Sentence Similarity
sentence-transformers
Safetensors
English
bert
feature-extraction
Generated from Trainer
dataset_size:6300
loss:MatryoshkaLoss
loss:MultipleNegativesRankingLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use Hritikmore/bge-base-financial-matryoshka with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use Hritikmore/bge-base-financial-matryoshka with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Hritikmore/bge-base-financial-matryoshka") sentences = [ "Forward-looking statements may appear throughout this report, including without limitation, the following sections: “Management's Discussion and Analysis,” “Risk Factors” and \"Notes 4, 8 and 13 to the Consolidated Financial Statements.\"", "How does a one-year adjustment in the 2023 expected retirement age for U.S. plans affect income before income taxes?", "Which sections of the report might contain forward-looking statements according to the text?", "What was the allowance for loan and lease losses at Bank of America as of December 31, 2022?" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
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