE Background Patents for Generation 2 Technology in any application outside of Carbon Capture Applications and Hydrogen Applications.",
'Aucta should continue to receive 15% of Net Sales Royalty for as long as ETON is selling the Product(s) in the Territory, unless otherwise agreed to under this Agreement.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.7920, 0.3253],
# [0.7920, 1.0000, 0.4614],
# [0.3253, 0.4614, 1.0000]])Usagepython
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
'License Grant',
"In the event FCE notifies ExxonMobil that it has formally decided not to pursue Generation 2 Technology for Power Applications, then upon ExxonMobil's written request, FCE agrees to negotiate a grant to ExxonMobil and its Affiliates, under commercially reasonable terms to be determined in good faith, a worldwide, royalty-bearing (with the royalty to be negotiated), non-exclusive, sub-licensable right and license to practice FCE Background Information and FCE Background Patents for Generation 2 Technology in any application outside of Carbon Capture Applications and Hydrogen Applications.",
'Aucta should continue to receive 15% of Net Sales Royalty for as long as ETON is selling the Product(s) in the Territory, unless otherwise agreed to under this Agreement.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.7920, 0.3253],
# [0.7920, 1.0000, 0.4614],
# [0.3253, 0.4614, 1.0000]])Usagepython
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
'License Grant',
"In the event FCE notifies ExxonMobil that it has formally decided not to pursue Generation 2 Technology for Power Applications, then upon ExxonMobil's written request, FCE agrees to negotiate a grant to ExxonMobil and its Affiliates, under commercially reasonable terms to be determined in good faith, a worldwide, royalty-bearing (with the royalty to be negotiated), non-exclusive, sub-licensable right and license to practice FCE Background Information and FCE Background Patents for Generation 2 Technology in any application outside of Carbon Capture Applications and Hydrogen Applications.",
'Aucta should continue to receive 15% of Net Sales Royalty for as long as ETON is selling the Product(s) in the Territory, unless otherwise agreed to under this Agreement.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.7920, 0.3253],
# [0.7920, 1.0000, 0.4614],
# [0.3253, 0.4614, 1.0000]])Usagepython
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
'License Grant',
"In the event FCE notifies ExxonMobil that it has formally decided not to pursue Generation 2 Technology for Power Applications, then upon ExxonMobil's written request, FCE agrees to negotiate a grant to ExxonMobil and its Affiliates, under commercially reasonable terms to be determined in good faith, a worldwide, royalty-bearing (with the royalty to be negotiated), non-exclusive, sub-licensable right and license to practice FCE Background Information and FCE Background Patents for Generation 2 Technology in any application outside of Carbon Capture Applications and Hydrogen Applications.",
'Aucta should continue to receive 15% of Net Sales Royalty for as long as ETON is selling the Product(s) in the Territory, unless otherwise agreed to under this Agreement.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.7920, 0.3253],
# [0.7920, 1.0000, 0.4614],
# [0.3253, 0.4614, 1.0000]])Usagepython
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
'License Grant',
"In the event FCE notifies ExxonMobil that it has formally decided not to pursue Generation 2 Technology for Power Applications, then upon ExxonMobil's written request, FCE agrees to negotiate a grant to ExxonMobil and its Affiliates, under commercially reasonable terms to be determined in good faith, a worldwide, royalty-bearing (with the royalty to be negotiated), non-exclusive, sub-licensable right and license to practice FCE Background Information and FCE Background Patents for Generation 2 Technology in any application outside of Carbon Capture Applications and Hydrogen Applications.",
'Aucta should continue to receive 15% of Net Sales Royalty for as long as ETON is selling the Product(s) in the Territory, unless otherwise agreed to under this Agreement.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.7920, 0.3253],
# [0.7920, 1.0000, 0.4614],
# [0.3253, 0.4614, 1.0000]])Usagepython
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
'License Grant',
"In the event FCE notifies ExxonMobil that it has formally decided not to pursue Generation 2 Technology for Power Applications, then upon ExxonMobil's written request, FCE agrees to negotiate a grant to ExxonMobil and its Affiliates, under commercially reasonable terms to be determined in good faith, a worldwide, royalty-bearing (with the royalty to be negotiated), non-exclusive, sub-licensable right and license to practice FCE Background Information and FCE Background Patents for Generation 2 Technology in any application outside of Carbon Capture Applications and Hydrogen Applications.",
'Aucta should continue to receive 15% of Net Sales Royalty for as long as ETON is selling the Product(s) in the Territory, unless otherwise agreed to under this Agreement.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.7920, 0.3253],
# [0.7920, 1.0000, 0.4614],
# [0.3253, 0.4614, 1.0000]])Usagepython
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
'License Grant',
"In the event FCE notifies ExxonMobil that it has formally decided not to pursue Generation 2 Technology for Power Applications, then upon ExxonMobil's written request, FCE agrees to negotiate a grant to ExxonMobil and its Affiliates, under commercially reasonable terms to be determined in good faith, a worldwide, royalty-bearing (with the royalty to be negotiated), non-exclusive, sub-licensable right and license to practice FCE Background Information and FCE Background Patents for Generation 2 Technology in any application outside of Carbon Capture Applications and Hydrogen Applications.",
'Aucta should continue to receive 15% of Net Sales Royalty for as long as ETON is selling the Product(s) in the Territory, unless otherwise agreed to under this Agreement.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.7920, 0.3253],
# [0.7920, 1.0000, 0.4614],
# [0.3253, 0.4614, 1.0000]])Usagepython
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
'License Grant',
"In the event FCE notifies ExxonMobil that it has formally decided not to pursue Generation 2 Technology for Power Applications, then upon ExxonMobil's written request, FCE agrees to negotiate a grant to ExxonMobil and its Affiliates, under commercially reasonable terms to be determined in good faith, a worldwide, royalty-bearing (with the royalty to be negotiated), non-exclusive, sub-licensable right and license to practice FCE Background Information and FCE Background Patents for Generation 2 Technology in any application outside of Carbon Capture Applications and Hydrogen Applications.",
'Aucta should continue to receive 15% of Net Sales Royalty for as long as ETON is selling the Product(s) in the Territory, unless otherwise agreed to under this Agreement.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.7920, 0.3253],
# [0.7920, 1.0000, 0.4614],
# [0.3253, 0.4614, 1.0000]])Usagepython
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
'License Grant',
"In the event FCE notifies ExxonMobil that it has formally decided not to pursue Generation 2 Technology for Power Applications, then upon ExxonMobil's written request, FCE agrees to negotiate a grant to ExxonMobil and its Affiliates, under commercially reasonable terms to be determined in good faith, a worldwide, royalty-bearing (with the royalty to be negotiated), non-exclusive, sub-licensable right and license to practice FCE Background Information and FCE Background Patents for Generation 2 Technology in any application outside of Carbon Capture Applications and Hydrogen Applications.",
'Aucta should continue to receive 15% of Net Sales Royalty for as long as ETON is selling the Product(s) in the Territory, unless otherwise agreed to under this Agreement.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.7920, 0.3253],
# [0.7920, 1.0000, 0.4614],
# [0.3253, 0.4614, 1.0000]])Usagepython
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
'License Grant',
"In the event FCE notifies ExxonMobil that it has formally decided not to pursue Generation 2 Technology for Power Applications, then upon ExxonMobil's written request, FCE agrees to negotiate a grant to ExxonMobil and its Affiliates, under commercially reasonable terms to be determined in good faith, a worldwide, royalty-bearing (with the royalty to be negotiated), non-exclusive, sub-licensable right and license to practice FCE Background Information and FCE Background Patents for Generation 2 Technology in any application outside of Carbon Capture Applications and Hydrogen Applications.",
'Aucta should continue to receive 15% of Net Sales Royalty for as long as ETON is selling the Product(s) in the Territory, unless otherwise agreed to under this Agreement.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.7920, 0.3253],
# [0.7920, 1.0000, 0.4614],
# [0.3253, 0.4614, 1.0000]])Usagepython
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
'License Grant',
"In the event FCE notifies ExxonMobil that it has formally decided not to pursue Generation 2 Technology for Power Applications, then upon ExxonMobil's written request, FCE agrees to negotiate a grant to ExxonMobil and its Affiliates, under commercially reasonable terms to be determined in good faith, a worldwide, royalty-bearing (with the royalty to be negotiated), non-exclusive, sub-licensable right and license to practice FCE Background Information and FCE Background Patents for Generation 2 Technology in any application outside of Carbon Capture Applications and Hydrogen Applications.",
'Aucta should continue to receive 15% of Net Sales Royalty for as long as ETON is selling the Product(s) in the Territory, unless otherwise agreed to under this Agreement.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.7920, 0.3253],
# [0.7920, 1.0000, 0.4614],
# [0.3253, 0.4614, 1.0000]])Usagepython
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
'License Grant',
"In the event FCE notifies ExxonMobil that it has formally decided not to pursue Generation 2 Technology for Power Applications, then upon ExxonMobil's written request, FCE agrees to negotiate a grant to ExxonMobil and its Affiliates, under commercially reasonable terms to be determined in good faith, a worldwide, royalty-bearing (with the royalty to be negotiated), non-exclusive, sub-licensable right and license to practice FCE Background Information and FCE Background Patents for Generation 2 Technology in any application outside of Carbon Capture Applications and Hydrogen Applications.",
'Aucta should continue to receive 15% of Net Sales Royalty for as long as ETON is selling the Product(s) in the Territory, unless otherwise agreed to under this Agreement.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.7920, 0.3253],
# [0.7920, 1.0000, 0.4614],
# [0.3253, 0.4614, 1.0000]])Usagepython
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
'License Grant',
"In the event FCE notifies ExxonMobil that it has formally decided not to pursue Generation 2 Technology for Power Applications, then upon ExxonMobil's written request, FCE agrees to negotiate a grant to ExxonMobil and its Affiliates, under commercially reasonable terms to be determined in good faith, a worldwide, royalty-bearing (with the royalty to be negotiated), non-exclusive, sub-licensable right and license to practice FCE Background Information and FCE Background Patents for Generation 2 Technology in any application outside of Carbon Capture Applications and Hydrogen Applications.",
'Aucta should continue to receive 15% of Net Sales Royalty for as long as ETON is selling the Product(s) in the Territory, unless otherwise agreed to under this Agreement.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.7920, 0.3253],
# [0.7920, 1.0000, 0.4614],
# [0.3253, 0.4614, 1.0000]])Usagepython
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
'License Grant',
"In the event FCE notifies ExxonMobil that it has formally decided not to pursue Generation 2 Technology for Power Applications, then upon ExxonMobil's written request, FCE agrees to negotiate a grant to ExxonMobil and its Affiliates, under commercially reasonable terms to be determined in good faith, a worldwide, royalty-bearing (with the royalty to be negotiated), non-exclusive, sub-licensable right and license to practice FCE Background Information and FCE Background Patents for Generation 2 Technology in any application outside of Carbon Capture Applications and Hydrogen Applications.",
'Aucta should continue to receive 15% of Net Sales Royalty for as long as ETON is selling the Product(s) in the Territory, unless otherwise agreed to under this Agreement.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.7920, 0.3253],
# [0.7920, 1.0000, 0.4614],
# [0.3253, 0.4614, 1.0000]])Usagepython
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
'License Grant',
