bge-small-en-v1-5-ft-orc-093-hce

1
—
by
magnifi
Embedding Model
OTHER
1705.00652B params
New
1 downloads
Early-stage
Try on Hugging FaceAdd 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
Usagebash
pip install -U sentence-transformers
Usagebash
pip install -U sentence-transformers
Usagebash
pip install -U sentence-transformers
Usagebash
pip install -U sentence-transformers
Usagebash
pip install -U sentence-transformers
Usagebash
pip install -U sentence-transformers
Usagebash
pip install -U sentence-transformers
Usagebash
pip install -U sentence-transformers
Usagebash
pip install -U sentence-transformers
Usagebash
pip install -U sentence-transformers
Usagebash
pip install -U sentence-transformers
Usagebash
pip install -U sentence-transformers
Usagebash
pip install -U sentence-transformers
Usagebash
pip install -U sentence-transformers
Usagebash
pip install -U sentence-transformers
Usagebash
pip install -U sentence-transformers
Usagebash
pip install -U sentence-transformers
Usagebash
pip install -U sentence-transformers
Usagebash
pip install -U sentence-transformers
Usagebash
pip install -U sentence-transformers
Usagebash
pip install -U sentence-transformers
Usagebash
pip install -U sentence-transformers
Usagebash
pip install -U sentence-transformers
Usagebash
pip install -U sentence-transformers
Usagebash
pip install -U sentence-transformers
Usagebash
pip install -U sentence-transformers
Usagebash
pip install -U sentence-transformers
Usagebash
pip install -U sentence-transformers
Usagebash
pip install -U sentence-transformers
Usagebash
pip install -U sentence-transformers
Usagebash
pip install -U sentence-transformers
Usagebash
pip install -U sentence-transformers
Usagebash
pip install -U sentence-transformers
Usagebash
pip install -U sentence-transformers
Usagebash
pip install -U sentence-transformers
Usagebash
pip install -U sentence-transformers
Usagebash
pip install -U sentence-transformers
Usagebash
pip install -U sentence-transformers
Usagebash
pip install -U sentence-transformers
Usagebash
pip install -U sentence-transformers
Usagebash
pip install -U sentence-transformers
Usagebash
pip install -U sentence-transformers
Usagebash
pip install -U sentence-transformers
Usagebash
pip install -U sentence-transformers
Usagebash
pip install -U sentence-transformers
Usagebash
pip install -U sentence-transformers
Usagebash
pip install -U sentence-transformers
Usagebash
pip install -U sentence-transformers
Usagebash
pip install -U sentence-transformers
Usagebash
pip install -U sentence-transformers
Usagebash
pip install -U sentence-transformers
Usagebash
pip install -U sentence-transformers
Usagebash
pip install -U sentence-transformers
Usagebash
pip install -U sentence-transformers
Usagebash
pip install -U sentence-transformers
Usagepython
from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
    'Give me a quick summary of [NEWSLETTER_NAME_1]',
    '[{"newsletter_search([\'<NEWSLETTER_NAME_1>\'],None,None,\'<DATES>\',True)": "newsletter_chunks"}]',
    '[{"get_portfolio([\'type\', \'marketValue\'],True,<PORTFOLIO_NAME_1>)": "portfolio"}, {"filter(\'portfolio\',\'type\',\'==\',\'CASH\')": "portfolio"}, {"aggregate(\'portfolio\',\'ticker\',\'marketValue\',\'sum\',None)": "buying_power"}]',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

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

# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
    'Give me a quick summary of [NEWSLETTER_NAME_1]',
    '[{"newsletter_search([\'<NEWSLETTER_NAME_1>\'],None,None,\'<DATES>\',True)": "newsletter_chunks"}]',
    '[{"get_portfolio([\'type\', \'marketValue\'],True,<PORTFOLIO_NAME_1>)": "portfolio"}, {"filter(\'portfolio\',\'type\',\'==\',\'CASH\')": "portfolio"}, {"aggregate(\'portfolio\',\'ticker\',\'marketValue\',\'sum\',None)": "buying_power"}]',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

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

# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
    'Give me a quick summary of [NEWSLETTER_NAME_1]',
    '[{"newsletter_search([\'<NEWSLETTER_NAME_1>\'],None,None,\'<DATES>\',True)": "newsletter_chunks"}]',
    '[{"get_portfolio([\'type\', \'marketValue\'],True,<PORTFOLIO_NAME_1>)": "portfolio"}, {"filter(\'portfolio\',\'type\',\'==\',\'CASH\')": "portfolio"}, {"aggregate(\'portfolio\',\'ticker\',\'marketValue\',\'sum\',None)": "buying_power"}]',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

