OpenMed/OpenMed-NER-ChemicalDetect-ElectraMed-33M
Specialized model for Chemical Entity Recognition - Identifies chemical compounds and substances in biomedical literature
README
widget: text: "The patient was administered acetylsalicylic acid for pain relief." text: "Treatment with doxorubicin showed significant improvement in tumor regression." text: "The compound benzylpenicillin demonstrated strong antimicrobial activity." text: "Further studies are needed to understand the effects of methotrexate on rheumatoid arthritis." text: "The synthesis of vancomycin remains a significant challenge in organic chemistry." tags: token-classification named-entity-recognition…
Source attribution
- HuggingFace — OpenMed/OpenMed-NER-ChemicalDetect-ElectraMed-33M
Related resources
This model is a lightweight model pre-trained on SELFIES (Self-Referencing Embedded Strings) representations of molecules. It is trained on 2.7M unique and valid molecules taken from COCONUTDB and ChemBL34, with 7.3M total generated masked examples.
This model is a BERT-like sequence classifier for 221 human protein drug targets, fine-tuned from gbyuvd/chemselfies-base-bertmlm on a dataset derived ChemBL34 (Zdrazil et al. 2023). It predicts potential drug targets using chemical structures represented as SELFIES (Self-Referencing Embedded…
## Description: Geneformer is a foundational transformer model pretrained on a large-scale corpus of single-cell transcriptomes to enable context-specific predictions in settings with limited data in network biology. This model version was continually pretrained on ~14 million cancer transcriptomes…
ctheodoris/Geneformer
by ctheodoris# Geneformer Geneformer is a foundational transformer model pretrained on a large-scale corpus of human single cell transcriptomes to enable context-aware predictions in settings with limited data in network biology.
datasets: - UMLS
PII Detection Model | 44M Parameters | Open Source