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---
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language:
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- english
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thumbnail:
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tags:
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- token classification
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license: agpl-3.0
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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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---
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# sd-smallmol-roles
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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). It has then been fine-tuned for token classification on the SourceData [sd-panels](https://huggingface.co/datasets/EMBO/sd-panels) dataset with the `SMALL_MOL_ROLES` configuration to perform pure context-dependent semantic role classification 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 to infer the semantic role of small molecules with regard to the causal hypotheses tested in experiments reported in scientific papers.
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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>The <mask> overexpression in cells caused an increase in <mask> expression.</s>"""
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tokenizer = RobertaTokenizerFast.from_pretrained('roberta-base', max_len=512)
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model = RobertaForTokenClassification.from_pretrained('EMBO/sd-smallmol-roles')
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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/sd-panels) which 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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- Model fine tuned: EMBL/bio-lm
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- Tokenizer vocab size: 50265
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- Training data: EMBO/sd-panels
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- Dataset configuration: SMALL_MOL_ROLES
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- Training with 48771 examples.
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- Evaluating on 13801 examples.
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- Training on 15 features: O, I-CONTROLLED_VAR, B-CONTROLLED_VAR, I-MEASURED_VAR, B-MEASURED_VAR
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- Epochs: 0.33
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- `per_device_train_batch_size`: 16
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- `per_device_eval_batch_size`: 16
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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 7178 example of test set with `sklearn.metrics`:
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```
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precision recall f1-score support
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CONTROLLED_VAR 0.76 0.90 0.83 2946
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MEASURED_VAR 0.60 0.71 0.65 852
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micro avg 0.73 0.86 0.79 3798
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macro avg 0.68 0.80 0.74 3798
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weighted avg 0.73 0.86 0.79 3798
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{'test_loss': 0.011743436567485332, 'test_accuracy_score': 0.9951612532624371, 'test_precision': 0.7261345852895149, 'test_recall': 0.8551869404949973, 'test_f1': 0.7853947527505744, 'test_runtime': 58.0378, 'test_samples_per_second': 123.678, 'test_steps_per_second': 1.947}
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```
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