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

# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
    'Give me a quick summary of [NEWSLETTER_NAME_1]',
    '[{"newsletter_search([\'<NEWSLETTER_NAME_1>\'],None,None,\'<DATES>\',True)": "newsletter_chunks"}]',
    '[{"get_portfolio([\'type\', \'marketValue\'],True,<PORTFOLIO_NAME_1>)": "portfolio"}, {"filter(\'portfolio\',\'type\',\'==\',\'CASH\')": "portfolio"}, {"aggregate(\'portfolio\',\'ticker\',\'marketValue\',\'sum\',None)": "buying_power"}]',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

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

# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
    'Give me a quick summary of [NEWSLETTER_NAME_1]',
    '[{"newsletter_search([\'<NEWSLETTER_NAME_1>\'],None,None,\'<DATES>\',True)": "newsletter_chunks"}]',
    '[{"get_portfolio([\'type\', \'marketValue\'],True,<PORTFOLIO_NAME_1>)": "portfolio"}, {"filter(\'portfolio\',\'type\',\'==\',\'CASH\')": "portfolio"}, {"aggregate(\'portfolio\',\'ticker\',\'marketValue\',\'sum\',None)": "buying_power"}]',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

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

# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
    'Give me a quick summary of [NEWSLETTER_NAME_1]',
    '[{"newsletter_search([\'<NEWSLETTER_NAME_1>\'],None,None,\'<DATES>\',True)": "newsletter_chunks"}]',
    '[{"get_portfolio([\'type\', \'marketValue\'],True,<PORTFOLIO_NAME_1>)": "portfolio"}, {"filter(\'portfolio\',\'type\',\'==\',\'CASH\')": "portfolio"}, {"aggregate(\'portfolio\',\'ticker\',\'marketValue\',\'sum\',None)": "buying_power"}]',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

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

# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
    'Give me a quick summary of [NEWSLETTER_NAME_1]',
    '[{"newsletter_search([\'<NEWSLETTER_NAME_1>\'],None,None,\'<DATES>\',True)": "newsletter_chunks"}]',
    '[{"get_portfolio([\'type\', \'marketValue\'],True,<PORTFOLIO_NAME_1>)": "portfolio"}, {"filter(\'portfolio\',\'type\',\'==\',\'CASH\')": "portfolio"}, {"aggregate(\'portfolio\',\'ticker\',\'marketValue\',\'sum\',None)": "buying_power"}]',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

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

# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
    'Give me a quick summary of [NEWSLETTER_NAME_1]',
    '[{"newsletter_search([\'<NEWSLETTER_NAME_1>\'],None,None,\'<DATES>\',True)": "newsletter_chunks"}]',
    '[{"get_portfolio([\'type\', \'marketValue\'],True,<PORTFOLIO_NAME_1>)": "portfolio"}, {"filter(\'portfolio\',\'type\',\'==\',\'CASH\')": "portfolio"}, {"aggregate(\'portfolio\',\'ticker\',\'marketValue\',\'sum\',None)": "buying_power"}]',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

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

# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
    'Give me a quick summary of [NEWSLETTER_NAME_1]',
    '[{"newsletter_search([\'<NEWSLETTER_NAME_1>\'],None,None,\'<DATES>\',True)": "newsletter_chunks"}]',
    '[{"get_portfolio([\'type\', \'marketValue\'],True,<PORTFOLIO_NAME_1>)": "portfolio"}, {"filter(\'portfolio\',\'type\',\'==\',\'CASH\')": "portfolio"}, {"aggregate(\'portfolio\',\'ticker\',\'marketValue\',\'sum\',None)": "buying_power"}]',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

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

# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
    'Give me a quick summary of [NEWSLETTER_NAME_1]',
    '[{"newsletter_search([\'<NEWSLETTER_NAME_1>\'],None,None,\'<DATES>\',True)": "newsletter_chunks"}]',
    '[{"get_portfolio([\'type\', \'marketValue\'],True,<PORTFOLIO_NAME_1>)": "portfolio"}, {"filter(\'portfolio\',\'type\',\'==\',\'CASH\')": "portfolio"}, {"aggregate(\'portfolio\',\'ticker\',\'marketValue\',\'sum\',None)": "buying_power"}]',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

