cambridgeltl/SapBERT-from-PubMedBERT-fulltext
feature-extraction model by cambridgeltl
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Model card & documentation
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datasets: [news] A cross-lingual extension of SapBERT will appear in the main onference of ACL 2021! [news] SapBERT will appear in the conference proceedings of NAACL 2021! SapBERT by Liu et al. (2020). Trained with UMLS 2020AA (English only), using microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext as the base model. The input should be a string of biomedical entity names, e.g., "covid…
Read the full model card ↗ · Preview checked 2026-10-29T21:46:25.152Z
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Model overview
cambridgeltl/SapBERT-from-PubMedBERT-fulltext is a feature-extraction model repository published by cambridgeltl on Hugging Face. The source reports the transformers library.
This page summarizes Hub metadata. For intended use, training data, evaluation results and limitations, consult the original model card.
Model facts
- Task
- feature-extraction
- Library
- transformers
- Recent downloads (30 days)
- 1,417,733
- Cumulative likes
- 89
- Hugging Face trending score
- 1
- Reported safetensors parameters
- 109,482,752
- Architecture
- BertModel
- License
- apache-2.0
- Access
- Not gated by Hugging Face
- Created
- 2022-03-02T23:29:05.000Z
- Last modified
- 2023-06-14T19:03:02.000Z
Compatibility and lineage
Reported languages: en
Base-model lineage was not included.
Parameter count is not a RAM/VRAM requirement. Check precision, quantization, context length and runtime compatibility in the model card; no hardware or API-cost claim is inferred here.
Use and evaluate
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Source and freshness
Source: Hugging Face Hub. Metadata observed 2026-11-01T06:54:29.511Z. Daily imports are snapshots, not real-time monitoring.
Popularity and source listings do not establish security, suitability, licensing rights or benchmark performance.