
Named Entity Recognition (NER) model to recognize chemical entities. 

[PubMedBERT](https://huggingface.co/microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext) fine-tuned on the following datasets:

- [Chemdner patents CEMP corpus](https://biocreative.bioinformatics.udel.edu/resources/corpora/chemdner-patents-cemp-corpus/) (train, dev, test sets)
- [DDI corpus](https://github.com/isegura/DDICorpus) (train, dev, test sets): entity types "GROUP", "DRUG", "DRUG_N"
- [GREC Corpus](http://www.nactem.ac.uk/GREC/standoff.php) (train, dev, test sets): entity type  "organic_compounds"
- [MLEE](http://nactem.ac.uk/MLEE/) (train, dev, test sets): entity type "Drug or compound"
- [NLM-CHEM](https://ftp.ncbi.nlm.nih.gov/pub/lu/NLMChem/) (train, dev, test sets)
- [CHEMDNER](https://biocreative.bioinformatics.udel.edu/resources/) (train, dev, test sets)
- [Chebi Corpus](http://www.nactem.ac.uk/chebi/) (train, dev, test sets): entity types "Metabolite", "Chemical"
- [PHAEDRA](http://www.nactem.ac.uk/PHAEDRA/) (train, dev, test sets): entity type "Pharmalogical_substance"
- [Chemprot](https://biocreative.bioinformatics.udel.edu/tasks/biocreative-vi/track-5/) (train, dev, test sets)
- [PGx Corpus](https://github.com/practikpharma/PGxCorpus) (train, dev, test sets): entity type "Chemical"
- [BioNLP11ID](https://github.com/cambridgeltl/MTL-Bioinformatics-2016/tree/master/data/BioNLP11ID-chem-IOB) (train, dev, test sets): entity type "Chemical"
- [BioNLP13CG]() (train, dev, test sets): entity type "Chemical"
- [BC4CHEMD](https://github.com/cambridgeltl/MTL-Bioinformatics-2016/tree/master/data/BC4CHEMD) (train, dev, test sets)
- [CRAFT corpus](https://github.com/UCDenver-ccp/CRAFT/tree/master/concept-annotation) (train, dev, test sets): entity type "ChEBI"
- [BC5CDR]() (train, dev, test sets): entity type "Chemical"
{"language": ["en"], "pipeline_tag": "token-classification"}
transformers pytorch bert token-classification en autotrain_compatible endpoints_compatible has_space region:us
TAGS
#transformers #pytorch #bert #token-classification #en #autotrain_compatible #endpoints_compatible #has_space #region-us 


Named Entity Recognition (NER) model to recognize chemical entities. 

PubMedBERT fine-tuned on the following datasets:

- Chemdner patents CEMP corpus (train, dev, test sets)
- DDI corpus (train, dev, test sets): entity types "GROUP", "DRUG", "DRUG_N"
- GREC Corpus (train, dev, test sets): entity type  "organic_compounds"
- MLEE (train, dev, test sets): entity type "Drug or compound"
- NLM-CHEM (train, dev, test sets)
- CHEMDNER (train, dev, test sets)
- Chebi Corpus (train, dev, test sets): entity types "Metabolite", "Chemical"
- PHAEDRA (train, dev, test sets): entity type "Pharmalogical_substance"
- Chemprot (train, dev, test sets)
- PGx Corpus (train, dev, test sets): entity type "Chemical"
- BioNLP11ID (train, dev, test sets): entity type "Chemical"
- [BioNLP13CG]() (train, dev, test sets): entity type "Chemical"
- BC4CHEMD (train, dev, test sets)
- CRAFT corpus (train, dev, test sets): entity type "ChEBI"
- [BC5CDR]() (train, dev, test sets): entity type "Chemical"
TAGS
#transformers #pytorch #bert #token-classification #en #autotrain_compatible #endpoints_compatible #has_space #region-us 

TAGS
#transformers #pytorch #bert #token-classification #en #autotrain_compatible #endpoints_compatible #has_space #region-us 