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

# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
    'Give me a quick summary of [NEWSLETTER_NAME_1]',
    '[{"newsletter_search([\'<NEWSLETTER_NAME_1>\'],None,None,\'<DATES>\',True)": "newsletter_chunks"}]',
    '[{"get_portfolio([\'type\', \'marketValue\'],True,<PORTFOLIO_NAME_1>)": "portfolio"}, {"filter(\'portfolio\',\'type\',\'==\',\'CASH\')": "portfolio"}, {"aggregate(\'portfolio\',\'ticker\',\'marketValue\',\'sum\',None)": "buying_power"}]',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

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

# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
    'Give me a quick summary of [NEWSLETTER_NAME_1]',
    '[{"newsletter_search([\'<NEWSLETTER_NAME_1>\'],None,None,\'<DATES>\',True)": "newsletter_chunks"}]',
    '[{"get_portfolio([\'type\', \'marketValue\'],True,<PORTFOLIO_NAME_1>)": "portfolio"}, {"filter(\'portfolio\',\'type\',\'==\',\'CASH\')": "portfolio"}, {"aggregate(\'portfolio\',\'ticker\',\'marketValue\',\'sum\',None)": "buying_power"}]',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

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

# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
    'Give me a quick summary of [NEWSLETTER_NAME_1]',
    '[{"newsletter_search([\'<NEWSLETTER_NAME_1>\'],None,None,\'<DATES>\',True)": "newsletter_chunks"}]',
    '[{"get_portfolio([\'type\', \'marketValue\'],True,<PORTFOLIO_NAME_1>)": "portfolio"}, {"filter(\'portfolio\',\'type\',\'==\',\'CASH\')": "portfolio"}, {"aggregate(\'portfolio\',\'ticker\',\'marketValue\',\'sum\',None)": "buying_power"}]',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

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

# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
    'Give me a quick summary of [NEWSLETTER_NAME_1]',
    '[{"newsletter_search([\'<NEWSLETTER_NAME_1>\'],None,None,\'<DATES>\',True)": "newsletter_chunks"}]',
    '[{"get_portfolio([\'type\', \'marketValue\'],True,<PORTFOLIO_NAME_1>)": "portfolio"}, {"filter(\'portfolio\',\'type\',\'==\',\'CASH\')": "portfolio"}, {"aggregate(\'portfolio\',\'ticker\',\'marketValue\',\'sum\',None)": "buying_power"}]',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

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

# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
    'Give me a quick summary of [NEWSLETTER_NAME_1]',
    '[{"newsletter_search([\'<NEWSLETTER_NAME_1>\'],None,None,\'<DATES>\',True)": "newsletter_chunks"}]',
    '[{"get_portfolio([\'type\', \'marketValue\'],True,<PORTFOLIO_NAME_1>)": "portfolio"}, {"filter(\'portfolio\',\'type\',\'==\',\'CASH\')": "portfolio"}, {"aggregate(\'portfolio\',\'ticker\',\'marketValue\',\'sum\',None)": "buying_power"}]',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

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

# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
    'Give me a quick summary of [NEWSLETTER_NAME_1]',
    '[{"newsletter_search([\'<NEWSLETTER_NAME_1>\'],None,None,\'<DATES>\',True)": "newsletter_chunks"}]',
    '[{"get_portfolio([\'type\', \'marketValue\'],True,<PORTFOLIO_NAME_1>)": "portfolio"}, {"filter(\'portfolio\',\'type\',\'==\',\'CASH\')": "portfolio"}, {"aggregate(\'portfolio\',\'ticker\',\'marketValue\',\'sum\',None)": "buying_power"}]',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

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

# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
    'Give me a quick summary of [NEWSLETTER_NAME_1]',
    '[{"newsletter_search([\'<NEWSLETTER_NAME_1>\'],None,None,\'<DATES>\',True)": "newsletter_chunks"}]',
    '[{"get_portfolio([\'type\', \'marketValue\'],True,<PORTFOLIO_NAME_1>)": "portfolio"}, {"filter(\'portfolio\',\'type\',\'==\',\'CASH\')": "portfolio"}, {"aggregate(\'portfolio\',\'ticker\',\'marketValue\',\'sum\',None)": "buying_power"}]',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

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

# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
    'Give me a quick summary of [NEWSLETTER_NAME_1]',
    '[{"newsletter_search([\'<NEWSLETTER_NAME_1>\'],None,None,\'<DATES>\',True)": "newsletter_chunks"}]',
    '[{"get_portfolio([\'type\', \'marketValue\'],True,<PORTFOLIO_NAME_1>)": "portfolio"}, {"filter(\'portfolio\',\'type\',\'==\',\'CASH\')": "portfolio"}, {"aggregate(\'portfolio\',\'ticker\',\'marketValue\',\'sum\',None)": "buying_power"}]',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

