| --- | |
| language: en | |
| license: agpl-3.0 | |
| tags: | |
| - token classification | |
| datasets: | |
| - EMBO/sd-nlp | |
| metrics: [] | |
| --- | |
| # sd-smallmol-roles | |
| ## Model description | |
| 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-nlp](https://huggingface.co/datasets/EMBO/sd-nlp) dataset with the `SMALL_MOL_ROLES` configuration to perform pure context-dependent semantic role classification of bioentities. | |
| ## Intended uses & limitations | |
| #### How to use | |
| 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. | |
| To have a quick check of the model: | |
| ```python | |
| from transformers import pipeline, RobertaTokenizerFast, RobertaForTokenClassification | |
| example = """<s>The <mask> overexpression in cells caused an increase in <mask> expression.</s>""" | |
| tokenizer = RobertaTokenizerFast.from_pretrained('roberta-base', max_len=512) | |
| model = RobertaForTokenClassification.from_pretrained('EMBO/sd-smallmol-roles') | |
| 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 `roberta-base` tokenizer. | |
| ## Training data | |
| The model was trained for token classification using the [EMBO/sd-nlp dataset](https://huggingface.co/datasets/EMBO/sd-nlp) which includes manually annotated examples. | |
| ## Training procedure | |
| 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 | |
| - Model fine tuned: EMBL/bio-lm | |
| - Tokenizer vocab size: 50265 | |
| - Training data: EMBO/sd-nlp | |
| - Dataset configuration: SMALL_MOL_ROLES | |
| - Training with 48771 examples. | |
| - Evaluating on 13801 examples. | |
| - Training on 15 features: O, I-CONTROLLED_VAR, B-CONTROLLED_VAR, I-MEASURED_VAR, B-MEASURED_VAR | |
| - Epochs: 0.33 | |
| - `per_device_train_batch_size`: 16 | |
| - `per_device_eval_batch_size`: 16 | |
| - `learning_rate`: 0.0001 | |
| - `weight_decay`: 0.0 | |
| - `adam_beta1`: 0.9 | |
| - `adam_beta2`: 0.999 | |
| - `adam_epsilon`: 1e-08 | |
| - `max_grad_norm`: 1.0 | |
| ## Eval results | |
| On 7178 example of test set with `sklearn.metrics`: | |
| ``` | |
| precision recall f1-score support | |
| CONTROLLED_VAR 0.76 0.90 0.83 2946 | |
| MEASURED_VAR 0.60 0.71 0.65 852 | |
| micro avg 0.73 0.86 0.79 3798 | |
| macro avg 0.68 0.80 0.74 3798 | |
| weighted avg 0.73 0.86 0.79 3798 | |
| {'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} | |
| ``` | |