optimized-semcache-embeds-en-final

Name: optimized-semcache-embeds-en-final
Author: magnifi

—

magnifi

Embedding Model

OTHER

1705.00652B params

New

3 downloads

Early-stage

Try on Hugging Face Add to Compare

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

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pip install -U sentence-transformers

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pip install -U sentence-transformers

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pip install -U sentence-transformers

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pip install -U sentence-transformers

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pip install -U sentence-transformers

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pip install -U sentence-transformers

Usagepython

from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
    'what sectors are contributing the most to my performance [DATES]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'returns\')": "portfolio"}]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'portfolio\')": "portfolio"}]',
]
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("sentence_transformers_model_id")
# Run inference
sentences = [
    'what sectors are contributing the most to my performance [DATES]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'returns\')": "portfolio"}]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'portfolio\')": "portfolio"}]',
]
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("sentence_transformers_model_id")
# Run inference
sentences = [
    'what sectors are contributing the most to my performance [DATES]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'returns\')": "portfolio"}]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'portfolio\')": "portfolio"}]',
]
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("sentence_transformers_model_id")
# Run inference
sentences = [
    'what sectors are contributing the most to my performance [DATES]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'returns\')": "portfolio"}]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'portfolio\')": "portfolio"}]',
]
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("sentence_transformers_model_id")
# Run inference
sentences = [
    'what sectors are contributing the most to my performance [DATES]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'returns\')": "portfolio"}]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'portfolio\')": "portfolio"}]',
]
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("sentence_transformers_model_id")
# Run inference
sentences = [
    'what sectors are contributing the most to my performance [DATES]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'returns\')": "portfolio"}]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'portfolio\')": "portfolio"}]',
]
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("sentence_transformers_model_id")
# Run inference
sentences = [
    'what sectors are contributing the most to my performance [DATES]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'returns\')": "portfolio"}]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'portfolio\')": "portfolio"}]',
]
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("sentence_transformers_model_id")
# Run inference
sentences = [
    'what sectors are contributing the most to my performance [DATES]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'returns\')": "portfolio"}]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'portfolio\')": "portfolio"}]',
]
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("sentence_transformers_model_id")
# Run inference
sentences = [
    'what sectors are contributing the most to my performance [DATES]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'returns\')": "portfolio"}]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'portfolio\')": "portfolio"}]',
]
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("sentence_transformers_model_id")
# Run inference
sentences = [
    'what sectors are contributing the most to my performance [DATES]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'returns\')": "portfolio"}]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'portfolio\')": "portfolio"}]',
]
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("sentence_transformers_model_id")
# Run inference
sentences = [
    'what sectors are contributing the most to my performance [DATES]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'returns\')": "portfolio"}]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'portfolio\')": "portfolio"}]',
]
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("sentence_transformers_model_id")
# Run inference
sentences = [
    'what sectors are contributing the most to my performance [DATES]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'returns\')": "portfolio"}]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'portfolio\')": "portfolio"}]',
]
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("sentence_transformers_model_id")
# Run inference
sentences = [
    'what sectors are contributing the most to my performance [DATES]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'returns\')": "portfolio"}]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'portfolio\')": "portfolio"}]',
]
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("sentence_transformers_model_id")
# Run inference
sentences = [
    'what sectors are contributing the most to my performance [DATES]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'returns\')": "portfolio"}]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'portfolio\')": "portfolio"}]',
]
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("sentence_transformers_model_id")
# Run inference
sentences = [
    'what sectors are contributing the most to my performance [DATES]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'returns\')": "portfolio"}]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'portfolio\')": "portfolio"}]',
]
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("sentence_transformers_model_id")
# Run inference
sentences = [
    'what sectors are contributing the most to my performance [DATES]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'returns\')": "portfolio"}]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'portfolio\')": "portfolio"}]',
]
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("sentence_transformers_model_id")
# Run inference
sentences = [
    'what sectors are contributing the most to my performance [DATES]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'returns\')": "portfolio"}]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'portfolio\')": "portfolio"}]',
]
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("sentence_transformers_model_id")
# Run inference
sentences = [
    'what sectors are contributing the most to my performance [DATES]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'returns\')": "portfolio"}]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'portfolio\')": "portfolio"}]',
]
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("sentence_transformers_model_id")
# Run inference
sentences = [
    'what sectors are contributing the most to my performance [DATES]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'returns\')": "portfolio"}]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'portfolio\')": "portfolio"}]',
]
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("sentence_transformers_model_id")
# Run inference
sentences = [
    'what sectors are contributing the most to my performance [DATES]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'returns\')": "portfolio"}]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'portfolio\')": "portfolio"}]',
]
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("sentence_transformers_model_id")
# Run inference
sentences = [
    'what sectors are contributing the most to my performance [DATES]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'returns\')": "portfolio"}]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'portfolio\')": "portfolio"}]',
]
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("sentence_transformers_model_id")
# Run inference
sentences = [
    'what sectors are contributing the most to my performance [DATES]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'returns\')": "portfolio"}]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'portfolio\')": "portfolio"}]',
]
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("sentence_transformers_model_id")
# Run inference
sentences = [
    'what sectors are contributing the most to my performance [DATES]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'returns\')": "portfolio"}]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'portfolio\')": "portfolio"}]',
]
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("sentence_transformers_model_id")
# Run inference
sentences = [
    'what sectors are contributing the most to my performance [DATES]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'returns\')": "portfolio"}]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'portfolio\')": "portfolio"}]',
]
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("sentence_transformers_model_id")
# Run inference
sentences = [
    'what sectors are contributing the most to my performance [DATES]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'returns\')": "portfolio"}]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'portfolio\')": "portfolio"}]',
]
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("sentence_transformers_model_id")
# Run inference
sentences = [
    'what sectors are contributing the most to my performance [DATES]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'returns\')": "portfolio"}]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'portfolio\')": "portfolio"}]',
]
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("sentence_transformers_model_id")
# Run inference
sentences = [
    'what sectors are contributing the most to my performance [DATES]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'returns\')": "portfolio"}]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'portfolio\')": "portfolio"}]',
]
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("sentence_transformers_model_id")
# Run inference
sentences = [
    'what sectors are contributing the most to my performance [DATES]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'returns\')": "portfolio"}]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'portfolio\')": "portfolio"}]',
]
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("sentence_transformers_model_id")
# Run inference
sentences = [
    'what sectors are contributing the most to my performance [DATES]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'returns\')": "portfolio"}]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'portfolio\')": "portfolio"}]',
]