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

# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
    'Give me a quick summary of [NEWSLETTER_NAME_1]',
    '[{"newsletter_search([\'<NEWSLETTER_NAME_1>\'],None,None,\'<DATES>\',True)": "newsletter_chunks"}]',
    '[{"get_portfolio([\'type\', \'marketValue\'],True,<PORTFOLIO_NAME_1>)": "portfolio"}, {"filter(\'portfolio\',\'type\',\'==\',\'CASH\')": "portfolio"}, {"aggregate(\'portfolio\',\'ticker\',\'marketValue\',\'sum\',None)": "buying_power"}]',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

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

# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
    'Give me a quick summary of [NEWSLETTER_NAME_1]',
    '[{"newsletter_search([\'<NEWSLETTER_NAME_1>\'],None,None,\'<DATES>\',True)": "newsletter_chunks"}]',
    '[{"get_portfolio([\'type\', \'marketValue\'],True,<PORTFOLIO_NAME_1>)": "portfolio"}, {"filter(\'portfolio\',\'type\',\'==\',\'CASH\')": "portfolio"}, {"aggregate(\'portfolio\',\'ticker\',\'marketValue\',\'sum\',None)": "buying_power"}]',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

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

# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
    'Give me a quick summary of [NEWSLETTER_NAME_1]',
    '[{"newsletter_search([\'<NEWSLETTER_NAME_1>\'],None,None,\'<DATES>\',True)": "newsletter_chunks"}]',
    '[{"get_portfolio([\'type\', \'marketValue\'],True,<PORTFOLIO_NAME_1>)": "portfolio"}, {"filter(\'portfolio\',\'type\',\'==\',\'CASH\')": "portfolio"}, {"aggregate(\'portfolio\',\'ticker\',\'marketValue\',\'sum\',None)": "buying_power"}]',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

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

# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
    'Give me a quick summary of [NEWSLETTER_NAME_1]',
    '[{"newsletter_search([\'<NEWSLETTER_NAME_1>\'],None,None,\'<DATES>\',True)": "newsletter_chunks"}]',
    '[{"get_portfolio([\'type\', \'marketValue\'],True,<PORTFOLIO_NAME_1>)": "portfolio"}, {"filter(\'portfolio\',\'type\',\'==\',\'CASH\')": "portfolio"}, {"aggregate(\'portfolio\',\'ticker\',\'marketValue\',\'sum\',None)": "buying_power"}]',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

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

# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
    'Give me a quick summary of [NEWSLETTER_NAME_1]',
    '[{"newsletter_search([\'<NEWSLETTER_NAME_1>\'],None,None,\'<DATES>\',True)": "newsletter_chunks"}]',
    '[{"get_portfolio([\'type\', \'marketValue\'],True,<PORTFOLIO_NAME_1>)": "portfolio"}, {"filter(\'portfolio\',\'type\',\'==\',\'CASH\')": "portfolio"}, {"aggregate(\'portfolio\',\'ticker\',\'marketValue\',\'sum\',None)": "buying_power"}]',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

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

# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
    'Give me a quick summary of [NEWSLETTER_NAME_1]',
    '[{"newsletter_search([\'<NEWSLETTER_NAME_1>\'],None,None,\'<DATES>\',True)": "newsletter_chunks"}]',
    '[{"get_portfolio([\'type\', \'marketValue\'],True,<PORTFOLIO_NAME_1>)": "portfolio"}, {"filter(\'portfolio\',\'type\',\'==\',\'CASH\')": "portfolio"}, {"aggregate(\'portfolio\',\'ticker\',\'marketValue\',\'sum\',None)": "buying_power"}]',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

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

# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
    'Give me a quick summary of [NEWSLETTER_NAME_1]',
    '[{"newsletter_search([\'<NEWSLETTER_NAME_1>\'],None,None,\'<DATES>\',True)": "newsletter_chunks"}]',
    '[{"get_portfolio([\'type\', \'marketValue\'],True,<PORTFOLIO_NAME_1>)": "portfolio"}, {"filter(\'portfolio\',\'type\',\'==\',\'CASH\')": "portfolio"}, {"aggregate(\'portfolio\',\'ticker\',\'marketValue\',\'sum\',None)": "buying_power"}]',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

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

# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
    'Give me a quick summary of [NEWSLETTER_NAME_1]',
    '[{"newsletter_search([\'<NEWSLETTER_NAME_1>\'],None,None,\'<DATES>\',True)": "newsletter_chunks"}]',
    '[{"get_portfolio([\'type\', \'marketValue\'],True,<PORTFOLIO_NAME_1>)": "portfolio"}, {"filter(\'portfolio\',\'type\',\'==\',\'CASH\')": "portfolio"}, {"aggregate(\'portfolio\',\'ticker\',\'marketValue\',\'sum\',None)": "buying_power"}]',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

