Sentence Similarity
sentence-transformers
PyTorch
Transformers
bert
feature-extraction
text-embeddings-inference
Instructions to use kamalkraj/BioSimCSE-BioLinkBERT-BASE with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use kamalkraj/BioSimCSE-BioLinkBERT-BASE with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("kamalkraj/BioSimCSE-BioLinkBERT-BASE") sentences = [ "The up-regulation of miR-146a was also detected in cervical cancer tissues.", "The expression of miR-146a has been found to be up-regulated in cervical cancer.", "Only concomitant ablation of ERK1 and ERK2 impairs tumor growth." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] - Transformers
How to use kamalkraj/BioSimCSE-BioLinkBERT-BASE with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("kamalkraj/BioSimCSE-BioLinkBERT-BASE") model = AutoModel.from_pretrained("kamalkraj/BioSimCSE-BioLinkBERT-BASE", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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# kamalkraj/BioSimCSE-BioLinkBERT-BASE
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- feature-extraction
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- sentence-similarity
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- transformers
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widget:
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- source_sentence: "The up-regulation of miR-146a was also detected in cervical cancer tissues."
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sentences: ["The expression of miR-146a has been found to be up-regulated in cervical cancer.", "Only concomitant ablation of ERK1 and ERK2 impairs tumor growth."]
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example_title: "BioNLP Example"
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# kamalkraj/BioSimCSE-BioLinkBERT-BASE
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