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--- |
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language: en |
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tags: |
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- token classification |
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datasets: |
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- EMBO/sd-panels |
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metrics: [] |
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--- |
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# sd-ner |
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## Model description |
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This model is a [RoBERTa base model](https://huggingface.co/roberta-base) 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](https://huggingface.co/datasets/EMBO/biolang) and fine-tuned for token classification on the SourceData [sd-panels](https://huggingface.co/datasets/EMBO/sd-panels) dataset to perform Named Entity Recognition of bioentities. |
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## Intended uses & limitations |
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#### How to use |
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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. |
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To have a quick check of the model: |
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```python |
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from transformers import pipeline, RobertaTokenizerFast, RobertaForTokenClassification |
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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>""" |
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tokenizer = RobertaTokenizerFast.from_pretrained('roberta-base', max_len=512) |
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model = RobertaForTokenClassification.from_pretrained('EMBO/sd-ner') |
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ner = pipeline('ner', model, tokenizer=tokenizer) |
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res = ner(example) |
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for r in res: |
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print(r['word'], r['entity']) |
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``` |
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#### Limitations and bias |
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The model must be used with the `roberta-base` tokenizer. |
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## Training data |
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The model was trained for token classification using the [EMBO/sd-panels dataset](https://huggingface.co/datasets/EMBO/biolang) wich includes manually annotated examples. |
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## Training procedure |
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The training was run on a NVIDIA DGX Station with 4XTesla V100 GPUs. |
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Training code is available at https://github.com/source-data/soda-roberta |
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- Command: `python -m tokcl.train /data/json/sd_panels NER --num_train_epochs=3.5` |
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- Tokenizer vocab size: 50265 |
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- Training data: EMBO/biolang MLM |
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- Training with 31410 examples. |
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- Evaluating on 8861 examples. |
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- 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 |
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- Epochs: 3.5 |
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- `per_device_train_batch_size`: 32 |
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- `per_device_eval_batch_size`: 32 |
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- `learning_rate`: 0.0001 |
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- `weight_decay`: 0.0 |
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- `adam_beta1`: 0.9 |
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- `adam_beta2`: 0.999 |
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- `adam_epsilon`: 1e-08 |
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- `max_grad_norm`: 1.0 |
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## Eval results |
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On test set with `sklearn.metrics`: |
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``` |
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precision recall f1-score support |
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CELL 0.77 0.81 0.79 3477 |
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EXP_ASSAY 0.71 0.70 0.71 7049 |
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GENEPROD 0.86 0.90 0.88 16140 |
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ORGANISM 0.80 0.82 0.81 2759 |
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SMALL_MOLECULE 0.78 0.82 0.80 4446 |
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SUBCELLULAR 0.71 0.75 0.73 2125 |
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TISSUE 0.70 0.75 0.73 1971 |
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micro avg 0.79 0.82 0.81 37967 |
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macro avg 0.76 0.79 0.78 37967 |
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weighted avg 0.79 0.82 0.81 37967 |
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``` |
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