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

# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
    'Give me a quick summary of [NEWSLETTER_NAME_1]',
    '[{"newsletter_search([\'<NEWSLETTER_NAME_1>\'],None,None,\'<DATES>\',True)": "newsletter_chunks"}]',
    '[{"get_portfolio([\'type\', \'marketValue\'],True,<PORTFOLIO_NAME_1>)": "portfolio"}, {"filter(\'portfolio\',\'type\',\'==\',\'CASH\')": "portfolio"}, {"aggregate(\'portfolio\',\'ticker\',\'marketValue\',\'sum\',None)": "buying_power"}]',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

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

# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
    'Give me a quick summary of [NEWSLETTER_NAME_1]',
    '[{"newsletter_search([\'<NEWSLETTER_NAME_1>\'],None,None,\'<DATES>\',True)": "newsletter_chunks"}]',
    '[{"get_portfolio([\'type\', \'marketValue\'],True,<PORTFOLIO_NAME_1>)": "portfolio"}, {"filter(\'portfolio\',\'type\',\'==\',\'CASH\')": "portfolio"}, {"aggregate(\'portfolio\',\'ticker\',\'marketValue\',\'sum\',None)": "buying_power"}]',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

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

# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
    'Give me a quick summary of [NEWSLETTER_NAME_1]',
    '[{"newsletter_search([\'<NEWSLETTER_NAME_1>\'],None,None,\'<DATES>\',True)": "newsletter_chunks"}]',
    '[{"get_portfolio([\'type\', \'marketValue\'],True,<PORTFOLIO_NAME_1>)": "portfolio"}, {"filter(\'portfolio\',\'type\',\'==\',\'CASH\')": "portfolio"}, {"aggregate(\'portfolio\',\'ticker\',\'marketValue\',\'sum\',None)": "buying_power"}]',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

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

# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
    'Give me a quick summary of [NEWSLETTER_NAME_1]',
    '[{"newsletter_search([\'<NEWSLETTER_NAME_1>\'],None,None,\'<DATES>\',True)": "newsletter_chunks"}]',
    '[{"get_portfolio([\'type\', \'marketValue\'],True,<PORTFOLIO_NAME_1>)": "portfolio"}, {"filter(\'portfolio\',\'type\',\'==\',\'CASH\')": "portfolio"}, {"aggregate(\'portfolio\',\'ticker\',\'marketValue\',\'sum\',None)": "buying_power"}]',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

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

# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
    'Give me a quick summary of [NEWSLETTER_NAME_1]',
    '[{"newsletter_search([\'<NEWSLETTER_NAME_1>\'],None,None,\'<DATES>\',True)": "newsletter_chunks"}]',
    '[{"get_portfolio([\'type\', \'marketValue\'],True,<PORTFOLIO_NAME_1>)": "portfolio"}, {"filter(\'portfolio\',\'type\',\'==\',\'CASH\')": "portfolio"}, {"aggregate(\'portfolio\',\'ticker\',\'marketValue\',\'sum\',None)": "buying_power"}]',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

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

# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
    'Give me a quick summary of [NEWSLETTER_NAME_1]',
    '[{"newsletter_search([\'<NEWSLETTER_NAME_1>\'],None,None,\'<DATES>\',True)": "newsletter_chunks"}]',
    '[{"get_portfolio([\'type\', \'marketValue\'],True,<PORTFOLIO_NAME_1>)": "portfolio"}, {"filter(\'portfolio\',\'type\',\'==\',\'CASH\')": "portfolio"}, {"aggregate(\'portfolio\',\'ticker\',\'marketValue\',\'sum\',None)": "buying_power"}]',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

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

# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
    'Give me a quick summary of [NEWSLETTER_NAME_1]',
    '[{"newsletter_search([\'<NEWSLETTER_NAME_1>\'],None,None,\'<DATES>\',True)": "newsletter_chunks"}]',
    '[{"get_portfolio([\'type\', \'marketValue\'],True,<PORTFOLIO_NAME_1>)": "portfolio"}, {"filter(\'portfolio\',\'type\',\'==\',\'CASH\')": "portfolio"}, {"aggregate(\'portfolio\',\'ticker\',\'marketValue\',\'sum\',None)": "buying_power"}]',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

