Sentence Similarity
sentence-transformers
Safetensors
English
bert
feature-extraction
Generated from Trainer
dataset_size:150
loss:MatryoshkaLoss
loss:MultipleNegativesRankingLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use Snorkeler/BGE-Finetuned-FinBench with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use Snorkeler/BGE-Finetuned-FinBench with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Snorkeler/BGE-Finetuned-FinBench") sentences = [ "Worldwide Sales Change By Business SegmentOrganic salesAcquisitionsDivestituresTranslationTotal sales changeSafety and Industrial1.0 % % %(4.2) %(3.2) %Transportation and Electronics1.2 (0.5)(4.6)(3.9)Health Care3.2 (1.4)(3.8)(2.0)Consumer(0.9) (0.4)(2.6)(3.9)Total Company1.2 (0.5)(3.9)(3.2)", "Has MGM Resorts paid dividends to common shareholders in FY2022?", "If we exclude the impact of M&A, which segment has dragged down 3M's overall growth in 2022?", "In 2022 Q2, which of JPM's business segments had the highest net income?" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
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