Sentence Similarity
sentence-transformers
Safetensors
English
modernbert
feature-extraction
finance
financial-nlp
embeddings
finmteb
text-embeddings-inference
Instructions to use BatuhanECB/FinModernBERT-embed-large-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use BatuhanECB/FinModernBERT-embed-large-v1 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("BatuhanECB/FinModernBERT-embed-large-v1") sentences = [ "That is a happy person", "That is a happy dog", "That is a very happy person", "Today is a sunny day" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
Commit History
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docs: add official FinMTEB leaderboard placement (STS #2, Summ #2 of 18)
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Card: link the released SEC-synthetic pairs dataset
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Card: rename to -v1 naming, cross-links updated
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Card: report compute as GPU-time (~120 GPU-hours / ~5 days on 2x3090), not calendar time
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FinModernBERT-embed-large v1: DAPT + contrastive + WiSE-FT alpha=0.65 (FinMTEB-EN 35-task evaluated)
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initial commit
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