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
FinModernBERT-embed-large v1: DAPT + contrastive + WiSE-FT alpha=0.65 (FinMTEB-EN 35-task evaluated)
96ec6bc verified Download tokenizer.json from BatuhanECB/FinModernBERT-embed-large-v1: direct link, hf CLI and curl.
- Browser
- Download file 3.58 MB
-
https://huggingface.co/BatuhanECB/FinModernBERT-embed-large-v1/resolve/main/tokenizer.json
- Command line
-
hf download hf://BatuhanECB/FinModernBERT-embed-large-v1/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/BatuhanECB/FinModernBERT-embed-large-v1/resolve/main/tokenizer.json
3.58 MB
File too large to display, you can check the raw version instead.