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("sentence_transformers_model_id")
# Run inference
sentences = [
    'what sectors are contributing the most to my performance [DATES]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'returns\')": "portfolio"}]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'portfolio\')": "portfolio"}]',
]
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("sentence_transformers_model_id")
# Run inference
sentences = [
    'what sectors are contributing the most to my performance [DATES]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'returns\')": "portfolio"}]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'portfolio\')": "portfolio"}]',
]
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("sentence_transformers_model_id")
# Run inference
sentences = [
    'what sectors are contributing the most to my performance [DATES]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'returns\')": "portfolio"}]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'portfolio\')": "portfolio"}]',
]
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("sentence_transformers_model_id")
# Run inference
sentences = [
    'what sectors are contributing the most to my performance [DATES]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'returns\')": "portfolio"}]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'portfolio\')": "portfolio"}]',
]
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("sentence_transformers_model_id")
# Run inference
sentences = [
    'what sectors are contributing the most to my performance [DATES]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'returns\')": "portfolio"}]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'portfolio\')": "portfolio"}]',
]
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("sentence_transformers_model_id")
# Run inference
sentences = [
    'what sectors are contributing the most to my performance [DATES]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'returns\')": "portfolio"}]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'portfolio\')": "portfolio"}]',
]
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("sentence_transformers_model_id")
# Run inference
sentences = [
    'what sectors are contributing the most to my performance [DATES]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'returns\')": "portfolio"}]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'portfolio\')": "portfolio"}]',
]
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("sentence_transformers_model_id")
# Run inference
sentences = [
    'what sectors are contributing the most to my performance [DATES]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'returns\')": "portfolio"}]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'portfolio\')": "portfolio"}]',
]
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("sentence_transformers_model_id")
# Run inference
sentences = [
    'what sectors are contributing the most to my performance [DATES]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'returns\')": "portfolio"}]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'portfolio\')": "portfolio"}]',
]
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("sentence_transformers_model_id")
# Run inference
sentences = [
    'what sectors are contributing the most to my performance [DATES]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'returns\')": "portfolio"}]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'portfolio\')": "portfolio"}]',
]
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("sentence_transformers_model_id")
# Run inference
sentences = [
    'what sectors are contributing the most to my performance [DATES]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'returns\')": "portfolio"}]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'portfolio\')": "portfolio"}]',
]
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("sentence_transformers_model_id")
# Run inference
sentences = [
    'what sectors are contributing the most to my performance [DATES]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'returns\')": "portfolio"}]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'portfolio\')": "portfolio"}]',
]
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("sentence_transformers_model_id")
# Run inference
sentences = [
    'what sectors are contributing the most to my performance [DATES]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'returns\')": "portfolio"}]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'portfolio\')": "portfolio"}]',
]
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("sentence_transformers_model_id")
# Run inference
sentences = [
    'what sectors are contributing the most to my performance [DATES]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'returns\')": "portfolio"}]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'portfolio\')": "portfolio"}]',
]
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("sentence_transformers_model_id")
# Run inference
sentences = [
    'what sectors are contributing the most to my performance [DATES]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'returns\')": "portfolio"}]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'portfolio\')": "portfolio"}]',
]
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("sentence_transformers_model_id")
# Run inference
sentences = [
    'what sectors are contributing the most to my performance [DATES]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'returns\')": "portfolio"}]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'portfolio\')": "portfolio"}]',
]
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("sentence_transformers_model_id")
# Run inference
sentences = [
    'what sectors are contributing the most to my performance [DATES]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'returns\')": "portfolio"}]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'portfolio\')": "portfolio"}]',
]
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("sentence_transformers_model_id")
# Run inference
sentences = [
    'what sectors are contributing the most to my performance [DATES]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'returns\')": "portfolio"}]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'portfolio\')": "portfolio"}]',
]
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("sentence_transformers_model_id")
# Run inference
sentences = [
    'what sectors are contributing the most to my performance [DATES]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'returns\')": "portfolio"}]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'portfolio\')": "portfolio"}]',
]
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("sentence_transformers_model_id")
# Run inference
sentences = [
    'what sectors are contributing the most to my performance [DATES]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'returns\')": "portfolio"}]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'portfolio\')": "portfolio"}]',
]
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("sentence_transformers_model_id")
# Run inference
sentences = [
    'what sectors are contributing the most to my performance [DATES]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'returns\')": "portfolio"}]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'portfolio\')": "portfolio"}]',
]
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("sentence_transformers_model_id")
# Run inference
sentences = [
    'what sectors are contributing the most to my performance [DATES]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'returns\')": "portfolio"}]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'portfolio\')": "portfolio"}]',
]
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("sentence_transformers_model_id")
# Run inference
sentences = [
    'what sectors are contributing the most to my performance [DATES]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'returns\')": "portfolio"}]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'portfolio\')": "portfolio"}]',
]
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("sentence_transformers_model_id")
# Run inference
sentences = [
    'what sectors are contributing the most to my performance [DATES]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'returns\')": "portfolio"}]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'portfolio\')": "portfolio"}]',
]
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("sentence_transformers_model_id")
# Run inference
sentences = [
    'what sectors are contributing the most to my performance [DATES]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'returns\')": "portfolio"}]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'portfolio\')": "portfolio"}]',
]
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("sentence_transformers_model_id")
# Run inference
sentences = [
    'what sectors are contributing the most to my performance [DATES]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'returns\')": "portfolio"}]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'portfolio\')": "portfolio"}]',
]
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("sentence_transformers_model_id")
# Run inference
sentences = [
    'what sectors are contributing the most to my performance [DATES]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'returns\')": "portfolio"}]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'portfolio\')": "portfolio"}]',
]
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("sentence_transformers_model_id")
# Run inference
sentences = [
    'what sectors are contributing the most to my performance [DATES]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'returns\')": "portfolio"}]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'portfolio\')": "portfolio"}]',
]
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("sentence_transformers_model_id")
# Run inference
sentences = [
    'what sectors are contributing the most to my performance [DATES]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'returns\')": "portfolio"}]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'portfolio\')": "portfolio"}]',
]
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("sentence_transformers_model_id")
# Run inference
sentences = [
    'what sectors are contributing the most to my performance [DATES]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'returns\')": "portfolio"}]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'portfolio\')": "portfolio"}]',
]
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("sentence_transformers_model_id")
# Run inference
sentences = [
    'what sectors are contributing the most to my performance [DATES]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'returns\')": "portfolio"}]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'portfolio\')": "portfolio"}]',
]
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("sentence_transformers_model_id")