"In the event FCE notifies ExxonMobil that it has formally decided not to pursue Generation 2 Technology for Power Applications, then upon ExxonMobil's written request, FCE agrees to negotiate a grant to ExxonMobil and its Affiliates, under commercially reasonable terms to be determined in good faith, a worldwide, royalty-bearing (with the royalty to be negotiated), non-exclusive, sub-licensable right and license to practice FCE Background Information and FCE Background Patents for Generation 2 Technology in any application outside of Carbon Capture Applications and Hydrogen Applications.",
'Aucta should continue to receive 15% of Net Sales Royalty for as long as ETON is selling the Product(s) in the Territory, unless otherwise agreed to under this Agreement.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.7920, 0.3253],
# [0.7920, 1.0000, 0.4614],
# [0.3253, 0.4614, 1.0000]])Usagepython
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
'License Grant',
"In the event FCE notifies ExxonMobil that it has formally decided not to pursue Generation 2 Technology for Power Applications, then upon ExxonMobil's written request, FCE agrees to negotiate a grant to ExxonMobil and its Affiliates, under commercially reasonable terms to be determined in good faith, a worldwide, royalty-bearing (with the royalty to be negotiated), non-exclusive, sub-licensable right and license to practice FCE Background Information and FCE Background Patents for Generation 2 Technology in any application outside of Carbon Capture Applications and Hydrogen Applications.",
'Aucta should continue to receive 15% of Net Sales Royalty for as long as ETON is selling the Product(s) in the Territory, unless otherwise agreed to under this Agreement.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.7920, 0.3253],
# [0.7920, 1.0000, 0.4614],
# [0.3253, 0.4614, 1.0000]])Usagepython
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
'License Grant',
"In the event FCE notifies ExxonMobil that it has formally decided not to pursue Generation 2 Technology for Power Applications, then upon ExxonMobil's written request, FCE agrees to negotiate a grant to ExxonMobil and its Affiliates, under commercially reasonable terms to be determined in good faith, a worldwide, royalty-bearing (with the royalty to be negotiated), non-exclusive, sub-licensable right and license to practice FCE Background Information and FCE Background Patents for Generation 2 Technology in any application outside of Carbon Capture Applications and Hydrogen Applications.",
'Aucta should continue to receive 15% of Net Sales Royalty for as long as ETON is selling the Product(s) in the Territory, unless otherwise agreed to under this Agreement.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.7920, 0.3253],
# [0.7920, 1.0000, 0.4614],
# [0.3253, 0.4614, 1.0000]])Usagepython
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
'License Grant',
"In the event FCE notifies ExxonMobil that it has formally decided not to pursue Generation 2 Technology for Power Applications, then upon ExxonMobil's written request, FCE agrees to negotiate a grant to ExxonMobil and its Affiliates, under commercially reasonable terms to be determined in good faith, a worldwide, royalty-bearing (with the royalty to be negotiated), non-exclusive, sub-licensable right and license to practice FCE Background Information and FCE Background Patents for Generation 2 Technology in any application outside of Carbon Capture Applications and Hydrogen Applications.",
'Aucta should continue to receive 15% of Net Sales Royalty for as long as ETON is selling the Product(s) in the Territory, unless otherwise agreed to under this Agreement.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.7920, 0.3253],
# [0.7920, 1.0000, 0.4614],
# [0.3253, 0.4614, 1.0000]])Usagepython
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
'License Grant',
"In the event FCE notifies ExxonMobil that it has formally decided not to pursue Generation 2 Technology for Power Applications, then upon ExxonMobil's written request, FCE agrees to negotiate a grant to ExxonMobil and its Affiliates, under commercially reasonable terms to be determined in good faith, a worldwide, royalty-bearing (with the royalty to be negotiated), non-exclusive, sub-licensable right and license to practice FCE Background Information and FCE Background Patents for Generation 2 Technology in any application outside of Carbon Capture Applications and Hydrogen Applications.",
'Aucta should continue to receive 15% of Net Sales Royalty for as long as ETON is selling the Product(s) in the Territory, unless otherwise agreed to under this Agreement.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.7920, 0.3253],
# [0.7920, 1.0000, 0.4614],
# [0.3253, 0.4614, 1.0000]])Usagepython
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
'License Grant',
"In the event FCE notifies ExxonMobil that it has formally decided not to pursue Generation 2 Technology for Power Applications, then upon ExxonMobil's written request, FCE agrees to negotiate a grant to ExxonMobil and its Affiliates, under commercially reasonable terms to be determined in good faith, a worldwide, royalty-bearing (with the royalty to be negotiated), non-exclusive, sub-licensable right and license to practice FCE Background Information and FCE Background Patents for Generation 2 Technology in any application outside of Carbon Capture Applications and Hydrogen Applications.",
'Aucta should continue to receive 15% of Net Sales Royalty for as long as ETON is selling the Product(s) in the Territory, unless otherwise agreed to under this Agreement.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.7920, 0.3253],
# [0.7920, 1.0000, 0.4614],
# [0.3253, 0.4614, 1.0000]])Usagepython
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
'License Grant',
"In the event FCE notifies ExxonMobil that it has formally decided not to pursue Generation 2 Technology for Power Applications, then upon ExxonMobil's written request, FCE agrees to negotiate a grant to ExxonMobil and its Affiliates, under commercially reasonable terms to be determined in good faith, a worldwide, royalty-bearing (with the royalty to be negotiated), non-exclusive, sub-licensable right and license to practice FCE Background Information and FCE Background Patents for Generation 2 Technology in any application outside of Carbon Capture Applications and Hydrogen Applications.",
'Aucta should continue to receive 15% of Net Sales Royalty for as long as ETON is selling the Product(s) in the Territory, unless otherwise agreed to under this Agreement.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.7920, 0.3253],
# [0.7920, 1.0000, 0.4614],
# [0.3253, 0.4614, 1.0000]])Usagepython
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
'License Grant',
"In the event FCE notifies ExxonMobil that it has formally decided not to pursue Generation 2 Technology for Power Applications, then upon ExxonMobil's written request, FCE agrees to negotiate a grant to ExxonMobil and its Affiliates, under commercially reasonable terms to be determined in good faith, a worldwide, royalty-bearing (with the royalty to be negotiated), non-exclusive, sub-licensable right and license to practice FCE Background Information and FCE Background Patents for Generation 2 Technology in any application outside of Carbon Capture Applications and Hydrogen Applications.",
'Aucta should continue to receive 15% of Net Sales Royalty for as long as ETON is selling the Product(s) in the Territory, unless otherwise agreed to under this Agreement.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.7920, 0.3253],
# [0.7920, 1.0000, 0.4614],
# [0.3253, 0.4614, 1.0000]])Usagepython
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
'License Grant',
"In the event FCE notifies ExxonMobil that it has formally decided not to pursue Generation 2 Technology for Power Applications, then upon ExxonMobil's written request, FCE agrees to negotiate a grant to ExxonMobil and its Affiliates, under commercially reasonable terms to be determined in good faith, a worldwide, royalty-bearing (with the royalty to be negotiated), non-exclusive, sub-licensable right and license to practice FCE Background Information and FCE Background Patents for Generation 2 Technology in any application outside of Carbon Capture Applications and Hydrogen Applications.",
'Aucta should continue to receive 15% of Net Sales Royalty for as long as ETON is selling the Product(s) in the Territory, unless otherwise agreed to under this Agreement.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.7920, 0.3253],
# [0.7920, 1.0000, 0.4614],
# [0.3253, 0.4614, 1.0000]])Usagepython
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
'License Grant',
"In the event FCE notifies ExxonMobil that it has formally decided not to pursue Generation 2 Technology for Power Applications, then upon ExxonMobil's written request, FCE agrees to negotiate a grant to ExxonMobil and its Affiliates, under commercially reasonable terms to be determined in good faith, a worldwide, royalty-bearing (with the royalty to be negotiated), non-exclusive, sub-licensable right and license to practice FCE Background Information and FCE Background Patents for Generation 2 Technology in any application outside of Carbon Capture Applications and Hydrogen Applications.",
'Aucta should continue to receive 15% of Net Sales Royalty for as long as ETON is selling the Product(s) in the Territory, unless otherwise agreed to under this Agreement.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.7920, 0.3253],
# [0.7920, 1.0000, 0.4614],
# [0.3253, 0.4614, 1.0000]])Usagepython
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
'License Grant',
"In the event FCE notifies ExxonMobil that it has formally decided not to pursue Generation 2 Technology for Power Applications, then upon ExxonMobil's written request, FCE agrees to negotiate a grant to ExxonMobil and its Affiliates, under commercially reasonable terms to be determined in good faith, a worldwide, royalty-bearing (with the royalty to be negotiated), non-exclusive, sub-licensable right and license to practice FCE Background Information and FCE Background Patents for Generation 2 Technology in any application outside of Carbon Capture Applications and Hydrogen Applications.",
'Aucta should continue to receive 15% of Net Sales Royalty for as long as ETON is selling the Product(s) in the Territory, unless otherwise agreed to under this Agreement.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.7920, 0.3253],
# [0.7920, 1.0000, 0.4614],
# [0.3253, 0.4614, 1.0000]])Usagepython
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
'License Grant',
"In the event FCE notifies ExxonMobil that it has formally decided not to pursue Generation 2 Technology for Power Applications, then upon ExxonMobil's written request, FCE agrees to negotiate a grant to ExxonMobil and its Affiliates, under commercially reasonable terms to be determined in good faith, a worldwide, royalty-bearing (with the royalty to be negotiated), non-exclusive, sub-licensable right and license to practice FCE Background Information and FCE Background Patents for Generation 2 Technology in any application outside of Carbon Capture Applications and Hydrogen Applications.",
'Aucta should continue to receive 15% of Net Sales Royalty for as long as ETON is selling the Product(s) in the Territory, unless otherwise agreed to under this Agreement.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.7920, 0.3253],
# [0.7920, 1.0000, 0.4614],
# [0.3253, 0.4614, 1.0000]])Usagepython
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
'License Grant',
"In the event FCE notifies ExxonMobil that it has formally decided not to pursue Generation 2 Technology for Power Applications, then upon ExxonMobil's written request, FCE agrees to negotiate a grant to ExxonMobil and its Affiliates, under commercially reasonable terms to be determined in good faith, a worldwide, royalty-bearing (with the royalty to be negotiated), non-exclusive, sub-licensable right and license to practice FCE Background Information and FCE Background Patents for Generation 2 Technology in any application outside of Carbon Capture Applications and Hydrogen Applications.",
'Aucta should continue to receive 15% of Net Sales Royalty for as long as ETON is selling the Product(s) in the Territory, unless otherwise agreed to under this Agreement.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.7920, 0.3253],
# [0.7920, 1.0000, 0.4614],
# [0.3253, 0.4614, 1.0000]])Usagepython
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
'License Grant',
"In the event FCE notifies ExxonMobil that it has formally decided not to pursue Generation 2 Technology for Power Applications, then upon ExxonMobil's written request, FCE agrees to negotiate a grant to ExxonMobil and its Affiliates, under commercially reasonable terms to be determined in good faith, a worldwide, royalty-bearing (with the royalty to be negotiated), non-exclusive, sub-licensable right and license to practice FCE Background Information and FCE Background Patents for Generation 2 Technology in any application outside of Carbon Capture Applications and Hydrogen Applications.",
