bge-small-en-v1-5-ft-test-run

Name: bge-small-en-v1-5-ft-test-run
Author: magnifi

—

magnifi

Embedding Model

OTHER

1705.00652B params

New

1 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

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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

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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

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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 = [
    'Market news from [DATES]',
    '[{"get_news_articles(None,None,None,\'<DATES>\')": "news_data"}, {"get_attribute([\'SPY\'],[\'returns\'],\'<DATES>\')":"SPY_returns"},  {"get_attribute([\'DIA\'],[\'returns\'],\'<DATES>\')":"DIA_returns"}, {"get_attribute([\'QQQ\'],[\'returns\'],\'<DATES>\')":"QQQ_returns"}]',
    '[{"get_dividend_history([\'<TICKER>\'],None)": "<TICKER>_dividend_history"}]',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for 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 = [
    'Market news from [DATES]',
    '[{"get_news_articles(None,None,None,\'<DATES>\')": "news_data"}, {"get_attribute([\'SPY\'],[\'returns\'],\'<DATES>\')":"SPY_returns"},  {"get_attribute([\'DIA\'],[\'returns\'],\'<DATES>\')":"DIA_returns"}, {"get_attribute([\'QQQ\'],[\'returns\'],\'<DATES>\')":"QQQ_returns"}]',
    '[{"get_dividend_history([\'<TICKER>\'],None)": "<TICKER>_dividend_history"}]',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for 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 = [
    'Market news from [DATES]',
    '[{"get_news_articles(None,None,None,\'<DATES>\')": "news_data"}, {"get_attribute([\'SPY\'],[\'returns\'],\'<DATES>\')":"SPY_returns"},  {"get_attribute([\'DIA\'],[\'returns\'],\'<DATES>\')":"DIA_returns"}, {"get_attribute([\'QQQ\'],[\'returns\'],\'<DATES>\')":"QQQ_returns"}]',
    '[{"get_dividend_history([\'<TICKER>\'],None)": "<TICKER>_dividend_history"}]',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for 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 = [
    'Market news from [DATES]',
    '[{"get_news_articles(None,None,None,\'<DATES>\')": "news_data"}, {"get_attribute([\'SPY\'],[\'returns\'],\'<DATES>\')":"SPY_returns"},  {"get_attribute([\'DIA\'],[\'returns\'],\'<DATES>\')":"DIA_returns"}, {"get_attribute([\'QQQ\'],[\'returns\'],\'<DATES>\')":"QQQ_returns"}]',
    '[{"get_dividend_history([\'<TICKER>\'],None)": "<TICKER>_dividend_history"}]',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for 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 = [
    'Market news from [DATES]',
    '[{"get_news_articles(None,None,None,\'<DATES>\')": "news_data"}, {"get_attribute([\'SPY\'],[\'returns\'],\'<DATES>\')":"SPY_returns"},  {"get_attribute([\'DIA\'],[\'returns\'],\'<DATES>\')":"DIA_returns"}, {"get_attribute([\'QQQ\'],[\'returns\'],\'<DATES>\')":"QQQ_returns"}]',
    '[{"get_dividend_history([\'<TICKER>\'],None)": "<TICKER>_dividend_history"}]',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for 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 = [
    'Market news from [DATES]',
    '[{"get_news_articles(None,None,None,\'<DATES>\')": "news_data"}, {"get_attribute([\'SPY\'],[\'returns\'],\'<DATES>\')":"SPY_returns"},  {"get_attribute([\'DIA\'],[\'returns\'],\'<DATES>\')":"DIA_returns"}, {"get_attribute([\'QQQ\'],[\'returns\'],\'<DATES>\')":"QQQ_returns"}]',
    '[{"get_dividend_history([\'<TICKER>\'],None)": "<TICKER>_dividend_history"}]',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for 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 = [
    'Market news from [DATES]',
    '[{"get_news_articles(None,None,None,\'<DATES>\')": "news_data"}, {"get_attribute([\'SPY\'],[\'returns\'],\'<DATES>\')":"SPY_returns"},  {"get_attribute([\'DIA\'],[\'returns\'],\'<DATES>\')":"DIA_returns"}, {"get_attribute([\'QQQ\'],[\'returns\'],\'<DATES>\')":"QQQ_returns"}]',
    '[{"get_dividend_history([\'<TICKER>\'],None)": "<TICKER>_dividend_history"}]',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for 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 = [
    'Market news from [DATES]',
    '[{"get_news_articles(None,None,None,\'<DATES>\')": "news_data"}, {"get_attribute([\'SPY\'],[\'returns\'],\'<DATES>\')":"SPY_returns"},  {"get_attribute([\'DIA\'],[\'returns\'],\'<DATES>\')":"DIA_returns"}, {"get_attribute([\'QQQ\'],[\'returns\'],\'<DATES>\')":"QQQ_returns"}]',
    '[{"get_dividend_history([\'<TICKER>\'],None)": "<TICKER>_dividend_history"}]',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for 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 = [
    'Market news from [DATES]',
    '[{"get_news_articles(None,None,None,\'<DATES>\')": "news_data"}, {"get_attribute([\'SPY\'],[\'returns\'],\'<DATES>\')":"SPY_returns"},  {"get_attribute([\'DIA\'],[\'returns\'],\'<DATES>\')":"DIA_returns"}, {"get_attribute([\'QQQ\'],[\'returns\'],\'<DATES>\')":"QQQ_returns"}]',
    '[{"get_dividend_history([\'<TICKER>\'],None)": "<TICKER>_dividend_history"}]',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for 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 = [
    'Market news from [DATES]',
    '[{"get_news_articles(None,None,None,\'<DATES>\')": "news_data"}, {"get_attribute([\'SPY\'],[\'returns\'],\'<DATES>\')":"SPY_returns"},  {"get_attribute([\'DIA\'],[\'returns\'],\'<DATES>\')":"DIA_returns"}, {"get_attribute([\'QQQ\'],[\'returns\'],\'<DATES>\')":"QQQ_returns"}]',
    '[{"get_dividend_history([\'<TICKER>\'],None)": "<TICKER>_dividend_history"}]',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for 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 = [
    'Market news from [DATES]',
    '[{"get_news_articles(None,None,None,\'<DATES>\')": "news_data"}, {"get_attribute([\'SPY\'],[\'returns\'],\'<DATES>\')":"SPY_returns"},  {"get_attribute([\'DIA\'],[\'returns\'],\'<DATES>\')":"DIA_returns"}, {"get_attribute([\'QQQ\'],[\'returns\'],\'<DATES>\')":"QQQ_returns"}]',
    '[{"get_dividend_history([\'<TICKER>\'],None)": "<TICKER>_dividend_history"}]',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for 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 = [
    'Market news from [DATES]',
    '[{"get_news_articles(None,None,None,\'<DATES>\')": "news_data"}, {"get_attribute([\'SPY\'],[\'returns\'],\'<DATES>\')":"SPY_returns"},  {"get_attribute([\'DIA\'],[\'returns\'],\'<DATES>\')":"DIA_returns"}, {"get_attribute([\'QQQ\'],[\'returns\'],\'<DATES>\')":"QQQ_returns"}]',
    '[{"get_dividend_history([\'<TICKER>\'],None)": "<TICKER>_dividend_history"}]',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for 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 = [
    'Market news from [DATES]',
    '[{"get_news_articles(None,None,None,\'<DATES>\')": "news_data"}, {"get_attribute([\'SPY\'],[\'returns\'],\'<DATES>\')":"SPY_returns"},  {"get_attribute([\'DIA\'],[\'returns\'],\'<DATES>\')":"DIA_returns"}, {"get_attribute([\'QQQ\'],[\'returns\'],\'<DATES>\')":"QQQ_returns"}]',
    '[{"get_dividend_history([\'<TICKER>\'],None)": "<TICKER>_dividend_history"}]',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for 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 = [
    'Market news from [DATES]',
    '[{"get_news_articles(None,None,None,\'<DATES>\')": "news_data"}, {"get_attribute([\'SPY\'],[\'returns\'],\'<DATES>\')":"SPY_returns"},  {"get_attribute([\'DIA\'],[\'returns\'],\'<DATES>\')":"DIA_returns"}, {"get_attribute([\'QQQ\'],[\'returns\'],\'<DATES>\')":"QQQ_returns"}]',
    '[{"get_dividend_history([\'<TICKER>\'],None)": "<TICKER>_dividend_history"}]',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for 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 = [
    'Market news from [DATES]',
    '[{"get_news_articles(None,None,None,\'<DATES>\')": "news_data"}, {"get_attribute([\'SPY\'],[\'returns\'],\'<DATES>\')":"SPY_returns"},  {"get_attribute([\'DIA\'],[\'returns\'],\'<DATES>\')":"DIA_returns"}, {"get_attribute([\'QQQ\'],[\'returns\'],\'<DATES>\')":"QQQ_returns"}]',
    '[{"get_dividend_history([\'<TICKER>\'],None)": "<TICKER>_dividend_history"}]',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for 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 = [
    'Market news from [DATES]',
    '[{"get_news_articles(None,None,None,\'<DATES>\')": "news_data"}, {"get_attribute([\'SPY\'],[\'returns\'],\'<DATES>\')":"SPY_returns"},  {"get_attribute([\'DIA\'],[\'returns\'],\'<DATES>\')":"DIA_returns"}, {"get_attribute([\'QQQ\'],[\'returns\'],\'<DATES>\')":"QQQ_returns"}]',
    '[{"get_dividend_history([\'<TICKER>\'],None)": "<TICKER>_dividend_history"}]',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for 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 = [
    'Market news from [DATES]',
    '[{"get_news_articles(None,None,None,\'<DATES>\')": "news_data"}, {"get_attribute([\'SPY\'],[\'returns\'],\'<DATES>\')":"SPY_returns"},  {"get_attribute([\'DIA\'],[\'returns\'],\'<DATES>\')":"DIA_returns"}, {"get_attribute([\'QQQ\'],[\'returns\'],\'<DATES>\')":"QQQ_returns"}]',
    '[{"get_dividend_history([\'<TICKER>\'],None)": "<TICKER>_dividend_history"}]',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for 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 = [
    'Market news from [DATES]',
    '[{"get_news_articles(None,None,None,\'<DATES>\')": "news_data"}, {"get_attribute([\'SPY\'],[\'returns\'],\'<DATES>\')":"SPY_returns"},  {"get_attribute([\'DIA\'],[\'returns\'],\'<DATES>\')":"DIA_returns"}, {"get_attribute([\'QQQ\'],[\'returns\'],\'<DATES>\')":"QQQ_returns"}]',
    '[{"get_dividend_history([\'<TICKER>\'],None)": "<TICKER>_dividend_history"}]',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for 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 = [
    'Market news from [DATES]',
    '[{"get_news_articles(None,None,None,\'<DATES>\')": "news_data"}, {"get_attribute([\'SPY\'],[\'returns\'],\'<DATES>\')":"SPY_returns"},  {"get_attribute([\'DIA\'],[\'returns\'],\'<DATES>\')":"DIA_returns"}, {"get_attribute([\'QQQ\'],[\'returns\'],\'<DATES>\')":"QQQ_returns"}]',
    '[{"get_dividend_history([\'<TICKER>\'],None)": "<TICKER>_dividend_history"}]',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for 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 = [
    'Market news from [DATES]',
    '[{"get_news_articles(None,None,None,\'<DATES>\')": "news_data"}, {"get_attribute([\'SPY\'],[\'returns\'],\'<DATES>\')":"SPY_returns"},  {"get_attribute([\'DIA\'],[\'returns\'],\'<DATES>\')":"DIA_returns"}, {"get_attribute([\'QQQ\'],[\'returns\'],\'<DATES>\')":"QQQ_returns"}]',
    '[{"get_dividend_history([\'<TICKER>\'],None)": "<TICKER>_dividend_history"}]',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for 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 = [
    'Market news from [DATES]',
    '[{"get_news_articles(None,None,None,\'<DATES>\')": "news_data"}, {"get_attribute([\'SPY\'],[\'returns\'],\'<DATES>\')":"SPY_returns"},  {"get_attribute([\'DIA\'],[\'returns\'],\'<DATES>\')":"DIA_returns"}, {"get_attribute([\'QQQ\'],[\'returns\'],\'<DATES>\')":"QQQ_returns"}]',
    '[{"get_dividend_history([\'<TICKER>\'],None)": "<TICKER>_dividend_history"}]',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for 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 = [
    'Market news from [DATES]',
    '[{"get_news_articles(None,None,None,\'<DATES>\')": "news_data"}, {"get_attribute([\'SPY\'],[\'returns\'],\'<DATES>\')":"SPY_returns"},  {"get_attribute([\'DIA\'],[\'returns\'],\'<DATES>\')":"DIA_returns"}, {"get_attribute([\'QQQ\'],[\'returns\'],\'<DATES>\')":"QQQ_returns"}]',
    '[{"get_dividend_history([\'<TICKER>\'],None)": "<TICKER>_dividend_history"}]',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for 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 = [
    'Market news from [DATES]',
    '[{"get_news_articles(None,None,None,\'<DATES>\')": "news_data"}, {"get_attribute([\'SPY\'],[\'returns\'],\'<DATES>\')":"SPY_returns"},  {"get_attribute([\'DIA\'],[\'returns\'],\'<DATES>\')":"DIA_returns"}, {"get_attribute([\'QQQ\'],[\'returns\'],\'<DATES>\')":"QQQ_returns"}]',
    '[{"get_dividend_history([\'<TICKER>\'],None)": "<TICKER>_dividend_history"}]',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for 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 = [
    'Market news from [DATES]',
    '[{"get_news_articles(None,None,None,\'<DATES>\')": "news_data"}, {"get_attribute([\'SPY\'],[\'returns\'],\'<DATES>\')":"SPY_returns"},  {"get_attribute([\'DIA\'],[\'returns\'],\'<DATES>\')":"DIA_returns"}, {"get_attribute([\'QQQ\'],[\'returns\'],\'<DATES>\')":"QQQ_returns"}]',
    '[{"get_dividend_history([\'<TICKER>\'],None)": "<TICKER>_dividend_history"}]',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for 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 = [
    'Market news from [DATES]',
    '[{"get_news_articles(None,None,None,\'<DATES>\')": "news_data"}, {"get_attribute([\'SPY\'],[\'returns\'],\'<DATES>\')":"SPY_returns"},  {"get_attribute([\'DIA\'],[\'returns\'],\'<DATES>\')":"DIA_returns"}, {"get_attribute([\'QQQ\'],[\'returns\'],\'<DATES>\')":"QQQ_returns"}]',
    '[{"get_dividend_history([\'<TICKER>\'],None)": "<TICKER>_dividend_history"}]',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for 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 = [
    'Market news from [DATES]',
    '[{"get_news_articles(None,None,None,\'<DATES>\')": "news_data"}, {"get_attribute([\'SPY\'],[\'returns\'],\'<DATES>\')":"SPY_returns"},  {"get_attribute([\'DIA\'],[\'returns\'],\'<DATES>\')":"DIA_returns"}, {"get_attribute([\'QQQ\'],[\'returns\'],\'<DATES>\')":"QQQ_returns"}]',
    '[{"get_dividend_history([\'<TICKER>\'],None)": "<TICKER>_dividend_history"}]',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for 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 = [
    'Market news from [DATES]',
    '[{"get_news_articles(None,None,None,\'<DATES>\')": "news_data"}, {"get_attribute([\'SPY\'],[\'returns\'],\'<DATES>\')":"SPY_returns"},  {"get_attribute([\'DIA\'],[\'returns\'],\'<DATES>\')":"DIA_returns"}, {"get_attribute([\'QQQ\'],[\'returns\'],\'<DATES>\')":"QQQ_returns"}]',
    '[{"get_dividend_history([\'<TICKER>\'],None)": "<TICKER>_dividend_history"}]',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for 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 = [
    'Market news from [DATES]',
    '[{"get_news_articles(None,None,None,\'<DATES>\')": "news_data"}, {"get_attribute([\'SPY\'],[\'returns\'],\'<DATES>\')":"SPY_returns"},  {"get_attribute([\'DIA\'],[\'returns\'],\'<DATES>\')":"DIA_returns"}, {"get_attribute([\'QQQ\'],[\'returns\'],\'<DATES>\')":"QQQ_returns"}]',
    '[{"get_dividend_history([\'<TICKER>\'],None)": "<TICKER>_dividend_history"}]',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for 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 = [
    'Market news from [DATES]',
    '[{"get_news_articles(None,None,None,\'<DATES>\')": "news_data"}, {"get_attribute([\'SPY\'],[\'returns\'],\'<DATES>\')":"SPY_returns"},  {"get_attribute([\'DIA\'],[\'returns\'],\'<DATES>\')":"DIA_returns"}, {"get_attribute([\'QQQ\'],[\'returns\'],\'<DATES>\')":"QQQ_returns"}]',
