Sentence Similarity
sentence-transformers
Safetensors
bert
feature-extraction
Generated from Trainer
dataset_size:36223
loss:MultipleNegativesRankingLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use baconnier/Finance_embedding_large_en-V1.5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use baconnier/Finance_embedding_large_en-V1.5 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("baconnier/Finance_embedding_large_en-V1.5") sentences = [ "How many Rights will XYZ Bank issue for each outstanding share of Acme Inc. stock in the event of a takeover attempt?", "Sarah took out a 30-year mortgage and has been paying for 3 years, so she has 27 years left to pay if she continues making regular payments. The remaining principal balance after 3 years is the original $300,000 minus the principal portion of the 36 payments made. If Sarah continues making payments, the remaining principal balance will decrease with each payment until it reaches $0 at the end of the 30-year term.\nSarah has 27 years left on her mortgage if she continues making regular payments. The remaining principal balance will steadily decrease with each payment and will be $0 when the mortgage is fully paid off.", "The passage does not provide information about the premium John will receive for writing the options. The premium depends on factors like the stock price, strike price, time to expiration, and implied volatility, which are not mentioned in the given context.\nThere is not enough information provided to determine the premium income John will receive.", "In the event of a takeover attempt, XYZ Bank, the Rights Agent, will issue Rights to Acme Inc. shareholders. The context states that XYZ Bank will issue one Right for each outstanding share of Acme Inc. stock.\nXYZ Bank will issue one Right for each outstanding share of Acme Inc. stock in the event of a takeover attempt." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
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