'Aucta should continue to receive 15% of Net Sales Royalty for as long as ETON is selling the Product(s) in the Territory, unless otherwise agreed to under this Agreement.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.7920, 0.3253],
# [0.7920, 1.0000, 0.4614],
# [0.3253, 0.4614, 1.0000]])Usagepython
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
'License Grant',
"In the event FCE notifies ExxonMobil that it has formally decided not to pursue Generation 2 Technology for Power Applications, then upon ExxonMobil's written request, FCE agrees to negotiate a grant to ExxonMobil and its Affiliates, under commercially reasonable terms to be determined in good faith, a worldwide, royalty-bearing (with the royalty to be negotiated), non-exclusive, sub-licensable right and license to practice FCE Background Information and FCE Background Patents for Generation 2 Technology in any application outside of Carbon Capture Applications and Hydrogen Applications.",
'Aucta should continue to receive 15% of Net Sales Royalty for as long as ETON is selling the Product(s) in the Territory, unless otherwise agreed to under this Agreement.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.7920, 0.3253],
# [0.7920, 1.0000, 0.4614],
# [0.3253, 0.4614, 1.0000]])Usagepython
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
'License Grant',
"In the event FCE notifies ExxonMobil that it has formally decided not to pursue Generation 2 Technology for Power Applications, then upon ExxonMobil's written request, FCE agrees to negotiate a grant to ExxonMobil and its Affiliates, under commercially reasonable terms to be determined in good faith, a worldwide, royalty-bearing (with the royalty to be negotiated), non-exclusive, sub-licensable right and license to practice FCE Background Information and FCE Background Patents for Generation 2 Technology in any application outside of Carbon Capture Applications and Hydrogen Applications.",
'Aucta should continue to receive 15% of Net Sales Royalty for as long as ETON is selling the Product(s) in the Territory, unless otherwise agreed to under this Agreement.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.7920, 0.3253],
# [0.7920, 1.0000, 0.4614],
# [0.3253, 0.4614, 1.0000]])Usagepython
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
'License Grant',
"In the event FCE notifies ExxonMobil that it has formally decided not to pursue Generation 2 Technology for Power Applications, then upon ExxonMobil's written request, FCE agrees to negotiate a grant to ExxonMobil and its Affiliates, under commercially reasonable terms to be determined in good faith, a worldwide, royalty-bearing (with the royalty to be negotiated), non-exclusive, sub-licensable right and license to practice FCE Background Information and FCE Background Patents for Generation 2 Technology in any application outside of Carbon Capture Applications and Hydrogen Applications.",
'Aucta should continue to receive 15% of Net Sales Royalty for as long as ETON is selling the Product(s) in the Territory, unless otherwise agreed to under this Agreement.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.7920, 0.3253],
# [0.7920, 1.0000, 0.4614],
# [0.3253, 0.4614, 1.0000]])Usagepython
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
'License Grant',
"In the event FCE notifies ExxonMobil that it has formally decided not to pursue Generation 2 Technology for Power Applications, then upon ExxonMobil's written request, FCE agrees to negotiate a grant to ExxonMobil and its Affiliates, under commercially reasonable terms to be determined in good faith, a worldwide, royalty-bearing (with the royalty to be negotiated), non-exclusive, sub-licensable right and license to practice FCE Background Information and FCE Background Patents for Generation 2 Technology in any application outside of Carbon Capture Applications and Hydrogen Applications.",
'Aucta should continue to receive 15% of Net Sales Royalty for as long as ETON is selling the Product(s) in the Territory, unless otherwise agreed to under this Agreement.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.7920, 0.3253],
# [0.7920, 1.0000, 0.4614],
# [0.3253, 0.4614, 1.0000]])Usagepython
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
'License Grant',
"In the event FCE notifies ExxonMobil that it has formally decided not to pursue Generation 2 Technology for Power Applications, then upon ExxonMobil's written request, FCE agrees to negotiate a grant to ExxonMobil and its Affiliates, under commercially reasonable terms to be determined in good faith, a worldwide, royalty-bearing (with the royalty to be negotiated), non-exclusive, sub-licensable right and license to practice FCE Background Information and FCE Background Patents for Generation 2 Technology in any application outside of Carbon Capture Applications and Hydrogen Applications.",
'Aucta should continue to receive 15% of Net Sales Royalty for as long as ETON is selling the Product(s) in the Territory, unless otherwise agreed to under this Agreement.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.7920, 0.3253],
# [0.7920, 1.0000, 0.4614],
# [0.3253, 0.4614, 1.0000]])Usagepython
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
'License Grant',
"In the event FCE notifies ExxonMobil that it has formally decided not to pursue Generation 2 Technology for Power Applications, then upon ExxonMobil's written request, FCE agrees to negotiate a grant to ExxonMobil and its Affiliates, under commercially reasonable terms to be determined in good faith, a worldwide, royalty-bearing (with the royalty to be negotiated), non-exclusive, sub-licensable right and license to practice FCE Background Information and FCE Background Patents for Generation 2 Technology in any application outside of Carbon Capture Applications and Hydrogen Applications.",
'Aucta should continue to receive 15% of Net Sales Royalty for as long as ETON is selling the Product(s) in the Territory, unless otherwise agreed to under this Agreement.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.7920, 0.3253],
# [0.7920, 1.0000, 0.4614],
# [0.3253, 0.4614, 1.0000]])Usagepython
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
'License Grant',
"In the event FCE notifies ExxonMobil that it has formally decided not to pursue Generation 2 Technology for Power Applications, then upon ExxonMobil's written request, FCE agrees to negotiate a grant to ExxonMobil and its Affiliates, under commercially reasonable terms to be determined in good faith, a worldwide, royalty-bearing (with the royalty to be negotiated), non-exclusive, sub-licensable right and license to practice FCE Background Information and FCE Background Patents for Generation 2 Technology in any application outside of Carbon Capture Applications and Hydrogen Applications.",
'Aucta should continue to receive 15% of Net Sales Royalty for as long as ETON is selling the Product(s) in the Territory, unless otherwise agreed to under this Agreement.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.7920, 0.3253],
# [0.7920, 1.0000, 0.4614],
# [0.3253, 0.4614, 1.0000]])Usagepython
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
'License Grant',
"In the event FCE notifies ExxonMobil that it has formally decided not to pursue Generation 2 Technology for Power Applications, then upon ExxonMobil's written request, FCE agrees to negotiate a grant to ExxonMobil and its Affiliates, under commercially reasonable terms to be determined in good faith, a worldwide, royalty-bearing (with the royalty to be negotiated), non-exclusive, sub-licensable right and license to practice FCE Background Information and FCE Background Patents for Generation 2 Technology in any application outside of Carbon Capture Applications and Hydrogen Applications.",
'Aucta should continue to receive 15% of Net Sales Royalty for as long as ETON is selling the Product(s) in the Territory, unless otherwise agreed to under this Agreement.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.7920, 0.3253],
# [0.7920, 1.0000, 0.4614],
# [0.3253, 0.4614, 1.0000]])Usagepython
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
'License Grant',
"In the event FCE notifies ExxonMobil that it has formally decided not to pursue Generation 2 Technology for Power Applications, then upon ExxonMobil's written request, FCE agrees to negotiate a grant to ExxonMobil and its Affiliates, under commercially reasonable terms to be determined in good faith, a worldwide, royalty-bearing (with the royalty to be negotiated), non-exclusive, sub-licensable right and license to practice FCE Background Information and FCE Background Patents for Generation 2 Technology in any application outside of Carbon Capture Applications and Hydrogen Applications.",
'Aucta should continue to receive 15% of Net Sales Royalty for as long as ETON is selling the Product(s) in the Territory, unless otherwise agreed to under this Agreement.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.7920, 0.3253],
# [0.7920, 1.0000, 0.4614],
# [0.3253, 0.4614, 1.0000]])Usagepython
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
'License Grant',
"In the event FCE notifies ExxonMobil that it has formally decided not to pursue Generation 2 Technology for Power Applications, then upon ExxonMobil's written request, FCE agrees to negotiate a grant to ExxonMobil and its Affiliates, under commercially reasonable terms to be determined in good faith, a worldwide, royalty-bearing (with the royalty to be negotiated), non-exclusive, sub-licensable right and license to practice FCE Background Information and FCE Background Patents for Generation 2 Technology in any application outside of Carbon Capture Applications and Hydrogen Applications.",
'Aucta should continue to receive 15% of Net Sales Royalty for as long as ETON is selling the Product(s) in the Territory, unless otherwise agreed to under this Agreement.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.7920, 0.3253],
# [0.7920, 1.0000, 0.4614],
# [0.3253, 0.4614, 1.0000]])Usagepython
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
'License Grant',
"In the event FCE notifies ExxonMobil that it has formally decided not to pursue Generation 2 Technology for Power Applications, then upon ExxonMobil's written request, FCE agrees to negotiate a grant to ExxonMobil and its Affiliates, under commercially reasonable terms to be determined in good faith, a worldwide, royalty-bearing (with the royalty to be negotiated), non-exclusive, sub-licensable right and license to practice FCE Background Information and FCE Background Patents for Generation 2 Technology in any application outside of Carbon Capture Applications and Hydrogen Applications.",
'Aucta should continue to receive 15% of Net Sales Royalty for as long as ETON is selling the Product(s) in the Territory, unless otherwise agreed to under this Agreement.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.7920, 0.3253],
# [0.7920, 1.0000, 0.4614],
# [0.3253, 0.4614, 1.0000]])Usagepython
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
'License Grant',
"In the event FCE notifies ExxonMobil that it has formally decided not to pursue Generation 2 Technology for Power Applications, then upon ExxonMobil's written request, FCE agrees to negotiate a grant to ExxonMobil and its Affiliates, under commercially reasonable terms to be determined in good faith, a worldwide, royalty-bearing (with the royalty to be negotiated), non-exclusive, sub-licensable right and license to practice FCE Background Information and FCE Background Patents for Generation 2 Technology in any application outside of Carbon Capture Applications and Hydrogen Applications.",
'Aucta should continue to receive 15% of Net Sales Royalty for as long as ETON is selling the Product(s) in the Territory, unless otherwise agreed to under this Agreement.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.7920, 0.3253],
# [0.7920, 1.0000, 0.4614],
# [0.3253, 0.4614, 1.0000]])Usagepython
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
'License Grant',
"In the event FCE notifies ExxonMobil that it has formally decided not to pursue Generation 2 Technology for Power Applications, then upon ExxonMobil's written request, FCE agrees to negotiate a grant to ExxonMobil and its Affiliates, under commercially reasonable terms to be determined in good faith, a worldwide, royalty-bearing (with the royalty to be negotiated), non-exclusive, sub-licensable right and license to practice FCE Background Information and FCE Background Patents for Generation 2 Technology in any application outside of Carbon Capture Applications and Hydrogen Applications.",
'Aucta should continue to receive 15% of Net Sales Royalty for as long as ETON is selling the Product(s) in the Territory, unless otherwise agreed to under this Agreement.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.7920, 0.3253],
# [0.7920, 1.0000, 0.4614],
# [0.3253, 0.4614, 1.0000]])Usagepython
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
'License Grant',