    '[{"get_dividend_history([\'<TICKER>\'],None)": "<TICKER>_dividend_history"}]',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for 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 = [
    'Market news from [DATES]',
    '[{"get_news_articles(None,None,None,\'<DATES>\')": "news_data"}, {"get_attribute([\'SPY\'],[\'returns\'],\'<DATES>\')":"SPY_returns"},  {"get_attribute([\'DIA\'],[\'returns\'],\'<DATES>\')":"DIA_returns"}, {"get_attribute([\'QQQ\'],[\'returns\'],\'<DATES>\')":"QQQ_returns"}]',
    '[{"get_dividend_history([\'<TICKER>\'],None)": "<TICKER>_dividend_history"}]',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for 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 = [
    'Market news from [DATES]',
    '[{"get_news_articles(None,None,None,\'<DATES>\')": "news_data"}, {"get_attribute([\'SPY\'],[\'returns\'],\'<DATES>\')":"SPY_returns"},  {"get_attribute([\'DIA\'],[\'returns\'],\'<DATES>\')":"DIA_returns"}, {"get_attribute([\'QQQ\'],[\'returns\'],\'<DATES>\')":"QQQ_returns"}]',
    '[{"get_dividend_history([\'<TICKER>\'],None)": "<TICKER>_dividend_history"}]',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for 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 = [
    'Market news from [DATES]',
    '[{"get_news_articles(None,None,None,\'<DATES>\')": "news_data"}, {"get_attribute([\'SPY\'],[\'returns\'],\'<DATES>\')":"SPY_returns"},  {"get_attribute([\'DIA\'],[\'returns\'],\'<DATES>\')":"DIA_returns"}, {"get_attribute([\'QQQ\'],[\'returns\'],\'<DATES>\')":"QQQ_returns"}]',
    '[{"get_dividend_history([\'<TICKER>\'],None)": "<TICKER>_dividend_history"}]',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for 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 = [
    'Market news from [DATES]',
    '[{"get_news_articles(None,None,None,\'<DATES>\')": "news_data"}, {"get_attribute([\'SPY\'],[\'returns\'],\'<DATES>\')":"SPY_returns"},  {"get_attribute([\'DIA\'],[\'returns\'],\'<DATES>\')":"DIA_returns"}, {"get_attribute([\'QQQ\'],[\'returns\'],\'<DATES>\')":"QQQ_returns"}]',
    '[{"get_dividend_history([\'<TICKER>\'],None)": "<TICKER>_dividend_history"}]',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for 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 = [
    'Market news from [DATES]',
    '[{"get_news_articles(None,None,None,\'<DATES>\')": "news_data"}, {"get_attribute([\'SPY\'],[\'returns\'],\'<DATES>\')":"SPY_returns"},  {"get_attribute([\'DIA\'],[\'returns\'],\'<DATES>\')":"DIA_returns"}, {"get_attribute([\'QQQ\'],[\'returns\'],\'<DATES>\')":"QQQ_returns"}]',
    '[{"get_dividend_history([\'<TICKER>\'],None)": "<TICKER>_dividend_history"}]',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for 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 = [
    'Market news from [DATES]',
    '[{"get_news_articles(None,None,None,\'<DATES>\')": "news_data"}, {"get_attribute([\'SPY\'],[\'returns\'],\'<DATES>\')":"SPY_returns"},  {"get_attribute([\'DIA\'],[\'returns\'],\'<DATES>\')":"DIA_returns"}, {"get_attribute([\'QQQ\'],[\'returns\'],\'<DATES>\')":"QQQ_returns"}]',
    '[{"get_dividend_history([\'<TICKER>\'],None)": "<TICKER>_dividend_history"}]',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for 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 = [
    'Market news from [DATES]',
    '[{"get_news_articles(None,None,None,\'<DATES>\')": "news_data"}, {"get_attribute([\'SPY\'],[\'returns\'],\'<DATES>\')":"SPY_returns"},  {"get_attribute([\'DIA\'],[\'returns\'],\'<DATES>\')":"DIA_returns"}, {"get_attribute([\'QQQ\'],[\'returns\'],\'<DATES>\')":"QQQ_returns"}]',
    '[{"get_dividend_history([\'<TICKER>\'],None)": "<TICKER>_dividend_history"}]',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for 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 = [
    'Market news from [DATES]',
    '[{"get_news_articles(None,None,None,\'<DATES>\')": "news_data"}, {"get_attribute([\'SPY\'],[\'returns\'],\'<DATES>\')":"SPY_returns"},  {"get_attribute([\'DIA\'],[\'returns\'],\'<DATES>\')":"DIA_returns"}, {"get_attribute([\'QQQ\'],[\'returns\'],\'<DATES>\')":"QQQ_returns"}]',
    '[{"get_dividend_history([\'<TICKER>\'],None)": "<TICKER>_dividend_history"}]',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for 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 = [
    'Market news from [DATES]',
    '[{"get_news_articles(None,None,None,\'<DATES>\')": "news_data"}, {"get_attribute([\'SPY\'],[\'returns\'],\'<DATES>\')":"SPY_returns"},  {"get_attribute([\'DIA\'],[\'returns\'],\'<DATES>\')":"DIA_returns"}, {"get_attribute([\'QQQ\'],[\'returns\'],\'<DATES>\')":"QQQ_returns"}]',
    '[{"get_dividend_history([\'<TICKER>\'],None)": "<TICKER>_dividend_history"}]',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for 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 = [
    'Market news from [DATES]',
    '[{"get_news_articles(None,None,None,\'<DATES>\')": "news_data"}, {"get_attribute([\'SPY\'],[\'returns\'],\'<DATES>\')":"SPY_returns"},  {"get_attribute([\'DIA\'],[\'returns\'],\'<DATES>\')":"DIA_returns"}, {"get_attribute([\'QQQ\'],[\'returns\'],\'<DATES>\')":"QQQ_returns"}]',
    '[{"get_dividend_history([\'<TICKER>\'],None)": "<TICKER>_dividend_history"}]',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for 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 = [
    'Market news from [DATES]',
    '[{"get_news_articles(None,None,None,\'<DATES>\')": "news_data"}, {"get_attribute([\'SPY\'],[\'returns\'],\'<DATES>\')":"SPY_returns"},  {"get_attribute([\'DIA\'],[\'returns\'],\'<DATES>\')":"DIA_returns"}, {"get_attribute([\'QQQ\'],[\'returns\'],\'<DATES>\')":"QQQ_returns"}]',
    '[{"get_dividend_history([\'<TICKER>\'],None)": "<TICKER>_dividend_history"}]',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for 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 = [
    'Market news from [DATES]',
    '[{"get_news_articles(None,None,None,\'<DATES>\')": "news_data"}, {"get_attribute([\'SPY\'],[\'returns\'],\'<DATES>\')":"SPY_returns"},  {"get_attribute([\'DIA\'],[\'returns\'],\'<DATES>\')":"DIA_returns"}, {"get_attribute([\'QQQ\'],[\'returns\'],\'<DATES>\')":"QQQ_returns"}]',
    '[{"get_dividend_history([\'<TICKER>\'],None)": "<TICKER>_dividend_history"}]',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for 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 = [
    'Market news from [DATES]',
    '[{"get_news_articles(None,None,None,\'<DATES>\')": "news_data"}, {"get_attribute([\'SPY\'],[\'returns\'],\'<DATES>\')":"SPY_returns"},  {"get_attribute([\'DIA\'],[\'returns\'],\'<DATES>\')":"DIA_returns"}, {"get_attribute([\'QQQ\'],[\'returns\'],\'<DATES>\')":"QQQ_returns"}]',
    '[{"get_dividend_history([\'<TICKER>\'],None)": "<TICKER>_dividend_history"}]',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for 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 = [
    'Market news from [DATES]',
    '[{"get_news_articles(None,None,None,\'<DATES>\')": "news_data"}, {"get_attribute([\'SPY\'],[\'returns\'],\'<DATES>\')":"SPY_returns"},  {"get_attribute([\'DIA\'],[\'returns\'],\'<DATES>\')":"DIA_returns"}, {"get_attribute([\'QQQ\'],[\'returns\'],\'<DATES>\')":"QQQ_returns"}]',
    '[{"get_dividend_history([\'<TICKER>\'],None)": "<TICKER>_dividend_history"}]',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for 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 = [
    'Market news from [DATES]',
    '[{"get_news_articles(None,None,None,\'<DATES>\')": "news_data"}, {"get_attribute([\'SPY\'],[\'returns\'],\'<DATES>\')":"SPY_returns"},  {"get_attribute([\'DIA\'],[\'returns\'],\'<DATES>\')":"DIA_returns"}, {"get_attribute([\'QQQ\'],[\'returns\'],\'<DATES>\')":"QQQ_returns"}]',
    '[{"get_dividend_history([\'<TICKER>\'],None)": "<TICKER>_dividend_history"}]',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for 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 = [
    'Market news from [DATES]',
    '[{"get_news_articles(None,None,None,\'<DATES>\')": "news_data"}, {"get_attribute([\'SPY\'],[\'returns\'],\'<DATES>\')":"SPY_returns"},  {"get_attribute([\'DIA\'],[\'returns\'],\'<DATES>\')":"DIA_returns"}, {"get_attribute([\'QQQ\'],[\'returns\'],\'<DATES>\')":"QQQ_returns"}]',
    '[{"get_dividend_history([\'<TICKER>\'],None)": "<TICKER>_dividend_history"}]',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for 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 = [
    'Market news from [DATES]',
    '[{"get_news_articles(None,None,None,\'<DATES>\')": "news_data"}, {"get_attribute([\'SPY\'],[\'returns\'],\'<DATES>\')":"SPY_returns"},  {"get_attribute([\'DIA\'],[\'returns\'],\'<DATES>\')":"DIA_returns"}, {"get_attribute([\'QQQ\'],[\'returns\'],\'<DATES>\')":"QQQ_returns"}]',
    '[{"get_dividend_history([\'<TICKER>\'],None)": "<TICKER>_dividend_history"}]',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for 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 = [
    'Market news from [DATES]',
    '[{"get_news_articles(None,None,None,\'<DATES>\')": "news_data"}, {"get_attribute([\'SPY\'],[\'returns\'],\'<DATES>\')":"SPY_returns"},  {"get_attribute([\'DIA\'],[\'returns\'],\'<DATES>\')":"DIA_returns"}, {"get_attribute([\'QQQ\'],[\'returns\'],\'<DATES>\')":"QQQ_returns"}]',
    '[{"get_dividend_history([\'<TICKER>\'],None)": "<TICKER>_dividend_history"}]',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for 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 = [
    'Market news from [DATES]',
    '[{"get_news_articles(None,None,None,\'<DATES>\')": "news_data"}, {"get_attribute([\'SPY\'],[\'returns\'],\'<DATES>\')":"SPY_returns"},  {"get_attribute([\'DIA\'],[\'returns\'],\'<DATES>\')":"DIA_returns"}, {"get_attribute([\'QQQ\'],[\'returns\'],\'<DATES>\')":"QQQ_returns"}]',
    '[{"get_dividend_history([\'<TICKER>\'],None)": "<TICKER>_dividend_history"}]',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for 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 = [
    'Market news from [DATES]',
    '[{"get_news_articles(None,None,None,\'<DATES>\')": "news_data"}, {"get_attribute([\'SPY\'],[\'returns\'],\'<DATES>\')":"SPY_returns"},  {"get_attribute([\'DIA\'],[\'returns\'],\'<DATES>\')":"DIA_returns"}, {"get_attribute([\'QQQ\'],[\'returns\'],\'<DATES>\')":"QQQ_returns"}]',
    '[{"get_dividend_history([\'<TICKER>\'],None)": "<TICKER>_dividend_history"}]',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for 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 = [
    'Market news from [DATES]',
    '[{"get_news_articles(None,None,None,\'<DATES>\')": "news_data"}, {"get_attribute([\'SPY\'],[\'returns\'],\'<DATES>\')":"SPY_returns"},  {"get_attribute([\'DIA\'],[\'returns\'],\'<DATES>\')":"DIA_returns"}, {"get_attribute([\'QQQ\'],[\'returns\'],\'<DATES>\')":"QQQ_returns"}]',
    '[{"get_dividend_history([\'<TICKER>\'],None)": "<TICKER>_dividend_history"}]',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for 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 = [
    'Market news from [DATES]',
    '[{"get_news_articles(None,None,None,\'<DATES>\')": "news_data"}, {"get_attribute([\'SPY\'],[\'returns\'],\'<DATES>\')":"SPY_returns"},  {"get_attribute([\'DIA\'],[\'returns\'],\'<DATES>\')":"DIA_returns"}, {"get_attribute([\'QQQ\'],[\'returns\'],\'<DATES>\')":"QQQ_returns"}]',
    '[{"get_dividend_history([\'<TICKER>\'],None)": "<TICKER>_dividend_history"}]',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for 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 = [
    'Market news from [DATES]',
    '[{"get_news_articles(None,None,None,\'<DATES>\')": "news_data"}, {"get_attribute([\'SPY\'],[\'returns\'],\'<DATES>\')":"SPY_returns"},  {"get_attribute([\'DIA\'],[\'returns\'],\'<DATES>\')":"DIA_returns"}, {"get_attribute([\'QQQ\'],[\'returns\'],\'<DATES>\')":"QQQ_returns"}]',
    '[{"get_dividend_history([\'<TICKER>\'],None)": "<TICKER>_dividend_history"}]',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for 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 = [
    'Market news from [DATES]',
    '[{"get_news_articles(None,None,None,\'<DATES>\')": "news_data"}, {"get_attribute([\'SPY\'],[\'returns\'],\'<DATES>\')":"SPY_returns"},  {"get_attribute([\'DIA\'],[\'returns\'],\'<DATES>\')":"DIA_returns"}, {"get_attribute([\'QQQ\'],[\'returns\'],\'<DATES>\')":"QQQ_returns"}]',
    '[{"get_dividend_history([\'<TICKER>\'],None)": "<TICKER>_dividend_history"}]',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for 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 = [
    'Market news from [DATES]',
    '[{"get_news_articles(None,None,None,\'<DATES>\')": "news_data"}, {"get_attribute([\'SPY\'],[\'returns\'],\'<DATES>\')":"SPY_returns"},  {"get_attribute([\'DIA\'],[\'returns\'],\'<DATES>\')":"DIA_returns"}, {"get_attribute([\'QQQ\'],[\'returns\'],\'<DATES>\')":"QQQ_returns"}]',
    '[{"get_dividend_history([\'<TICKER>\'],None)": "<TICKER>_dividend_history"}]',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for 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 = [
    'Market news from [DATES]',
    '[{"get_news_articles(None,None,None,\'<DATES>\')": "news_data"}, {"get_attribute([\'SPY\'],[\'returns\'],\'<DATES>\')":"SPY_returns"},  {"get_attribute([\'DIA\'],[\'returns\'],\'<DATES>\')":"DIA_returns"}, {"get_attribute([\'QQQ\'],[\'returns\'],\'<DATES>\')":"QQQ_returns"}]',
    '[{"get_dividend_history([\'<TICKER>\'],None)": "<TICKER>_dividend_history"}]',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for 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 = [
    'Market news from [DATES]',
    '[{"get_news_articles(None,None,None,\'<DATES>\')": "news_data"}, {"get_attribute([\'SPY\'],[\'returns\'],\'<DATES>\')":"SPY_returns"},  {"get_attribute([\'DIA\'],[\'returns\'],\'<DATES>\')":"DIA_returns"}, {"get_attribute([\'QQQ\'],[\'returns\'],\'<DATES>\')":"QQQ_returns"}]',
    '[{"get_dividend_history([\'<TICKER>\'],None)": "<TICKER>_dividend_history"}]',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for 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 = [
    'Market news from [DATES]',
    '[{"get_news_articles(None,None,None,\'<DATES>\')": "news_data"}, {"get_attribute([\'SPY\'],[\'returns\'],\'<DATES>\')":"SPY_returns"},  {"get_attribute([\'DIA\'],[\'returns\'],\'<DATES>\')":"DIA_returns"}, {"get_attribute([\'QQQ\'],[\'returns\'],\'<DATES>\')":"QQQ_returns"}]',
    '[{"get_dividend_history([\'<TICKER>\'],None)": "<TICKER>_dividend_history"}]',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for 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 = [
    'Market news from [DATES]',
    '[{"get_news_articles(None,None,None,\'<DATES>\')": "news_data"}, {"get_attribute([\'SPY\'],[\'returns\'],\'<DATES>\')":"SPY_returns"},  {"get_attribute([\'DIA\'],[\'returns\'],\'<DATES>\')":"DIA_returns"}, {"get_attribute([\'QQQ\'],[\'returns\'],\'<DATES>\')":"QQQ_returns"}]',
    '[{"get_dividend_history([\'<TICKER>\'],None)": "<TICKER>_dividend_history"}]',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for 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 = [
    'Market news from [DATES]',
    '[{"get_news_articles(None,None,None,\'<DATES>\')": "news_data"}, {"get_attribute([\'SPY\'],[\'returns\'],\'<DATES>\')":"SPY_returns"},  {"get_attribute([\'DIA\'],[\'returns\'],\'<DATES>\')":"DIA_returns"}, {"get_attribute([\'QQQ\'],[\'returns\'],\'<DATES>\')":"QQQ_returns"}]',
