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 aired/bge-base-financial-matryoshka with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use aired/bge-base-financial-matryoshka with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("aired/bge-base-financial-matryoshka") sentences = [ "We enter into forward currency contracts in order to hedge a portion of the foreign currency exposure associated with the translation of our net investment in our Canadian subsidiary.", "How much did Delta Air Lines spend on debt and finance lease obligations in 2023?", "What mechanisms does the company use to hedge foreign currency exposure for its Canadian subsidiary?", "How did operating overhead expenses change for NIKE from fiscal 2022 to fiscal 2023?" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
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