static-similarity-mrl-multilingual-v1

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license:apache-2.0
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sentence-transformers
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Quick Summary

AI model with specialized capabilities.

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
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
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("tomaarsen/static-similarity-mrl-multilingual-v1")
# Run inference
sentences = [
    'It is known for its dry red chili powder .',
    'It is popular for dry red chili powder .',
    'These monsters will move in large groups .',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]

# 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("tomaarsen/static-similarity-mrl-multilingual-v1")
# Run inference
sentences = [
    'It is known for its dry red chili powder .',
    'It is popular for dry red chili powder .',
    'These monsters will move in large groups .',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]

# 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("tomaarsen/static-similarity-mrl-multilingual-v1")
# Run inference
sentences = [
    'It is known for its dry red chili powder .',
    'It is popular for dry red chili powder .',
    'These monsters will move in large groups .',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]

# 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("tomaarsen/static-similarity-mrl-multilingual-v1")
# Run inference
sentences = [
    'It is known for its dry red chili powder .',
    'It is popular for dry red chili powder .',
    'These monsters will move in large groups .',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]

# 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("tomaarsen/static-similarity-mrl-multilingual-v1")
# Run inference
sentences = [
    'It is known for its dry red chili powder .',
    'It is popular for dry red chili powder .',
    'These monsters will move in large groups .',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]

# 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("tomaarsen/static-similarity-mrl-multilingual-v1")
# Run inference
sentences = [
    'It is known for its dry red chili powder .',
    'It is popular for dry red chili powder .',
    'These monsters will move in large groups .',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]

# 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("tomaarsen/static-similarity-mrl-multilingual-v1")
# Run inference
sentences = [
    'It is known for its dry red chili powder .',
    'It is popular for dry red chili powder .',
    'These monsters will move in large groups .',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]

# 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("tomaarsen/static-similarity-mrl-multilingual-v1")
# Run inference
sentences = [
    'It is known for its dry red chili powder .',
    'It is popular for dry red chili powder .',
    'These monsters will move in large groups .',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]

# 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("tomaarsen/static-similarity-mrl-multilingual-v1")
# Run inference
sentences = [
    'It is known for its dry red chili powder .',
    'It is popular for dry red chili powder .',
    'These monsters will move in large groups .',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]

# 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("tomaarsen/static-similarity-mrl-multilingual-v1")
# Run inference
sentences = [
    'It is known for its dry red chili powder .',
    'It is popular for dry red chili powder .',
    'These monsters will move in large groups .',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]

# 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("tomaarsen/static-similarity-mrl-multilingual-v1")
# Run inference
sentences = [
    'It is known for its dry red chili powder .',
    'It is popular for dry red chili powder .',
    'These monsters will move in large groups .',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]

# 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("tomaarsen/static-similarity-mrl-multilingual-v1")
# Run inference
sentences = [
    'It is known for its dry red chili powder .',
    'It is popular for dry red chili powder .',
    'These monsters will move in large groups .',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]

# 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("tomaarsen/static-similarity-mrl-multilingual-v1")
# Run inference
sentences = [
    'It is known for its dry red chili powder .',
    'It is popular for dry red chili powder .',
    'These monsters will move in large groups .',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]

# 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("tomaarsen/static-similarity-mrl-multilingual-v1")
# Run inference
sentences = [
    'It is known for its dry red chili powder .',
    'It is popular for dry red chili powder .',
    'These monsters will move in large groups .',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]

# 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("tomaarsen/static-similarity-mrl-multilingual-v1")
# Run inference
sentences = [
    'It is known for its dry red chili powder .',
    'It is popular for dry red chili powder .',
    'These monsters will move in large groups .',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]

# 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("tomaarsen/static-similarity-mrl-multilingual-v1")
# Run inference
sentences = [
    'It is known for its dry red chili powder .',
    'It is popular for dry red chili powder .',
    'These monsters will move in large groups .',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]

# 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("tomaarsen/static-similarity-mrl-multilingual-v1")
# Run inference
sentences = [
    'It is known for its dry red chili powder .',
    'It is popular for dry red chili powder .',
    'These monsters will move in large groups .',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]

# 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("tomaarsen/static-similarity-mrl-multilingual-v1")
# Run inference
sentences = [
    'It is known for its dry red chili powder .',
    'It is popular for dry red chili powder .',
    'These monsters will move in large groups .',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]

# 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("tomaarsen/static-similarity-mrl-multilingual-v1")
# Run inference
sentences = [
    'It is known for its dry red chili powder .',
    'It is popular for dry red chili powder .',
    'These monsters will move in large groups .',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]

# 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("tomaarsen/static-similarity-mrl-multilingual-v1")
# Run inference
sentences = [
    'It is known for its dry red chili powder .',
    'It is popular for dry red chili powder .',
    'These monsters will move in large groups .',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]