    '[{"get_dividend_history([\'<TICKER>\'],None)": "<TICKER>_dividend_history"}]',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for 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 = [
    'Market news from [DATES]',
    '[{"get_news_articles(None,None,None,\'<DATES>\')": "news_data"}, {"get_attribute([\'SPY\'],[\'returns\'],\'<DATES>\')":"SPY_returns"},  {"get_attribute([\'DIA\'],[\'returns\'],\'<DATES>\')":"DIA_returns"}, {"get_attribute([\'QQQ\'],[\'returns\'],\'<DATES>\')":"QQQ_returns"}]',
    '[{"get_dividend_history([\'<TICKER>\'],None)": "<TICKER>_dividend_history"}]',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for 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 = [
    'Market news from [DATES]',
    '[{"get_news_articles(None,None,None,\'<DATES>\')": "news_data"}, {"get_attribute([\'SPY\'],[\'returns\'],\'<DATES>\')":"SPY_returns"},  {"get_attribute([\'DIA\'],[\'returns\'],\'<DATES>\')":"DIA_returns"}, {"get_attribute([\'QQQ\'],[\'returns\'],\'<DATES>\')":"QQQ_returns"}]',
    '[{"get_dividend_history([\'<TICKER>\'],None)": "<TICKER>_dividend_history"}]',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for 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 = [
    'Market news from [DATES]',
    '[{"get_news_articles(None,None,None,\'<DATES>\')": "news_data"}, {"get_attribute([\'SPY\'],[\'returns\'],\'<DATES>\')":"SPY_returns"},  {"get_attribute([\'DIA\'],[\'returns\'],\'<DATES>\')":"DIA_returns"}, {"get_attribute([\'QQQ\'],[\'returns\'],\'<DATES>\')":"QQQ_returns"}]',
    '[{"get_dividend_history([\'<TICKER>\'],None)": "<TICKER>_dividend_history"}]',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for 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 = [
    'Market news from [DATES]',
    '[{"get_news_articles(None,None,None,\'<DATES>\')": "news_data"}, {"get_attribute([\'SPY\'],[\'returns\'],\'<DATES>\')":"SPY_returns"},  {"get_attribute([\'DIA\'],[\'returns\'],\'<DATES>\')":"DIA_returns"}, {"get_attribute([\'QQQ\'],[\'returns\'],\'<DATES>\')":"QQQ_returns"}]',
    '[{"get_dividend_history([\'<TICKER>\'],None)": "<TICKER>_dividend_history"}]',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for 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 = [
    'Market news from [DATES]',
    '[{"get_news_articles(None,None,None,\'<DATES>\')": "news_data"}, {"get_attribute([\'SPY\'],[\'returns\'],\'<DATES>\')":"SPY_returns"},  {"get_attribute([\'DIA\'],[\'returns\'],\'<DATES>\')":"DIA_returns"}, {"get_attribute([\'QQQ\'],[\'returns\'],\'<DATES>\')":"QQQ_returns"}]',
    '[{"get_dividend_history([\'<TICKER>\'],None)": "<TICKER>_dividend_history"}]',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for 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 = [
    'Market news from [DATES]',
    '[{"get_news_articles(None,None,None,\'<DATES>\')": "news_data"}, {"get_attribute([\'SPY\'],[\'returns\'],\'<DATES>\')":"SPY_returns"},  {"get_attribute([\'DIA\'],[\'returns\'],\'<DATES>\')":"DIA_returns"}, {"get_attribute([\'QQQ\'],[\'returns\'],\'<DATES>\')":"QQQ_returns"}]',
    '[{"get_dividend_history([\'<TICKER>\'],None)": "<TICKER>_dividend_history"}]',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for 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 = [
    'Market news from [DATES]',
    '[{"get_news_articles(None,None,None,\'<DATES>\')": "news_data"}, {"get_attribute([\'SPY\'],[\'returns\'],\'<DATES>\')":"SPY_returns"},  {"get_attribute([\'DIA\'],[\'returns\'],\'<DATES>\')":"DIA_returns"}, {"get_attribute([\'QQQ\'],[\'returns\'],\'<DATES>\')":"QQQ_returns"}]',
    '[{"get_dividend_history([\'<TICKER>\'],None)": "<TICKER>_dividend_history"}]',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for 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 = [
    'Market news from [DATES]',
    '[{"get_news_articles(None,None,None,\'<DATES>\')": "news_data"}, {"get_attribute([\'SPY\'],[\'returns\'],\'<DATES>\')":"SPY_returns"},  {"get_attribute([\'DIA\'],[\'returns\'],\'<DATES>\')":"DIA_returns"}, {"get_attribute([\'QQQ\'],[\'returns\'],\'<DATES>\')":"QQQ_returns"}]',
    '[{"get_dividend_history([\'<TICKER>\'],None)": "<TICKER>_dividend_history"}]',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for 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 = [
    'Market news from [DATES]',
    '[{"get_news_articles(None,None,None,\'<DATES>\')": "news_data"}, {"get_attribute([\'SPY\'],[\'returns\'],\'<DATES>\')":"SPY_returns"},  {"get_attribute([\'DIA\'],[\'returns\'],\'<DATES>\')":"DIA_returns"}, {"get_attribute([\'QQQ\'],[\'returns\'],\'<DATES>\')":"QQQ_returns"}]',
    '[{"get_dividend_history([\'<TICKER>\'],None)": "<TICKER>_dividend_history"}]',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for 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 = [
    'Market news from [DATES]',
    '[{"get_news_articles(None,None,None,\'<DATES>\')": "news_data"}, {"get_attribute([\'SPY\'],[\'returns\'],\'<DATES>\')":"SPY_returns"},  {"get_attribute([\'DIA\'],[\'returns\'],\'<DATES>\')":"DIA_returns"}, {"get_attribute([\'QQQ\'],[\'returns\'],\'<DATES>\')":"QQQ_returns"}]',
    '[{"get_dividend_history([\'<TICKER>\'],None)": "<TICKER>_dividend_history"}]',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for 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 = [
    'Market news from [DATES]',
    '[{"get_news_articles(None,None,None,\'<DATES>\')": "news_data"}, {"get_attribute([\'SPY\'],[\'returns\'],\'<DATES>\')":"SPY_returns"},  {"get_attribute([\'DIA\'],[\'returns\'],\'<DATES>\')":"DIA_returns"}, {"get_attribute([\'QQQ\'],[\'returns\'],\'<DATES>\')":"QQQ_returns"}]',
    '[{"get_dividend_history([\'<TICKER>\'],None)": "<TICKER>_dividend_history"}]',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for 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 = [
    'Market news from [DATES]',
    '[{"get_news_articles(None,None,None,\'<DATES>\')": "news_data"}, {"get_attribute([\'SPY\'],[\'returns\'],\'<DATES>\')":"SPY_returns"},  {"get_attribute([\'DIA\'],[\'returns\'],\'<DATES>\')":"DIA_returns"}, {"get_attribute([\'QQQ\'],[\'returns\'],\'<DATES>\')":"QQQ_returns"}]',
    '[{"get_dividend_history([\'<TICKER>\'],None)": "<TICKER>_dividend_history"}]',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for 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 = [
    'Market news from [DATES]',
    '[{"get_news_articles(None,None,None,\'<DATES>\')": "news_data"}, {"get_attribute([\'SPY\'],[\'returns\'],\'<DATES>\')":"SPY_returns"},  {"get_attribute([\'DIA\'],[\'returns\'],\'<DATES>\')":"DIA_returns"}, {"get_attribute([\'QQQ\'],[\'returns\'],\'<DATES>\')":"QQQ_returns"}]',
    '[{"get_dividend_history([\'<TICKER>\'],None)": "<TICKER>_dividend_history"}]',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for 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 = [
    'Market news from [DATES]',
    '[{"get_news_articles(None,None,None,\'<DATES>\')": "news_data"}, {"get_attribute([\'SPY\'],[\'returns\'],\'<DATES>\')":"SPY_returns"},  {"get_attribute([\'DIA\'],[\'returns\'],\'<DATES>\')":"DIA_returns"}, {"get_attribute([\'QQQ\'],[\'returns\'],\'<DATES>\')":"QQQ_returns"}]',
    '[{"get_dividend_history([\'<TICKER>\'],None)": "<TICKER>_dividend_history"}]',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for 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 = [
    'Market news from [DATES]',
    '[{"get_news_articles(None,None,None,\'<DATES>\')": "news_data"}, {"get_attribute([\'SPY\'],[\'returns\'],\'<DATES>\')":"SPY_returns"},  {"get_attribute([\'DIA\'],[\'returns\'],\'<DATES>\')":"DIA_returns"}, {"get_attribute([\'QQQ\'],[\'returns\'],\'<DATES>\')":"QQQ_returns"}]',
    '[{"get_dividend_history([\'<TICKER>\'],None)": "<TICKER>_dividend_history"}]',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for 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 = [
    'Market news from [DATES]',
    '[{"get_news_articles(None,None,None,\'<DATES>\')": "news_data"}, {"get_attribute([\'SPY\'],[\'returns\'],\'<DATES>\')":"SPY_returns"},  {"get_attribute([\'DIA\'],[\'returns\'],\'<DATES>\')":"DIA_returns"}, {"get_attribute([\'QQQ\'],[\'returns\'],\'<DATES>\')":"QQQ_returns"}]',
    '[{"get_dividend_history([\'<TICKER>\'],None)": "<TICKER>_dividend_history"}]',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for 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 = [
    'Market news from [DATES]',
    '[{"get_news_articles(None,None,None,\'<DATES>\')": "news_data"}, {"get_attribute([\'SPY\'],[\'returns\'],\'<DATES>\')":"SPY_returns"},  {"get_attribute([\'DIA\'],[\'returns\'],\'<DATES>\')":"DIA_returns"}, {"get_attribute([\'QQQ\'],[\'returns\'],\'<DATES>\')":"QQQ_returns"}]',
    '[{"get_dividend_history([\'<TICKER>\'],None)": "<TICKER>_dividend_history"}]',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for 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 = [
    'Market news from [DATES]',
    '[{"get_news_articles(None,None,None,\'<DATES>\')": "news_data"}, {"get_attribute([\'SPY\'],[\'returns\'],\'<DATES>\')":"SPY_returns"},  {"get_attribute([\'DIA\'],[\'returns\'],\'<DATES>\')":"DIA_returns"}, {"get_attribute([\'QQQ\'],[\'returns\'],\'<DATES>\')":"QQQ_returns"}]',
    '[{"get_dividend_history([\'<TICKER>\'],None)": "<TICKER>_dividend_history"}]',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for 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 = [
    'Market news from [DATES]',
    '[{"get_news_articles(None,None,None,\'<DATES>\')": "news_data"}, {"get_attribute([\'SPY\'],[\'returns\'],\'<DATES>\')":"SPY_returns"},  {"get_attribute([\'DIA\'],[\'returns\'],\'<DATES>\')":"DIA_returns"}, {"get_attribute([\'QQQ\'],[\'returns\'],\'<DATES>\')":"QQQ_returns"}]',
    '[{"get_dividend_history([\'<TICKER>\'],None)": "<TICKER>_dividend_history"}]',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for 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 = [
    'Market news from [DATES]',
    '[{"get_news_articles(None,None,None,\'<DATES>\')": "news_data"}, {"get_attribute([\'SPY\'],[\'returns\'],\'<DATES>\')":"SPY_returns"},  {"get_attribute([\'DIA\'],[\'returns\'],\'<DATES>\')":"DIA_returns"}, {"get_attribute([\'QQQ\'],[\'returns\'],\'<DATES>\')":"QQQ_returns"}]',
    '[{"get_dividend_history([\'<TICKER>\'],None)": "<TICKER>_dividend_history"}]',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for 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 = [
    'Market news from [DATES]',
    '[{"get_news_articles(None,None,None,\'<DATES>\')": "news_data"}, {"get_attribute([\'SPY\'],[\'returns\'],\'<DATES>\')":"SPY_returns"},  {"get_attribute([\'DIA\'],[\'returns\'],\'<DATES>\')":"DIA_returns"}, {"get_attribute([\'QQQ\'],[\'returns\'],\'<DATES>\')":"QQQ_returns"}]',
    '[{"get_dividend_history([\'<TICKER>\'],None)": "<TICKER>_dividend_history"}]',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for 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 = [
    'Market news from [DATES]',
    '[{"get_news_articles(None,None,None,\'<DATES>\')": "news_data"}, {"get_attribute([\'SPY\'],[\'returns\'],\'<DATES>\')":"SPY_returns"},  {"get_attribute([\'DIA\'],[\'returns\'],\'<DATES>\')":"DIA_returns"}, {"get_attribute([\'QQQ\'],[\'returns\'],\'<DATES>\')":"QQQ_returns"}]',
    '[{"get_dividend_history([\'<TICKER>\'],None)": "<TICKER>_dividend_history"}]',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for 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 = [
    'Market news from [DATES]',
    '[{"get_news_articles(None,None,None,\'<DATES>\')": "news_data"}, {"get_attribute([\'SPY\'],[\'returns\'],\'<DATES>\')":"SPY_returns"},  {"get_attribute([\'DIA\'],[\'returns\'],\'<DATES>\')":"DIA_returns"}, {"get_attribute([\'QQQ\'],[\'returns\'],\'<DATES>\')":"QQQ_returns"}]',
    '[{"get_dividend_history([\'<TICKER>\'],None)": "<TICKER>_dividend_history"}]',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for 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 = [
    'Market news from [DATES]',
    '[{"get_news_articles(None,None,None,\'<DATES>\')": "news_data"}, {"get_attribute([\'SPY\'],[\'returns\'],\'<DATES>\')":"SPY_returns"},  {"get_attribute([\'DIA\'],[\'returns\'],\'<DATES>\')":"DIA_returns"}, {"get_attribute([\'QQQ\'],[\'returns\'],\'<DATES>\')":"QQQ_returns"}]',
    '[{"get_dividend_history([\'<TICKER>\'],None)": "<TICKER>_dividend_history"}]',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for 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 = [
    'Market news from [DATES]',
    '[{"get_news_articles(None,None,None,\'<DATES>\')": "news_data"}, {"get_attribute([\'SPY\'],[\'returns\'],\'<DATES>\')":"SPY_returns"},  {"get_attribute([\'DIA\'],[\'returns\'],\'<DATES>\')":"DIA_returns"}, {"get_attribute([\'QQQ\'],[\'returns\'],\'<DATES>\')":"QQQ_returns"}]',
    '[{"get_dividend_history([\'<TICKER>\'],None)": "<TICKER>_dividend_history"}]',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for 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 = [
    'Market news from [DATES]',
    '[{"get_news_articles(None,None,None,\'<DATES>\')": "news_data"}, {"get_attribute([\'SPY\'],[\'returns\'],\'<DATES>\')":"SPY_returns"},  {"get_attribute([\'DIA\'],[\'returns\'],\'<DATES>\')":"DIA_returns"}, {"get_attribute([\'QQQ\'],[\'returns\'],\'<DATES>\')":"QQQ_returns"}]',
    '[{"get_dividend_history([\'<TICKER>\'],None)": "<TICKER>_dividend_history"}]',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for 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 = [
    'Market news from [DATES]',
    '[{"get_news_articles(None,None,None,\'<DATES>\')": "news_data"}, {"get_attribute([\'SPY\'],[\'returns\'],\'<DATES>\')":"SPY_returns"},  {"get_attribute([\'DIA\'],[\'returns\'],\'<DATES>\')":"DIA_returns"}, {"get_attribute([\'QQQ\'],[\'returns\'],\'<DATES>\')":"QQQ_returns"}]',
    '[{"get_dividend_history([\'<TICKER>\'],None)": "<TICKER>_dividend_history"}]',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for 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 = [
    'Market news from [DATES]',
    '[{"get_news_articles(None,None,None,\'<DATES>\')": "news_data"}, {"get_attribute([\'SPY\'],[\'returns\'],\'<DATES>\')":"SPY_returns"},  {"get_attribute([\'DIA\'],[\'returns\'],\'<DATES>\')":"DIA_returns"}, {"get_attribute([\'QQQ\'],[\'returns\'],\'<DATES>\')":"QQQ_returns"}]',
    '[{"get_dividend_history([\'<TICKER>\'],None)": "<TICKER>_dividend_history"}]',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for 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 = [
    'Market news from [DATES]',
    '[{"get_news_articles(None,None,None,\'<DATES>\')": "news_data"}, {"get_attribute([\'SPY\'],[\'returns\'],\'<DATES>\')":"SPY_returns"},  {"get_attribute([\'DIA\'],[\'returns\'],\'<DATES>\')":"DIA_returns"}, {"get_attribute([\'QQQ\'],[\'returns\'],\'<DATES>\')":"QQQ_returns"}]',
    '[{"get_dividend_history([\'<TICKER>\'],None)": "<TICKER>_dividend_history"}]',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for 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 = [
    'Market news from [DATES]',