# Run inference
sentences = [
    'what sectors are contributing the most to my performance [DATES]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'returns\')": "portfolio"}]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'portfolio\')": "portfolio"}]',
]
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("sentence_transformers_model_id")
# Run inference
sentences = [
    'what sectors are contributing the most to my performance [DATES]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'returns\')": "portfolio"}]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'portfolio\')": "portfolio"}]',
]
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("sentence_transformers_model_id")
# Run inference
sentences = [
    'what sectors are contributing the most to my performance [DATES]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'returns\')": "portfolio"}]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'portfolio\')": "portfolio"}]',
]
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("sentence_transformers_model_id")
# Run inference
sentences = [
    'what sectors are contributing the most to my performance [DATES]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'returns\')": "portfolio"}]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'portfolio\')": "portfolio"}]',
]
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("sentence_transformers_model_id")
# Run inference
sentences = [
    'what sectors are contributing the most to my performance [DATES]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'returns\')": "portfolio"}]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'portfolio\')": "portfolio"}]',
]
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("sentence_transformers_model_id")
# Run inference
sentences = [
    'what sectors are contributing the most to my performance [DATES]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'returns\')": "portfolio"}]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'portfolio\')": "portfolio"}]',
]
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("sentence_transformers_model_id")
# Run inference
sentences = [
    'what sectors are contributing the most to my performance [DATES]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'returns\')": "portfolio"}]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'portfolio\')": "portfolio"}]',
]
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("sentence_transformers_model_id")
# Run inference
sentences = [
    'what sectors are contributing the most to my performance [DATES]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'returns\')": "portfolio"}]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'portfolio\')": "portfolio"}]',
]
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("sentence_transformers_model_id")
# Run inference
sentences = [
    'what sectors are contributing the most to my performance [DATES]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'returns\')": "portfolio"}]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'portfolio\')": "portfolio"}]',
]
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("sentence_transformers_model_id")
# Run inference
sentences = [
    'what sectors are contributing the most to my performance [DATES]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'returns\')": "portfolio"}]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'portfolio\')": "portfolio"}]',
]
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("sentence_transformers_model_id")
# Run inference
sentences = [
    'what sectors are contributing the most to my performance [DATES]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'returns\')": "portfolio"}]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'portfolio\')": "portfolio"}]',
]
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("sentence_transformers_model_id")
# Run inference
sentences = [
    'what sectors are contributing the most to my performance [DATES]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'returns\')": "portfolio"}]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'portfolio\')": "portfolio"}]',
]
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("sentence_transformers_model_id")
# Run inference
sentences = [
    'what sectors are contributing the most to my performance [DATES]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'returns\')": "portfolio"}]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'portfolio\')": "portfolio"}]',
]
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("sentence_transformers_model_id")
# Run inference
sentences = [
    'what sectors are contributing the most to my performance [DATES]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'returns\')": "portfolio"}]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'portfolio\')": "portfolio"}]',
]
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("sentence_transformers_model_id")
# Run inference
sentences = [
    'what sectors are contributing the most to my performance [DATES]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'returns\')": "portfolio"}]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'portfolio\')": "portfolio"}]',
]
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("sentence_transformers_model_id")
# Run inference
sentences = [
    'what sectors are contributing the most to my performance [DATES]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'returns\')": "portfolio"}]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'portfolio\')": "portfolio"}]',
]
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("sentence_transformers_model_id")
# Run inference
sentences = [
    'what sectors are contributing the most to my performance [DATES]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'returns\')": "portfolio"}]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'portfolio\')": "portfolio"}]',
]
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("sentence_transformers_model_id")
# Run inference
sentences = [
    'what sectors are contributing the most to my performance [DATES]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'returns\')": "portfolio"}]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'portfolio\')": "portfolio"}]',
]
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("sentence_transformers_model_id")
# Run inference
sentences = [
    'what sectors are contributing the most to my performance [DATES]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'returns\')": "portfolio"}]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'portfolio\')": "portfolio"}]',
]
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("sentence_transformers_model_id")
# Run inference
sentences = [
    'what sectors are contributing the most to my performance [DATES]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'returns\')": "portfolio"}]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'portfolio\')": "portfolio"}]',
]
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("sentence_transformers_model_id")
# Run inference
sentences = [
    'what sectors are contributing the most to my performance [DATES]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'returns\')": "portfolio"}]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'portfolio\')": "portfolio"}]',
]
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("sentence_transformers_model_id")
# Run inference
sentences = [
    'what sectors are contributing the most to my performance [DATES]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'returns\')": "portfolio"}]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'portfolio\')": "portfolio"}]',
]
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("sentence_transformers_model_id")
# Run inference
sentences = [
    'what sectors are contributing the most to my performance [DATES]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'returns\')": "portfolio"}]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'portfolio\')": "portfolio"}]',
]
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("sentence_transformers_model_id")
# Run inference
sentences = [
    'what sectors are contributing the most to my performance [DATES]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'returns\')": "portfolio"}]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'portfolio\')": "portfolio"}]',
]
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("sentence_transformers_model_id")
# Run inference
sentences = [
    'what sectors are contributing the most to my performance [DATES]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'returns\')": "portfolio"}]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'portfolio\')": "portfolio"}]',
]
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("sentence_transformers_model_id")
# Run inference
sentences = [
    'what sectors are contributing the most to my performance [DATES]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'returns\')": "portfolio"}]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'portfolio\')": "portfolio"}]',
]
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("sentence_transformers_model_id")
# Run inference
sentences = [
    'what sectors are contributing the most to my performance [DATES]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'returns\')": "portfolio"}]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'portfolio\')": "portfolio"}]',
]
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("sentence_transformers_model_id")
# Run inference
sentences = [
    'what sectors are contributing the most to my performance [DATES]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'returns\')": "portfolio"}]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'portfolio\')": "portfolio"}]',
]
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("sentence_transformers_model_id")
# Run inference
sentences = [
    'what sectors are contributing the most to my performance [DATES]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'returns\')": "portfolio"}]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'portfolio\')": "portfolio"}]',
]
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("sentence_transformers_model_id")
# Run inference
sentences = [
    'what sectors are contributing the most to my performance [DATES]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'returns\')": "portfolio"}]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'portfolio\')": "portfolio"}]',
]
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("sentence_transformers_model_id")
# Run inference
sentences = [
    'what sectors are contributing the most to my performance [DATES]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'returns\')": "portfolio"}]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'portfolio\')": "portfolio"}]',
]
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("sentence_transformers_model_id")
# Run inference
sentences = [
