distilbert-base-multilingual-cased-aligned

1
—
by
agentlans
Embedding Model
OTHER
1705.00652B params
New
1 downloads
Early-stage
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Edge AI:
Mobile
Laptop
Server
3811GB+ RAM
Mobile
Laptop
Server
Quick Summary

AI model with specialized capabilities.

Device Compatibility

Mobile
4-6GB RAM
Laptop
16GB RAM
Server
GPU
Minimum Recommended
1588GB+ 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
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/distilbert-base-multilingual-cased-aligned")
# Run inference
sentences = [
    'Palm DOC Conduit for KPilot',
    'PalmDOC- conduit foar KPilot',
    'Man nepatinka gyventi kaime.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]

# 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/distilbert-base-multilingual-cased-aligned")
# Run inference
sentences = [
    'Palm DOC Conduit for KPilot',
    'PalmDOC- conduit foar KPilot',
    'Man nepatinka gyventi kaime.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]

# 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/distilbert-base-multilingual-cased-aligned")
# Run inference
sentences = [
    'Palm DOC Conduit for KPilot',
    'PalmDOC- conduit foar KPilot',
    'Man nepatinka gyventi kaime.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]

# 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/distilbert-base-multilingual-cased-aligned")
# Run inference
sentences = [
    'Palm DOC Conduit for KPilot',
    'PalmDOC- conduit foar KPilot',
    'Man nepatinka gyventi kaime.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]

# 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/distilbert-base-multilingual-cased-aligned")
# Run inference
sentences = [
    'Palm DOC Conduit for KPilot',
    'PalmDOC- conduit foar KPilot',
    'Man nepatinka gyventi kaime.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]

# 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/distilbert-base-multilingual-cased-aligned")
# Run inference
sentences = [
    'Palm DOC Conduit for KPilot',
    'PalmDOC- conduit foar KPilot',
    'Man nepatinka gyventi kaime.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]

# 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/distilbert-base-multilingual-cased-aligned")
# Run inference
sentences = [
    'Palm DOC Conduit for KPilot',
    'PalmDOC- conduit foar KPilot',
    'Man nepatinka gyventi kaime.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]

# 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/distilbert-base-multilingual-cased-aligned")
# Run inference
sentences = [
    'Palm DOC Conduit for KPilot',
    'PalmDOC- conduit foar KPilot',
    'Man nepatinka gyventi kaime.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]

# 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/distilbert-base-multilingual-cased-aligned")
# Run inference
sentences = [
    'Palm DOC Conduit for KPilot',
    'PalmDOC- conduit foar KPilot',
    'Man nepatinka gyventi kaime.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]

# 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/distilbert-base-multilingual-cased-aligned")
# Run inference
sentences = [
    'Palm DOC Conduit for KPilot',
    'PalmDOC- conduit foar KPilot',
    'Man nepatinka gyventi kaime.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]

# 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/distilbert-base-multilingual-cased-aligned")
# Run inference
sentences = [
    'Palm DOC Conduit for KPilot',
    'PalmDOC- conduit foar KPilot',
    'Man nepatinka gyventi kaime.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]

# 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/distilbert-base-multilingual-cased-aligned")
# Run inference
sentences = [
    'Palm DOC Conduit for KPilot',
    'PalmDOC- conduit foar KPilot',
    'Man nepatinka gyventi kaime.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]

# 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/distilbert-base-multilingual-cased-aligned")
# Run inference
sentences = [
    'Palm DOC Conduit for KPilot',
    'PalmDOC- conduit foar KPilot',
    'Man nepatinka gyventi kaime.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]

# 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/distilbert-base-multilingual-cased-aligned")
# Run inference
sentences = [
    'Palm DOC Conduit for KPilot',
    'PalmDOC- conduit foar KPilot',
    'Man nepatinka gyventi kaime.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]

# 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/distilbert-base-multilingual-cased-aligned")
# Run inference
sentences = [
    'Palm DOC Conduit for KPilot',
    'PalmDOC- conduit foar KPilot',
    'Man nepatinka gyventi kaime.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]

# 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/distilbert-base-multilingual-cased-aligned")
# Run inference
sentences = [
    'Palm DOC Conduit for KPilot',
    'PalmDOC- conduit foar KPilot',
    'Man nepatinka gyventi kaime.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]

# 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/distilbert-base-multilingual-cased-aligned")
# Run inference
sentences = [
    'Palm DOC Conduit for KPilot',
    'PalmDOC- conduit foar KPilot',
    'Man nepatinka gyventi kaime.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]

# 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/distilbert-base-multilingual-cased-aligned")
# Run inference
sentences = [
    'Palm DOC Conduit for KPilot',
    'PalmDOC- conduit foar KPilot',
    'Man nepatinka gyventi kaime.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]

# 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/distilbert-base-multilingual-cased-aligned")
# Run inference
sentences = [
    'Palm DOC Conduit for KPilot',
    'PalmDOC- conduit foar KPilot',
    'Man nepatinka gyventi kaime.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]

# 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/distilbert-base-multilingual-cased-aligned")
# Run inference
sentences = [
    'Palm DOC Conduit for KPilot',
    'PalmDOC- conduit foar KPilot',
    'Man nepatinka gyventi kaime.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]

# 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/distilbert-base-multilingual-cased-aligned")
# Run inference
sentences = [
    'Palm DOC Conduit for KPilot',
    'PalmDOC- conduit foar KPilot',
    'Man nepatinka gyventi kaime.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]

# 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/distilbert-base-multilingual-cased-aligned")
# Run inference
sentences = [
    'Palm DOC Conduit for KPilot',
    'PalmDOC- conduit foar KPilot',
    'Man nepatinka gyventi kaime.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]

# 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/distilbert-base-multilingual-cased-aligned")
# Run inference
sentences = [
    'Palm DOC Conduit for KPilot',
    'PalmDOC- conduit foar KPilot',
    'Man nepatinka gyventi kaime.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]

# 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/distilbert-base-multilingual-cased-aligned")
# Run inference
sentences = [
    'Palm DOC Conduit for KPilot',
    'PalmDOC- conduit foar KPilot',
    'Man nepatinka gyventi kaime.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]

# 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/distilbert-base-multilingual-cased-aligned")
# Run inference
sentences = [
    'Palm DOC Conduit for KPilot',
    'PalmDOC- conduit foar KPilot',
    'Man nepatinka gyventi kaime.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]

# 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/distilbert-base-multilingual-cased-aligned")
# Run inference
sentences = [
    'Palm DOC Conduit for KPilot',
    'PalmDOC- conduit foar KPilot',
    'Man nepatinka gyventi kaime.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]

# 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/distilbert-base-multilingual-cased-aligned")
# Run inference
sentences = [
    'Palm DOC Conduit for KPilot',
    'PalmDOC- conduit foar KPilot',
    'Man nepatinka gyventi kaime.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]

# 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/distilbert-base-multilingual-cased-aligned")
# Run inference
sentences = [
    'Palm DOC Conduit for KPilot',
    'PalmDOC- conduit foar KPilot',
    'Man nepatinka gyventi kaime.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]

# 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/distilbert-base-multilingual-cased-aligned")
# Run inference
sentences = [
    'Palm DOC Conduit for KPilot',
    'PalmDOC- conduit foar KPilot',
    'Man nepatinka gyventi kaime.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]

# 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/distilbert-base-multilingual-cased-aligned")
# Run inference
sentences = [
    'Palm DOC Conduit for KPilot',
    'PalmDOC- conduit foar KPilot',
    'Man nepatinka gyventi kaime.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]

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

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