deberta-v3-xsmall-zyda-2-v2

2
—
by
agentlans
Embedding Model
OTHER
1908.10084B params
New
0 downloads
Early-stage
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Edge AI:
Mobile
Laptop
Server
4265GB+ RAM
Mobile
Laptop
Server
Quick Summary

AI model with specialized capabilities.

Device Compatibility

Mobile
4-6GB RAM
Laptop
16GB RAM
Server
GPU
Minimum Recommended
1778GB+ RAM

Code Examples

Usagebash
pip install -U sentence-transformers
Usagebash
pip install -U sentence-transformers
Usagebash
pip install -U sentence-transformers
Usagebash
pip install -U sentence-transformers
Usagebash
pip install -U sentence-transformers
Usagebash
pip install -U sentence-transformers
Usagebash
pip install -U sentence-transformers
Usagebash
pip install -U sentence-transformers
Usagebash
pip install -U sentence-transformers
Usagebash
pip install -U sentence-transformers
Usagebash
pip install -U sentence-transformers
Usagebash
pip install -U sentence-transformers
Usagebash
pip install -U sentence-transformers
Usagebash
pip install -U sentence-transformers
Usagebash
pip install -U sentence-transformers
Usagebash
pip install -U sentence-transformers
Usagebash
pip install -U sentence-transformers
Usagepython
from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("agentlans/deberta-v3-xsmall-zyda-2-v2")
# Run inference
sentences = [
    'The expansion of European colonies resulted in the dissemination of their cultural ideas and institutions to other regions.',
    'How long do dogs bleed during menstruation?',
    'The team added a second car for Thed Björk in 2006 , and was replaced by Richard Göransson in 2009 .',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]
Usagepython
from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("agentlans/deberta-v3-xsmall-zyda-2-v2")
# Run inference
sentences = [
    'The expansion of European colonies resulted in the dissemination of their cultural ideas and institutions to other regions.',
    'How long do dogs bleed during menstruation?',
    'The team added a second car for Thed Björk in 2006 , and was replaced by Richard Göransson in 2009 .',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]
Usagepython
from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("agentlans/deberta-v3-xsmall-zyda-2-v2")
# Run inference
sentences = [
    'The expansion of European colonies resulted in the dissemination of their cultural ideas and institutions to other regions.',
    'How long do dogs bleed during menstruation?',
    'The team added a second car for Thed Björk in 2006 , and was replaced by Richard Göransson in 2009 .',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]
Usagepython
from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("agentlans/deberta-v3-xsmall-zyda-2-v2")
# Run inference
sentences = [
    'The expansion of European colonies resulted in the dissemination of their cultural ideas and institutions to other regions.',
    'How long do dogs bleed during menstruation?',
    'The team added a second car for Thed Björk in 2006 , and was replaced by Richard Göransson in 2009 .',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]
Usagepython
from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("agentlans/deberta-v3-xsmall-zyda-2-v2")
# Run inference
sentences = [
    'The expansion of European colonies resulted in the dissemination of their cultural ideas and institutions to other regions.',
    'How long do dogs bleed during menstruation?',
    'The team added a second car for Thed Björk in 2006 , and was replaced by Richard Göransson in 2009 .',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]
Usagepython
from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("agentlans/deberta-v3-xsmall-zyda-2-v2")
# Run inference
sentences = [
    'The expansion of European colonies resulted in the dissemination of their cultural ideas and institutions to other regions.',
    'How long do dogs bleed during menstruation?',
    'The team added a second car for Thed Björk in 2006 , and was replaced by Richard Göransson in 2009 .',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]
Usagepython
from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("agentlans/deberta-v3-xsmall-zyda-2-v2")
# Run inference
sentences = [
    'The expansion of European colonies resulted in the dissemination of their cultural ideas and institutions to other regions.',
    'How long do dogs bleed during menstruation?',
    'The team added a second car for Thed Björk in 2006 , and was replaced by Richard Göransson in 2009 .',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]
Usagepython
from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("agentlans/deberta-v3-xsmall-zyda-2-v2")
# Run inference
sentences = [
    'The expansion of European colonies resulted in the dissemination of their cultural ideas and institutions to other regions.',
    'How long do dogs bleed during menstruation?',
    'The team added a second car for Thed Björk in 2006 , and was replaced by Richard Göransson in 2009 .',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]
Usagepython
from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("agentlans/deberta-v3-xsmall-zyda-2-v2")
# Run inference
sentences = [
    'The expansion of European colonies resulted in the dissemination of their cultural ideas and institutions to other regions.',
    'How long do dogs bleed during menstruation?',
    'The team added a second car for Thed Björk in 2006 , and was replaced by Richard Göransson in 2009 .',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]
Usagepython
from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("agentlans/deberta-v3-xsmall-zyda-2-v2")
# Run inference
sentences = [
    'The expansion of European colonies resulted in the dissemination of their cultural ideas and institutions to other regions.',
    'How long do dogs bleed during menstruation?',
    'The team added a second car for Thed Björk in 2006 , and was replaced by Richard Göransson in 2009 .',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]
Usagepython
from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("agentlans/deberta-v3-xsmall-zyda-2-v2")
# Run inference
sentences = [
    'The expansion of European colonies resulted in the dissemination of their cultural ideas and institutions to other regions.',
    'How long do dogs bleed during menstruation?',
    'The team added a second car for Thed Björk in 2006 , and was replaced by Richard Göransson in 2009 .',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]
Usagepython
from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("agentlans/deberta-v3-xsmall-zyda-2-v2")
# Run inference
sentences = [
    'The expansion of European colonies resulted in the dissemination of their cultural ideas and institutions to other regions.',
    'How long do dogs bleed during menstruation?',
    'The team added a second car for Thed Björk in 2006 , and was replaced by Richard Göransson in 2009 .',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]
Usagepython
from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("agentlans/deberta-v3-xsmall-zyda-2-v2")
# Run inference
sentences = [
    'The expansion of European colonies resulted in the dissemination of their cultural ideas and institutions to other regions.',
    'How long do dogs bleed during menstruation?',
    'The team added a second car for Thed Björk in 2006 , and was replaced by Richard Göransson in 2009 .',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]
Usagepython
from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("agentlans/deberta-v3-xsmall-zyda-2-v2")
# Run inference
sentences = [
    'The expansion of European colonies resulted in the dissemination of their cultural ideas and institutions to other regions.',
    'How long do dogs bleed during menstruation?',
    'The team added a second car for Thed Björk in 2006 , and was replaced by Richard Göransson in 2009 .',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]
Usagepython
from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("agentlans/deberta-v3-xsmall-zyda-2-v2")
# Run inference
sentences = [
    'The expansion of European colonies resulted in the dissemination of their cultural ideas and institutions to other regions.',
    'How long do dogs bleed during menstruation?',
    'The team added a second car for Thed Björk in 2006 , and was replaced by Richard Göransson in 2009 .',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]
Usagepython
from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("agentlans/deberta-v3-xsmall-zyda-2-v2")
# Run inference
sentences = [
    'The expansion of European colonies resulted in the dissemination of their cultural ideas and institutions to other regions.',
    'How long do dogs bleed during menstruation?',
    'The team added a second car for Thed Björk in 2006 , and was replaced by Richard Göransson in 2009 .',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]
Usagepython
from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("agentlans/deberta-v3-xsmall-zyda-2-v2")
# Run inference
sentences = [
    'The expansion of European colonies resulted in the dissemination of their cultural ideas and institutions to other regions.',
    'How long do dogs bleed during menstruation?',
    'The team added a second car for Thed Björk in 2006 , and was replaced by Richard Göransson in 2009 .',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]

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