    'what sectors are contributing the most to my performance [DATES]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'returns\')": "portfolio"}]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'portfolio\')": "portfolio"}]',
]
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("sentence_transformers_model_id")
# Run inference
sentences = [
    'what sectors are contributing the most to my performance [DATES]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'returns\')": "portfolio"}]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'portfolio\')": "portfolio"}]',
]
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("sentence_transformers_model_id")
# Run inference
sentences = [
    'what sectors are contributing the most to my performance [DATES]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'returns\')": "portfolio"}]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'portfolio\')": "portfolio"}]',
]
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("sentence_transformers_model_id")
# Run inference
sentences = [
    'what sectors are contributing the most to my performance [DATES]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'returns\')": "portfolio"}]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'portfolio\')": "portfolio"}]',
]
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("sentence_transformers_model_id")
# Run inference
sentences = [
    'what sectors are contributing the most to my performance [DATES]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'returns\')": "portfolio"}]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'portfolio\')": "portfolio"}]',
]
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("sentence_transformers_model_id")
# Run inference
sentences = [
    'what sectors are contributing the most to my performance [DATES]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'returns\')": "portfolio"}]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'portfolio\')": "portfolio"}]',
]
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("sentence_transformers_model_id")
# Run inference
sentences = [
    'what sectors are contributing the most to my performance [DATES]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'returns\')": "portfolio"}]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'portfolio\')": "portfolio"}]',
]
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("sentence_transformers_model_id")
# Run inference
sentences = [
    'what sectors are contributing the most to my performance [DATES]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'returns\')": "portfolio"}]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'portfolio\')": "portfolio"}]',
]
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("sentence_transformers_model_id")
# Run inference
sentences = [
    'what sectors are contributing the most to my performance [DATES]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'returns\')": "portfolio"}]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'portfolio\')": "portfolio"}]',
]
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("sentence_transformers_model_id")
# Run inference
sentences = [
    'what sectors are contributing the most to my performance [DATES]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'returns\')": "portfolio"}]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'portfolio\')": "portfolio"}]',
]
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("sentence_transformers_model_id")
# Run inference
sentences = [
    'what sectors are contributing the most to my performance [DATES]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'returns\')": "portfolio"}]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'portfolio\')": "portfolio"}]',
]
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("sentence_transformers_model_id")
# Run inference
sentences = [
    'what sectors are contributing the most to my performance [DATES]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'returns\')": "portfolio"}]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'portfolio\')": "portfolio"}]',
]
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("sentence_transformers_model_id")
# Run inference
sentences = [
    'what sectors are contributing the most to my performance [DATES]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'returns\')": "portfolio"}]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'portfolio\')": "portfolio"}]',
]
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("sentence_transformers_model_id")
# Run inference
sentences = [
    'what sectors are contributing the most to my performance [DATES]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'returns\')": "portfolio"}]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'portfolio\')": "portfolio"}]',
]
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("sentence_transformers_model_id")
# Run inference
sentences = [
    'what sectors are contributing the most to my performance [DATES]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'returns\')": "portfolio"}]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'portfolio\')": "portfolio"}]',
]
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("sentence_transformers_model_id")
# Run inference
sentences = [
    'what sectors are contributing the most to my performance [DATES]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'returns\')": "portfolio"}]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'portfolio\')": "portfolio"}]',
]
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("sentence_transformers_model_id")
# Run inference
sentences = [
    'what sectors are contributing the most to my performance [DATES]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'returns\')": "portfolio"}]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'portfolio\')": "portfolio"}]',
]
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("sentence_transformers_model_id")
# Run inference
sentences = [
    'what sectors are contributing the most to my performance [DATES]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'returns\')": "portfolio"}]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'portfolio\')": "portfolio"}]',
]
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("sentence_transformers_model_id")
# Run inference
sentences = [
    'what sectors are contributing the most to my performance [DATES]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'returns\')": "portfolio"}]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'portfolio\')": "portfolio"}]',
]
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("sentence_transformers_model_id")
# Run inference
sentences = [
    'what sectors are contributing the most to my performance [DATES]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'returns\')": "portfolio"}]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'portfolio\')": "portfolio"}]',
]
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("sentence_transformers_model_id")
# Run inference
sentences = [
    'what sectors are contributing the most to my performance [DATES]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'returns\')": "portfolio"}]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'portfolio\')": "portfolio"}]',
]
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("sentence_transformers_model_id")
# Run inference
sentences = [
    'what sectors are contributing the most to my performance [DATES]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'returns\')": "portfolio"}]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'portfolio\')": "portfolio"}]',
]
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("sentence_transformers_model_id")
# Run inference
sentences = [
    'what sectors are contributing the most to my performance [DATES]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'returns\')": "portfolio"}]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'portfolio\')": "portfolio"}]',
]
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("sentence_transformers_model_id")
# Run inference
sentences = [
    'what sectors are contributing the most to my performance [DATES]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'returns\')": "portfolio"}]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'portfolio\')": "portfolio"}]',
]
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("sentence_transformers_model_id")
# Run inference
sentences = [
    'what sectors are contributing the most to my performance [DATES]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'returns\')": "portfolio"}]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'portfolio\')": "portfolio"}]',
]
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("sentence_transformers_model_id")
# Run inference
sentences = [
    'what sectors are contributing the most to my performance [DATES]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'returns\')": "portfolio"}]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'portfolio\')": "portfolio"}]',
]
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("sentence_transformers_model_id")
# Run inference
sentences = [
    'what sectors are contributing the most to my performance [DATES]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'returns\')": "portfolio"}]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'portfolio\')": "portfolio"}]',
]
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("sentence_transformers_model_id")
# Run inference
sentences = [
    'what sectors are contributing the most to my performance [DATES]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'returns\')": "portfolio"}]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'portfolio\')": "portfolio"}]',
]
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("sentence_transformers_model_id")
# Run inference
sentences = [
    'what sectors are contributing the most to my performance [DATES]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'returns\')": "portfolio"}]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'portfolio\')": "portfolio"}]',
]
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("sentence_transformers_model_id")
# Run inference
sentences = [
    'what sectors are contributing the most to my performance [DATES]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'returns\')": "portfolio"}]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'portfolio\')": "portfolio"}]',
]
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("sentence_transformers_model_id")
# Run inference
sentences = [
    'what sectors are contributing the most to my performance [DATES]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'returns\')": "portfolio"}]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'portfolio\')": "portfolio"}]',
]
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("sentence_transformers_model_id")
# Run inference
sentences = [