    '[{"get_news_articles(None,None,None,\'<DATES>\')": "news_data"}, {"get_attribute([\'SPY\'],[\'returns\'],\'<DATES>\')":"SPY_returns"},  {"get_attribute([\'DIA\'],[\'returns\'],\'<DATES>\')":"DIA_returns"}, {"get_attribute([\'QQQ\'],[\'returns\'],\'<DATES>\')":"QQQ_returns"}]',
    '[{"get_dividend_history([\'<TICKER>\'],None)": "<TICKER>_dividend_history"}]',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for 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 = [
    'Market news from [DATES]',
    '[{"get_news_articles(None,None,None,\'<DATES>\')": "news_data"}, {"get_attribute([\'SPY\'],[\'returns\'],\'<DATES>\')":"SPY_returns"},  {"get_attribute([\'DIA\'],[\'returns\'],\'<DATES>\')":"DIA_returns"}, {"get_attribute([\'QQQ\'],[\'returns\'],\'<DATES>\')":"QQQ_returns"}]',
    '[{"get_dividend_history([\'<TICKER>\'],None)": "<TICKER>_dividend_history"}]',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for 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 = [
    'Market news from [DATES]',
    '[{"get_news_articles(None,None,None,\'<DATES>\')": "news_data"}, {"get_attribute([\'SPY\'],[\'returns\'],\'<DATES>\')":"SPY_returns"},  {"get_attribute([\'DIA\'],[\'returns\'],\'<DATES>\')":"DIA_returns"}, {"get_attribute([\'QQQ\'],[\'returns\'],\'<DATES>\')":"QQQ_returns"}]',
    '[{"get_dividend_history([\'<TICKER>\'],None)": "<TICKER>_dividend_history"}]',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for 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 = [
    'Market news from [DATES]',
    '[{"get_news_articles(None,None,None,\'<DATES>\')": "news_data"}, {"get_attribute([\'SPY\'],[\'returns\'],\'<DATES>\')":"SPY_returns"},  {"get_attribute([\'DIA\'],[\'returns\'],\'<DATES>\')":"DIA_returns"}, {"get_attribute([\'QQQ\'],[\'returns\'],\'<DATES>\')":"QQQ_returns"}]',
    '[{"get_dividend_history([\'<TICKER>\'],None)": "<TICKER>_dividend_history"}]',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for 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 = [
    'Market news from [DATES]',
    '[{"get_news_articles(None,None,None,\'<DATES>\')": "news_data"}, {"get_attribute([\'SPY\'],[\'returns\'],\'<DATES>\')":"SPY_returns"},  {"get_attribute([\'DIA\'],[\'returns\'],\'<DATES>\')":"DIA_returns"}, {"get_attribute([\'QQQ\'],[\'returns\'],\'<DATES>\')":"QQQ_returns"}]',
    '[{"get_dividend_history([\'<TICKER>\'],None)": "<TICKER>_dividend_history"}]',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for 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 = [
    'Market news from [DATES]',
    '[{"get_news_articles(None,None,None,\'<DATES>\')": "news_data"}, {"get_attribute([\'SPY\'],[\'returns\'],\'<DATES>\')":"SPY_returns"},  {"get_attribute([\'DIA\'],[\'returns\'],\'<DATES>\')":"DIA_returns"}, {"get_attribute([\'QQQ\'],[\'returns\'],\'<DATES>\')":"QQQ_returns"}]',
    '[{"get_dividend_history([\'<TICKER>\'],None)": "<TICKER>_dividend_history"}]',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for 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 = [
    'Market news from [DATES]',
    '[{"get_news_articles(None,None,None,\'<DATES>\')": "news_data"}, {"get_attribute([\'SPY\'],[\'returns\'],\'<DATES>\')":"SPY_returns"},  {"get_attribute([\'DIA\'],[\'returns\'],\'<DATES>\')":"DIA_returns"}, {"get_attribute([\'QQQ\'],[\'returns\'],\'<DATES>\')":"QQQ_returns"}]',
    '[{"get_dividend_history([\'<TICKER>\'],None)": "<TICKER>_dividend_history"}]',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for 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 = [
    'Market news from [DATES]',
    '[{"get_news_articles(None,None,None,\'<DATES>\')": "news_data"}, {"get_attribute([\'SPY\'],[\'returns\'],\'<DATES>\')":"SPY_returns"},  {"get_attribute([\'DIA\'],[\'returns\'],\'<DATES>\')":"DIA_returns"}, {"get_attribute([\'QQQ\'],[\'returns\'],\'<DATES>\')":"QQQ_returns"}]',
    '[{"get_dividend_history([\'<TICKER>\'],None)": "<TICKER>_dividend_history"}]',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for 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 = [
    'Market news from [DATES]',
    '[{"get_news_articles(None,None,None,\'<DATES>\')": "news_data"}, {"get_attribute([\'SPY\'],[\'returns\'],\'<DATES>\')":"SPY_returns"},  {"get_attribute([\'DIA\'],[\'returns\'],\'<DATES>\')":"DIA_returns"}, {"get_attribute([\'QQQ\'],[\'returns\'],\'<DATES>\')":"QQQ_returns"}]',
    '[{"get_dividend_history([\'<TICKER>\'],None)": "<TICKER>_dividend_history"}]',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for 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 = [
    'Market news from [DATES]',
    '[{"get_news_articles(None,None,None,\'<DATES>\')": "news_data"}, {"get_attribute([\'SPY\'],[\'returns\'],\'<DATES>\')":"SPY_returns"},  {"get_attribute([\'DIA\'],[\'returns\'],\'<DATES>\')":"DIA_returns"}, {"get_attribute([\'QQQ\'],[\'returns\'],\'<DATES>\')":"QQQ_returns"}]',
    '[{"get_dividend_history([\'<TICKER>\'],None)": "<TICKER>_dividend_history"}]',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for 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 = [
    'Market news from [DATES]',
    '[{"get_news_articles(None,None,None,\'<DATES>\')": "news_data"}, {"get_attribute([\'SPY\'],[\'returns\'],\'<DATES>\')":"SPY_returns"},  {"get_attribute([\'DIA\'],[\'returns\'],\'<DATES>\')":"DIA_returns"}, {"get_attribute([\'QQQ\'],[\'returns\'],\'<DATES>\')":"QQQ_returns"}]',
    '[{"get_dividend_history([\'<TICKER>\'],None)": "<TICKER>_dividend_history"}]',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for 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 = [
    'Market news from [DATES]',
    '[{"get_news_articles(None,None,None,\'<DATES>\')": "news_data"}, {"get_attribute([\'SPY\'],[\'returns\'],\'<DATES>\')":"SPY_returns"},  {"get_attribute([\'DIA\'],[\'returns\'],\'<DATES>\')":"DIA_returns"}, {"get_attribute([\'QQQ\'],[\'returns\'],\'<DATES>\')":"QQQ_returns"}]',
    '[{"get_dividend_history([\'<TICKER>\'],None)": "<TICKER>_dividend_history"}]',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for 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 = [
    'Market news from [DATES]',
    '[{"get_news_articles(None,None,None,\'<DATES>\')": "news_data"}, {"get_attribute([\'SPY\'],[\'returns\'],\'<DATES>\')":"SPY_returns"},  {"get_attribute([\'DIA\'],[\'returns\'],\'<DATES>\')":"DIA_returns"}, {"get_attribute([\'QQQ\'],[\'returns\'],\'<DATES>\')":"QQQ_returns"}]',
    '[{"get_dividend_history([\'<TICKER>\'],None)": "<TICKER>_dividend_history"}]',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for 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 = [
    'Market news from [DATES]',
    '[{"get_news_articles(None,None,None,\'<DATES>\')": "news_data"}, {"get_attribute([\'SPY\'],[\'returns\'],\'<DATES>\')":"SPY_returns"},  {"get_attribute([\'DIA\'],[\'returns\'],\'<DATES>\')":"DIA_returns"}, {"get_attribute([\'QQQ\'],[\'returns\'],\'<DATES>\')":"QQQ_returns"}]',
    '[{"get_dividend_history([\'<TICKER>\'],None)": "<TICKER>_dividend_history"}]',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for 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 = [
    'Market news from [DATES]',
    '[{"get_news_articles(None,None,None,\'<DATES>\')": "news_data"}, {"get_attribute([\'SPY\'],[\'returns\'],\'<DATES>\')":"SPY_returns"},  {"get_attribute([\'DIA\'],[\'returns\'],\'<DATES>\')":"DIA_returns"}, {"get_attribute([\'QQQ\'],[\'returns\'],\'<DATES>\')":"QQQ_returns"}]',
    '[{"get_dividend_history([\'<TICKER>\'],None)": "<TICKER>_dividend_history"}]',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for 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 = [
    'Market news from [DATES]',
    '[{"get_news_articles(None,None,None,\'<DATES>\')": "news_data"}, {"get_attribute([\'SPY\'],[\'returns\'],\'<DATES>\')":"SPY_returns"},  {"get_attribute([\'DIA\'],[\'returns\'],\'<DATES>\')":"DIA_returns"}, {"get_attribute([\'QQQ\'],[\'returns\'],\'<DATES>\')":"QQQ_returns"}]',
    '[{"get_dividend_history([\'<TICKER>\'],None)": "<TICKER>_dividend_history"}]',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for 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 = [
    'Market news from [DATES]',
    '[{"get_news_articles(None,None,None,\'<DATES>\')": "news_data"}, {"get_attribute([\'SPY\'],[\'returns\'],\'<DATES>\')":"SPY_returns"},  {"get_attribute([\'DIA\'],[\'returns\'],\'<DATES>\')":"DIA_returns"}, {"get_attribute([\'QQQ\'],[\'returns\'],\'<DATES>\')":"QQQ_returns"}]',
    '[{"get_dividend_history([\'<TICKER>\'],None)": "<TICKER>_dividend_history"}]',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for 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 = [
    'Market news from [DATES]',
    '[{"get_news_articles(None,None,None,\'<DATES>\')": "news_data"}, {"get_attribute([\'SPY\'],[\'returns\'],\'<DATES>\')":"SPY_returns"},  {"get_attribute([\'DIA\'],[\'returns\'],\'<DATES>\')":"DIA_returns"}, {"get_attribute([\'QQQ\'],[\'returns\'],\'<DATES>\')":"QQQ_returns"}]',
    '[{"get_dividend_history([\'<TICKER>\'],None)": "<TICKER>_dividend_history"}]',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for 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 = [
    'Market news from [DATES]',
    '[{"get_news_articles(None,None,None,\'<DATES>\')": "news_data"}, {"get_attribute([\'SPY\'],[\'returns\'],\'<DATES>\')":"SPY_returns"},  {"get_attribute([\'DIA\'],[\'returns\'],\'<DATES>\')":"DIA_returns"}, {"get_attribute([\'QQQ\'],[\'returns\'],\'<DATES>\')":"QQQ_returns"}]',
    '[{"get_dividend_history([\'<TICKER>\'],None)": "<TICKER>_dividend_history"}]',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for 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 = [
    'Market news from [DATES]',
    '[{"get_news_articles(None,None,None,\'<DATES>\')": "news_data"}, {"get_attribute([\'SPY\'],[\'returns\'],\'<DATES>\')":"SPY_returns"},  {"get_attribute([\'DIA\'],[\'returns\'],\'<DATES>\')":"DIA_returns"}, {"get_attribute([\'QQQ\'],[\'returns\'],\'<DATES>\')":"QQQ_returns"}]',
    '[{"get_dividend_history([\'<TICKER>\'],None)": "<TICKER>_dividend_history"}]',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for 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 = [
    'Market news from [DATES]',
    '[{"get_news_articles(None,None,None,\'<DATES>\')": "news_data"}, {"get_attribute([\'SPY\'],[\'returns\'],\'<DATES>\')":"SPY_returns"},  {"get_attribute([\'DIA\'],[\'returns\'],\'<DATES>\')":"DIA_returns"}, {"get_attribute([\'QQQ\'],[\'returns\'],\'<DATES>\')":"QQQ_returns"}]',
    '[{"get_dividend_history([\'<TICKER>\'],None)": "<TICKER>_dividend_history"}]',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for 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 = [
    'Market news from [DATES]',
    '[{"get_news_articles(None,None,None,\'<DATES>\')": "news_data"}, {"get_attribute([\'SPY\'],[\'returns\'],\'<DATES>\')":"SPY_returns"},  {"get_attribute([\'DIA\'],[\'returns\'],\'<DATES>\')":"DIA_returns"}, {"get_attribute([\'QQQ\'],[\'returns\'],\'<DATES>\')":"QQQ_returns"}]',
    '[{"get_dividend_history([\'<TICKER>\'],None)": "<TICKER>_dividend_history"}]',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for 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 = [
    'Market news from [DATES]',
    '[{"get_news_articles(None,None,None,\'<DATES>\')": "news_data"}, {"get_attribute([\'SPY\'],[\'returns\'],\'<DATES>\')":"SPY_returns"},  {"get_attribute([\'DIA\'],[\'returns\'],\'<DATES>\')":"DIA_returns"}, {"get_attribute([\'QQQ\'],[\'returns\'],\'<DATES>\')":"QQQ_returns"}]',
    '[{"get_dividend_history([\'<TICKER>\'],None)": "<TICKER>_dividend_history"}]',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for 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 = [
    'Market news from [DATES]',
    '[{"get_news_articles(None,None,None,\'<DATES>\')": "news_data"}, {"get_attribute([\'SPY\'],[\'returns\'],\'<DATES>\')":"SPY_returns"},  {"get_attribute([\'DIA\'],[\'returns\'],\'<DATES>\')":"DIA_returns"}, {"get_attribute([\'QQQ\'],[\'returns\'],\'<DATES>\')":"QQQ_returns"}]',
    '[{"get_dividend_history([\'<TICKER>\'],None)": "<TICKER>_dividend_history"}]',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for 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 = [
    'Market news from [DATES]',
    '[{"get_news_articles(None,None,None,\'<DATES>\')": "news_data"}, {"get_attribute([\'SPY\'],[\'returns\'],\'<DATES>\')":"SPY_returns"},  {"get_attribute([\'DIA\'],[\'returns\'],\'<DATES>\')":"DIA_returns"}, {"get_attribute([\'QQQ\'],[\'returns\'],\'<DATES>\')":"QQQ_returns"}]',
    '[{"get_dividend_history([\'<TICKER>\'],None)": "<TICKER>_dividend_history"}]',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for 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 = [
    'Market news from [DATES]',
    '[{"get_news_articles(None,None,None,\'<DATES>\')": "news_data"}, {"get_attribute([\'SPY\'],[\'returns\'],\'<DATES>\')":"SPY_returns"},  {"get_attribute([\'DIA\'],[\'returns\'],\'<DATES>\')":"DIA_returns"}, {"get_attribute([\'QQQ\'],[\'returns\'],\'<DATES>\')":"QQQ_returns"}]',
    '[{"get_dividend_history([\'<TICKER>\'],None)": "<TICKER>_dividend_history"}]',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for 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 = [
    'Market news from [DATES]',
    '[{"get_news_articles(None,None,None,\'<DATES>\')": "news_data"}, {"get_attribute([\'SPY\'],[\'returns\'],\'<DATES>\')":"SPY_returns"},  {"get_attribute([\'DIA\'],[\'returns\'],\'<DATES>\')":"DIA_returns"}, {"get_attribute([\'QQQ\'],[\'returns\'],\'<DATES>\')":"QQQ_returns"}]',
    '[{"get_dividend_history([\'<TICKER>\'],None)": "<TICKER>_dividend_history"}]',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for 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 = [
    'Market news from [DATES]',
    '[{"get_news_articles(None,None,None,\'<DATES>\')": "news_data"}, {"get_attribute([\'SPY\'],[\'returns\'],\'<DATES>\')":"SPY_returns"},  {"get_attribute([\'DIA\'],[\'returns\'],\'<DATES>\')":"DIA_returns"}, {"get_attribute([\'QQQ\'],[\'returns\'],\'<DATES>\')":"QQQ_returns"}]',
    '[{"get_dividend_history([\'<TICKER>\'],None)": "<TICKER>_dividend_history"}]',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for 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 = [
    'Market news from [DATES]',
    '[{"get_news_articles(None,None,None,\'<DATES>\')": "news_data"}, {"get_attribute([\'SPY\'],[\'returns\'],\'<DATES>\')":"SPY_returns"},  {"get_attribute([\'DIA\'],[\'returns\'],\'<DATES>\')":"DIA_returns"}, {"get_attribute([\'QQQ\'],[\'returns\'],\'<DATES>\')":"QQQ_returns"}]',
    '[{"get_dividend_history([\'<TICKER>\'],None)": "<TICKER>_dividend_history"}]',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for 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 = [
    'Market news from [DATES]',
    '[{"get_news_articles(None,None,None,\'<DATES>\')": "news_data"}, {"get_attribute([\'SPY\'],[\'returns\'],\'<DATES>\')":"SPY_returns"},  {"get_attribute([\'DIA\'],[\'returns\'],\'<DATES>\')":"DIA_returns"}, {"get_attribute([\'QQQ\'],[\'returns\'],\'<DATES>\')":"QQQ_returns"}]',
    '[{"get_dividend_history([\'<TICKER>\'],None)": "<TICKER>_dividend_history"}]',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for 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 = [