"In the event FCE notifies ExxonMobil that it has formally decided not to pursue Generation 2 Technology for Power Applications, then upon ExxonMobil's written request, FCE agrees to negotiate a grant to ExxonMobil and its Affiliates, under commercially reasonable terms to be determined in good faith, a worldwide, royalty-bearing (with the royalty to be negotiated), non-exclusive, sub-licensable right and license to practice FCE Background Information and FCE Background Patents for Generation 2 Technology in any application outside of Carbon Capture Applications and Hydrogen Applications.",
'Aucta should continue to receive 15% of Net Sales Royalty for as long as ETON is selling the Product(s) in the Territory, unless otherwise agreed to under this Agreement.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.7920, 0.3253],
# [0.7920, 1.0000, 0.4614],
# [0.3253, 0.4614, 1.0000]])Usagepython
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
'License Grant',
"In the event FCE notifies ExxonMobil that it has formally decided not to pursue Generation 2 Technology for Power Applications, then upon ExxonMobil's written request, FCE agrees to negotiate a grant to ExxonMobil and its Affiliates, under commercially reasonable terms to be determined in good faith, a worldwide, royalty-bearing (with the royalty to be negotiated), non-exclusive, sub-licensable right and license to practice FCE Background Information and FCE Background Patents for Generation 2 Technology in any application outside of Carbon Capture Applications and Hydrogen Applications.",
'Aucta should continue to receive 15% of Net Sales Royalty for as long as ETON is selling the Product(s) in the Territory, unless otherwise agreed to under this Agreement.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.7920, 0.3253],
# [0.7920, 1.0000, 0.4614],
# [0.3253, 0.4614, 1.0000]])Usagepython
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
'License Grant',
"In the event FCE notifies ExxonMobil that it has formally decided not to pursue Generation 2 Technology for Power Applications, then upon ExxonMobil's written request, FCE agrees to negotiate a grant to ExxonMobil and its Affiliates, under commercially reasonable terms to be determined in good faith, a worldwide, royalty-bearing (with the royalty to be negotiated), non-exclusive, sub-licensable right and license to practice FCE Background Information and FCE Background Patents for Generation 2 Technology in any application outside of Carbon Capture Applications and Hydrogen Applications.",
'Aucta should continue to receive 15% of Net Sales Royalty for as long as ETON is selling the Product(s) in the Territory, unless otherwise agreed to under this Agreement.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.7920, 0.3253],
# [0.7920, 1.0000, 0.4614],
# [0.3253, 0.4614, 1.0000]])Usagepython
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
'License Grant',
"In the event FCE notifies ExxonMobil that it has formally decided not to pursue Generation 2 Technology for Power Applications, then upon ExxonMobil's written request, FCE agrees to negotiate a grant to ExxonMobil and its Affiliates, under commercially reasonable terms to be determined in good faith, a worldwide, royalty-bearing (with the royalty to be negotiated), non-exclusive, sub-licensable right and license to practice FCE Background Information and FCE Background Patents for Generation 2 Technology in any application outside of Carbon Capture Applications and Hydrogen Applications.",
'Aucta should continue to receive 15% of Net Sales Royalty for as long as ETON is selling the Product(s) in the Territory, unless otherwise agreed to under this Agreement.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.7920, 0.3253],
# [0.7920, 1.0000, 0.4614],
# [0.3253, 0.4614, 1.0000]])Usagepython
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
'License Grant',
"In the event FCE notifies ExxonMobil that it has formally decided not to pursue Generation 2 Technology for Power Applications, then upon ExxonMobil's written request, FCE agrees to negotiate a grant to ExxonMobil and its Affiliates, under commercially reasonable terms to be determined in good faith, a worldwide, royalty-bearing (with the royalty to be negotiated), non-exclusive, sub-licensable right and license to practice FCE Background Information and FCE Background Patents for Generation 2 Technology in any application outside of Carbon Capture Applications and Hydrogen Applications.",
'Aucta should continue to receive 15% of Net Sales Royalty for as long as ETON is selling the Product(s) in the Territory, unless otherwise agreed to under this Agreement.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.7920, 0.3253],
# [0.7920, 1.0000, 0.4614],
# [0.3253, 0.4614, 1.0000]])Usagepython
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
'License Grant',
"In the event FCE notifies ExxonMobil that it has formally decided not to pursue Generation 2 Technology for Power Applications, then upon ExxonMobil's written request, FCE agrees to negotiate a grant to ExxonMobil and its Affiliates, under commercially reasonable terms to be determined in good faith, a worldwide, royalty-bearing (with the royalty to be negotiated), non-exclusive, sub-licensable right and license to practice FCE Background Information and FCE Background Patents for Generation 2 Technology in any application outside of Carbon Capture Applications and Hydrogen Applications.",
'Aucta should continue to receive 15% of Net Sales Royalty for as long as ETON is selling the Product(s) in the Territory, unless otherwise agreed to under this Agreement.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.7920, 0.3253],
# [0.7920, 1.0000, 0.4614],
# [0.3253, 0.4614, 1.0000]])Usagepython
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
'License Grant',
"In the event FCE notifies ExxonMobil that it has formally decided not to pursue Generation 2 Technology for Power Applications, then upon ExxonMobil's written request, FCE agrees to negotiate a grant to ExxonMobil and its Affiliates, under commercially reasonable terms to be determined in good faith, a worldwide, royalty-bearing (with the royalty to be negotiated), non-exclusive, sub-licensable right and license to practice FCE Background Information and FCE Background Patents for Generation 2 Technology in any application outside of Carbon Capture Applications and Hydrogen Applications.",
'Aucta should continue to receive 15% of Net Sales Royalty for as long as ETON is selling the Product(s) in the Territory, unless otherwise agreed to under this Agreement.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.7920, 0.3253],
# [0.7920, 1.0000, 0.4614],
# [0.3253, 0.4614, 1.0000]])Usagepython
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
'License Grant',
"In the event FCE notifies ExxonMobil that it has formally decided not to pursue Generation 2 Technology for Power Applications, then upon ExxonMobil's written request, FCE agrees to negotiate a grant to ExxonMobil and its Affiliates, under commercially reasonable terms to be determined in good faith, a worldwide, royalty-bearing (with the royalty to be negotiated), non-exclusive, sub-licensable right and license to practice FCE Background Information and FCE Background Patents for Generation 2 Technology in any application outside of Carbon Capture Applications and Hydrogen Applications.",
'Aucta should continue to receive 15% of Net Sales Royalty for as long as ETON is selling the Product(s) in the Territory, unless otherwise agreed to under this Agreement.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.7920, 0.3253],
# [0.7920, 1.0000, 0.4614],
# [0.3253, 0.4614, 1.0000]])Usagepython
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
'License Grant',
"In the event FCE notifies ExxonMobil that it has formally decided not to pursue Generation 2 Technology for Power Applications, then upon ExxonMobil's written request, FCE agrees to negotiate a grant to ExxonMobil and its Affiliates, under commercially reasonable terms to be determined in good faith, a worldwide, royalty-bearing (with the royalty to be negotiated), non-exclusive, sub-licensable right and license to practice FCE Background Information and FCE Background Patents for Generation 2 Technology in any application outside of Carbon Capture Applications and Hydrogen Applications.",
'Aucta should continue to receive 15% of Net Sales Royalty for as long as ETON is selling the Product(s) in the Territory, unless otherwise agreed to under this Agreement.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.7920, 0.3253],
# [0.7920, 1.0000, 0.4614],
# [0.3253, 0.4614, 1.0000]])Usagepython
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
'License Grant',
"In the event FCE notifies ExxonMobil that it has formally decided not to pursue Generation 2 Technology for Power Applications, then upon ExxonMobil's written request, FCE agrees to negotiate a grant to ExxonMobil and its Affiliates, under commercially reasonable terms to be determined in good faith, a worldwide, royalty-bearing (with the royalty to be negotiated), non-exclusive, sub-licensable right and license to practice FCE Background Information and FCE Background Patents for Generation 2 Technology in any application outside of Carbon Capture Applications and Hydrogen Applications.",
'Aucta should continue to receive 15% of Net Sales Royalty for as long as ETON is selling the Product(s) in the Territory, unless otherwise agreed to under this Agreement.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.7920, 0.3253],
# [0.7920, 1.0000, 0.4614],
# [0.3253, 0.4614, 1.0000]])Usagepython
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
'License Grant',
"In the event FCE notifies ExxonMobil that it has formally decided not to pursue Generation 2 Technology for Power Applications, then upon ExxonMobil's written request, FCE agrees to negotiate a grant to ExxonMobil and its Affiliates, under commercially reasonable terms to be determined in good faith, a worldwide, royalty-bearing (with the royalty to be negotiated), non-exclusive, sub-licensable right and license to practice FCE Background Information and FCE Background Patents for Generation 2 Technology in any application outside of Carbon Capture Applications and Hydrogen Applications.",
'Aucta should continue to receive 15% of Net Sales Royalty for as long as ETON is selling the Product(s) in the Territory, unless otherwise agreed to under this Agreement.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.7920, 0.3253],
# [0.7920, 1.0000, 0.4614],
# [0.3253, 0.4614, 1.0000]])Usagepython
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
'License Grant',
"In the event FCE notifies ExxonMobil that it has formally decided not to pursue Generation 2 Technology for Power Applications, then upon ExxonMobil's written request, FCE agrees to negotiate a grant to ExxonMobil and its Affiliates, under commercially reasonable terms to be determined in good faith, a worldwide, royalty-bearing (with the royalty to be negotiated), non-exclusive, sub-licensable right and license to practice FCE Background Information and FCE Background Patents for Generation 2 Technology in any application outside of Carbon Capture Applications and Hydrogen Applications.",
'Aucta should continue to receive 15% of Net Sales Royalty for as long as ETON is selling the Product(s) in the Territory, unless otherwise agreed to under this Agreement.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.7920, 0.3253],
# [0.7920, 1.0000, 0.4614],
# [0.3253, 0.4614, 1.0000]])Usagepython
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
'License Grant',
"In the event FCE notifies ExxonMobil that it has formally decided not to pursue Generation 2 Technology for Power Applications, then upon ExxonMobil's written request, FCE agrees to negotiate a grant to ExxonMobil and its Affiliates, under commercially reasonable terms to be determined in good faith, a worldwide, royalty-bearing (with the royalty to be negotiated), non-exclusive, sub-licensable right and license to practice FCE Background Information and FCE Background Patents for Generation 2 Technology in any application outside of Carbon Capture Applications and Hydrogen Applications.",
'Aucta should continue to receive 15% of Net Sales Royalty for as long as ETON is selling the Product(s) in the Territory, unless otherwise agreed to under this Agreement.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.7920, 0.3253],
# [0.7920, 1.0000, 0.4614],
# [0.3253, 0.4614, 1.0000]])Usagepython
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
'License Grant',
"In the event FCE notifies ExxonMobil that it has formally decided not to pursue Generation 2 Technology for Power Applications, then upon ExxonMobil's written request, FCE agrees to negotiate a grant to ExxonMobil and its Affiliates, under commercially reasonable terms to be determined in good faith, a worldwide, royalty-bearing (with the royalty to be negotiated), non-exclusive, sub-licensable right and license to practice FCE Background Information and FCE Background Patents for Generation 2 Technology in any application outside of Carbon Capture Applications and Hydrogen Applications.",
