Feature Extraction
Transformers
PyTorch
TensorFlow
JAX
Safetensors
English
bert
biomedical
lexical semantics
bionlp
biology
science
embedding
entity linking
Instructions to use cambridgeltl/SapBERT-from-PubMedBERT-fulltext with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use cambridgeltl/SapBERT-from-PubMedBERT-fulltext with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="cambridgeltl/SapBERT-from-PubMedBERT-fulltext")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("cambridgeltl/SapBERT-from-PubMedBERT-fulltext") model = AutoModel.from_pretrained("cambridgeltl/SapBERT-from-PubMedBERT-fulltext", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
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datasets:
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- UMLS
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### SapBERT-PubMedBERT
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SapBERT by [Liu et al. (2020)](https://arxiv.org/pdf/2010.11784.pdf). Trained with [UMLS](https://www.nlm.nih.gov/research/umls/licensedcontent/umlsknowledgesources.html) 2020AA (English only), using [microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext](https://huggingface.co/microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext) as the base model. Please use [CLS] as the representation of the input.
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datasets:
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- UMLS
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[Update] SapBERT will appear in the conference proceedings of NAACL 2021!
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### SapBERT-PubMedBERT
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SapBERT by [Liu et al. (2020)](https://arxiv.org/pdf/2010.11784.pdf). Trained with [UMLS](https://www.nlm.nih.gov/research/umls/licensedcontent/umlsknowledgesources.html) 2020AA (English only), using [microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext](https://huggingface.co/microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext) as the base model. Please use [CLS] as the representation of the input.
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