    'Market news from [DATES]',
    '[{"get_news_articles(None,None,None,\'<DATES>\')": "news_data"}, {"get_attribute([\'SPY\'],[\'returns\'],\'<DATES>\')":"SPY_returns"},  {"get_attribute([\'DIA\'],[\'returns\'],\'<DATES>\')":"DIA_returns"}, {"get_attribute([\'QQQ\'],[\'returns\'],\'<DATES>\')":"QQQ_returns"}]',
    '[{"get_dividend_history([\'<TICKER>\'],None)": "<TICKER>_dividend_history"}]',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for 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 = [
    'Market news from [DATES]',
    '[{"get_news_articles(None,None,None,\'<DATES>\')": "news_data"}, {"get_attribute([\'SPY\'],[\'returns\'],\'<DATES>\')":"SPY_returns"},  {"get_attribute([\'DIA\'],[\'returns\'],\'<DATES>\')":"DIA_returns"}, {"get_attribute([\'QQQ\'],[\'returns\'],\'<DATES>\')":"QQQ_returns"}]',
    '[{"get_dividend_history([\'<TICKER>\'],None)": "<TICKER>_dividend_history"}]',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for 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 = [
    'Market news from [DATES]',
    '[{"get_news_articles(None,None,None,\'<DATES>\')": "news_data"}, {"get_attribute([\'SPY\'],[\'returns\'],\'<DATES>\')":"SPY_returns"},  {"get_attribute([\'DIA\'],[\'returns\'],\'<DATES>\')":"DIA_returns"}, {"get_attribute([\'QQQ\'],[\'returns\'],\'<DATES>\')":"QQQ_returns"}]',
    '[{"get_dividend_history([\'<TICKER>\'],None)": "<TICKER>_dividend_history"}]',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for 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 = [
    'Market news from [DATES]',
    '[{"get_news_articles(None,None,None,\'<DATES>\')": "news_data"}, {"get_attribute([\'SPY\'],[\'returns\'],\'<DATES>\')":"SPY_returns"},  {"get_attribute([\'DIA\'],[\'returns\'],\'<DATES>\')":"DIA_returns"}, {"get_attribute([\'QQQ\'],[\'returns\'],\'<DATES>\')":"QQQ_returns"}]',
    '[{"get_dividend_history([\'<TICKER>\'],None)": "<TICKER>_dividend_history"}]',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for 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 = [
    'Market news from [DATES]',
    '[{"get_news_articles(None,None,None,\'<DATES>\')": "news_data"}, {"get_attribute([\'SPY\'],[\'returns\'],\'<DATES>\')":"SPY_returns"},  {"get_attribute([\'DIA\'],[\'returns\'],\'<DATES>\')":"DIA_returns"}, {"get_attribute([\'QQQ\'],[\'returns\'],\'<DATES>\')":"QQQ_returns"}]',
    '[{"get_dividend_history([\'<TICKER>\'],None)": "<TICKER>_dividend_history"}]',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for 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 = [
    'Market news from [DATES]',
    '[{"get_news_articles(None,None,None,\'<DATES>\')": "news_data"}, {"get_attribute([\'SPY\'],[\'returns\'],\'<DATES>\')":"SPY_returns"},  {"get_attribute([\'DIA\'],[\'returns\'],\'<DATES>\')":"DIA_returns"}, {"get_attribute([\'QQQ\'],[\'returns\'],\'<DATES>\')":"QQQ_returns"}]',
    '[{"get_dividend_history([\'<TICKER>\'],None)": "<TICKER>_dividend_history"}]',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for 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 = [
    'Market news from [DATES]',
    '[{"get_news_articles(None,None,None,\'<DATES>\')": "news_data"}, {"get_attribute([\'SPY\'],[\'returns\'],\'<DATES>\')":"SPY_returns"},  {"get_attribute([\'DIA\'],[\'returns\'],\'<DATES>\')":"DIA_returns"}, {"get_attribute([\'QQQ\'],[\'returns\'],\'<DATES>\')":"QQQ_returns"}]',
    '[{"get_dividend_history([\'<TICKER>\'],None)": "<TICKER>_dividend_history"}]',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for 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 = [
    'Market news from [DATES]',
    '[{"get_news_articles(None,None,None,\'<DATES>\')": "news_data"}, {"get_attribute([\'SPY\'],[\'returns\'],\'<DATES>\')":"SPY_returns"},  {"get_attribute([\'DIA\'],[\'returns\'],\'<DATES>\')":"DIA_returns"}, {"get_attribute([\'QQQ\'],[\'returns\'],\'<DATES>\')":"QQQ_returns"}]',
    '[{"get_dividend_history([\'<TICKER>\'],None)": "<TICKER>_dividend_history"}]',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for 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 = [
    'Market news from [DATES]',
    '[{"get_news_articles(None,None,None,\'<DATES>\')": "news_data"}, {"get_attribute([\'SPY\'],[\'returns\'],\'<DATES>\')":"SPY_returns"},  {"get_attribute([\'DIA\'],[\'returns\'],\'<DATES>\')":"DIA_returns"}, {"get_attribute([\'QQQ\'],[\'returns\'],\'<DATES>\')":"QQQ_returns"}]',
    '[{"get_dividend_history([\'<TICKER>\'],None)": "<TICKER>_dividend_history"}]',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for 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 = [
    'Market news from [DATES]',
    '[{"get_news_articles(None,None,None,\'<DATES>\')": "news_data"}, {"get_attribute([\'SPY\'],[\'returns\'],\'<DATES>\')":"SPY_returns"},  {"get_attribute([\'DIA\'],[\'returns\'],\'<DATES>\')":"DIA_returns"}, {"get_attribute([\'QQQ\'],[\'returns\'],\'<DATES>\')":"QQQ_returns"}]',
    '[{"get_dividend_history([\'<TICKER>\'],None)": "<TICKER>_dividend_history"}]',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for 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 = [
    'Market news from [DATES]',
    '[{"get_news_articles(None,None,None,\'<DATES>\')": "news_data"}, {"get_attribute([\'SPY\'],[\'returns\'],\'<DATES>\')":"SPY_returns"},  {"get_attribute([\'DIA\'],[\'returns\'],\'<DATES>\')":"DIA_returns"}, {"get_attribute([\'QQQ\'],[\'returns\'],\'<DATES>\')":"QQQ_returns"}]',
    '[{"get_dividend_history([\'<TICKER>\'],None)": "<TICKER>_dividend_history"}]',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for 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 = [
    'Market news from [DATES]',
    '[{"get_news_articles(None,None,None,\'<DATES>\')": "news_data"}, {"get_attribute([\'SPY\'],[\'returns\'],\'<DATES>\')":"SPY_returns"},  {"get_attribute([\'DIA\'],[\'returns\'],\'<DATES>\')":"DIA_returns"}, {"get_attribute([\'QQQ\'],[\'returns\'],\'<DATES>\')":"QQQ_returns"}]',
    '[{"get_dividend_history([\'<TICKER>\'],None)": "<TICKER>_dividend_history"}]',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for 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 = [
    'Market news from [DATES]',
    '[{"get_news_articles(None,None,None,\'<DATES>\')": "news_data"}, {"get_attribute([\'SPY\'],[\'returns\'],\'<DATES>\')":"SPY_returns"},  {"get_attribute([\'DIA\'],[\'returns\'],\'<DATES>\')":"DIA_returns"}, {"get_attribute([\'QQQ\'],[\'returns\'],\'<DATES>\')":"QQQ_returns"}]',
    '[{"get_dividend_history([\'<TICKER>\'],None)": "<TICKER>_dividend_history"}]',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for 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 = [
    'Market news from [DATES]',
    '[{"get_news_articles(None,None,None,\'<DATES>\')": "news_data"}, {"get_attribute([\'SPY\'],[\'returns\'],\'<DATES>\')":"SPY_returns"},  {"get_attribute([\'DIA\'],[\'returns\'],\'<DATES>\')":"DIA_returns"}, {"get_attribute([\'QQQ\'],[\'returns\'],\'<DATES>\')":"QQQ_returns"}]',
    '[{"get_dividend_history([\'<TICKER>\'],None)": "<TICKER>_dividend_history"}]',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for 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 = [
    'Market news from [DATES]',
    '[{"get_news_articles(None,None,None,\'<DATES>\')": "news_data"}, {"get_attribute([\'SPY\'],[\'returns\'],\'<DATES>\')":"SPY_returns"},  {"get_attribute([\'DIA\'],[\'returns\'],\'<DATES>\')":"DIA_returns"}, {"get_attribute([\'QQQ\'],[\'returns\'],\'<DATES>\')":"QQQ_returns"}]',
    '[{"get_dividend_history([\'<TICKER>\'],None)": "<TICKER>_dividend_history"}]',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for 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 = [
    'Market news from [DATES]',
    '[{"get_news_articles(None,None,None,\'<DATES>\')": "news_data"}, {"get_attribute([\'SPY\'],[\'returns\'],\'<DATES>\')":"SPY_returns"},  {"get_attribute([\'DIA\'],[\'returns\'],\'<DATES>\')":"DIA_returns"}, {"get_attribute([\'QQQ\'],[\'returns\'],\'<DATES>\')":"QQQ_returns"}]',
    '[{"get_dividend_history([\'<TICKER>\'],None)": "<TICKER>_dividend_history"}]',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for 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 = [
    'Market news from [DATES]',
    '[{"get_news_articles(None,None,None,\'<DATES>\')": "news_data"}, {"get_attribute([\'SPY\'],[\'returns\'],\'<DATES>\')":"SPY_returns"},  {"get_attribute([\'DIA\'],[\'returns\'],\'<DATES>\')":"DIA_returns"}, {"get_attribute([\'QQQ\'],[\'returns\'],\'<DATES>\')":"QQQ_returns"}]',
    '[{"get_dividend_history([\'<TICKER>\'],None)": "<TICKER>_dividend_history"}]',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for 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 = [
    'Market news from [DATES]',
    '[{"get_news_articles(None,None,None,\'<DATES>\')": "news_data"}, {"get_attribute([\'SPY\'],[\'returns\'],\'<DATES>\')":"SPY_returns"},  {"get_attribute([\'DIA\'],[\'returns\'],\'<DATES>\')":"DIA_returns"}, {"get_attribute([\'QQQ\'],[\'returns\'],\'<DATES>\')":"QQQ_returns"}]',
    '[{"get_dividend_history([\'<TICKER>\'],None)": "<TICKER>_dividend_history"}]',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for 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 = [
    'Market news from [DATES]',
    '[{"get_news_articles(None,None,None,\'<DATES>\')": "news_data"}, {"get_attribute([\'SPY\'],[\'returns\'],\'<DATES>\')":"SPY_returns"},  {"get_attribute([\'DIA\'],[\'returns\'],\'<DATES>\')":"DIA_returns"}, {"get_attribute([\'QQQ\'],[\'returns\'],\'<DATES>\')":"QQQ_returns"}]',
    '[{"get_dividend_history([\'<TICKER>\'],None)": "<TICKER>_dividend_history"}]',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for 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 = [
    'Market news from [DATES]',
    '[{"get_news_articles(None,None,None,\'<DATES>\')": "news_data"}, {"get_attribute([\'SPY\'],[\'returns\'],\'<DATES>\')":"SPY_returns"},  {"get_attribute([\'DIA\'],[\'returns\'],\'<DATES>\')":"DIA_returns"}, {"get_attribute([\'QQQ\'],[\'returns\'],\'<DATES>\')":"QQQ_returns"}]',
    '[{"get_dividend_history([\'<TICKER>\'],None)": "<TICKER>_dividend_history"}]',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for 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 = [
    'Market news from [DATES]',
    '[{"get_news_articles(None,None,None,\'<DATES>\')": "news_data"}, {"get_attribute([\'SPY\'],[\'returns\'],\'<DATES>\')":"SPY_returns"},  {"get_attribute([\'DIA\'],[\'returns\'],\'<DATES>\')":"DIA_returns"}, {"get_attribute([\'QQQ\'],[\'returns\'],\'<DATES>\')":"QQQ_returns"}]',
    '[{"get_dividend_history([\'<TICKER>\'],None)": "<TICKER>_dividend_history"}]',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for 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 = [
    'Market news from [DATES]',
    '[{"get_news_articles(None,None,None,\'<DATES>\')": "news_data"}, {"get_attribute([\'SPY\'],[\'returns\'],\'<DATES>\')":"SPY_returns"},  {"get_attribute([\'DIA\'],[\'returns\'],\'<DATES>\')":"DIA_returns"}, {"get_attribute([\'QQQ\'],[\'returns\'],\'<DATES>\')":"QQQ_returns"}]',
    '[{"get_dividend_history([\'<TICKER>\'],None)": "<TICKER>_dividend_history"}]',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for 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 = [
    'Market news from [DATES]',
    '[{"get_news_articles(None,None,None,\'<DATES>\')": "news_data"}, {"get_attribute([\'SPY\'],[\'returns\'],\'<DATES>\')":"SPY_returns"},  {"get_attribute([\'DIA\'],[\'returns\'],\'<DATES>\')":"DIA_returns"}, {"get_attribute([\'QQQ\'],[\'returns\'],\'<DATES>\')":"QQQ_returns"}]',
    '[{"get_dividend_history([\'<TICKER>\'],None)": "<TICKER>_dividend_history"}]',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for 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 = [
    'Market news from [DATES]',
    '[{"get_news_articles(None,None,None,\'<DATES>\')": "news_data"}, {"get_attribute([\'SPY\'],[\'returns\'],\'<DATES>\')":"SPY_returns"},  {"get_attribute([\'DIA\'],[\'returns\'],\'<DATES>\')":"DIA_returns"}, {"get_attribute([\'QQQ\'],[\'returns\'],\'<DATES>\')":"QQQ_returns"}]',
    '[{"get_dividend_history([\'<TICKER>\'],None)": "<TICKER>_dividend_history"}]',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for 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 = [
    'Market news from [DATES]',
    '[{"get_news_articles(None,None,None,\'<DATES>\')": "news_data"}, {"get_attribute([\'SPY\'],[\'returns\'],\'<DATES>\')":"SPY_returns"},  {"get_attribute([\'DIA\'],[\'returns\'],\'<DATES>\')":"DIA_returns"}, {"get_attribute([\'QQQ\'],[\'returns\'],\'<DATES>\')":"QQQ_returns"}]',
    '[{"get_dividend_history([\'<TICKER>\'],None)": "<TICKER>_dividend_history"}]',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for 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 = [
    'Market news from [DATES]',
    '[{"get_news_articles(None,None,None,\'<DATES>\')": "news_data"}, {"get_attribute([\'SPY\'],[\'returns\'],\'<DATES>\')":"SPY_returns"},  {"get_attribute([\'DIA\'],[\'returns\'],\'<DATES>\')":"DIA_returns"}, {"get_attribute([\'QQQ\'],[\'returns\'],\'<DATES>\')":"QQQ_returns"}]',
    '[{"get_dividend_history([\'<TICKER>\'],None)": "<TICKER>_dividend_history"}]',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for 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 = [
    'Market news from [DATES]',
    '[{"get_news_articles(None,None,None,\'<DATES>\')": "news_data"}, {"get_attribute([\'SPY\'],[\'returns\'],\'<DATES>\')":"SPY_returns"},  {"get_attribute([\'DIA\'],[\'returns\'],\'<DATES>\')":"DIA_returns"}, {"get_attribute([\'QQQ\'],[\'returns\'],\'<DATES>\')":"QQQ_returns"}]',
    '[{"get_dividend_history([\'<TICKER>\'],None)": "<TICKER>_dividend_history"}]',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for 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 = [
    'Market news from [DATES]',
    '[{"get_news_articles(None,None,None,\'<DATES>\')": "news_data"}, {"get_attribute([\'SPY\'],[\'returns\'],\'<DATES>\')":"SPY_returns"},  {"get_attribute([\'DIA\'],[\'returns\'],\'<DATES>\')":"DIA_returns"}, {"get_attribute([\'QQQ\'],[\'returns\'],\'<DATES>\')":"QQQ_returns"}]',
    '[{"get_dividend_history([\'<TICKER>\'],None)": "<TICKER>_dividend_history"}]',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for 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 = [
    'Market news from [DATES]',
    '[{"get_news_articles(None,None,None,\'<DATES>\')": "news_data"}, {"get_attribute([\'SPY\'],[\'returns\'],\'<DATES>\')":"SPY_returns"},  {"get_attribute([\'DIA\'],[\'returns\'],\'<DATES>\')":"DIA_returns"}, {"get_attribute([\'QQQ\'],[\'returns\'],\'<DATES>\')":"QQQ_returns"}]',
    '[{"get_dividend_history([\'<TICKER>\'],None)": "<TICKER>_dividend_history"}]',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for 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 = [
    'Market news from [DATES]',
    '[{"get_news_articles(None,None,None,\'<DATES>\')": "news_data"}, {"get_attribute([\'SPY\'],[\'returns\'],\'<DATES>\')":"SPY_returns"},  {"get_attribute([\'DIA\'],[\'returns\'],\'<DATES>\')":"DIA_returns"}, {"get_attribute([\'QQQ\'],[\'returns\'],\'<DATES>\')":"QQQ_returns"}]',