    'what sectors are contributing the most to my performance [DATES]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'returns\')": "portfolio"}]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'portfolio\')": "portfolio"}]',
]
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("sentence_transformers_model_id")
# Run inference
sentences = [
    'what sectors are contributing the most to my performance [DATES]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'returns\')": "portfolio"}]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'portfolio\')": "portfolio"}]',
]
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("sentence_transformers_model_id")
# Run inference
sentences = [
    'what sectors are contributing the most to my performance [DATES]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'returns\')": "portfolio"}]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'portfolio\')": "portfolio"}]',
]
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("sentence_transformers_model_id")
# Run inference
sentences = [
    'what sectors are contributing the most to my performance [DATES]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'returns\')": "portfolio"}]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'portfolio\')": "portfolio"}]',
]
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("sentence_transformers_model_id")
# Run inference
sentences = [
    'what sectors are contributing the most to my performance [DATES]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'returns\')": "portfolio"}]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'portfolio\')": "portfolio"}]',
]
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("sentence_transformers_model_id")
# Run inference
sentences = [
    'what sectors are contributing the most to my performance [DATES]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'returns\')": "portfolio"}]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'portfolio\')": "portfolio"}]',
]
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("sentence_transformers_model_id")
# Run inference
sentences = [
    'what sectors are contributing the most to my performance [DATES]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'returns\')": "portfolio"}]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'portfolio\')": "portfolio"}]',
]
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("sentence_transformers_model_id")
# Run inference
sentences = [
    'what sectors are contributing the most to my performance [DATES]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'returns\')": "portfolio"}]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'portfolio\')": "portfolio"}]',
]
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("sentence_transformers_model_id")
# Run inference
sentences = [
    'what sectors are contributing the most to my performance [DATES]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'returns\')": "portfolio"}]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'portfolio\')": "portfolio"}]',
]
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("sentence_transformers_model_id")
# Run inference
sentences = [
    'what sectors are contributing the most to my performance [DATES]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'returns\')": "portfolio"}]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'portfolio\')": "portfolio"}]',
]
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("sentence_transformers_model_id")
# Run inference
sentences = [
    'what sectors are contributing the most to my performance [DATES]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'returns\')": "portfolio"}]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'portfolio\')": "portfolio"}]',
]
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("sentence_transformers_model_id")
# Run inference
sentences = [
    'what sectors are contributing the most to my performance [DATES]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'returns\')": "portfolio"}]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'portfolio\')": "portfolio"}]',
]
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("sentence_transformers_model_id")
# Run inference
sentences = [
    'what sectors are contributing the most to my performance [DATES]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'returns\')": "portfolio"}]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'portfolio\')": "portfolio"}]',
]
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("sentence_transformers_model_id")
# Run inference
sentences = [
    'what sectors are contributing the most to my performance [DATES]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'returns\')": "portfolio"}]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'portfolio\')": "portfolio"}]',
]
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("sentence_transformers_model_id")
# Run inference
sentences = [
    'what sectors are contributing the most to my performance [DATES]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'returns\')": "portfolio"}]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'portfolio\')": "portfolio"}]',
]
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("sentence_transformers_model_id")
# Run inference
sentences = [
    'what sectors are contributing the most to my performance [DATES]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'returns\')": "portfolio"}]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'portfolio\')": "portfolio"}]',
]
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("sentence_transformers_model_id")
# Run inference
sentences = [
    'what sectors are contributing the most to my performance [DATES]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'returns\')": "portfolio"}]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'portfolio\')": "portfolio"}]',
]
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("sentence_transformers_model_id")
# Run inference
sentences = [
    'what sectors are contributing the most to my performance [DATES]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'returns\')": "portfolio"}]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'portfolio\')": "portfolio"}]',
]
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("sentence_transformers_model_id")
# Run inference
sentences = [
    'what sectors are contributing the most to my performance [DATES]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'returns\')": "portfolio"}]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'portfolio\')": "portfolio"}]',
]
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("sentence_transformers_model_id")
# Run inference
sentences = [
    'what sectors are contributing the most to my performance [DATES]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'returns\')": "portfolio"}]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'portfolio\')": "portfolio"}]',
]
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("sentence_transformers_model_id")
# Run inference
sentences = [
    'what sectors are contributing the most to my performance [DATES]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'returns\')": "portfolio"}]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'portfolio\')": "portfolio"}]',
]
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("sentence_transformers_model_id")
# Run inference
sentences = [
    'what sectors are contributing the most to my performance [DATES]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'returns\')": "portfolio"}]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'portfolio\')": "portfolio"}]',
]
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("sentence_transformers_model_id")
# Run inference
sentences = [
    'what sectors are contributing the most to my performance [DATES]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'returns\')": "portfolio"}]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'portfolio\')": "portfolio"}]',
]
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("sentence_transformers_model_id")
# Run inference
sentences = [
    'what sectors are contributing the most to my performance [DATES]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'returns\')": "portfolio"}]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'portfolio\')": "portfolio"}]',
]
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("sentence_transformers_model_id")
# Run inference
sentences = [
    'what sectors are contributing the most to my performance [DATES]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'returns\')": "portfolio"}]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'portfolio\')": "portfolio"}]',
]
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("sentence_transformers_model_id")
# Run inference
sentences = [
    'what sectors are contributing the most to my performance [DATES]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'returns\')": "portfolio"}]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'portfolio\')": "portfolio"}]',
]
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("sentence_transformers_model_id")
# Run inference
sentences = [
    'what sectors are contributing the most to my performance [DATES]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'returns\')": "portfolio"}]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'portfolio\')": "portfolio"}]',
]
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("sentence_transformers_model_id")
# Run inference
sentences = [
    'what sectors are contributing the most to my performance [DATES]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'returns\')": "portfolio"}]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'portfolio\')": "portfolio"}]',
]
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("sentence_transformers_model_id")
# Run inference
sentences = [
    'what sectors are contributing the most to my performance [DATES]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'returns\')": "portfolio"}]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'portfolio\')": "portfolio"}]',
]
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("sentence_transformers_model_id")
# Run inference
sentences = [
    'what sectors are contributing the most to my performance [DATES]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'returns\')": "portfolio"}]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'portfolio\')": "portfolio"}]',
]
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("sentence_transformers_model_id")
# Run inference
sentences = [
    'what sectors are contributing the most to my performance [DATES]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'returns\')": "portfolio"}]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'portfolio\')": "portfolio"}]',
]
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("sentence_transformers_model_id")
# Run inference
sentences = [
    'what sectors are contributing the most to my performance [DATES]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'returns\')": "portfolio"}]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'portfolio\')": "portfolio"}]',
