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_ner_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_ner_model_v2_large with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("seanfarrell/icd11_ner_model_v2_large") sentences = [ "hypoxic ischaemic encephalopathy in diseases classified elsewhere", "other specified hypoxic-ischaemic encephalopathy", "monocytopenia", "isolated benign cerebral vasculitis" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from seanfarrell/icd11_ner_model_v2_large: direct link, hf CLI and curl.
- Browser
- Download file 33.4 MB
-
https://huggingface.co/seanfarrell/icd11_ner_model_v2_large/resolve/main/tokenizer.json
- Command line
-
hf download hf://seanfarrell/icd11_ner_model_v2_large/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/seanfarrell/icd11_ner_model_v2_large/resolve/main/tokenizer.json
33.4 MB
- Xet hash:
- 5aeb78ec7d1f573e7b71d6cd37db0d99b9ef0d66c5cded5fd4734990fdd0038c
- Size of remote file:
- 33.4 MB
- SHA256:
- c8252b6b3d55018d76a8f6928ddfa5fe84767bd23f12cd6c495d51fa6f73b3f4
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