sentence-transformers
Safetensors
English
bert
medembed
medical-embedding
clinical-embedding
information-retrieval
Instructions to use abhinand/MedEmbed-base-v0.1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use abhinand/MedEmbed-base-v0.1 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("abhinand/MedEmbed-base-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
Add `feature-extraction` tag
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by chiragjn - opened
README.md
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---
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language: en
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tags:
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- medembed
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- medical-embedding
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- clinical-embedding
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- information-retrieval
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- sentence-transformers
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license: apache-2.0
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datasets:
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- MedicalQARetrieval
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- MRR
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base_model:
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- BAAI/bge-base-en-v1.5
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---
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# MedEmbed: Specialized Embedding Model for Medical and Clinical Information Retrieval
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---
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language: en
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tags:
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- feature-extraction
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- medembed
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- medical-embedding
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- clinical-embedding
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- information-retrieval
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- sentence-transformers
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- sentence-similarity
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license: apache-2.0
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datasets:
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- MedicalQARetrieval
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- MRR
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base_model:
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- BAAI/bge-base-en-v1.5
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pipeline_tag: feature-extraction
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---
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# MedEmbed: Specialized Embedding Model for Medical and Clinical Information Retrieval
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