]
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("sentence_transformers_model_id")
# Run inference
sentences = [
    'what sectors are contributing the most to my performance [DATES]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'returns\')": "portfolio"}]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'portfolio\')": "portfolio"}]',
]
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("sentence_transformers_model_id")
# Run inference
sentences = [
    'what sectors are contributing the most to my performance [DATES]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'returns\')": "portfolio"}]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'portfolio\')": "portfolio"}]',
]
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("sentence_transformers_model_id")
# Run inference
sentences = [
    'what sectors are contributing the most to my performance [DATES]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'returns\')": "portfolio"}]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'portfolio\')": "portfolio"}]',
]
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("sentence_transformers_model_id")
# Run inference
sentences = [
    'what sectors are contributing the most to my performance [DATES]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'returns\')": "portfolio"}]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'portfolio\')": "portfolio"}]',
]
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("sentence_transformers_model_id")
# Run inference
sentences = [
    'what sectors are contributing the most to my performance [DATES]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'returns\')": "portfolio"}]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'portfolio\')": "portfolio"}]',
]
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("sentence_transformers_model_id")
# Run inference
sentences = [
    'what sectors are contributing the most to my performance [DATES]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'returns\')": "portfolio"}]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'portfolio\')": "portfolio"}]',
]
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("sentence_transformers_model_id")
# Run inference
sentences = [
    'what sectors are contributing the most to my performance [DATES]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'returns\')": "portfolio"}]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'portfolio\')": "portfolio"}]',
]
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("sentence_transformers_model_id")
# Run inference
sentences = [
    'what sectors are contributing the most to my performance [DATES]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'returns\')": "portfolio"}]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'portfolio\')": "portfolio"}]',
]
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("sentence_transformers_model_id")
# Run inference
sentences = [
    'what sectors are contributing the most to my performance [DATES]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'returns\')": "portfolio"}]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'portfolio\')": "portfolio"}]',
]
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("sentence_transformers_model_id")
# Run inference
sentences = [
    'what sectors are contributing the most to my performance [DATES]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'returns\')": "portfolio"}]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'portfolio\')": "portfolio"}]',
]
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("sentence_transformers_model_id")
# Run inference
sentences = [
    'what sectors are contributing the most to my performance [DATES]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'returns\')": "portfolio"}]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'portfolio\')": "portfolio"}]',
]
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("sentence_transformers_model_id")
# Run inference
sentences = [
    'what sectors are contributing the most to my performance [DATES]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'returns\')": "portfolio"}]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'portfolio\')": "portfolio"}]',
]
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("sentence_transformers_model_id")
# Run inference
sentences = [
    'what sectors are contributing the most to my performance [DATES]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'returns\')": "portfolio"}]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'portfolio\')": "portfolio"}]',
]
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("sentence_transformers_model_id")
# Run inference
sentences = [
    'what sectors are contributing the most to my performance [DATES]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'returns\')": "portfolio"}]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'portfolio\')": "portfolio"}]',
]
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("sentence_transformers_model_id")
# Run inference
sentences = [
    'what sectors are contributing the most to my performance [DATES]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'returns\')": "portfolio"}]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'portfolio\')": "portfolio"}]',
]
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("sentence_transformers_model_id")
# Run inference
sentences = [
    'what sectors are contributing the most to my performance [DATES]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'returns\')": "portfolio"}]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'portfolio\')": "portfolio"}]',
]
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("sentence_transformers_model_id")
# Run inference
sentences = [
    'what sectors are contributing the most to my performance [DATES]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'returns\')": "portfolio"}]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'portfolio\')": "portfolio"}]',
]
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("sentence_transformers_model_id")
# Run inference
sentences = [
    'what sectors are contributing the most to my performance [DATES]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'returns\')": "portfolio"}]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'portfolio\')": "portfolio"}]',
]
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("sentence_transformers_model_id")
# Run inference
sentences = [
    'what sectors are contributing the most to my performance [DATES]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'returns\')": "portfolio"}]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'portfolio\')": "portfolio"}]',
]
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("sentence_transformers_model_id")
# Run inference
sentences = [
    'what sectors are contributing the most to my performance [DATES]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'returns\')": "portfolio"}]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'portfolio\')": "portfolio"}]',
]
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("sentence_transformers_model_id")
# Run inference
sentences = [
    'what sectors are contributing the most to my performance [DATES]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'returns\')": "portfolio"}]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'portfolio\')": "portfolio"}]',
]
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("sentence_transformers_model_id")
# Run inference
sentences = [
    'what sectors are contributing the most to my performance [DATES]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'returns\')": "portfolio"}]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'portfolio\')": "portfolio"}]',
]
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("sentence_transformers_model_id")
# Run inference
sentences = [
    'what sectors are contributing the most to my performance [DATES]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'returns\')": "portfolio"}]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'portfolio\')": "portfolio"}]',
]
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("sentence_transformers_model_id")
# Run inference
sentences = [
    'what sectors are contributing the most to my performance [DATES]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'returns\')": "portfolio"}]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'portfolio\')": "portfolio"}]',
]
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("sentence_transformers_model_id")
# Run inference
sentences = [
    'what sectors are contributing the most to my performance [DATES]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'returns\')": "portfolio"}]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'portfolio\')": "portfolio"}]',
]
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("sentence_transformers_model_id")
# Run inference
sentences = [
    'what sectors are contributing the most to my performance [DATES]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'returns\')": "portfolio"}]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'portfolio\')": "portfolio"}]',
]
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("sentence_transformers_model_id")
# Run inference
sentences = [
    'what sectors are contributing the most to my performance [DATES]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'returns\')": "portfolio"}]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'portfolio\')": "portfolio"}]',
]
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("sentence_transformers_model_id")
# Run inference
sentences = [
    'what sectors are contributing the most to my performance [DATES]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'returns\')": "portfolio"}]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'portfolio\')": "portfolio"}]',
]
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("sentence_transformers_model_id")
# Run inference
sentences = [
    'what sectors are contributing the most to my performance [DATES]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'returns\')": "portfolio"}]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'portfolio\')": "portfolio"}]',
]
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("sentence_transformers_model_id")
# Run inference
sentences = [
    'what sectors are contributing the most to my performance [DATES]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'returns\')": "portfolio"}]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'portfolio\')": "portfolio"}]',
]
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("sentence_transformers_model_id")
# Run inference
sentences = [
    'what sectors are contributing the most to my performance [DATES]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'returns\')": "portfolio"}]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'portfolio\')": "portfolio"}]',
]