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

# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
    'Give me a quick summary of [NEWSLETTER_NAME_1]',
    '[{"newsletter_search([\'<NEWSLETTER_NAME_1>\'],None,None,\'<DATES>\',True)": "newsletter_chunks"}]',
    '[{"get_portfolio([\'type\', \'marketValue\'],True,<PORTFOLIO_NAME_1>)": "portfolio"}, {"filter(\'portfolio\',\'type\',\'==\',\'CASH\')": "portfolio"}, {"aggregate(\'portfolio\',\'ticker\',\'marketValue\',\'sum\',None)": "buying_power"}]',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

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

# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
    'Give me a quick summary of [NEWSLETTER_NAME_1]',
    '[{"newsletter_search([\'<NEWSLETTER_NAME_1>\'],None,None,\'<DATES>\',True)": "newsletter_chunks"}]',
    '[{"get_portfolio([\'type\', \'marketValue\'],True,<PORTFOLIO_NAME_1>)": "portfolio"}, {"filter(\'portfolio\',\'type\',\'==\',\'CASH\')": "portfolio"}, {"aggregate(\'portfolio\',\'ticker\',\'marketValue\',\'sum\',None)": "buying_power"}]',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

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

# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
    'Give me a quick summary of [NEWSLETTER_NAME_1]',
    '[{"newsletter_search([\'<NEWSLETTER_NAME_1>\'],None,None,\'<DATES>\',True)": "newsletter_chunks"}]',
    '[{"get_portfolio([\'type\', \'marketValue\'],True,<PORTFOLIO_NAME_1>)": "portfolio"}, {"filter(\'portfolio\',\'type\',\'==\',\'CASH\')": "portfolio"}, {"aggregate(\'portfolio\',\'ticker\',\'marketValue\',\'sum\',None)": "buying_power"}]',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

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

# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
    'Give me a quick summary of [NEWSLETTER_NAME_1]',
    '[{"newsletter_search([\'<NEWSLETTER_NAME_1>\'],None,None,\'<DATES>\',True)": "newsletter_chunks"}]',
    '[{"get_portfolio([\'type\', \'marketValue\'],True,<PORTFOLIO_NAME_1>)": "portfolio"}, {"filter(\'portfolio\',\'type\',\'==\',\'CASH\')": "portfolio"}, {"aggregate(\'portfolio\',\'ticker\',\'marketValue\',\'sum\',None)": "buying_power"}]',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

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

# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
    'Give me a quick summary of [NEWSLETTER_NAME_1]',
    '[{"newsletter_search([\'<NEWSLETTER_NAME_1>\'],None,None,\'<DATES>\',True)": "newsletter_chunks"}]',
    '[{"get_portfolio([\'type\', \'marketValue\'],True,<PORTFOLIO_NAME_1>)": "portfolio"}, {"filter(\'portfolio\',\'type\',\'==\',\'CASH\')": "portfolio"}, {"aggregate(\'portfolio\',\'ticker\',\'marketValue\',\'sum\',None)": "buying_power"}]',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

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

# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
    'Give me a quick summary of [NEWSLETTER_NAME_1]',
    '[{"newsletter_search([\'<NEWSLETTER_NAME_1>\'],None,None,\'<DATES>\',True)": "newsletter_chunks"}]',
    '[{"get_portfolio([\'type\', \'marketValue\'],True,<PORTFOLIO_NAME_1>)": "portfolio"}, {"filter(\'portfolio\',\'type\',\'==\',\'CASH\')": "portfolio"}, {"aggregate(\'portfolio\',\'ticker\',\'marketValue\',\'sum\',None)": "buying_power"}]',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

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

# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
    'Give me a quick summary of [NEWSLETTER_NAME_1]',
    '[{"newsletter_search([\'<NEWSLETTER_NAME_1>\'],None,None,\'<DATES>\',True)": "newsletter_chunks"}]',
    '[{"get_portfolio([\'type\', \'marketValue\'],True,<PORTFOLIO_NAME_1>)": "portfolio"}, {"filter(\'portfolio\',\'type\',\'==\',\'CASH\')": "portfolio"}, {"aggregate(\'portfolio\',\'ticker\',\'marketValue\',\'sum\',None)": "buying_power"}]',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

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

# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
    'Give me a quick summary of [NEWSLETTER_NAME_1]',
    '[{"newsletter_search([\'<NEWSLETTER_NAME_1>\'],None,None,\'<DATES>\',True)": "newsletter_chunks"}]',
    '[{"get_portfolio([\'type\', \'marketValue\'],True,<PORTFOLIO_NAME_1>)": "portfolio"}, {"filter(\'portfolio\',\'type\',\'==\',\'CASH\')": "portfolio"}, {"aggregate(\'portfolio\',\'ticker\',\'marketValue\',\'sum\',None)": "buying_power"}]',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