'Aucta should continue to receive 15% of Net Sales Royalty for as long as ETON is selling the Product(s) in the Territory, unless otherwise agreed to under this Agreement.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.7920, 0.3253],
# [0.7920, 1.0000, 0.4614],
# [0.3253, 0.4614, 1.0000]])Usagepython
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
'License Grant',
"In the event FCE notifies ExxonMobil that it has formally decided not to pursue Generation 2 Technology for Power Applications, then upon ExxonMobil's written request, FCE agrees to negotiate a grant to ExxonMobil and its Affiliates, under commercially reasonable terms to be determined in good faith, a worldwide, royalty-bearing (with the royalty to be negotiated), non-exclusive, sub-licensable right and license to practice FCE Background Information and FCE Background Patents for Generation 2 Technology in any application outside of Carbon Capture Applications and Hydrogen Applications.",
'Aucta should continue to receive 15% of Net Sales Royalty for as long as ETON is selling the Product(s) in the Territory, unless otherwise agreed to under this Agreement.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.7920, 0.3253],
# [0.7920, 1.0000, 0.4614],
# [0.3253, 0.4614, 1.0000]])Usagepython
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
'License Grant',
"In the event FCE notifies ExxonMobil that it has formally decided not to pursue Generation 2 Technology for Power Applications, then upon ExxonMobil's written request, FCE agrees to negotiate a grant to ExxonMobil and its Affiliates, under commercially reasonable terms to be determined in good faith, a worldwide, royalty-bearing (with the royalty to be negotiated), non-exclusive, sub-licensable right and license to practice FCE Background Information and FCE Background Patents for Generation 2 Technology in any application outside of Carbon Capture Applications and Hydrogen Applications.",
'Aucta should continue to receive 15% of Net Sales Royalty for as long as ETON is selling the Product(s) in the Territory, unless otherwise agreed to under this Agreement.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.7920, 0.3253],
# [0.7920, 1.0000, 0.4614],
# [0.3253, 0.4614, 1.0000]])Usagepython
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
'License Grant',
"In the event FCE notifies ExxonMobil that it has formally decided not to pursue Generation 2 Technology for Power Applications, then upon ExxonMobil's written request, FCE agrees to negotiate a grant to ExxonMobil and its Affiliates, under commercially reasonable terms to be determined in good faith, a worldwide, royalty-bearing (with the royalty to be negotiated), non-exclusive, sub-licensable right and license to practice FCE Background Information and FCE Background Patents for Generation 2 Technology in any application outside of Carbon Capture Applications and Hydrogen Applications.",
'Aucta should continue to receive 15% of Net Sales Royalty for as long as ETON is selling the Product(s) in the Territory, unless otherwise agreed to under this Agreement.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.7920, 0.3253],
# [0.7920, 1.0000, 0.4614],
# [0.3253, 0.4614, 1.0000]])Usagepython
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
'License Grant',
"In the event FCE notifies ExxonMobil that it has formally decided not to pursue Generation 2 Technology for Power Applications, then upon ExxonMobil's written request, FCE agrees to negotiate a grant to ExxonMobil and its Affiliates, under commercially reasonable terms to be determined in good faith, a worldwide, royalty-bearing (with the royalty to be negotiated), non-exclusive, sub-licensable right and license to practice FCE Background Information and FCE Background Patents for Generation 2 Technology in any application outside of Carbon Capture Applications and Hydrogen Applications.",
'Aucta should continue to receive 15% of Net Sales Royalty for as long as ETON is selling the Product(s) in the Territory, unless otherwise agreed to under this Agreement.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.7920, 0.3253],
# [0.7920, 1.0000, 0.4614],
# [0.3253, 0.4614, 1.0000]])Usagepython
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
'License Grant',
"In the event FCE notifies ExxonMobil that it has formally decided not to pursue Generation 2 Technology for Power Applications, then upon ExxonMobil's written request, FCE agrees to negotiate a grant to ExxonMobil and its Affiliates, under commercially reasonable terms to be determined in good faith, a worldwide, royalty-bearing (with the royalty to be negotiated), non-exclusive, sub-licensable right and license to practice FCE Background Information and FCE Background Patents for Generation 2 Technology in any application outside of Carbon Capture Applications and Hydrogen Applications.",
'Aucta should continue to receive 15% of Net Sales Royalty for as long as ETON is selling the Product(s) in the Territory, unless otherwise agreed to under this Agreement.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.7920, 0.3253],
# [0.7920, 1.0000, 0.4614],
# [0.3253, 0.4614, 1.0000]])Usagepython
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
'License Grant',
"In the event FCE notifies ExxonMobil that it has formally decided not to pursue Generation 2 Technology for Power Applications, then upon ExxonMobil's written request, FCE agrees to negotiate a grant to ExxonMobil and its Affiliates, under commercially reasonable terms to be determined in good faith, a worldwide, royalty-bearing (with the royalty to be negotiated), non-exclusive, sub-licensable right and license to practice FCE Background Information and FCE Background Patents for Generation 2 Technology in any application outside of Carbon Capture Applications and Hydrogen Applications.",
'Aucta should continue to receive 15% of Net Sales Royalty for as long as ETON is selling the Product(s) in the Territory, unless otherwise agreed to under this Agreement.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.7920, 0.3253],
# [0.7920, 1.0000, 0.4614],
# [0.3253, 0.4614, 1.0000]])Usagepython
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
'License Grant',
"In the event FCE notifies ExxonMobil that it has formally decided not to pursue Generation 2 Technology for Power Applications, then upon ExxonMobil's written request, FCE agrees to negotiate a grant to ExxonMobil and its Affiliates, under commercially reasonable terms to be determined in good faith, a worldwide, royalty-bearing (with the royalty to be negotiated), non-exclusive, sub-licensable right and license to practice FCE Background Information and FCE Background Patents for Generation 2 Technology in any application outside of Carbon Capture Applications and Hydrogen Applications.",
'Aucta should continue to receive 15% of Net Sales Royalty for as long as ETON is selling the Product(s) in the Territory, unless otherwise agreed to under this Agreement.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.7920, 0.3253],
# [0.7920, 1.0000, 0.4614],
# [0.3253, 0.4614, 1.0000]])Usagepython
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
'License Grant',
"In the event FCE notifies ExxonMobil that it has formally decided not to pursue Generation 2 Technology for Power Applications, then upon ExxonMobil's written request, FCE agrees to negotiate a grant to ExxonMobil and its Affiliates, under commercially reasonable terms to be determined in good faith, a worldwide, royalty-bearing (with the royalty to be negotiated), non-exclusive, sub-licensable right and license to practice FCE Background Information and FCE Background Patents for Generation 2 Technology in any application outside of Carbon Capture Applications and Hydrogen Applications.",
'Aucta should continue to receive 15% of Net Sales Royalty for as long as ETON is selling the Product(s) in the Territory, unless otherwise agreed to under this Agreement.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.7920, 0.3253],
# [0.7920, 1.0000, 0.4614],
# [0.3253, 0.4614, 1.0000]])Usagepython
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
'License Grant',
"In the event FCE notifies ExxonMobil that it has formally decided not to pursue Generation 2 Technology for Power Applications, then upon ExxonMobil's written request, FCE agrees to negotiate a grant to ExxonMobil and its Affiliates, under commercially reasonable terms to be determined in good faith, a worldwide, royalty-bearing (with the royalty to be negotiated), non-exclusive, sub-licensable right and license to practice FCE Background Information and FCE Background Patents for Generation 2 Technology in any application outside of Carbon Capture Applications and Hydrogen Applications.",
'Aucta should continue to receive 15% of Net Sales Royalty for as long as ETON is selling the Product(s) in the Territory, unless otherwise agreed to under this Agreement.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.7920, 0.3253],
# [0.7920, 1.0000, 0.4614],
# [0.3253, 0.4614, 1.0000]])Usagepython
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
'License Grant',
"In the event FCE notifies ExxonMobil that it has formally decided not to pursue Generation 2 Technology for Power Applications, then upon ExxonMobil's written request, FCE agrees to negotiate a grant to ExxonMobil and its Affiliates, under commercially reasonable terms to be determined in good faith, a worldwide, royalty-bearing (with the royalty to be negotiated), non-exclusive, sub-licensable right and license to practice FCE Background Information and FCE Background Patents for Generation 2 Technology in any application outside of Carbon Capture Applications and Hydrogen Applications.",
'Aucta should continue to receive 15% of Net Sales Royalty for as long as ETON is selling the Product(s) in the Territory, unless otherwise agreed to under this Agreement.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.7920, 0.3253],
# [0.7920, 1.0000, 0.4614],
# [0.3253, 0.4614, 1.0000]])Usagepython
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
'License Grant',
"In the event FCE notifies ExxonMobil that it has formally decided not to pursue Generation 2 Technology for Power Applications, then upon ExxonMobil's written request, FCE agrees to negotiate a grant to ExxonMobil and its Affiliates, under commercially reasonable terms to be determined in good faith, a worldwide, royalty-bearing (with the royalty to be negotiated), non-exclusive, sub-licensable right and license to practice FCE Background Information and FCE Background Patents for Generation 2 Technology in any application outside of Carbon Capture Applications and Hydrogen Applications.",
'Aucta should continue to receive 15% of Net Sales Royalty for as long as ETON is selling the Product(s) in the Territory, unless otherwise agreed to under this Agreement.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.7920, 0.3253],
# [0.7920, 1.0000, 0.4614],
# [0.3253, 0.4614, 1.0000]])Usagepython
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
'License Grant',
"In the event FCE notifies ExxonMobil that it has formally decided not to pursue Generation 2 Technology for Power Applications, then upon ExxonMobil's written request, FCE agrees to negotiate a grant to ExxonMobil and its Affiliates, under commercially reasonable terms to be determined in good faith, a worldwide, royalty-bearing (with the royalty to be negotiated), non-exclusive, sub-licensable right and license to practice FCE Background Information and FCE Background Patents for Generation 2 Technology in any application outside of Carbon Capture Applications and Hydrogen Applications.",
'Aucta should continue to receive 15% of Net Sales Royalty for as long as ETON is selling the Product(s) in the Territory, unless otherwise agreed to under this Agreement.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.7920, 0.3253],
# [0.7920, 1.0000, 0.4614],
# [0.3253, 0.4614, 1.0000]])Usagepython
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
'License Grant',
"In the event FCE notifies ExxonMobil that it has formally decided not to pursue Generation 2 Technology for Power Applications, then upon ExxonMobil's written request, FCE agrees to negotiate a grant to ExxonMobil and its Affiliates, under commercially reasonable terms to be determined in good faith, a worldwide, royalty-bearing (with the royalty to be negotiated), non-exclusive, sub-licensable right and license to practice FCE Background Information and FCE Background Patents for Generation 2 Technology in any application outside of Carbon Capture Applications and Hydrogen Applications.",
'Aucta should continue to receive 15% of Net Sales Royalty for as long as ETON is selling the Product(s) in the Territory, unless otherwise agreed to under this Agreement.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.7920, 0.3253],
# [0.7920, 1.0000, 0.4614],
# [0.3253, 0.4614, 1.0000]])Usagepython
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
'License Grant',