    '[{"get_dividend_history([\'<TICKER>\'],None)": "<TICKER>_dividend_history"}]',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for 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 = [
    'Market news from [DATES]',
    '[{"get_news_articles(None,None,None,\'<DATES>\')": "news_data"}, {"get_attribute([\'SPY\'],[\'returns\'],\'<DATES>\')":"SPY_returns"},  {"get_attribute([\'DIA\'],[\'returns\'],\'<DATES>\')":"DIA_returns"}, {"get_attribute([\'QQQ\'],[\'returns\'],\'<DATES>\')":"QQQ_returns"}]',
    '[{"get_dividend_history([\'<TICKER>\'],None)": "<TICKER>_dividend_history"}]',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for 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 = [
    'Market news from [DATES]',
    '[{"get_news_articles(None,None,None,\'<DATES>\')": "news_data"}, {"get_attribute([\'SPY\'],[\'returns\'],\'<DATES>\')":"SPY_returns"},  {"get_attribute([\'DIA\'],[\'returns\'],\'<DATES>\')":"DIA_returns"}, {"get_attribute([\'QQQ\'],[\'returns\'],\'<DATES>\')":"QQQ_returns"}]',
    '[{"get_dividend_history([\'<TICKER>\'],None)": "<TICKER>_dividend_history"}]',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for 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 = [
    'Market news from [DATES]',
    '[{"get_news_articles(None,None,None,\'<DATES>\')": "news_data"}, {"get_attribute([\'SPY\'],[\'returns\'],\'<DATES>\')":"SPY_returns"},  {"get_attribute([\'DIA\'],[\'returns\'],\'<DATES>\')":"DIA_returns"}, {"get_attribute([\'QQQ\'],[\'returns\'],\'<DATES>\')":"QQQ_returns"}]',
    '[{"get_dividend_history([\'<TICKER>\'],None)": "<TICKER>_dividend_history"}]',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for 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 = [
    'Market news from [DATES]',
    '[{"get_news_articles(None,None,None,\'<DATES>\')": "news_data"}, {"get_attribute([\'SPY\'],[\'returns\'],\'<DATES>\')":"SPY_returns"},  {"get_attribute([\'DIA\'],[\'returns\'],\'<DATES>\')":"DIA_returns"}, {"get_attribute([\'QQQ\'],[\'returns\'],\'<DATES>\')":"QQQ_returns"}]',
    '[{"get_dividend_history([\'<TICKER>\'],None)": "<TICKER>_dividend_history"}]',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for 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 = [
    'Market news from [DATES]',
    '[{"get_news_articles(None,None,None,\'<DATES>\')": "news_data"}, {"get_attribute([\'SPY\'],[\'returns\'],\'<DATES>\')":"SPY_returns"},  {"get_attribute([\'DIA\'],[\'returns\'],\'<DATES>\')":"DIA_returns"}, {"get_attribute([\'QQQ\'],[\'returns\'],\'<DATES>\')":"QQQ_returns"}]',
    '[{"get_dividend_history([\'<TICKER>\'],None)": "<TICKER>_dividend_history"}]',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for 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 = [
    'Market news from [DATES]',
    '[{"get_news_articles(None,None,None,\'<DATES>\')": "news_data"}, {"get_attribute([\'SPY\'],[\'returns\'],\'<DATES>\')":"SPY_returns"},  {"get_attribute([\'DIA\'],[\'returns\'],\'<DATES>\')":"DIA_returns"}, {"get_attribute([\'QQQ\'],[\'returns\'],\'<DATES>\')":"QQQ_returns"}]',
    '[{"get_dividend_history([\'<TICKER>\'],None)": "<TICKER>_dividend_history"}]',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for 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 = [
    'Market news from [DATES]',
    '[{"get_news_articles(None,None,None,\'<DATES>\')": "news_data"}, {"get_attribute([\'SPY\'],[\'returns\'],\'<DATES>\')":"SPY_returns"},  {"get_attribute([\'DIA\'],[\'returns\'],\'<DATES>\')":"DIA_returns"}, {"get_attribute([\'QQQ\'],[\'returns\'],\'<DATES>\')":"QQQ_returns"}]',
    '[{"get_dividend_history([\'<TICKER>\'],None)": "<TICKER>_dividend_history"}]',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for 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 = [
    'Market news from [DATES]',
    '[{"get_news_articles(None,None,None,\'<DATES>\')": "news_data"}, {"get_attribute([\'SPY\'],[\'returns\'],\'<DATES>\')":"SPY_returns"},  {"get_attribute([\'DIA\'],[\'returns\'],\'<DATES>\')":"DIA_returns"}, {"get_attribute([\'QQQ\'],[\'returns\'],\'<DATES>\')":"QQQ_returns"}]',
    '[{"get_dividend_history([\'<TICKER>\'],None)": "<TICKER>_dividend_history"}]',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for 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 = [
    'Market news from [DATES]',
    '[{"get_news_articles(None,None,None,\'<DATES>\')": "news_data"}, {"get_attribute([\'SPY\'],[\'returns\'],\'<DATES>\')":"SPY_returns"},  {"get_attribute([\'DIA\'],[\'returns\'],\'<DATES>\')":"DIA_returns"}, {"get_attribute([\'QQQ\'],[\'returns\'],\'<DATES>\')":"QQQ_returns"}]',
    '[{"get_dividend_history([\'<TICKER>\'],None)": "<TICKER>_dividend_history"}]',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for 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 = [
    'Market news from [DATES]',
    '[{"get_news_articles(None,None,None,\'<DATES>\')": "news_data"}, {"get_attribute([\'SPY\'],[\'returns\'],\'<DATES>\')":"SPY_returns"},  {"get_attribute([\'DIA\'],[\'returns\'],\'<DATES>\')":"DIA_returns"}, {"get_attribute([\'QQQ\'],[\'returns\'],\'<DATES>\')":"QQQ_returns"}]',
    '[{"get_dividend_history([\'<TICKER>\'],None)": "<TICKER>_dividend_history"}]',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for 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 = [
    'Market news from [DATES]',
    '[{"get_news_articles(None,None,None,\'<DATES>\')": "news_data"}, {"get_attribute([\'SPY\'],[\'returns\'],\'<DATES>\')":"SPY_returns"},  {"get_attribute([\'DIA\'],[\'returns\'],\'<DATES>\')":"DIA_returns"}, {"get_attribute([\'QQQ\'],[\'returns\'],\'<DATES>\')":"QQQ_returns"}]',
    '[{"get_dividend_history([\'<TICKER>\'],None)": "<TICKER>_dividend_history"}]',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for 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 = [
    'Market news from [DATES]',
    '[{"get_news_articles(None,None,None,\'<DATES>\')": "news_data"}, {"get_attribute([\'SPY\'],[\'returns\'],\'<DATES>\')":"SPY_returns"},  {"get_attribute([\'DIA\'],[\'returns\'],\'<DATES>\')":"DIA_returns"}, {"get_attribute([\'QQQ\'],[\'returns\'],\'<DATES>\')":"QQQ_returns"}]',
    '[{"get_dividend_history([\'<TICKER>\'],None)": "<TICKER>_dividend_history"}]',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for 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 = [
    'Market news from [DATES]',
    '[{"get_news_articles(None,None,None,\'<DATES>\')": "news_data"}, {"get_attribute([\'SPY\'],[\'returns\'],\'<DATES>\')":"SPY_returns"},  {"get_attribute([\'DIA\'],[\'returns\'],\'<DATES>\')":"DIA_returns"}, {"get_attribute([\'QQQ\'],[\'returns\'],\'<DATES>\')":"QQQ_returns"}]',
    '[{"get_dividend_history([\'<TICKER>\'],None)": "<TICKER>_dividend_history"}]',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for 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 = [
    'Market news from [DATES]',
    '[{"get_news_articles(None,None,None,\'<DATES>\')": "news_data"}, {"get_attribute([\'SPY\'],[\'returns\'],\'<DATES>\')":"SPY_returns"},  {"get_attribute([\'DIA\'],[\'returns\'],\'<DATES>\')":"DIA_returns"}, {"get_attribute([\'QQQ\'],[\'returns\'],\'<DATES>\')":"QQQ_returns"}]',
    '[{"get_dividend_history([\'<TICKER>\'],None)": "<TICKER>_dividend_history"}]',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for 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 = [
    'Market news from [DATES]',
    '[{"get_news_articles(None,None,None,\'<DATES>\')": "news_data"}, {"get_attribute([\'SPY\'],[\'returns\'],\'<DATES>\')":"SPY_returns"},  {"get_attribute([\'DIA\'],[\'returns\'],\'<DATES>\')":"DIA_returns"}, {"get_attribute([\'QQQ\'],[\'returns\'],\'<DATES>\')":"QQQ_returns"}]',
    '[{"get_dividend_history([\'<TICKER>\'],None)": "<TICKER>_dividend_history"}]',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for 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 = [
    'Market news from [DATES]',
    '[{"get_news_articles(None,None,None,\'<DATES>\')": "news_data"}, {"get_attribute([\'SPY\'],[\'returns\'],\'<DATES>\')":"SPY_returns"},  {"get_attribute([\'DIA\'],[\'returns\'],\'<DATES>\')":"DIA_returns"}, {"get_attribute([\'QQQ\'],[\'returns\'],\'<DATES>\')":"QQQ_returns"}]',
    '[{"get_dividend_history([\'<TICKER>\'],None)": "<TICKER>_dividend_history"}]',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for 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 = [
    'Market news from [DATES]',
    '[{"get_news_articles(None,None,None,\'<DATES>\')": "news_data"}, {"get_attribute([\'SPY\'],[\'returns\'],\'<DATES>\')":"SPY_returns"},  {"get_attribute([\'DIA\'],[\'returns\'],\'<DATES>\')":"DIA_returns"}, {"get_attribute([\'QQQ\'],[\'returns\'],\'<DATES>\')":"QQQ_returns"}]',
    '[{"get_dividend_history([\'<TICKER>\'],None)": "<TICKER>_dividend_history"}]',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for 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 = [
    'Market news from [DATES]',
    '[{"get_news_articles(None,None,None,\'<DATES>\')": "news_data"}, {"get_attribute([\'SPY\'],[\'returns\'],\'<DATES>\')":"SPY_returns"},  {"get_attribute([\'DIA\'],[\'returns\'],\'<DATES>\')":"DIA_returns"}, {"get_attribute([\'QQQ\'],[\'returns\'],\'<DATES>\')":"QQQ_returns"}]',
    '[{"get_dividend_history([\'<TICKER>\'],None)": "<TICKER>_dividend_history"}]',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for 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 = [
    'Market news from [DATES]',
    '[{"get_news_articles(None,None,None,\'<DATES>\')": "news_data"}, {"get_attribute([\'SPY\'],[\'returns\'],\'<DATES>\')":"SPY_returns"},  {"get_attribute([\'DIA\'],[\'returns\'],\'<DATES>\')":"DIA_returns"}, {"get_attribute([\'QQQ\'],[\'returns\'],\'<DATES>\')":"QQQ_returns"}]',
    '[{"get_dividend_history([\'<TICKER>\'],None)": "<TICKER>_dividend_history"}]',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for 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 = [
    'Market news from [DATES]',
    '[{"get_news_articles(None,None,None,\'<DATES>\')": "news_data"}, {"get_attribute([\'SPY\'],[\'returns\'],\'<DATES>\')":"SPY_returns"},  {"get_attribute([\'DIA\'],[\'returns\'],\'<DATES>\')":"DIA_returns"}, {"get_attribute([\'QQQ\'],[\'returns\'],\'<DATES>\')":"QQQ_returns"}]',
    '[{"get_dividend_history([\'<TICKER>\'],None)": "<TICKER>_dividend_history"}]',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for 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 = [
    'Market news from [DATES]',
    '[{"get_news_articles(None,None,None,\'<DATES>\')": "news_data"}, {"get_attribute([\'SPY\'],[\'returns\'],\'<DATES>\')":"SPY_returns"},  {"get_attribute([\'DIA\'],[\'returns\'],\'<DATES>\')":"DIA_returns"}, {"get_attribute([\'QQQ\'],[\'returns\'],\'<DATES>\')":"QQQ_returns"}]',
    '[{"get_dividend_history([\'<TICKER>\'],None)": "<TICKER>_dividend_history"}]',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for 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 = [
    'Market news from [DATES]',
    '[{"get_news_articles(None,None,None,\'<DATES>\')": "news_data"}, {"get_attribute([\'SPY\'],[\'returns\'],\'<DATES>\')":"SPY_returns"},  {"get_attribute([\'DIA\'],[\'returns\'],\'<DATES>\')":"DIA_returns"}, {"get_attribute([\'QQQ\'],[\'returns\'],\'<DATES>\')":"QQQ_returns"}]',
    '[{"get_dividend_history([\'<TICKER>\'],None)": "<TICKER>_dividend_history"}]',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for 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 = [
    'Market news from [DATES]',
    '[{"get_news_articles(None,None,None,\'<DATES>\')": "news_data"}, {"get_attribute([\'SPY\'],[\'returns\'],\'<DATES>\')":"SPY_returns"},  {"get_attribute([\'DIA\'],[\'returns\'],\'<DATES>\')":"DIA_returns"}, {"get_attribute([\'QQQ\'],[\'returns\'],\'<DATES>\')":"QQQ_returns"}]',
    '[{"get_dividend_history([\'<TICKER>\'],None)": "<TICKER>_dividend_history"}]',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for 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 = [
    'Market news from [DATES]',
    '[{"get_news_articles(None,None,None,\'<DATES>\')": "news_data"}, {"get_attribute([\'SPY\'],[\'returns\'],\'<DATES>\')":"SPY_returns"},  {"get_attribute([\'DIA\'],[\'returns\'],\'<DATES>\')":"DIA_returns"}, {"get_attribute([\'QQQ\'],[\'returns\'],\'<DATES>\')":"QQQ_returns"}]',
    '[{"get_dividend_history([\'<TICKER>\'],None)": "<TICKER>_dividend_history"}]',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for 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 = [
    'Market news from [DATES]',
    '[{"get_news_articles(None,None,None,\'<DATES>\')": "news_data"}, {"get_attribute([\'SPY\'],[\'returns\'],\'<DATES>\')":"SPY_returns"},  {"get_attribute([\'DIA\'],[\'returns\'],\'<DATES>\')":"DIA_returns"}, {"get_attribute([\'QQQ\'],[\'returns\'],\'<DATES>\')":"QQQ_returns"}]',
    '[{"get_dividend_history([\'<TICKER>\'],None)": "<TICKER>_dividend_history"}]',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for 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 = [
    'Market news from [DATES]',
    '[{"get_news_articles(None,None,None,\'<DATES>\')": "news_data"}, {"get_attribute([\'SPY\'],[\'returns\'],\'<DATES>\')":"SPY_returns"},  {"get_attribute([\'DIA\'],[\'returns\'],\'<DATES>\')":"DIA_returns"}, {"get_attribute([\'QQQ\'],[\'returns\'],\'<DATES>\')":"QQQ_returns"}]',
    '[{"get_dividend_history([\'<TICKER>\'],None)": "<TICKER>_dividend_history"}]',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for 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 = [
    'Market news from [DATES]',
    '[{"get_news_articles(None,None,None,\'<DATES>\')": "news_data"}, {"get_attribute([\'SPY\'],[\'returns\'],\'<DATES>\')":"SPY_returns"},  {"get_attribute([\'DIA\'],[\'returns\'],\'<DATES>\')":"DIA_returns"}, {"get_attribute([\'QQQ\'],[\'returns\'],\'<DATES>\')":"QQQ_returns"}]',
    '[{"get_dividend_history([\'<TICKER>\'],None)": "<TICKER>_dividend_history"}]',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for 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 = [
    'Market news from [DATES]',
    '[{"get_news_articles(None,None,None,\'<DATES>\')": "news_data"}, {"get_attribute([\'SPY\'],[\'returns\'],\'<DATES>\')":"SPY_returns"},  {"get_attribute([\'DIA\'],[\'returns\'],\'<DATES>\')":"DIA_returns"}, {"get_attribute([\'QQQ\'],[\'returns\'],\'<DATES>\')":"QQQ_returns"}]',
    '[{"get_dividend_history([\'<TICKER>\'],None)": "<TICKER>_dividend_history"}]',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for 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 = [
    'Market news from [DATES]',
    '[{"get_news_articles(None,None,None,\'<DATES>\')": "news_data"}, {"get_attribute([\'SPY\'],[\'returns\'],\'<DATES>\')":"SPY_returns"},  {"get_attribute([\'DIA\'],[\'returns\'],\'<DATES>\')":"DIA_returns"}, {"get_attribute([\'QQQ\'],[\'returns\'],\'<DATES>\')":"QQQ_returns"}]',
    '[{"get_dividend_history([\'<TICKER>\'],None)": "<TICKER>_dividend_history"}]',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for 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 = [
    'Market news from [DATES]',
    '[{"get_news_articles(None,None,None,\'<DATES>\')": "news_data"}, {"get_attribute([\'SPY\'],[\'returns\'],\'<DATES>\')":"SPY_returns"},  {"get_attribute([\'DIA\'],[\'returns\'],\'<DATES>\')":"DIA_returns"}, {"get_attribute([\'QQQ\'],[\'returns\'],\'<DATES>\')":"QQQ_returns"}]',
