Sentence Similarity
sentence-transformers
Safetensors
Model2Vec
Transformers
English
feature-extraction
embeddings
static-embeddings
Instructions to use NeuML/pubmedbert-base-embeddings-2M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use NeuML/pubmedbert-base-embeddings-2M with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("NeuML/pubmedbert-base-embeddings-2M") sentences = [ "That is a happy person", "That is a happy dog", "That is a very happy person", "Today is a sunny day" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Transformers
How to use NeuML/pubmedbert-base-embeddings-2M with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("NeuML/pubmedbert-base-embeddings-2M", dtype="auto") - Notebooks
- Google Colab
- Kaggle
Create modules.json
Browse files- modules.json +14 -0
modules.json
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{
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"idx": 0,
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"name": "0",
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"path": ".",
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"type": "sentence_transformers.models.StaticEmbedding"
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"idx": 1,
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"name": "1",
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"path": "1_Normalize",
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"type": "sentence_transformers.models.Normalize"
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}
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]
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