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 with the
PANELIZATION task to perform 'parsing' or 'segmentation' of figure legends into fragments corresponding to sub-panels.
Figures are usually composite representations of results obtained with heterogeneous experimental approaches and systems. Breaking figures into panels allows identifying more coherent descriptions of individual scientific experiments.
The intended use of this model is for 'parsing' figure legends into sub-fragments corresponding to individual panels as used in SourceData annotations (https://sourcedata.embo.org).
To have a quick check of the model:
from transformers import pipeline, RobertaTokenizerFast, RobertaForTokenClassification example = """Fig 4. a, Volume density of early (Avi) and late (Avd) autophagic vacuoles.a, Volume density of early (Avi) and late (Avd) autophagic vacuoles from four independent cultures. Examples of Avi and Avd are shown in b and c, respectively. Bars represent 0.4����m. d, Labelling density of cathepsin-D as estimated in two independent experiments. e, Labelling density of LAMP-1.""" tokenizer = RobertaTokenizerFast.from_pretrained('roberta-base', max_len=512) model = RobertaForTokenClassification.from_pretrained('EMBO/sd-panelization') ner = pipeline('ner', model, tokenizer=tokenizer) res = ner(example) for r in res: print(r['word'], r['entity'])
The model must be used with the
The model was trained for token classification using the
EMBO/sd-nlp PANELIZATION dataset which includes manually annotated examples.
The training was run on an NVIDIA DGX Station with 4XTesla V100 GPUs.
Training code is available at https://github.com/source-data/soda-roberta
- Model fine-tuned: EMBO/bio-lm
- Tokenizer vocab size: 50265
- Training data: EMBO/sd-nlp
- Dataset configuration: PANELIZATION
- TTraining with 2175 examples.
- Evaluating on 622 examples.
- Training on 2 features:
- Epochs: 1.3
Testing on 1802 examples from test set with
precision recall f1-score support PANEL_START 0.89 0.95 0.92 5427 micro avg 0.89 0.95 0.92 5427 macro avg 0.89 0.95 0.92 5427 weighted avg 0.89 0.95 0.92 5427
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