    '[{"get_dividend_history([\'<TICKER>\'],None)": "<TICKER>_dividend_history"}]',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for 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 = [
    'Market news from [DATES]',
    '[{"get_news_articles(None,None,None,\'<DATES>\')": "news_data"}, {"get_attribute([\'SPY\'],[\'returns\'],\'<DATES>\')":"SPY_returns"},  {"get_attribute([\'DIA\'],[\'returns\'],\'<DATES>\')":"DIA_returns"}, {"get_attribute([\'QQQ\'],[\'returns\'],\'<DATES>\')":"QQQ_returns"}]',
    '[{"get_dividend_history([\'<TICKER>\'],None)": "<TICKER>_dividend_history"}]',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for 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 = [
    'Market news from [DATES]',
    '[{"get_news_articles(None,None,None,\'<DATES>\')": "news_data"}, {"get_attribute([\'SPY\'],[\'returns\'],\'<DATES>\')":"SPY_returns"},  {"get_attribute([\'DIA\'],[\'returns\'],\'<DATES>\')":"DIA_returns"}, {"get_attribute([\'QQQ\'],[\'returns\'],\'<DATES>\')":"QQQ_returns"}]',
    '[{"get_dividend_history([\'<TICKER>\'],None)": "<TICKER>_dividend_history"}]',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for 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 = [
    'Market news from [DATES]',
    '[{"get_news_articles(None,None,None,\'<DATES>\')": "news_data"}, {"get_attribute([\'SPY\'],[\'returns\'],\'<DATES>\')":"SPY_returns"},  {"get_attribute([\'DIA\'],[\'returns\'],\'<DATES>\')":"DIA_returns"}, {"get_attribute([\'QQQ\'],[\'returns\'],\'<DATES>\')":"QQQ_returns"}]',
    '[{"get_dividend_history([\'<TICKER>\'],None)": "<TICKER>_dividend_history"}]',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for 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 = [
    'Market news from [DATES]',
    '[{"get_news_articles(None,None,None,\'<DATES>\')": "news_data"}, {"get_attribute([\'SPY\'],[\'returns\'],\'<DATES>\')":"SPY_returns"},  {"get_attribute([\'DIA\'],[\'returns\'],\'<DATES>\')":"DIA_returns"}, {"get_attribute([\'QQQ\'],[\'returns\'],\'<DATES>\')":"QQQ_returns"}]',
    '[{"get_dividend_history([\'<TICKER>\'],None)": "<TICKER>_dividend_history"}]',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for 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 = [
    'Market news from [DATES]',
    '[{"get_news_articles(None,None,None,\'<DATES>\')": "news_data"}, {"get_attribute([\'SPY\'],[\'returns\'],\'<DATES>\')":"SPY_returns"},  {"get_attribute([\'DIA\'],[\'returns\'],\'<DATES>\')":"DIA_returns"}, {"get_attribute([\'QQQ\'],[\'returns\'],\'<DATES>\')":"QQQ_returns"}]',
    '[{"get_dividend_history([\'<TICKER>\'],None)": "<TICKER>_dividend_history"}]',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for 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 = [
    'Market news from [DATES]',
    '[{"get_news_articles(None,None,None,\'<DATES>\')": "news_data"}, {"get_attribute([\'SPY\'],[\'returns\'],\'<DATES>\')":"SPY_returns"},  {"get_attribute([\'DIA\'],[\'returns\'],\'<DATES>\')":"DIA_returns"}, {"get_attribute([\'QQQ\'],[\'returns\'],\'<DATES>\')":"QQQ_returns"}]',
    '[{"get_dividend_history([\'<TICKER>\'],None)": "<TICKER>_dividend_history"}]',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for 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 = [
    'Market news from [DATES]',
    '[{"get_news_articles(None,None,None,\'<DATES>\')": "news_data"}, {"get_attribute([\'SPY\'],[\'returns\'],\'<DATES>\')":"SPY_returns"},  {"get_attribute([\'DIA\'],[\'returns\'],\'<DATES>\')":"DIA_returns"}, {"get_attribute([\'QQQ\'],[\'returns\'],\'<DATES>\')":"QQQ_returns"}]',
    '[{"get_dividend_history([\'<TICKER>\'],None)": "<TICKER>_dividend_history"}]',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for 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 = [
    'Market news from [DATES]',
    '[{"get_news_articles(None,None,None,\'<DATES>\')": "news_data"}, {"get_attribute([\'SPY\'],[\'returns\'],\'<DATES>\')":"SPY_returns"},  {"get_attribute([\'DIA\'],[\'returns\'],\'<DATES>\')":"DIA_returns"}, {"get_attribute([\'QQQ\'],[\'returns\'],\'<DATES>\')":"QQQ_returns"}]',
    '[{"get_dividend_history([\'<TICKER>\'],None)": "<TICKER>_dividend_history"}]',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for 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 = [
    'Market news from [DATES]',
    '[{"get_news_articles(None,None,None,\'<DATES>\')": "news_data"}, {"get_attribute([\'SPY\'],[\'returns\'],\'<DATES>\')":"SPY_returns"},  {"get_attribute([\'DIA\'],[\'returns\'],\'<DATES>\')":"DIA_returns"}, {"get_attribute([\'QQQ\'],[\'returns\'],\'<DATES>\')":"QQQ_returns"}]',
    '[{"get_dividend_history([\'<TICKER>\'],None)": "<TICKER>_dividend_history"}]',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for 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 = [
    'Market news from [DATES]',
    '[{"get_news_articles(None,None,None,\'<DATES>\')": "news_data"}, {"get_attribute([\'SPY\'],[\'returns\'],\'<DATES>\')":"SPY_returns"},  {"get_attribute([\'DIA\'],[\'returns\'],\'<DATES>\')":"DIA_returns"}, {"get_attribute([\'QQQ\'],[\'returns\'],\'<DATES>\')":"QQQ_returns"}]',
    '[{"get_dividend_history([\'<TICKER>\'],None)": "<TICKER>_dividend_history"}]',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for 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 = [
    'Market news from [DATES]',
    '[{"get_news_articles(None,None,None,\'<DATES>\')": "news_data"}, {"get_attribute([\'SPY\'],[\'returns\'],\'<DATES>\')":"SPY_returns"},  {"get_attribute([\'DIA\'],[\'returns\'],\'<DATES>\')":"DIA_returns"}, {"get_attribute([\'QQQ\'],[\'returns\'],\'<DATES>\')":"QQQ_returns"}]',
    '[{"get_dividend_history([\'<TICKER>\'],None)": "<TICKER>_dividend_history"}]',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for 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 = [
    'Market news from [DATES]',
    '[{"get_news_articles(None,None,None,\'<DATES>\')": "news_data"}, {"get_attribute([\'SPY\'],[\'returns\'],\'<DATES>\')":"SPY_returns"},  {"get_attribute([\'DIA\'],[\'returns\'],\'<DATES>\')":"DIA_returns"}, {"get_attribute([\'QQQ\'],[\'returns\'],\'<DATES>\')":"QQQ_returns"}]',
    '[{"get_dividend_history([\'<TICKER>\'],None)": "<TICKER>_dividend_history"}]',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for 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 = [
    'Market news from [DATES]',
    '[{"get_news_articles(None,None,None,\'<DATES>\')": "news_data"}, {"get_attribute([\'SPY\'],[\'returns\'],\'<DATES>\')":"SPY_returns"},  {"get_attribute([\'DIA\'],[\'returns\'],\'<DATES>\')":"DIA_returns"}, {"get_attribute([\'QQQ\'],[\'returns\'],\'<DATES>\')":"QQQ_returns"}]',
    '[{"get_dividend_history([\'<TICKER>\'],None)": "<TICKER>_dividend_history"}]',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for 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 = [
    'Market news from [DATES]',
    '[{"get_news_articles(None,None,None,\'<DATES>\')": "news_data"}, {"get_attribute([\'SPY\'],[\'returns\'],\'<DATES>\')":"SPY_returns"},  {"get_attribute([\'DIA\'],[\'returns\'],\'<DATES>\')":"DIA_returns"}, {"get_attribute([\'QQQ\'],[\'returns\'],\'<DATES>\')":"QQQ_returns"}]',
    '[{"get_dividend_history([\'<TICKER>\'],None)": "<TICKER>_dividend_history"}]',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for 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 = [
    'Market news from [DATES]',
    '[{"get_news_articles(None,None,None,\'<DATES>\')": "news_data"}, {"get_attribute([\'SPY\'],[\'returns\'],\'<DATES>\')":"SPY_returns"},  {"get_attribute([\'DIA\'],[\'returns\'],\'<DATES>\')":"DIA_returns"}, {"get_attribute([\'QQQ\'],[\'returns\'],\'<DATES>\')":"QQQ_returns"}]',
    '[{"get_dividend_history([\'<TICKER>\'],None)": "<TICKER>_dividend_history"}]',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for 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 = [
    'Market news from [DATES]',
    '[{"get_news_articles(None,None,None,\'<DATES>\')": "news_data"}, {"get_attribute([\'SPY\'],[\'returns\'],\'<DATES>\')":"SPY_returns"},  {"get_attribute([\'DIA\'],[\'returns\'],\'<DATES>\')":"DIA_returns"}, {"get_attribute([\'QQQ\'],[\'returns\'],\'<DATES>\')":"QQQ_returns"}]',
    '[{"get_dividend_history([\'<TICKER>\'],None)": "<TICKER>_dividend_history"}]',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for 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 = [
    'Market news from [DATES]',
    '[{"get_news_articles(None,None,None,\'<DATES>\')": "news_data"}, {"get_attribute([\'SPY\'],[\'returns\'],\'<DATES>\')":"SPY_returns"},  {"get_attribute([\'DIA\'],[\'returns\'],\'<DATES>\')":"DIA_returns"}, {"get_attribute([\'QQQ\'],[\'returns\'],\'<DATES>\')":"QQQ_returns"}]',
    '[{"get_dividend_history([\'<TICKER>\'],None)": "<TICKER>_dividend_history"}]',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for 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 = [
    'Market news from [DATES]',
    '[{"get_news_articles(None,None,None,\'<DATES>\')": "news_data"}, {"get_attribute([\'SPY\'],[\'returns\'],\'<DATES>\')":"SPY_returns"},  {"get_attribute([\'DIA\'],[\'returns\'],\'<DATES>\')":"DIA_returns"}, {"get_attribute([\'QQQ\'],[\'returns\'],\'<DATES>\')":"QQQ_returns"}]',
    '[{"get_dividend_history([\'<TICKER>\'],None)": "<TICKER>_dividend_history"}]',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for 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 = [
    'Market news from [DATES]',
    '[{"get_news_articles(None,None,None,\'<DATES>\')": "news_data"}, {"get_attribute([\'SPY\'],[\'returns\'],\'<DATES>\')":"SPY_returns"},  {"get_attribute([\'DIA\'],[\'returns\'],\'<DATES>\')":"DIA_returns"}, {"get_attribute([\'QQQ\'],[\'returns\'],\'<DATES>\')":"QQQ_returns"}]',
    '[{"get_dividend_history([\'<TICKER>\'],None)": "<TICKER>_dividend_history"}]',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for 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 = [
    'Market news from [DATES]',
    '[{"get_news_articles(None,None,None,\'<DATES>\')": "news_data"}, {"get_attribute([\'SPY\'],[\'returns\'],\'<DATES>\')":"SPY_returns"},  {"get_attribute([\'DIA\'],[\'returns\'],\'<DATES>\')":"DIA_returns"}, {"get_attribute([\'QQQ\'],[\'returns\'],\'<DATES>\')":"QQQ_returns"}]',
    '[{"get_dividend_history([\'<TICKER>\'],None)": "<TICKER>_dividend_history"}]',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for 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 = [
    'Market news from [DATES]',
    '[{"get_news_articles(None,None,None,\'<DATES>\')": "news_data"}, {"get_attribute([\'SPY\'],[\'returns\'],\'<DATES>\')":"SPY_returns"},  {"get_attribute([\'DIA\'],[\'returns\'],\'<DATES>\')":"DIA_returns"}, {"get_attribute([\'QQQ\'],[\'returns\'],\'<DATES>\')":"QQQ_returns"}]',
    '[{"get_dividend_history([\'<TICKER>\'],None)": "<TICKER>_dividend_history"}]',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for 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 = [
    'Market news from [DATES]',
    '[{"get_news_articles(None,None,None,\'<DATES>\')": "news_data"}, {"get_attribute([\'SPY\'],[\'returns\'],\'<DATES>\')":"SPY_returns"},  {"get_attribute([\'DIA\'],[\'returns\'],\'<DATES>\')":"DIA_returns"}, {"get_attribute([\'QQQ\'],[\'returns\'],\'<DATES>\')":"QQQ_returns"}]',
    '[{"get_dividend_history([\'<TICKER>\'],None)": "<TICKER>_dividend_history"}]',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for 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 = [
    'Market news from [DATES]',
    '[{"get_news_articles(None,None,None,\'<DATES>\')": "news_data"}, {"get_attribute([\'SPY\'],[\'returns\'],\'<DATES>\')":"SPY_returns"},  {"get_attribute([\'DIA\'],[\'returns\'],\'<DATES>\')":"DIA_returns"}, {"get_attribute([\'QQQ\'],[\'returns\'],\'<DATES>\')":"QQQ_returns"}]',
    '[{"get_dividend_history([\'<TICKER>\'],None)": "<TICKER>_dividend_history"}]',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for 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 = [
    'Market news from [DATES]',
    '[{"get_news_articles(None,None,None,\'<DATES>\')": "news_data"}, {"get_attribute([\'SPY\'],[\'returns\'],\'<DATES>\')":"SPY_returns"},  {"get_attribute([\'DIA\'],[\'returns\'],\'<DATES>\')":"DIA_returns"}, {"get_attribute([\'QQQ\'],[\'returns\'],\'<DATES>\')":"QQQ_returns"}]',
    '[{"get_dividend_history([\'<TICKER>\'],None)": "<TICKER>_dividend_history"}]',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for 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 = [
    'Market news from [DATES]',
    '[{"get_news_articles(None,None,None,\'<DATES>\')": "news_data"}, {"get_attribute([\'SPY\'],[\'returns\'],\'<DATES>\')":"SPY_returns"},  {"get_attribute([\'DIA\'],[\'returns\'],\'<DATES>\')":"DIA_returns"}, {"get_attribute([\'QQQ\'],[\'returns\'],\'<DATES>\')":"QQQ_returns"}]',
    '[{"get_dividend_history([\'<TICKER>\'],None)": "<TICKER>_dividend_history"}]',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for 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 = [
    'Market news from [DATES]',
    '[{"get_news_articles(None,None,None,\'<DATES>\')": "news_data"}, {"get_attribute([\'SPY\'],[\'returns\'],\'<DATES>\')":"SPY_returns"},  {"get_attribute([\'DIA\'],[\'returns\'],\'<DATES>\')":"DIA_returns"}, {"get_attribute([\'QQQ\'],[\'returns\'],\'<DATES>\')":"QQQ_returns"}]',
    '[{"get_dividend_history([\'<TICKER>\'],None)": "<TICKER>_dividend_history"}]',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for 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 = [
    'Market news from [DATES]',
    '[{"get_news_articles(None,None,None,\'<DATES>\')": "news_data"}, {"get_attribute([\'SPY\'],[\'returns\'],\'<DATES>\')":"SPY_returns"},  {"get_attribute([\'DIA\'],[\'returns\'],\'<DATES>\')":"DIA_returns"}, {"get_attribute([\'QQQ\'],[\'returns\'],\'<DATES>\')":"QQQ_returns"}]',
    '[{"get_dividend_history([\'<TICKER>\'],None)": "<TICKER>_dividend_history"}]',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for 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 = [
    'Market news from [DATES]',
    '[{"get_news_articles(None,None,None,\'<DATES>\')": "news_data"}, {"get_attribute([\'SPY\'],[\'returns\'],\'<DATES>\')":"SPY_returns"},  {"get_attribute([\'DIA\'],[\'returns\'],\'<DATES>\')":"DIA_returns"}, {"get_attribute([\'QQQ\'],[\'returns\'],\'<DATES>\')":"QQQ_returns"}]',
    '[{"get_dividend_history([\'<TICKER>\'],None)": "<TICKER>_dividend_history"}]',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for 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 = [
    'Market news from [DATES]',
    '[{"get_news_articles(None,None,None,\'<DATES>\')": "news_data"}, {"get_attribute([\'SPY\'],[\'returns\'],\'<DATES>\')":"SPY_returns"},  {"get_attribute([\'DIA\'],[\'returns\'],\'<DATES>\')":"DIA_returns"}, {"get_attribute([\'QQQ\'],[\'returns\'],\'<DATES>\')":"QQQ_returns"}]',
    '[{"get_dividend_history([\'<TICKER>\'],None)": "<TICKER>_dividend_history"}]',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for 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 = [
    'Market news from [DATES]',