# 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("tomaarsen/static-similarity-mrl-multilingual-v1")
# Run inference
sentences = [
    'It is known for its dry red chili powder .',
    'It is popular for dry red chili powder .',
    'These monsters will move in large groups .',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]

# 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("tomaarsen/static-similarity-mrl-multilingual-v1")
# Run inference
sentences = [
    'It is known for its dry red chili powder .',
    'It is popular for dry red chili powder .',
    'These monsters will move in large groups .',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]

# 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("tomaarsen/static-similarity-mrl-multilingual-v1")
# Run inference
sentences = [
    'It is known for its dry red chili powder .',
    'It is popular for dry red chili powder .',
    'These monsters will move in large groups .',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]

# 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("tomaarsen/static-similarity-mrl-multilingual-v1")
# Run inference
sentences = [
    'It is known for its dry red chili powder .',
    'It is popular for dry red chili powder .',
    'These monsters will move in large groups .',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]

# 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("tomaarsen/static-similarity-mrl-multilingual-v1")
# Run inference
sentences = [
    'It is known for its dry red chili powder .',
    'It is popular for dry red chili powder .',
    'These monsters will move in large groups .',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]

# 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("tomaarsen/static-similarity-mrl-multilingual-v1")
# Run inference
sentences = [
    'It is known for its dry red chili powder .',
    'It is popular for dry red chili powder .',
    'These monsters will move in large groups .',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]

# 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("tomaarsen/static-similarity-mrl-multilingual-v1")
# Run inference
sentences = [
    'It is known for its dry red chili powder .',
    'It is popular for dry red chili powder .',
    'These monsters will move in large groups .',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]

# 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("tomaarsen/static-similarity-mrl-multilingual-v1")
# Run inference
sentences = [
    'It is known for its dry red chili powder .',
    'It is popular for dry red chili powder .',
    'These monsters will move in large groups .',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]

# 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("tomaarsen/static-similarity-mrl-multilingual-v1")
# Run inference
sentences = [
    'It is known for its dry red chili powder .',
    'It is popular for dry red chili powder .',
    'These monsters will move in large groups .',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]

# 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("tomaarsen/static-similarity-mrl-multilingual-v1")
# Run inference
sentences = [
    'It is known for its dry red chili powder .',
    'It is popular for dry red chili powder .',
    'These monsters will move in large groups .',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]

# 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("tomaarsen/static-similarity-mrl-multilingual-v1")
# Run inference
sentences = [
    'It is known for its dry red chili powder .',
    'It is popular for dry red chili powder .',
    'These monsters will move in large groups .',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]

# 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("tomaarsen/static-similarity-mrl-multilingual-v1")
# Run inference
sentences = [
    'It is known for its dry red chili powder .',
    'It is popular for dry red chili powder .',
    'These monsters will move in large groups .',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]

# 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("tomaarsen/static-similarity-mrl-multilingual-v1")
# Run inference
sentences = [
    'It is known for its dry red chili powder .',
    'It is popular for dry red chili powder .',
    'These monsters will move in large groups .',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]

# 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("tomaarsen/static-similarity-mrl-multilingual-v1")
# Run inference
sentences = [
    'It is known for its dry red chili powder .',
    'It is popular for dry red chili powder .',
    'These monsters will move in large groups .',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]

# 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("tomaarsen/static-similarity-mrl-multilingual-v1")
# Run inference
sentences = [
    'It is known for its dry red chili powder .',
    'It is popular for dry red chili powder .',
    'These monsters will move in large groups .',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]

# 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("tomaarsen/static-similarity-mrl-multilingual-v1")
# Run inference
sentences = [
    'It is known for its dry red chili powder .',
    'It is popular for dry red chili powder .',
    'These monsters will move in large groups .',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]

# 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("tomaarsen/static-similarity-mrl-multilingual-v1")
# Run inference
sentences = [
    'It is known for its dry red chili powder .',
    'It is popular for dry red chili powder .',
    'These monsters will move in large groups .',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]

# 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("tomaarsen/static-similarity-mrl-multilingual-v1")
# Run inference
sentences = [
    'It is known for its dry red chili powder .',
    'It is popular for dry red chili powder .',
    'These monsters will move in large groups .',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]

# 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("tomaarsen/static-similarity-mrl-multilingual-v1")
# Run inference
sentences = [
    'It is known for its dry red chili powder .',
    'It is popular for dry red chili powder .',
    'These monsters will move in large groups .',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]

# 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("tomaarsen/static-similarity-mrl-multilingual-v1")
# Run inference
sentences = [
    'It is known for its dry red chili powder .',
    'It is popular for dry red chili powder .',
    'These monsters will move in large groups .',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]

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

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