"In the event FCE notifies ExxonMobil that it has formally decided not to pursue Generation 2 Technology for Power Applications, then upon ExxonMobil's written request, FCE agrees to negotiate a grant to ExxonMobil and its Affiliates, under commercially reasonable terms to be determined in good faith, a worldwide, royalty-bearing (with the royalty to be negotiated), non-exclusive, sub-licensable right and license to practice FCE Background Information and FCE Background Patents for Generation 2 Technology in any application outside of Carbon Capture Applications and Hydrogen Applications.",
'Aucta should continue to receive 15% of Net Sales Royalty for as long as ETON is selling the Product(s) in the Territory, unless otherwise agreed to under this Agreement.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.7920, 0.3253],
# [0.7920, 1.0000, 0.4614],
# [0.3253, 0.4614, 1.0000]])Usagepython
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
'License Grant',
"In the event FCE notifies ExxonMobil that it has formally decided not to pursue Generation 2 Technology for Power Applications, then upon ExxonMobil's written request, FCE agrees to negotiate a grant to ExxonMobil and its Affiliates, under commercially reasonable terms to be determined in good faith, a worldwide, royalty-bearing (with the royalty to be negotiated), non-exclusive, sub-licensable right and license to practice FCE Background Information and FCE Background Patents for Generation 2 Technology in any application outside of Carbon Capture Applications and Hydrogen Applications.",
'Aucta should continue to receive 15% of Net Sales Royalty for as long as ETON is selling the Product(s) in the Territory, unless otherwise agreed to under this Agreement.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.7920, 0.3253],
# [0.7920, 1.0000, 0.4614],
# [0.3253, 0.4614, 1.0000]])Usagepython
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
'License Grant',
"In the event FCE notifies ExxonMobil that it has formally decided not to pursue Generation 2 Technology for Power Applications, then upon ExxonMobil's written request, FCE agrees to negotiate a grant to ExxonMobil and its Affiliates, under commercially reasonable terms to be determined in good faith, a worldwide, royalty-bearing (with the royalty to be negotiated), non-exclusive, sub-licensable right and license to practice FCE Background Information and FCE Background Patents for Generation 2 Technology in any application outside of Carbon Capture Applications and Hydrogen Applications.",
'Aucta should continue to receive 15% of Net Sales Royalty for as long as ETON is selling the Product(s) in the Territory, unless otherwise agreed to under this Agreement.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.7920, 0.3253],
# [0.7920, 1.0000, 0.4614],
# [0.3253, 0.4614, 1.0000]])Usagepython
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
'License Grant',
"In the event FCE notifies ExxonMobil that it has formally decided not to pursue Generation 2 Technology for Power Applications, then upon ExxonMobil's written request, FCE agrees to negotiate a grant to ExxonMobil and its Affiliates, under commercially reasonable terms to be determined in good faith, a worldwide, royalty-bearing (with the royalty to be negotiated), non-exclusive, sub-licensable right and license to practice FCE Background Information and FCE Background Patents for Generation 2 Technology in any application outside of Carbon Capture Applications and Hydrogen Applications.",
'Aucta should continue to receive 15% of Net Sales Royalty for as long as ETON is selling the Product(s) in the Territory, unless otherwise agreed to under this Agreement.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.7920, 0.3253],
# [0.7920, 1.0000, 0.4614],
# [0.3253, 0.4614, 1.0000]])Usagepython
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
'License Grant',
"In the event FCE notifies ExxonMobil that it has formally decided not to pursue Generation 2 Technology for Power Applications, then upon ExxonMobil's written request, FCE agrees to negotiate a grant to ExxonMobil and its Affiliates, under commercially reasonable terms to be determined in good faith, a worldwide, royalty-bearing (with the royalty to be negotiated), non-exclusive, sub-licensable right and license to practice FCE Background Information and FCE Background Patents for Generation 2 Technology in any application outside of Carbon Capture Applications and Hydrogen Applications.",
'Aucta should continue to receive 15% of Net Sales Royalty for as long as ETON is selling the Product(s) in the Territory, unless otherwise agreed to under this Agreement.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.7920, 0.3253],
# [0.7920, 1.0000, 0.4614],
# [0.3253, 0.4614, 1.0000]])Usagepython
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
'License Grant',
"In the event FCE notifies ExxonMobil that it has formally decided not to pursue Generation 2 Technology for Power Applications, then upon ExxonMobil's written request, FCE agrees to negotiate a grant to ExxonMobil and its Affiliates, under commercially reasonable terms to be determined in good faith, a worldwide, royalty-bearing (with the royalty to be negotiated), non-exclusive, sub-licensable right and license to practice FCE Background Information and FCE Background Patents for Generation 2 Technology in any application outside of Carbon Capture Applications and Hydrogen Applications.",
'Aucta should continue to receive 15% of Net Sales Royalty for as long as ETON is selling the Product(s) in the Territory, unless otherwise agreed to under this Agreement.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.7920, 0.3253],
# [0.7920, 1.0000, 0.4614],
# [0.3253, 0.4614, 1.0000]])Usagepython
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
'License Grant',
"In the event FCE notifies ExxonMobil that it has formally decided not to pursue Generation 2 Technology for Power Applications, then upon ExxonMobil's written request, FCE agrees to negotiate a grant to ExxonMobil and its Affiliates, under commercially reasonable terms to be determined in good faith, a worldwide, royalty-bearing (with the royalty to be negotiated), non-exclusive, sub-licensable right and license to practice FCE Background Information and FCE Background Patents for Generation 2 Technology in any application outside of Carbon Capture Applications and Hydrogen Applications.",
'Aucta should continue to receive 15% of Net Sales Royalty for as long as ETON is selling the Product(s) in the Territory, unless otherwise agreed to under this Agreement.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.7920, 0.3253],
# [0.7920, 1.0000, 0.4614],
# [0.3253, 0.4614, 1.0000]])Usagepython
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
'License Grant',
"In the event FCE notifies ExxonMobil that it has formally decided not to pursue Generation 2 Technology for Power Applications, then upon ExxonMobil's written request, FCE agrees to negotiate a grant to ExxonMobil and its Affiliates, under commercially reasonable terms to be determined in good faith, a worldwide, royalty-bearing (with the royalty to be negotiated), non-exclusive, sub-licensable right and license to practice FCE Background Information and FCE Background Patents for Generation 2 Technology in any application outside of Carbon Capture Applications and Hydrogen Applications.",
'Aucta should continue to receive 15% of Net Sales Royalty for as long as ETON is selling the Product(s) in the Territory, unless otherwise agreed to under this Agreement.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.7920, 0.3253],
# [0.7920, 1.0000, 0.4614],
# [0.3253, 0.4614, 1.0000]])Usagepython
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
'License Grant',
"In the event FCE notifies ExxonMobil that it has formally decided not to pursue Generation 2 Technology for Power Applications, then upon ExxonMobil's written request, FCE agrees to negotiate a grant to ExxonMobil and its Affiliates, under commercially reasonable terms to be determined in good faith, a worldwide, royalty-bearing (with the royalty to be negotiated), non-exclusive, sub-licensable right and license to practice FCE Background Information and FCE Background Patents for Generation 2 Technology in any application outside of Carbon Capture Applications and Hydrogen Applications.",
'Aucta should continue to receive 15% of Net Sales Royalty for as long as ETON is selling the Product(s) in the Territory, unless otherwise agreed to under this Agreement.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.7920, 0.3253],
# [0.7920, 1.0000, 0.4614],
# [0.3253, 0.4614, 1.0000]])Usagepython
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
'License Grant',
"In the event FCE notifies ExxonMobil that it has formally decided not to pursue Generation 2 Technology for Power Applications, then upon ExxonMobil's written request, FCE agrees to negotiate a grant to ExxonMobil and its Affiliates, under commercially reasonable terms to be determined in good faith, a worldwide, royalty-bearing (with the royalty to be negotiated), non-exclusive, sub-licensable right and license to practice FCE Background Information and FCE Background Patents for Generation 2 Technology in any application outside of Carbon Capture Applications and Hydrogen Applications.",
'Aucta should continue to receive 15% of Net Sales Royalty for as long as ETON is selling the Product(s) in the Territory, unless otherwise agreed to under this Agreement.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.7920, 0.3253],
# [0.7920, 1.0000, 0.4614],
# [0.3253, 0.4614, 1.0000]])Usagepython
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
'License Grant',
"In the event FCE notifies ExxonMobil that it has formally decided not to pursue Generation 2 Technology for Power Applications, then upon ExxonMobil's written request, FCE agrees to negotiate a grant to ExxonMobil and its Affiliates, under commercially reasonable terms to be determined in good faith, a worldwide, royalty-bearing (with the royalty to be negotiated), non-exclusive, sub-licensable right and license to practice FCE Background Information and FCE Background Patents for Generation 2 Technology in any application outside of Carbon Capture Applications and Hydrogen Applications.",
'Aucta should continue to receive 15% of Net Sales Royalty for as long as ETON is selling the Product(s) in the Territory, unless otherwise agreed to under this Agreement.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.7920, 0.3253],
# [0.7920, 1.0000, 0.4614],
# [0.3253, 0.4614, 1.0000]])Usagepython
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
'License Grant',
"In the event FCE notifies ExxonMobil that it has formally decided not to pursue Generation 2 Technology for Power Applications, then upon ExxonMobil's written request, FCE agrees to negotiate a grant to ExxonMobil and its Affiliates, under commercially reasonable terms to be determined in good faith, a worldwide, royalty-bearing (with the royalty to be negotiated), non-exclusive, sub-licensable right and license to practice FCE Background Information and FCE Background Patents for Generation 2 Technology in any application outside of Carbon Capture Applications and Hydrogen Applications.",
'Aucta should continue to receive 15% of Net Sales Royalty for as long as ETON is selling the Product(s) in the Territory, unless otherwise agreed to under this Agreement.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.7920, 0.3253],
# [0.7920, 1.0000, 0.4614],
# [0.3253, 0.4614, 1.0000]])Usagepython
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
'License Grant',
"In the event FCE notifies ExxonMobil that it has formally decided not to pursue Generation 2 Technology for Power Applications, then upon ExxonMobil's written request, FCE agrees to negotiate a grant to ExxonMobil and its Affiliates, under commercially reasonable terms to be determined in good faith, a worldwide, royalty-bearing (with the royalty to be negotiated), non-exclusive, sub-licensable right and license to practice FCE Background Information and FCE Background Patents for Generation 2 Technology in any application outside of Carbon Capture Applications and Hydrogen Applications.",
'Aucta should continue to receive 15% of Net Sales Royalty for as long as ETON is selling the Product(s) in the Territory, unless otherwise agreed to under this Agreement.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.7920, 0.3253],
# [0.7920, 1.0000, 0.4614],
# [0.3253, 0.4614, 1.0000]])Usagepython
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
'License Grant',
"In the event FCE notifies ExxonMobil that it has formally decided not to pursue Generation 2 Technology for Power Applications, then upon ExxonMobil's written request, FCE agrees to negotiate a grant to ExxonMobil and its Affiliates, under commercially reasonable terms to be determined in good faith, a worldwide, royalty-bearing (with the royalty to be negotiated), non-exclusive, sub-licensable right and license to practice FCE Background Information and FCE Background Patents for Generation 2 Technology in any application outside of Carbon Capture Applications and Hydrogen Applications.",
'Aucta should continue to receive 15% of Net Sales Royalty for as long as ETON is selling the Product(s) in the Territory, unless otherwise agreed to under this Agreement.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.7920, 0.3253],
