|
---
|
|
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}
|
|
```
|
|
|