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("sentence_transformers_model_id")
# Run inference
sentences = [
    'what sectors are contributing the most to my performance [DATES]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'returns\')": "portfolio"}]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'portfolio\')": "portfolio"}]',
]
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("sentence_transformers_model_id")
# Run inference
sentences = [
    'what sectors are contributing the most to my performance [DATES]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'returns\')": "portfolio"}]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'portfolio\')": "portfolio"}]',
]
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("sentence_transformers_model_id")
# Run inference
sentences = [
    'what sectors are contributing the most to my performance [DATES]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'returns\')": "portfolio"}]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'portfolio\')": "portfolio"}]',
]
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("sentence_transformers_model_id")
# Run inference
sentences = [
    'what sectors are contributing the most to my performance [DATES]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'returns\')": "portfolio"}]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'portfolio\')": "portfolio"}]',
]
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("sentence_transformers_model_id")
# Run inference
sentences = [
    'what sectors are contributing the most to my performance [DATES]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'returns\')": "portfolio"}]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'portfolio\')": "portfolio"}]',
]
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("sentence_transformers_model_id")
# Run inference
sentences = [
    'what sectors are contributing the most to my performance [DATES]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'returns\')": "portfolio"}]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'portfolio\')": "portfolio"}]',
]
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("sentence_transformers_model_id")
# Run inference
sentences = [
    'what sectors are contributing the most to my performance [DATES]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'returns\')": "portfolio"}]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'portfolio\')": "portfolio"}]',
]
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("sentence_transformers_model_id")
# Run inference
sentences = [
    'what sectors are contributing the most to my performance [DATES]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'returns\')": "portfolio"}]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'portfolio\')": "portfolio"}]',
]
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("sentence_transformers_model_id")
# Run inference
sentences = [
    'what sectors are contributing the most to my performance [DATES]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'returns\')": "portfolio"}]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'portfolio\')": "portfolio"}]',
]
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("sentence_transformers_model_id")
# Run inference
sentences = [
    'what sectors are contributing the most to my performance [DATES]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'returns\')": "portfolio"}]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'portfolio\')": "portfolio"}]',
]
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("sentence_transformers_model_id")
# Run inference
sentences = [
    'what sectors are contributing the most to my performance [DATES]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'returns\')": "portfolio"}]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'portfolio\')": "portfolio"}]',
]
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("sentence_transformers_model_id")
# Run inference
sentences = [
    'what sectors are contributing the most to my performance [DATES]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'returns\')": "portfolio"}]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'portfolio\')": "portfolio"}]',
]
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("sentence_transformers_model_id")
# Run inference
sentences = [
    'what sectors are contributing the most to my performance [DATES]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'returns\')": "portfolio"}]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'portfolio\')": "portfolio"}]',
]
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("sentence_transformers_model_id")
# Run inference
sentences = [
    'what sectors are contributing the most to my performance [DATES]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'returns\')": "portfolio"}]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'portfolio\')": "portfolio"}]',
]
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("sentence_transformers_model_id")
# Run inference
sentences = [
    'what sectors are contributing the most to my performance [DATES]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'returns\')": "portfolio"}]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'portfolio\')": "portfolio"}]',
]
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("sentence_transformers_model_id")
# Run inference
sentences = [
    'what sectors are contributing the most to my performance [DATES]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'returns\')": "portfolio"}]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'portfolio\')": "portfolio"}]',
]
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("sentence_transformers_model_id")
# Run inference
sentences = [
    'what sectors are contributing the most to my performance [DATES]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'returns\')": "portfolio"}]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'portfolio\')": "portfolio"}]',
]
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("sentence_transformers_model_id")
# Run inference
sentences = [
    'what sectors are contributing the most to my performance [DATES]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'returns\')": "portfolio"}]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'portfolio\')": "portfolio"}]',
]
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("sentence_transformers_model_id")
# Run inference
sentences = [
    'what sectors are contributing the most to my performance [DATES]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'returns\')": "portfolio"}]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'portfolio\')": "portfolio"}]',
]
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("sentence_transformers_model_id")
# Run inference
sentences = [
    'what sectors are contributing the most to my performance [DATES]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'returns\')": "portfolio"}]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'portfolio\')": "portfolio"}]',
]
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("sentence_transformers_model_id")
# Run inference
sentences = [
    'what sectors are contributing the most to my performance [DATES]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'returns\')": "portfolio"}]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'portfolio\')": "portfolio"}]',
]
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("sentence_transformers_model_id")
# Run inference
sentences = [
    'what sectors are contributing the most to my performance [DATES]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'returns\')": "portfolio"}]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'portfolio\')": "portfolio"}]',
]
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("sentence_transformers_model_id")
# Run inference
sentences = [
    'what sectors are contributing the most to my performance [DATES]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'returns\')": "portfolio"}]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'portfolio\')": "portfolio"}]',
]
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("sentence_transformers_model_id")
# Run inference
sentences = [
    'what sectors are contributing the most to my performance [DATES]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'returns\')": "portfolio"}]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'portfolio\')": "portfolio"}]',
]
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("sentence_transformers_model_id")
# Run inference
sentences = [
    'what sectors are contributing the most to my performance [DATES]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'returns\')": "portfolio"}]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'portfolio\')": "portfolio"}]',
]
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("sentence_transformers_model_id")
# Run inference
sentences = [
    'what sectors are contributing the most to my performance [DATES]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'returns\')": "portfolio"}]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'portfolio\')": "portfolio"}]',
]
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("sentence_transformers_model_id")
# Run inference
sentences = [
    'what sectors are contributing the most to my performance [DATES]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'returns\')": "portfolio"}]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'portfolio\')": "portfolio"}]',
]
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("sentence_transformers_model_id")
# Run inference
sentences = [
    'what sectors are contributing the most to my performance [DATES]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'returns\')": "portfolio"}]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'portfolio\')": "portfolio"}]',
]
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("sentence_transformers_model_id")
# Run inference
sentences = [
    'what sectors are contributing the most to my performance [DATES]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'returns\')": "portfolio"}]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'portfolio\')": "portfolio"}]',
]
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("sentence_transformers_model_id")
# Run inference
sentences = [
    'what sectors are contributing the most to my performance [DATES]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'returns\')": "portfolio"}]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'portfolio\')": "portfolio"}]',
]
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("sentence_transformers_model_id")
# Run inference
sentences = [
    'what sectors are contributing the most to my performance [DATES]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'returns\')": "portfolio"}]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'portfolio\')": "portfolio"}]',
]
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("sentence_transformers_model_id")