# [0.7920, 1.0000, 0.4614],
# [0.3253, 0.4614, 1.0000]])Usagepython
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
'License Grant',
"In the event FCE notifies ExxonMobil that it has formally decided not to pursue Generation 2 Technology for Power Applications, then upon ExxonMobil's written request, FCE agrees to negotiate a grant to ExxonMobil and its Affiliates, under commercially reasonable terms to be determined in good faith, a worldwide, royalty-bearing (with the royalty to be negotiated), non-exclusive, sub-licensable right and license to practice FCE Background Information and FCE Background Patents for Generation 2 Technology in any application outside of Carbon Capture Applications and Hydrogen Applications.",
'Aucta should continue to receive 15% of Net Sales Royalty for as long as ETON is selling the Product(s) in the Territory, unless otherwise agreed to under this Agreement.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.7920, 0.3253],
# [0.7920, 1.0000, 0.4614],
# [0.3253, 0.4614, 1.0000]])Usagepython
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
'License Grant',
"In the event FCE notifies ExxonMobil that it has formally decided not to pursue Generation 2 Technology for Power Applications, then upon ExxonMobil's written request, FCE agrees to negotiate a grant to ExxonMobil and its Affiliates, under commercially reasonable terms to be determined in good faith, a worldwide, royalty-bearing (with the royalty to be negotiated), non-exclusive, sub-licensable right and license to practice FCE Background Information and FCE Background Patents for Generation 2 Technology in any application outside of Carbon Capture Applications and Hydrogen Applications.",
'Aucta should continue to receive 15% of Net Sales Royalty for as long as ETON is selling the Product(s) in the Territory, unless otherwise agreed to under this Agreement.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.7920, 0.3253],
# [0.7920, 1.0000, 0.4614],
# [0.3253, 0.4614, 1.0000]])Usagepython
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
'License Grant',
"In the event FCE notifies ExxonMobil that it has formally decided not to pursue Generation 2 Technology for Power Applications, then upon ExxonMobil's written request, FCE agrees to negotiate a grant to ExxonMobil and its Affiliates, under commercially reasonable terms to be determined in good faith, a worldwide, royalty-bearing (with the royalty to be negotiated), non-exclusive, sub-licensable right and license to practice FCE Background Information and FCE Background Patents for Generation 2 Technology in any application outside of Carbon Capture Applications and Hydrogen Applications.",
'Aucta should continue to receive 15% of Net Sales Royalty for as long as ETON is selling the Product(s) in the Territory, unless otherwise agreed to under this Agreement.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.7920, 0.3253],
# [0.7920, 1.0000, 0.4614],
# [0.3253, 0.4614, 1.0000]])Usagepython
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
'License Grant',
"In the event FCE notifies ExxonMobil that it has formally decided not to pursue Generation 2 Technology for Power Applications, then upon ExxonMobil's written request, FCE agrees to negotiate a grant to ExxonMobil and its Affiliates, under commercially reasonable terms to be determined in good faith, a worldwide, royalty-bearing (with the royalty to be negotiated), non-exclusive, sub-licensable right and license to practice FCE Background Information and FCE Background Patents for Generation 2 Technology in any application outside of Carbon Capture Applications and Hydrogen Applications.",
'Aucta should continue to receive 15% of Net Sales Royalty for as long as ETON is selling the Product(s) in the Territory, unless otherwise agreed to under this Agreement.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.7920, 0.3253],
# [0.7920, 1.0000, 0.4614],
# [0.3253, 0.4614, 1.0000]])Usagepython
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
'License Grant',
"In the event FCE notifies ExxonMobil that it has formally decided not to pursue Generation 2 Technology for Power Applications, then upon ExxonMobil's written request, FCE agrees to negotiate a grant to ExxonMobil and its Affiliates, under commercially reasonable terms to be determined in good faith, a worldwide, royalty-bearing (with the royalty to be negotiated), non-exclusive, sub-licensable right and license to practice FCE Background Information and FCE Background Patents for Generation 2 Technology in any application outside of Carbon Capture Applications and Hydrogen Applications.",
'Aucta should continue to receive 15% of Net Sales Royalty for as long as ETON is selling the Product(s) in the Territory, unless otherwise agreed to under this Agreement.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.7920, 0.3253],
# [0.7920, 1.0000, 0.4614],
# [0.3253, 0.4614, 1.0000]])Usagepython
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
'License Grant',
"In the event FCE notifies ExxonMobil that it has formally decided not to pursue Generation 2 Technology for Power Applications, then upon ExxonMobil's written request, FCE agrees to negotiate a grant to ExxonMobil and its Affiliates, under commercially reasonable terms to be determined in good faith, a worldwide, royalty-bearing (with the royalty to be negotiated), non-exclusive, sub-licensable right and license to practice FCE Background Information and FCE Background Patents for Generation 2 Technology in any application outside of Carbon Capture Applications and Hydrogen Applications.",
'Aucta should continue to receive 15% of Net Sales Royalty for as long as ETON is selling the Product(s) in the Territory, unless otherwise agreed to under this Agreement.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.7920, 0.3253],
# [0.7920, 1.0000, 0.4614],
# [0.3253, 0.4614, 1.0000]])Usagepython
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
'License Grant',
"In the event FCE notifies ExxonMobil that it has formally decided not to pursue Generation 2 Technology for Power Applications, then upon ExxonMobil's written request, FCE agrees to negotiate a grant to ExxonMobil and its Affiliates, under commercially reasonable terms to be determined in good faith, a worldwide, royalty-bearing (with the royalty to be negotiated), non-exclusive, sub-licensable right and license to practice FCE Background Information and FCE Background Patents for Generation 2 Technology in any application outside of Carbon Capture Applications and Hydrogen Applications.",
'Aucta should continue to receive 15% of Net Sales Royalty for as long as ETON is selling the Product(s) in the Territory, unless otherwise agreed to under this Agreement.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.7920, 0.3253],
# [0.7920, 1.0000, 0.4614],
# [0.3253, 0.4614, 1.0000]])Usagepython
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
'License Grant',
"In the event FCE notifies ExxonMobil that it has formally decided not to pursue Generation 2 Technology for Power Applications, then upon ExxonMobil's written request, FCE agrees to negotiate a grant to ExxonMobil and its Affiliates, under commercially reasonable terms to be determined in good faith, a worldwide, royalty-bearing (with the royalty to be negotiated), non-exclusive, sub-licensable right and license to practice FCE Background Information and FCE Background Patents for Generation 2 Technology in any application outside of Carbon Capture Applications and Hydrogen Applications.",
'Aucta should continue to receive 15% of Net Sales Royalty for as long as ETON is selling the Product(s) in the Territory, unless otherwise agreed to under this Agreement.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.7920, 0.3253],
# [0.7920, 1.0000, 0.4614],
# [0.3253, 0.4614, 1.0000]])Usagepython
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
'License Grant',
"In the event FCE notifies ExxonMobil that it has formally decided not to pursue Generation 2 Technology for Power Applications, then upon ExxonMobil's written request, FCE agrees to negotiate a grant to ExxonMobil and its Affiliates, under commercially reasonable terms to be determined in good faith, a worldwide, royalty-bearing (with the royalty to be negotiated), non-exclusive, sub-licensable right and license to practice FCE Background Information and FCE Background Patents for Generation 2 Technology in any application outside of Carbon Capture Applications and Hydrogen Applications.",
'Aucta should continue to receive 15% of Net Sales Royalty for as long as ETON is selling the Product(s) in the Territory, unless otherwise agreed to under this Agreement.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.7920, 0.3253],
# [0.7920, 1.0000, 0.4614],
# [0.3253, 0.4614, 1.0000]])Usagepython
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
'License Grant',
"In the event FCE notifies ExxonMobil that it has formally decided not to pursue Generation 2 Technology for Power Applications, then upon ExxonMobil's written request, FCE agrees to negotiate a grant to ExxonMobil and its Affiliates, under commercially reasonable terms to be determined in good faith, a worldwide, royalty-bearing (with the royalty to be negotiated), non-exclusive, sub-licensable right and license to practice FCE Background Information and FCE Background Patents for Generation 2 Technology in any application outside of Carbon Capture Applications and Hydrogen Applications.",
'Aucta should continue to receive 15% of Net Sales Royalty for as long as ETON is selling the Product(s) in the Territory, unless otherwise agreed to under this Agreement.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.7920, 0.3253],
# [0.7920, 1.0000, 0.4614],
# [0.3253, 0.4614, 1.0000]])Usagepython
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
'License Grant',
"In the event FCE notifies ExxonMobil that it has formally decided not to pursue Generation 2 Technology for Power Applications, then upon ExxonMobil's written request, FCE agrees to negotiate a grant to ExxonMobil and its Affiliates, under commercially reasonable terms to be determined in good faith, a worldwide, royalty-bearing (with the royalty to be negotiated), non-exclusive, sub-licensable right and license to practice FCE Background Information and FCE Background Patents for Generation 2 Technology in any application outside of Carbon Capture Applications and Hydrogen Applications.",
'Aucta should continue to receive 15% of Net Sales Royalty for as long as ETON is selling the Product(s) in the Territory, unless otherwise agreed to under this Agreement.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.7920, 0.3253],
# [0.7920, 1.0000, 0.4614],
# [0.3253, 0.4614, 1.0000]])Usagepython
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
'License Grant',
"In the event FCE notifies ExxonMobil that it has formally decided not to pursue Generation 2 Technology for Power Applications, then upon ExxonMobil's written request, FCE agrees to negotiate a grant to ExxonMobil and its Affiliates, under commercially reasonable terms to be determined in good faith, a worldwide, royalty-bearing (with the royalty to be negotiated), non-exclusive, sub-licensable right and license to practice FCE Background Information and FCE Background Patents for Generation 2 Technology in any application outside of Carbon Capture Applications and Hydrogen Applications.",
'Aucta should continue to receive 15% of Net Sales Royalty for as long as ETON is selling the Product(s) in the Territory, unless otherwise agreed to under this Agreement.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.7920, 0.3253],
# [0.7920, 1.0000, 0.4614],
# [0.3253, 0.4614, 1.0000]])Usagepython
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
'License Grant',
"In the event FCE notifies ExxonMobil that it has formally decided not to pursue Generation 2 Technology for Power Applications, then upon ExxonMobil's written request, FCE agrees to negotiate a grant to ExxonMobil and its Affiliates, under commercially reasonable terms to be determined in good faith, a worldwide, royalty-bearing (with the royalty to be negotiated), non-exclusive, sub-licensable right and license to practice FCE Background Information and FCE Background Patents for Generation 2 Technology in any application outside of Carbon Capture Applications and Hydrogen Applications.",
'Aucta should continue to receive 15% of Net Sales Royalty for as long as ETON is selling the Product(s) in the Territory, unless otherwise agreed to under this Agreement.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.7920, 0.3253],
# [0.7920, 1.0000, 0.4614],
# [0.3253, 0.4614, 1.0000]])Usagepython
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
'License Grant',
"In the event FCE notifies ExxonMobil that it has formally decided not to pursue Generation 2 Technology for Power Applications, then upon ExxonMobil's written request, FCE agrees to negotiate a grant to ExxonMobil and its Affiliates, under commercially reasonable terms to be determined in good faith, a worldwide, royalty-bearing (with the royalty to be negotiated), non-exclusive, sub-licensable right and license to practice FCE Background Information and FCE Background Patents for Generation 2 Technology in any application outside of Carbon Capture Applications and Hydrogen Applications.",
