Sentence Similarity
sentence-transformers
Safetensors
bert
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 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 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("seanfarrell/icd11_ner_model_v2") 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
File size: 429 Bytes
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{
"idx": 0,
"name": "0",
"path": "",
"type": "sentence_transformers.base.modules.transformer.Transformer"
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{
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"name": "1",
"path": "1_Pooling",
"type": "sentence_transformers.sentence_transformer.modules.pooling.Pooling"
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{
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"name": "2",
"path": "2_Normalize",
"type": "sentence_transformers.sentence_transformer.modules.normalize.Normalize"
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