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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SapBERT by [Liu et al. (2020)](https://arxiv.org/pdf/2010.11784.pdf). Please use [CLS] as the representation of the input.
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SapBERT by [Liu et al. (2020)](https://arxiv.org/pdf/2010.11784.pdf). Please use [CLS] as the representation of the input.
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```bibtex
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@article{liu2020self,
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title={Self-alignment Pre-training for Biomedical Entity Representations},
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author={Liu, Fangyu and Shareghi, Ehsan and Meng, Zaiqiao and Basaldella, Marco and Collier, Nigel},
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journal={arXiv preprint arXiv:2010.11784},
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year={2020}
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}
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```
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