Sentence Similarity
sentence-transformers
Safetensors
gemma3_text
feature-extraction
dense
Generated from Trainer
dataset_size:8089600
loss:MultipleNegativesRankingLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use seanfarrell/icd11_embed_model_v2_large with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use seanfarrell/icd11_embed_model_v2_large with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("seanfarrell/icd11_embed_model_v2_large") sentences = [ "anaemia", "anaemic nos", "scurvy", "foreign body" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from seanfarrell/icd11_embed_model_v2_large: direct link, hf CLI and curl.
- Browser
- Download file 33.4 MB
-
https://huggingface.co/seanfarrell/icd11_embed_model_v2_large/resolve/main/tokenizer.json
- Command line
-
hf download hf://seanfarrell/icd11_embed_model_v2_large/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/seanfarrell/icd11_embed_model_v2_large/resolve/main/tokenizer.json
33.4 MB
- Xet hash:
- 5dc934149b552d12a703231c8a613a0b2c95ac4c56ad5fe38d4a62bc4c37ed4d
- Size of remote file:
- 33.4 MB
- SHA256:
- 92e0e3eae4540616f795e1ad7e22b5a0859c46d6e3c36746d190014e4916e919
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