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

# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
    'Give me a quick summary of [NEWSLETTER_NAME_1]',
    '[{"newsletter_search([\'<NEWSLETTER_NAME_1>\'],None,None,\'<DATES>\',True)": "newsletter_chunks"}]',
    '[{"get_portfolio([\'type\', \'marketValue\'],True,<PORTFOLIO_NAME_1>)": "portfolio"}, {"filter(\'portfolio\',\'type\',\'==\',\'CASH\')": "portfolio"}, {"aggregate(\'portfolio\',\'ticker\',\'marketValue\',\'sum\',None)": "buying_power"}]',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

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

# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
    'Give me a quick summary of [NEWSLETTER_NAME_1]',
    '[{"newsletter_search([\'<NEWSLETTER_NAME_1>\'],None,None,\'<DATES>\',True)": "newsletter_chunks"}]',
    '[{"get_portfolio([\'type\', \'marketValue\'],True,<PORTFOLIO_NAME_1>)": "portfolio"}, {"filter(\'portfolio\',\'type\',\'==\',\'CASH\')": "portfolio"}, {"aggregate(\'portfolio\',\'ticker\',\'marketValue\',\'sum\',None)": "buying_power"}]',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

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

# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
    'Give me a quick summary of [NEWSLETTER_NAME_1]',
    '[{"newsletter_search([\'<NEWSLETTER_NAME_1>\'],None,None,\'<DATES>\',True)": "newsletter_chunks"}]',
    '[{"get_portfolio([\'type\', \'marketValue\'],True,<PORTFOLIO_NAME_1>)": "portfolio"}, {"filter(\'portfolio\',\'type\',\'==\',\'CASH\')": "portfolio"}, {"aggregate(\'portfolio\',\'ticker\',\'marketValue\',\'sum\',None)": "buying_power"}]',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

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

# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
    'Give me a quick summary of [NEWSLETTER_NAME_1]',
    '[{"newsletter_search([\'<NEWSLETTER_NAME_1>\'],None,None,\'<DATES>\',True)": "newsletter_chunks"}]',
    '[{"get_portfolio([\'type\', \'marketValue\'],True,<PORTFOLIO_NAME_1>)": "portfolio"}, {"filter(\'portfolio\',\'type\',\'==\',\'CASH\')": "portfolio"}, {"aggregate(\'portfolio\',\'ticker\',\'marketValue\',\'sum\',None)": "buying_power"}]',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

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

# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
    'Give me a quick summary of [NEWSLETTER_NAME_1]',
    '[{"newsletter_search([\'<NEWSLETTER_NAME_1>\'],None,None,\'<DATES>\',True)": "newsletter_chunks"}]',
    '[{"get_portfolio([\'type\', \'marketValue\'],True,<PORTFOLIO_NAME_1>)": "portfolio"}, {"filter(\'portfolio\',\'type\',\'==\',\'CASH\')": "portfolio"}, {"aggregate(\'portfolio\',\'ticker\',\'marketValue\',\'sum\',None)": "buying_power"}]',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

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

# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
    'Give me a quick summary of [NEWSLETTER_NAME_1]',
    '[{"newsletter_search([\'<NEWSLETTER_NAME_1>\'],None,None,\'<DATES>\',True)": "newsletter_chunks"}]',
    '[{"get_portfolio([\'type\', \'marketValue\'],True,<PORTFOLIO_NAME_1>)": "portfolio"}, {"filter(\'portfolio\',\'type\',\'==\',\'CASH\')": "portfolio"}, {"aggregate(\'portfolio\',\'ticker\',\'marketValue\',\'sum\',None)": "buying_power"}]',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

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

# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
    'Give me a quick summary of [NEWSLETTER_NAME_1]',
    '[{"newsletter_search([\'<NEWSLETTER_NAME_1>\'],None,None,\'<DATES>\',True)": "newsletter_chunks"}]',
    '[{"get_portfolio([\'type\', \'marketValue\'],True,<PORTFOLIO_NAME_1>)": "portfolio"}, {"filter(\'portfolio\',\'type\',\'==\',\'CASH\')": "portfolio"}, {"aggregate(\'portfolio\',\'ticker\',\'marketValue\',\'sum\',None)": "buying_power"}]',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

