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 4_Normalize/config.json from seanfarrell/icd11_embed_model_v2_large: direct link, hf CLI and curl.
- Browser
- Download file 97 Bytes
-
https://huggingface.co/seanfarrell/icd11_embed_model_v2_large/resolve/main/4_Normalize/config.json
- Command line
-
hf download hf://seanfarrell/icd11_embed_model_v2_large/4_Normalize/config.json
-
curl -L -o config.json https://huggingface.co/seanfarrell/icd11_embed_model_v2_large/resolve/main/4_Normalize/config.json
97 Bytes
| { | |
| "module_input_name": "sentence_embedding", | |
| "module_output_name": "sentence_embedding" | |
| } |