Feature Extraction
sentence-transformers
Safetensors
bert
sentence-similarity
biomedical
embeddings
life-sciences
scientific-text
SODA-VEC
EMBO
text-embeddings-inference
Instructions to use EMBO/negative_sampling_pmb with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use EMBO/negative_sampling_pmb with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("EMBO/negative_sampling_pmb") 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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README.md
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**Key Features:**
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- Trained on **26.5M biomedical title-abstract pairs** from PubMed Central
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- Based on **
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- Optimized for **biomedical text similarity** and **semantic search**
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- Produces **768-dimensional embeddings** with mean pooling
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**Key Features:**
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- Trained on **26.5M biomedical title-abstract pairs** from PubMed Central
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- Based on **microsoft/BiomedNLP-BiomedBERT-base-uncased-abstract-fulltext** architecture
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- Optimized for **biomedical text similarity** and **semantic search**
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- Produces **768-dimensional embeddings** with mean pooling
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