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

# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
    'Give me a quick summary of [NEWSLETTER_NAME_1]',
    '[{"newsletter_search([\'<NEWSLETTER_NAME_1>\'],None,None,\'<DATES>\',True)": "newsletter_chunks"}]',
    '[{"get_portfolio([\'type\', \'marketValue\'],True,<PORTFOLIO_NAME_1>)": "portfolio"}, {"filter(\'portfolio\',\'type\',\'==\',\'CASH\')": "portfolio"}, {"aggregate(\'portfolio\',\'ticker\',\'marketValue\',\'sum\',None)": "buying_power"}]',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

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

# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
    'Give me a quick summary of [NEWSLETTER_NAME_1]',
    '[{"newsletter_search([\'<NEWSLETTER_NAME_1>\'],None,None,\'<DATES>\',True)": "newsletter_chunks"}]',
    '[{"get_portfolio([\'type\', \'marketValue\'],True,<PORTFOLIO_NAME_1>)": "portfolio"}, {"filter(\'portfolio\',\'type\',\'==\',\'CASH\')": "portfolio"}, {"aggregate(\'portfolio\',\'ticker\',\'marketValue\',\'sum\',None)": "buying_power"}]',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

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

# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
    'Give me a quick summary of [NEWSLETTER_NAME_1]',
    '[{"newsletter_search([\'<NEWSLETTER_NAME_1>\'],None,None,\'<DATES>\',True)": "newsletter_chunks"}]',
    '[{"get_portfolio([\'type\', \'marketValue\'],True,<PORTFOLIO_NAME_1>)": "portfolio"}, {"filter(\'portfolio\',\'type\',\'==\',\'CASH\')": "portfolio"}, {"aggregate(\'portfolio\',\'ticker\',\'marketValue\',\'sum\',None)": "buying_power"}]',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

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

# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
    'Give me a quick summary of [NEWSLETTER_NAME_1]',
    '[{"newsletter_search([\'<NEWSLETTER_NAME_1>\'],None,None,\'<DATES>\',True)": "newsletter_chunks"}]',
    '[{"get_portfolio([\'type\', \'marketValue\'],True,<PORTFOLIO_NAME_1>)": "portfolio"}, {"filter(\'portfolio\',\'type\',\'==\',\'CASH\')": "portfolio"}, {"aggregate(\'portfolio\',\'ticker\',\'marketValue\',\'sum\',None)": "buying_power"}]',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

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

# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
    'Give me a quick summary of [NEWSLETTER_NAME_1]',
    '[{"newsletter_search([\'<NEWSLETTER_NAME_1>\'],None,None,\'<DATES>\',True)": "newsletter_chunks"}]',
    '[{"get_portfolio([\'type\', \'marketValue\'],True,<PORTFOLIO_NAME_1>)": "portfolio"}, {"filter(\'portfolio\',\'type\',\'==\',\'CASH\')": "portfolio"}, {"aggregate(\'portfolio\',\'ticker\',\'marketValue\',\'sum\',None)": "buying_power"}]',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

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

# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
    'Give me a quick summary of [NEWSLETTER_NAME_1]',
    '[{"newsletter_search([\'<NEWSLETTER_NAME_1>\'],None,None,\'<DATES>\',True)": "newsletter_chunks"}]',
    '[{"get_portfolio([\'type\', \'marketValue\'],True,<PORTFOLIO_NAME_1>)": "portfolio"}, {"filter(\'portfolio\',\'type\',\'==\',\'CASH\')": "portfolio"}, {"aggregate(\'portfolio\',\'ticker\',\'marketValue\',\'sum\',None)": "buying_power"}]',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

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

# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
    'Give me a quick summary of [NEWSLETTER_NAME_1]',
    '[{"newsletter_search([\'<NEWSLETTER_NAME_1>\'],None,None,\'<DATES>\',True)": "newsletter_chunks"}]',
    '[{"get_portfolio([\'type\', \'marketValue\'],True,<PORTFOLIO_NAME_1>)": "portfolio"}, {"filter(\'portfolio\',\'type\',\'==\',\'CASH\')": "portfolio"}, {"aggregate(\'portfolio\',\'ticker\',\'marketValue\',\'sum\',None)": "buying_power"}]',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

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

# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
    'Give me a quick summary of [NEWSLETTER_NAME_1]',
    '[{"newsletter_search([\'<NEWSLETTER_NAME_1>\'],None,None,\'<DATES>\',True)": "newsletter_chunks"}]',
    '[{"get_portfolio([\'type\', \'marketValue\'],True,<PORTFOLIO_NAME_1>)": "portfolio"}, {"filter(\'portfolio\',\'type\',\'==\',\'CASH\')": "portfolio"}, {"aggregate(\'portfolio\',\'ticker\',\'marketValue\',\'sum\',None)": "buying_power"}]',
]
embeddings = model.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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