'Aucta should continue to receive 15% of Net Sales Royalty for as long as ETON is selling the Product(s) in the Territory, unless otherwise agreed to under this Agreement.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.7920, 0.3253],
# [0.7920, 1.0000, 0.4614],
# [0.3253, 0.4614, 1.0000]])Usagepython
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
'License Grant',
"In the event FCE notifies ExxonMobil that it has formally decided not to pursue Generation 2 Technology for Power Applications, then upon ExxonMobil's written request, FCE agrees to negotiate a grant to ExxonMobil and its Affiliates, under commercially reasonable terms to be determined in good faith, a worldwide, royalty-bearing (with the royalty to be negotiated), non-exclusive, sub-licensable right and license to practice FCE Background Information and FCE Background Patents for Generation 2 Technology in any application outside of Carbon Capture Applications and Hydrogen Applications.",
'Aucta should continue to receive 15% of Net Sales Royalty for as long as ETON is selling the Product(s) in the Territory, unless otherwise agreed to under this Agreement.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.7920, 0.3253],
# [0.7920, 1.0000, 0.4614],
# [0.3253, 0.4614, 1.0000]])Usagepython
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
'License Grant',
"In the event FCE notifies ExxonMobil that it has formally decided not to pursue Generation 2 Technology for Power Applications, then upon ExxonMobil's written request, FCE agrees to negotiate a grant to ExxonMobil and its Affiliates, under commercially reasonable terms to be determined in good faith, a worldwide, royalty-bearing (with the royalty to be negotiated), non-exclusive, sub-licensable right and license to practice FCE Background Information and FCE Background Patents for Generation 2 Technology in any application outside of Carbon Capture Applications and Hydrogen Applications.",
'Aucta should continue to receive 15% of Net Sales Royalty for as long as ETON is selling the Product(s) in the Territory, unless otherwise agreed to under this Agreement.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.7920, 0.3253],
# [0.7920, 1.0000, 0.4614],
# [0.3253, 0.4614, 1.0000]])Usagepython
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
'License Grant',
"In the event FCE notifies ExxonMobil that it has formally decided not to pursue Generation 2 Technology for Power Applications, then upon ExxonMobil's written request, FCE agrees to negotiate a grant to ExxonMobil and its Affiliates, under commercially reasonable terms to be determined in good faith, a worldwide, royalty-bearing (with the royalty to be negotiated), non-exclusive, sub-licensable right and license to practice FCE Background Information and FCE Background Patents for Generation 2 Technology in any application outside of Carbon Capture Applications and Hydrogen Applications.",
'Aucta should continue to receive 15% of Net Sales Royalty for as long as ETON is selling the Product(s) in the Territory, unless otherwise agreed to under this Agreement.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.7920, 0.3253],
# [0.7920, 1.0000, 0.4614],
# [0.3253, 0.4614, 1.0000]])Usagepython
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
'License Grant',
"In the event FCE notifies ExxonMobil that it has formally decided not to pursue Generation 2 Technology for Power Applications, then upon ExxonMobil's written request, FCE agrees to negotiate a grant to ExxonMobil and its Affiliates, under commercially reasonable terms to be determined in good faith, a worldwide, royalty-bearing (with the royalty to be negotiated), non-exclusive, sub-licensable right and license to practice FCE Background Information and FCE Background Patents for Generation 2 Technology in any application outside of Carbon Capture Applications and Hydrogen Applications.",
'Aucta should continue to receive 15% of Net Sales Royalty for as long as ETON is selling the Product(s) in the Territory, unless otherwise agreed to under this Agreement.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.7920, 0.3253],
# [0.7920, 1.0000, 0.4614],
# [0.3253, 0.4614, 1.0000]])Usagepython
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
'License Grant',
"In the event FCE notifies ExxonMobil that it has formally decided not to pursue Generation 2 Technology for Power Applications, then upon ExxonMobil's written request, FCE agrees to negotiate a grant to ExxonMobil and its Affiliates, under commercially reasonable terms to be determined in good faith, a worldwide, royalty-bearing (with the royalty to be negotiated), non-exclusive, sub-licensable right and license to practice FCE Background Information and FCE Background Patents for Generation 2 Technology in any application outside of Carbon Capture Applications and Hydrogen Applications.",
'Aucta should continue to receive 15% of Net Sales Royalty for as long as ETON is selling the Product(s) in the Territory, unless otherwise agreed to under this Agreement.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.7920, 0.3253],
# [0.7920, 1.0000, 0.4614],
# [0.3253, 0.4614, 1.0000]])Usagepython
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
'License Grant',
"In the event FCE notifies ExxonMobil that it has formally decided not to pursue Generation 2 Technology for Power Applications, then upon ExxonMobil's written request, FCE agrees to negotiate a grant to ExxonMobil and its Affiliates, under commercially reasonable terms to be determined in good faith, a worldwide, royalty-bearing (with the royalty to be negotiated), non-exclusive, sub-licensable right and license to practice FCE Background Information and FCE Background Patents for Generation 2 Technology in any application outside of Carbon Capture Applications and Hydrogen Applications.",
'Aucta should continue to receive 15% of Net Sales Royalty for as long as ETON is selling the Product(s) in the Territory, unless otherwise agreed to under this Agreement.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.7920, 0.3253],
# [0.7920, 1.0000, 0.4614],
# [0.3253, 0.4614, 1.0000]])Usagepython
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
'License Grant',
"In the event FCE notifies ExxonMobil that it has formally decided not to pursue Generation 2 Technology for Power Applications, then upon ExxonMobil's written request, FCE agrees to negotiate a grant to ExxonMobil and its Affiliates, under commercially reasonable terms to be determined in good faith, a worldwide, royalty-bearing (with the royalty to be negotiated), non-exclusive, sub-licensable right and license to practice FCE Background Information and FCE Background Patents for Generation 2 Technology in any application outside of Carbon Capture Applications and Hydrogen Applications.",
'Aucta should continue to receive 15% of Net Sales Royalty for as long as ETON is selling the Product(s) in the Territory, unless otherwise agreed to under this Agreement.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.7920, 0.3253],
# [0.7920, 1.0000, 0.4614],
# [0.3253, 0.4614, 1.0000]])Usagepython
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
'License Grant',
"In the event FCE notifies ExxonMobil that it has formally decided not to pursue Generation 2 Technology for Power Applications, then upon ExxonMobil's written request, FCE agrees to negotiate a grant to ExxonMobil and its Affiliates, under commercially reasonable terms to be determined in good faith, a worldwide, royalty-bearing (with the royalty to be negotiated), non-exclusive, sub-licensable right and license to practice FCE Background Information and FCE Background Patents for Generation 2 Technology in any application outside of Carbon Capture Applications and Hydrogen Applications.",
'Aucta should continue to receive 15% of Net Sales Royalty for as long as ETON is selling the Product(s) in the Territory, unless otherwise agreed to under this Agreement.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.7920, 0.3253],
# [0.7920, 1.0000, 0.4614],
# [0.3253, 0.4614, 1.0000]])Usagepython
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
'License Grant',
"In the event FCE notifies ExxonMobil that it has formally decided not to pursue Generation 2 Technology for Power Applications, then upon ExxonMobil's written request, FCE agrees to negotiate a grant to ExxonMobil and its Affiliates, under commercially reasonable terms to be determined in good faith, a worldwide, royalty-bearing (with the royalty to be negotiated), non-exclusive, sub-licensable right and license to practice FCE Background Information and FCE Background Patents for Generation 2 Technology in any application outside of Carbon Capture Applications and Hydrogen Applications.",
'Aucta should continue to receive 15% of Net Sales Royalty for as long as ETON is selling the Product(s) in the Territory, unless otherwise agreed to under this Agreement.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.7920, 0.3253],
# [0.7920, 1.0000, 0.4614],
# [0.3253, 0.4614, 1.0000]])Usagepython
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
'License Grant',
"In the event FCE notifies ExxonMobil that it has formally decided not to pursue Generation 2 Technology for Power Applications, then upon ExxonMobil's written request, FCE agrees to negotiate a grant to ExxonMobil and its Affiliates, under commercially reasonable terms to be determined in good faith, a worldwide, royalty-bearing (with the royalty to be negotiated), non-exclusive, sub-licensable right and license to practice FCE Background Information and FCE Background Patents for Generation 2 Technology in any application outside of Carbon Capture Applications and Hydrogen Applications.",
'Aucta should continue to receive 15% of Net Sales Royalty for as long as ETON is selling the Product(s) in the Territory, unless otherwise agreed to under this Agreement.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.7920, 0.3253],
# [0.7920, 1.0000, 0.4614],
# [0.3253, 0.4614, 1.0000]])Usagepython
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
'License Grant',
"In the event FCE notifies ExxonMobil that it has formally decided not to pursue Generation 2 Technology for Power Applications, then upon ExxonMobil's written request, FCE agrees to negotiate a grant to ExxonMobil and its Affiliates, under commercially reasonable terms to be determined in good faith, a worldwide, royalty-bearing (with the royalty to be negotiated), non-exclusive, sub-licensable right and license to practice FCE Background Information and FCE Background Patents for Generation 2 Technology in any application outside of Carbon Capture Applications and Hydrogen Applications.",
'Aucta should continue to receive 15% of Net Sales Royalty for as long as ETON is selling the Product(s) in the Territory, unless otherwise agreed to under this Agreement.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.7920, 0.3253],
# [0.7920, 1.0000, 0.4614],
# [0.3253, 0.4614, 1.0000]])Usagepython
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
'License Grant',
"In the event FCE notifies ExxonMobil that it has formally decided not to pursue Generation 2 Technology for Power Applications, then upon ExxonMobil's written request, FCE agrees to negotiate a grant to ExxonMobil and its Affiliates, under commercially reasonable terms to be determined in good faith, a worldwide, royalty-bearing (with the royalty to be negotiated), non-exclusive, sub-licensable right and license to practice FCE Background Information and FCE Background Patents for Generation 2 Technology in any application outside of Carbon Capture Applications and Hydrogen Applications.",
'Aucta should continue to receive 15% of Net Sales Royalty for as long as ETON is selling the Product(s) in the Territory, unless otherwise agreed to under this Agreement.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.7920, 0.3253],
# [0.7920, 1.0000, 0.4614],
# [0.3253, 0.4614, 1.0000]])Usagepython
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
'License Grant',
"In the event FCE notifies ExxonMobil that it has formally decided not to pursue Generation 2 Technology for Power Applications, then upon ExxonMobil's written request, FCE agrees to negotiate a grant to ExxonMobil and its Affiliates, under commercially reasonable terms to be determined in good faith, a worldwide, royalty-bearing (with the royalty to be negotiated), non-exclusive, sub-licensable right and license to practice FCE Background Information and FCE Background Patents for Generation 2 Technology in any application outside of Carbon Capture Applications and Hydrogen Applications.",
'Aucta should continue to receive 15% of Net Sales Royalty for as long as ETON is selling the Product(s) in the Territory, unless otherwise agreed to under this Agreement.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.7920, 0.3253],
# [0.7920, 1.0000, 0.4614],
# [0.3253, 0.4614, 1.0000]])Deploy This Model
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