Models
This is the pretrained model presented in SciBERT: A Pretrained Language Model for Scientific Text, which is a BERT model trained on scientific
fill-mask artifact mirrored from the HuggingFace Hub.
Pretrained model on protein sequences using a masked language modeling (MLM) objective.
fill-mask artifact mirrored from the HuggingFace Hub.
ChemBERTa: Training a BERT-like transformer model for masked language modelling of chemical SMILES
A Named Entity Recognition model for clinical entities (problem, treatment,
This model card describes the ClinicalBERT model, which was trained on a large multicenter dataset with a large corpus of 1.2B words of diverse diseases we…
The Clinical Assertion and Negation Classification BERT is introduced in the paper Assertion Detection in Clinical Notes: Medical Language Models to the…
An Evolutionary-scale Model (ESM) for protein function prediction from amino acid sequences using the Gene Ontology (GO).
ESM Protein Localization Model Model description The ESM Protein Localization Model is a deep learning model trained on protein sequences to predict their…
An Evolutionary-scale Model (ESM) for protein function prediction from amino acid sequences using the Gene Ontology (GO).
Model artifact mirrored from the HuggingFace Hub.
Model Card for ChemBERTa-10M-MTR Model Details Model Description More information needed - Developed by: DeepChem - Shared by [Optional]:
An Evolutionary-scale Model (ESM) for protein function prediction from amino acid sequences using the Gene Ontology (GO).
Pretrained model on protein sequences using a masked language modeling (MLM) objective.
An encoder-only, half-precision version of the ProtT5-XL-UniRef50 model.
esm3-sm-open-v1 is trained on 2.78 billion natural proteins.
This is the official pre-trained model introduced in DNABERT-2: Efficient Foundation Model and Benchmark For Multi-Species
An Amharic-language variant of Gemma-3-27B-MeditronFO, from EPFL's LiGHT laboratory and the MeditronFO pipeline.