Spaces:
Runtime error
Runtime error
File size: 2,591 Bytes
ee21b96 |
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 |
# Finetuning RoBERTa on GLUE tasks
### 1) Download the data from GLUE website (https://gluebenchmark.com/tasks) using following commands:
```bash
wget https://gist.githubusercontent.com/W4ngatang/60c2bdb54d156a41194446737ce03e2e/raw/17b8dd0d724281ed7c3b2aeeda662b92809aadd5/download_glue_data.py
python download_glue_data.py --data_dir glue_data --tasks all
```
### 2) Preprocess GLUE task data:
```bash
./examples/roberta/preprocess_GLUE_tasks.sh glue_data <glue_task_name>
```
`glue_task_name` is one of the following:
`{ALL, QQP, MNLI, QNLI, MRPC, RTE, STS-B, SST-2, CoLA}`
Use `ALL` for preprocessing all the glue tasks.
### 3) Fine-tuning on GLUE task:
Example fine-tuning cmd for `RTE` task
```bash
ROBERTA_PATH=/path/to/roberta/model.pt
CUDA_VISIBLE_DEVICES=0 fairseq-hydra-train -config-dir examples/roberta/config/finetuning --config-name rte \
task.data=RTE-bin checkpoint.restore_file=$ROBERTA_PATH
```
There are additional config files for each of the GLUE tasks in the examples/roberta/config/finetuning directory.
**Note:**
a) Above cmd-args and hyperparams are tested on one Nvidia `V100` GPU with `32gb` of memory for each task. Depending on the GPU memory resources available to you, you can use increase `--update-freq` and reduce `--batch-size`.
b) All the settings in above table are suggested settings based on our hyperparam search within a fixed search space (for careful comparison across models). You might be able to find better metrics with wider hyperparam search.
### Inference on GLUE task
After training the model as mentioned in previous step, you can perform inference with checkpoints in `checkpoints/` directory using following python code snippet:
```python
from fairseq.models.roberta import RobertaModel
roberta = RobertaModel.from_pretrained(
'checkpoints/',
checkpoint_file='checkpoint_best.pt',
data_name_or_path='RTE-bin'
)
label_fn = lambda label: roberta.task.label_dictionary.string(
[label + roberta.task.label_dictionary.nspecial]
)
ncorrect, nsamples = 0, 0
roberta.cuda()
roberta.eval()
with open('glue_data/RTE/dev.tsv') as fin:
fin.readline()
for index, line in enumerate(fin):
tokens = line.strip().split('\t')
sent1, sent2, target = tokens[1], tokens[2], tokens[3]
tokens = roberta.encode(sent1, sent2)
prediction = roberta.predict('sentence_classification_head', tokens).argmax().item()
prediction_label = label_fn(prediction)
ncorrect += int(prediction_label == target)
nsamples += 1
print('| Accuracy: ', float(ncorrect)/float(nsamples))
```
|