sentence-transformers
Safetensors
English
bert
medembed
medical-embedding
clinical-embedding
information-retrieval
mteb
Eval Results (legacy)
Instructions to use abhinand/MedEmbed-large-v0.1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use abhinand/MedEmbed-large-v0.1 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("abhinand/MedEmbed-large-v0.1") sentences = [ "The weather is lovely today.", "It's so sunny outside!", "He drove to the stadium." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] - Notebooks
- Google Colab
- Kaggle
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| "rstrip": false, | |
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| "normalized": false, | |
| "rstrip": false, | |
| "single_word": false | |
| }, | |
| "pad_token": { | |
| "content": "[PAD]", | |
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| "normalized": false, | |
| "rstrip": false, | |
| "single_word": false | |
| }, | |
| "sep_token": { | |
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| }, | |
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| "normalized": false, | |
| "rstrip": false, | |
| "single_word": false | |
| } | |
| } | |