File size: 3,175 Bytes
f12cf9b c615d3a f12cf9b c615d3a f12cf9b c615d3a |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 |
---
language:
- english
thumbnail:
tags:
- token classification
license: agpl-3.0
datasets:
- EMBO/sd-panels
metrics:
-
---
# sd-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 as then fine-tuned for token classification on the SourceData [sd-nlp](https://huggingface.co/datasets/EMBO/sd-nlp) dataset with the `ROLES` task 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 gene products (genes and proteins) 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-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-panels dataset](https://huggingface.co/datasets/EMBO/sd-panels) 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
- Tokenizer vocab size: 50265
- Training data: EMBO/biolang MLM
- 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: 0.9
- `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 0.81 0.86 0.83 7835
MEASURED_VAR 0.82 0.85 0.84 9330
micro avg 0.82 0.85 0.83 17165
macro avg 0.82 0.85 0.83 17165
weighted avg 0.82 0.85 0.83 17165
{'test_loss': 0.03846803680062294, 'test_accuracy_score': 0.9854472664459946, 'test_precision': 0.8156312625250501, 'test_recall': 0.8535974366443344, 'test_f1': 0.8341825841897008, 'test_runtime': 58.7369, 'test_samples_per_second': 122.206, 'test_steps_per_second': 1.924}
```
|