# Run inference
sentences = [
    'what sectors are contributing the most to my performance [DATES]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'returns\')": "portfolio"}]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'portfolio\')": "portfolio"}]',
]
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("sentence_transformers_model_id")
# Run inference
sentences = [
    'what sectors are contributing the most to my performance [DATES]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'returns\')": "portfolio"}]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'portfolio\')": "portfolio"}]',
]
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("sentence_transformers_model_id")
# Run inference
sentences = [
    'what sectors are contributing the most to my performance [DATES]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'returns\')": "portfolio"}]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'portfolio\')": "portfolio"}]',
]
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("sentence_transformers_model_id")
# Run inference
sentences = [
    'what sectors are contributing the most to my performance [DATES]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'returns\')": "portfolio"}]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'portfolio\')": "portfolio"}]',
]
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("sentence_transformers_model_id")
# Run inference
sentences = [
    'what sectors are contributing the most to my performance [DATES]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'returns\')": "portfolio"}]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'portfolio\')": "portfolio"}]',
]
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("sentence_transformers_model_id")
# Run inference
sentences = [
    'what sectors are contributing the most to my performance [DATES]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'returns\')": "portfolio"}]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'portfolio\')": "portfolio"}]',
]
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("sentence_transformers_model_id")
# Run inference
sentences = [
    'what sectors are contributing the most to my performance [DATES]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'returns\')": "portfolio"}]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'portfolio\')": "portfolio"}]',
]
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("sentence_transformers_model_id")
# Run inference
sentences = [
    'what sectors are contributing the most to my performance [DATES]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'returns\')": "portfolio"}]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'portfolio\')": "portfolio"}]',
]
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("sentence_transformers_model_id")
# Run inference
sentences = [
    'what sectors are contributing the most to my performance [DATES]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'returns\')": "portfolio"}]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'portfolio\')": "portfolio"}]',
]
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("sentence_transformers_model_id")
# Run inference
sentences = [
    'what sectors are contributing the most to my performance [DATES]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'returns\')": "portfolio"}]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'portfolio\')": "portfolio"}]',
]
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("sentence_transformers_model_id")
# Run inference
sentences = [
    'what sectors are contributing the most to my performance [DATES]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'returns\')": "portfolio"}]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'portfolio\')": "portfolio"}]',
]
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("sentence_transformers_model_id")
# Run inference
sentences = [
    'what sectors are contributing the most to my performance [DATES]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'returns\')": "portfolio"}]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'portfolio\')": "portfolio"}]',
]
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("sentence_transformers_model_id")
# Run inference
sentences = [
    'what sectors are contributing the most to my performance [DATES]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'returns\')": "portfolio"}]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'portfolio\')": "portfolio"}]',
]
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("sentence_transformers_model_id")
# Run inference
sentences = [
    'what sectors are contributing the most to my performance [DATES]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'returns\')": "portfolio"}]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'portfolio\')": "portfolio"}]',
]
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("sentence_transformers_model_id")
# Run inference
sentences = [
    'what sectors are contributing the most to my performance [DATES]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'returns\')": "portfolio"}]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'portfolio\')": "portfolio"}]',
]
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("sentence_transformers_model_id")
# Run inference
sentences = [
    'what sectors are contributing the most to my performance [DATES]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'returns\')": "portfolio"}]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'portfolio\')": "portfolio"}]',
]
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("sentence_transformers_model_id")
# Run inference
sentences = [
    'what sectors are contributing the most to my performance [DATES]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'returns\')": "portfolio"}]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'portfolio\')": "portfolio"}]',
]
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("sentence_transformers_model_id")
# Run inference
sentences = [
    'what sectors are contributing the most to my performance [DATES]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'returns\')": "portfolio"}]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'portfolio\')": "portfolio"}]',
]
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("sentence_transformers_model_id")
# Run inference
sentences = [
    'what sectors are contributing the most to my performance [DATES]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'returns\')": "portfolio"}]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'portfolio\')": "portfolio"}]',
]
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("sentence_transformers_model_id")
# Run inference
sentences = [
    'what sectors are contributing the most to my performance [DATES]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'returns\')": "portfolio"}]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'portfolio\')": "portfolio"}]',
]
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("sentence_transformers_model_id")
# Run inference
sentences = [
    'what sectors are contributing the most to my performance [DATES]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'returns\')": "portfolio"}]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'portfolio\')": "portfolio"}]',
]
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("sentence_transformers_model_id")
# Run inference
sentences = [
    'what sectors are contributing the most to my performance [DATES]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'returns\')": "portfolio"}]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'portfolio\')": "portfolio"}]',
]
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("sentence_transformers_model_id")
# Run inference
sentences = [
    'what sectors are contributing the most to my performance [DATES]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'returns\')": "portfolio"}]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'portfolio\')": "portfolio"}]',
]
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("sentence_transformers_model_id")
# Run inference
sentences = [
    'what sectors are contributing the most to my performance [DATES]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'returns\')": "portfolio"}]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'portfolio\')": "portfolio"}]',
]
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("sentence_transformers_model_id")
# Run inference
sentences = [
    'what sectors are contributing the most to my performance [DATES]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'returns\')": "portfolio"}]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'portfolio\')": "portfolio"}]',
]
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("sentence_transformers_model_id")
# Run inference
sentences = [
    'what sectors are contributing the most to my performance [DATES]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'returns\')": "portfolio"}]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'portfolio\')": "portfolio"}]',
]
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("sentence_transformers_model_id")
# Run inference
sentences = [
    'what sectors are contributing the most to my performance [DATES]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'returns\')": "portfolio"}]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'portfolio\')": "portfolio"}]',
]
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("sentence_transformers_model_id")
# Run inference
sentences = [
    'what sectors are contributing the most to my performance [DATES]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'returns\')": "portfolio"}]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'portfolio\')": "portfolio"}]',
]
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("sentence_transformers_model_id")
# Run inference
sentences = [
    'what sectors are contributing the most to my performance [DATES]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'returns\')": "portfolio"}]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'portfolio\')": "portfolio"}]',
]
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("sentence_transformers_model_id")
# Run inference
sentences = [
    'what sectors are contributing the most to my performance [DATES]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'returns\')": "portfolio"}]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'portfolio\')": "portfolio"}]',
]
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("sentence_transformers_model_id")
# Run inference
sentences = [
    'what sectors are contributing the most to my performance [DATES]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'returns\')": "portfolio"}]',
    '[{"get_portfolio(None,True,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'sector\',None,\'portfolio\')": "portfolio"}]',
]
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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