| | ---
|
| | language:
|
| | - english
|
| | thumbnail:
|
| | tags:
|
| | - token classification
|
| | license: agpl-3.0
|
| | 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}
|
| | ```
|
| |
|