    '[{"get_news_articles(None,None,None,\'<DATES>\')": "news_data"}, {"get_attribute([\'SPY\'],[\'returns\'],\'<DATES>\')":"SPY_returns"},  {"get_attribute([\'DIA\'],[\'returns\'],\'<DATES>\')":"DIA_returns"}, {"get_attribute([\'QQQ\'],[\'returns\'],\'<DATES>\')":"QQQ_returns"}]',
    '[{"get_dividend_history([\'<TICKER>\'],None)": "<TICKER>_dividend_history"}]',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for 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 = [
    'Market news from [DATES]',
    '[{"get_news_articles(None,None,None,\'<DATES>\')": "news_data"}, {"get_attribute([\'SPY\'],[\'returns\'],\'<DATES>\')":"SPY_returns"},  {"get_attribute([\'DIA\'],[\'returns\'],\'<DATES>\')":"DIA_returns"}, {"get_attribute([\'QQQ\'],[\'returns\'],\'<DATES>\')":"QQQ_returns"}]',
    '[{"get_dividend_history([\'<TICKER>\'],None)": "<TICKER>_dividend_history"}]',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for 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 = [
    'Market news from [DATES]',
    '[{"get_news_articles(None,None,None,\'<DATES>\')": "news_data"}, {"get_attribute([\'SPY\'],[\'returns\'],\'<DATES>\')":"SPY_returns"},  {"get_attribute([\'DIA\'],[\'returns\'],\'<DATES>\')":"DIA_returns"}, {"get_attribute([\'QQQ\'],[\'returns\'],\'<DATES>\')":"QQQ_returns"}]',
    '[{"get_dividend_history([\'<TICKER>\'],None)": "<TICKER>_dividend_history"}]',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for 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 = [
    'Market news from [DATES]',
    '[{"get_news_articles(None,None,None,\'<DATES>\')": "news_data"}, {"get_attribute([\'SPY\'],[\'returns\'],\'<DATES>\')":"SPY_returns"},  {"get_attribute([\'DIA\'],[\'returns\'],\'<DATES>\')":"DIA_returns"}, {"get_attribute([\'QQQ\'],[\'returns\'],\'<DATES>\')":"QQQ_returns"}]',
    '[{"get_dividend_history([\'<TICKER>\'],None)": "<TICKER>_dividend_history"}]',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for 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 = [
    'Market news from [DATES]',
    '[{"get_news_articles(None,None,None,\'<DATES>\')": "news_data"}, {"get_attribute([\'SPY\'],[\'returns\'],\'<DATES>\')":"SPY_returns"},  {"get_attribute([\'DIA\'],[\'returns\'],\'<DATES>\')":"DIA_returns"}, {"get_attribute([\'QQQ\'],[\'returns\'],\'<DATES>\')":"QQQ_returns"}]',
    '[{"get_dividend_history([\'<TICKER>\'],None)": "<TICKER>_dividend_history"}]',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for 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 = [
    'Market news from [DATES]',
    '[{"get_news_articles(None,None,None,\'<DATES>\')": "news_data"}, {"get_attribute([\'SPY\'],[\'returns\'],\'<DATES>\')":"SPY_returns"},  {"get_attribute([\'DIA\'],[\'returns\'],\'<DATES>\')":"DIA_returns"}, {"get_attribute([\'QQQ\'],[\'returns\'],\'<DATES>\')":"QQQ_returns"}]',
    '[{"get_dividend_history([\'<TICKER>\'],None)": "<TICKER>_dividend_history"}]',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for 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 = [
    'Market news from [DATES]',
    '[{"get_news_articles(None,None,None,\'<DATES>\')": "news_data"}, {"get_attribute([\'SPY\'],[\'returns\'],\'<DATES>\')":"SPY_returns"},  {"get_attribute([\'DIA\'],[\'returns\'],\'<DATES>\')":"DIA_returns"}, {"get_attribute([\'QQQ\'],[\'returns\'],\'<DATES>\')":"QQQ_returns"}]',
    '[{"get_dividend_history([\'<TICKER>\'],None)": "<TICKER>_dividend_history"}]',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for 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 = [
    'Market news from [DATES]',
    '[{"get_news_articles(None,None,None,\'<DATES>\')": "news_data"}, {"get_attribute([\'SPY\'],[\'returns\'],\'<DATES>\')":"SPY_returns"},  {"get_attribute([\'DIA\'],[\'returns\'],\'<DATES>\')":"DIA_returns"}, {"get_attribute([\'QQQ\'],[\'returns\'],\'<DATES>\')":"QQQ_returns"}]',
    '[{"get_dividend_history([\'<TICKER>\'],None)": "<TICKER>_dividend_history"}]',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for 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 = [
    'Market news from [DATES]',
    '[{"get_news_articles(None,None,None,\'<DATES>\')": "news_data"}, {"get_attribute([\'SPY\'],[\'returns\'],\'<DATES>\')":"SPY_returns"},  {"get_attribute([\'DIA\'],[\'returns\'],\'<DATES>\')":"DIA_returns"}, {"get_attribute([\'QQQ\'],[\'returns\'],\'<DATES>\')":"QQQ_returns"}]',
    '[{"get_dividend_history([\'<TICKER>\'],None)": "<TICKER>_dividend_history"}]',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for 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 = [
    'Market news from [DATES]',
    '[{"get_news_articles(None,None,None,\'<DATES>\')": "news_data"}, {"get_attribute([\'SPY\'],[\'returns\'],\'<DATES>\')":"SPY_returns"},  {"get_attribute([\'DIA\'],[\'returns\'],\'<DATES>\')":"DIA_returns"}, {"get_attribute([\'QQQ\'],[\'returns\'],\'<DATES>\')":"QQQ_returns"}]',
    '[{"get_dividend_history([\'<TICKER>\'],None)": "<TICKER>_dividend_history"}]',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for 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 = [
    'Market news from [DATES]',
    '[{"get_news_articles(None,None,None,\'<DATES>\')": "news_data"}, {"get_attribute([\'SPY\'],[\'returns\'],\'<DATES>\')":"SPY_returns"},  {"get_attribute([\'DIA\'],[\'returns\'],\'<DATES>\')":"DIA_returns"}, {"get_attribute([\'QQQ\'],[\'returns\'],\'<DATES>\')":"QQQ_returns"}]',
    '[{"get_dividend_history([\'<TICKER>\'],None)": "<TICKER>_dividend_history"}]',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for 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 = [
    'Market news from [DATES]',
    '[{"get_news_articles(None,None,None,\'<DATES>\')": "news_data"}, {"get_attribute([\'SPY\'],[\'returns\'],\'<DATES>\')":"SPY_returns"},  {"get_attribute([\'DIA\'],[\'returns\'],\'<DATES>\')":"DIA_returns"}, {"get_attribute([\'QQQ\'],[\'returns\'],\'<DATES>\')":"QQQ_returns"}]',
    '[{"get_dividend_history([\'<TICKER>\'],None)": "<TICKER>_dividend_history"}]',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for 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 = [
    'Market news from [DATES]',
    '[{"get_news_articles(None,None,None,\'<DATES>\')": "news_data"}, {"get_attribute([\'SPY\'],[\'returns\'],\'<DATES>\')":"SPY_returns"},  {"get_attribute([\'DIA\'],[\'returns\'],\'<DATES>\')":"DIA_returns"}, {"get_attribute([\'QQQ\'],[\'returns\'],\'<DATES>\')":"QQQ_returns"}]',
    '[{"get_dividend_history([\'<TICKER>\'],None)": "<TICKER>_dividend_history"}]',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for 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 = [
    'Market news from [DATES]',
    '[{"get_news_articles(None,None,None,\'<DATES>\')": "news_data"}, {"get_attribute([\'SPY\'],[\'returns\'],\'<DATES>\')":"SPY_returns"},  {"get_attribute([\'DIA\'],[\'returns\'],\'<DATES>\')":"DIA_returns"}, {"get_attribute([\'QQQ\'],[\'returns\'],\'<DATES>\')":"QQQ_returns"}]',
    '[{"get_dividend_history([\'<TICKER>\'],None)": "<TICKER>_dividend_history"}]',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for 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 = [
    'Market news from [DATES]',
    '[{"get_news_articles(None,None,None,\'<DATES>\')": "news_data"}, {"get_attribute([\'SPY\'],[\'returns\'],\'<DATES>\')":"SPY_returns"},  {"get_attribute([\'DIA\'],[\'returns\'],\'<DATES>\')":"DIA_returns"}, {"get_attribute([\'QQQ\'],[\'returns\'],\'<DATES>\')":"QQQ_returns"}]',
    '[{"get_dividend_history([\'<TICKER>\'],None)": "<TICKER>_dividend_history"}]',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for 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 = [
    'Market news from [DATES]',
    '[{"get_news_articles(None,None,None,\'<DATES>\')": "news_data"}, {"get_attribute([\'SPY\'],[\'returns\'],\'<DATES>\')":"SPY_returns"},  {"get_attribute([\'DIA\'],[\'returns\'],\'<DATES>\')":"DIA_returns"}, {"get_attribute([\'QQQ\'],[\'returns\'],\'<DATES>\')":"QQQ_returns"}]',
    '[{"get_dividend_history([\'<TICKER>\'],None)": "<TICKER>_dividend_history"}]',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for 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 = [
    'Market news from [DATES]',
    '[{"get_news_articles(None,None,None,\'<DATES>\')": "news_data"}, {"get_attribute([\'SPY\'],[\'returns\'],\'<DATES>\')":"SPY_returns"},  {"get_attribute([\'DIA\'],[\'returns\'],\'<DATES>\')":"DIA_returns"}, {"get_attribute([\'QQQ\'],[\'returns\'],\'<DATES>\')":"QQQ_returns"}]',
    '[{"get_dividend_history([\'<TICKER>\'],None)": "<TICKER>_dividend_history"}]',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for 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 = [
    'Market news from [DATES]',
    '[{"get_news_articles(None,None,None,\'<DATES>\')": "news_data"}, {"get_attribute([\'SPY\'],[\'returns\'],\'<DATES>\')":"SPY_returns"},  {"get_attribute([\'DIA\'],[\'returns\'],\'<DATES>\')":"DIA_returns"}, {"get_attribute([\'QQQ\'],[\'returns\'],\'<DATES>\')":"QQQ_returns"}]',
    '[{"get_dividend_history([\'<TICKER>\'],None)": "<TICKER>_dividend_history"}]',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for 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 = [
    'Market news from [DATES]',
    '[{"get_news_articles(None,None,None,\'<DATES>\')": "news_data"}, {"get_attribute([\'SPY\'],[\'returns\'],\'<DATES>\')":"SPY_returns"},  {"get_attribute([\'DIA\'],[\'returns\'],\'<DATES>\')":"DIA_returns"}, {"get_attribute([\'QQQ\'],[\'returns\'],\'<DATES>\')":"QQQ_returns"}]',
    '[{"get_dividend_history([\'<TICKER>\'],None)": "<TICKER>_dividend_history"}]',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for 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 = [
    'Market news from [DATES]',
    '[{"get_news_articles(None,None,None,\'<DATES>\')": "news_data"}, {"get_attribute([\'SPY\'],[\'returns\'],\'<DATES>\')":"SPY_returns"},  {"get_attribute([\'DIA\'],[\'returns\'],\'<DATES>\')":"DIA_returns"}, {"get_attribute([\'QQQ\'],[\'returns\'],\'<DATES>\')":"QQQ_returns"}]',
    '[{"get_dividend_history([\'<TICKER>\'],None)": "<TICKER>_dividend_history"}]',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for 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 = [
    'Market news from [DATES]',
    '[{"get_news_articles(None,None,None,\'<DATES>\')": "news_data"}, {"get_attribute([\'SPY\'],[\'returns\'],\'<DATES>\')":"SPY_returns"},  {"get_attribute([\'DIA\'],[\'returns\'],\'<DATES>\')":"DIA_returns"}, {"get_attribute([\'QQQ\'],[\'returns\'],\'<DATES>\')":"QQQ_returns"}]',
    '[{"get_dividend_history([\'<TICKER>\'],None)": "<TICKER>_dividend_history"}]',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for 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 = [
    'Market news from [DATES]',
    '[{"get_news_articles(None,None,None,\'<DATES>\')": "news_data"}, {"get_attribute([\'SPY\'],[\'returns\'],\'<DATES>\')":"SPY_returns"},  {"get_attribute([\'DIA\'],[\'returns\'],\'<DATES>\')":"DIA_returns"}, {"get_attribute([\'QQQ\'],[\'returns\'],\'<DATES>\')":"QQQ_returns"}]',
    '[{"get_dividend_history([\'<TICKER>\'],None)": "<TICKER>_dividend_history"}]',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for 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 = [
    'Market news from [DATES]',
    '[{"get_news_articles(None,None,None,\'<DATES>\')": "news_data"}, {"get_attribute([\'SPY\'],[\'returns\'],\'<DATES>\')":"SPY_returns"},  {"get_attribute([\'DIA\'],[\'returns\'],\'<DATES>\')":"DIA_returns"}, {"get_attribute([\'QQQ\'],[\'returns\'],\'<DATES>\')":"QQQ_returns"}]',
    '[{"get_dividend_history([\'<TICKER>\'],None)": "<TICKER>_dividend_history"}]',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for 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 = [
    'Market news from [DATES]',
    '[{"get_news_articles(None,None,None,\'<DATES>\')": "news_data"}, {"get_attribute([\'SPY\'],[\'returns\'],\'<DATES>\')":"SPY_returns"},  {"get_attribute([\'DIA\'],[\'returns\'],\'<DATES>\')":"DIA_returns"}, {"get_attribute([\'QQQ\'],[\'returns\'],\'<DATES>\')":"QQQ_returns"}]',
    '[{"get_dividend_history([\'<TICKER>\'],None)": "<TICKER>_dividend_history"}]',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for 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 = [
    'Market news from [DATES]',
    '[{"get_news_articles(None,None,None,\'<DATES>\')": "news_data"}, {"get_attribute([\'SPY\'],[\'returns\'],\'<DATES>\')":"SPY_returns"},  {"get_attribute([\'DIA\'],[\'returns\'],\'<DATES>\')":"DIA_returns"}, {"get_attribute([\'QQQ\'],[\'returns\'],\'<DATES>\')":"QQQ_returns"}]',
    '[{"get_dividend_history([\'<TICKER>\'],None)": "<TICKER>_dividend_history"}]',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for 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 = [
    'Market news from [DATES]',
    '[{"get_news_articles(None,None,None,\'<DATES>\')": "news_data"}, {"get_attribute([\'SPY\'],[\'returns\'],\'<DATES>\')":"SPY_returns"},  {"get_attribute([\'DIA\'],[\'returns\'],\'<DATES>\')":"DIA_returns"}, {"get_attribute([\'QQQ\'],[\'returns\'],\'<DATES>\')":"QQQ_returns"}]',
    '[{"get_dividend_history([\'<TICKER>\'],None)": "<TICKER>_dividend_history"}]',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for 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 = [
    'Market news from [DATES]',
    '[{"get_news_articles(None,None,None,\'<DATES>\')": "news_data"}, {"get_attribute([\'SPY\'],[\'returns\'],\'<DATES>\')":"SPY_returns"},  {"get_attribute([\'DIA\'],[\'returns\'],\'<DATES>\')":"DIA_returns"}, {"get_attribute([\'QQQ\'],[\'returns\'],\'<DATES>\')":"QQQ_returns"}]',
    '[{"get_dividend_history([\'<TICKER>\'],None)": "<TICKER>_dividend_history"}]',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for 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 = [
    'Market news from [DATES]',
    '[{"get_news_articles(None,None,None,\'<DATES>\')": "news_data"}, {"get_attribute([\'SPY\'],[\'returns\'],\'<DATES>\')":"SPY_returns"},  {"get_attribute([\'DIA\'],[\'returns\'],\'<DATES>\')":"DIA_returns"}, {"get_attribute([\'QQQ\'],[\'returns\'],\'<DATES>\')":"QQQ_returns"}]',
    '[{"get_dividend_history([\'<TICKER>\'],None)": "<TICKER>_dividend_history"}]',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for 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 = [
    'Market news from [DATES]',
    '[{"get_news_articles(None,None,None,\'<DATES>\')": "news_data"}, {"get_attribute([\'SPY\'],[\'returns\'],\'<DATES>\')":"SPY_returns"},  {"get_attribute([\'DIA\'],[\'returns\'],\'<DATES>\')":"DIA_returns"}, {"get_attribute([\'QQQ\'],[\'returns\'],\'<DATES>\')":"QQQ_returns"}]',
    '[{"get_dividend_history([\'<TICKER>\'],None)": "<TICKER>_dividend_history"}]',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for 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 = [
    'Market news from [DATES]',
    '[{"get_news_articles(None,None,None,\'<DATES>\')": "news_data"}, {"get_attribute([\'SPY\'],[\'returns\'],\'<DATES>\')":"SPY_returns"},  {"get_attribute([\'DIA\'],[\'returns\'],\'<DATES>\')":"DIA_returns"}, {"get_attribute([\'QQQ\'],[\'returns\'],\'<DATES>\')":"QQQ_returns"}]',
    '[{"get_dividend_history([\'<TICKER>\'],None)": "<TICKER>_dividend_history"}]',
]
embeddings = model.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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