language: - english thumbnail: null tags: - token classification license: agpl-3.0 datasets: - EMBO/sd-nlp metrics: - null
This model is a RoBERTa base model that was further trained using a masked language modeling task on a compendium of english scientific textual examples from the life sciences using the BioLang dataset. It was then fine-tuned for token classification on the SourceData sd-nlp dataset wit the
NER task to perform Named Entity Recognition of bioentities.
Intended uses & limitations
How to use
The intended use of this model is for Named Entity Recognition of biological entitie used in SourceData annotations (https://sourcedata.embo.org), including small molecules, gene products (genes and proteins), subcellular components, cell line and cell types, organ and tissues, species as well as experimental methods.
To have a quick check of the model:
from transformers import pipeline, RobertaTokenizerFast, RobertaForTokenClassification example = """<s> F. Western blot of input and eluates of Upf1 domains purification in a Nmd4-HA strain. The band with the # might corresponds to a dimer of Upf1-CH, bands marked with a star correspond to residual signal with the anti-HA antibodies (Nmd4). Fragments in the eluate have a smaller size because the protein A part of the tag was removed by digestion with the TEV protease. G6PDH served as a loading control in the input samples </s>""" tokenizer = RobertaTokenizerFast.from_pretrained('roberta-base', max_len=512) model = RobertaForTokenClassification.from_pretrained('EMBO/sd-ner') ner = pipeline('ner', model, tokenizer=tokenizer) res = ner(example) for r in res: print(r['word'], r['entity'])
Limitations and bias
The model must be used with the
The model was trained for token classification using the EMBO/sd-nlp
NER dataset wich includes manually annotated examples.
The training was run on a NVIDIA DGX Station with 4XTesla V100 GPUs.
Training code is available at https://github.com/source-data/soda-roberta
python -m tokcl.train NER --num_train_epochs=3.5
- Tokenizer vocab size: 50265
- Training data: EMBO/sd-nlp NER
- Training with 31410 examples.
- Evaluating on 8861 examples.
- Training on 15 features: O, I-SMALL_MOLECULE, B-SMALL_MOLECULE, I-GENEPROD, B-GENEPROD, I-SUBCELLULAR, B-SUBCELLULAR, I-CELL, B-CELL, I-TISSUE, B-TISSUE, I-ORGANISM, B-ORGANISM, I-EXP_ASSAY, B-EXP_ASSAY
- Epochs: 3.5
Testing on 4224 examples of test set with
precision recall f1-score support CELL 0.77 0.81 0.79 3477 EXP_ASSAY 0.71 0.70 0.71 7049 GENEPROD 0.86 0.90 0.88 16140 ORGANISM 0.80 0.82 0.81 2759 SMALL_MOLECULE 0.78 0.82 0.80 4446 SUBCELLULAR 0.71 0.75 0.73 2125 TISSUE 0.70 0.75 0.73 1971 micro avg 0.79 0.82 0.81 37967 macro avg 0.76 0.79 0.78 37967 weighted avg 0.79 0.82 0.81 37967