Skip to content

Latest commit

 

History

History
99 lines (85 loc) · 3.98 KB

README.glue.md

File metadata and controls

99 lines (85 loc) · 3.98 KB
Raw

Fine-tuning BART on GLUE tasks

1) Download the data from GLUE website (https://gluebenchmark.com/tasks) using following commands:

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 (same as RoBERTa):

./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

TOTAL_NUM_UPDATES=2036  # 10 epochs through RTE for bsz 16
WARMUP_UPDATES=61      # 6 percent of the number of updates
LR=1e-05                # Peak LR for polynomial LR scheduler.
NUM_CLASSES=2
MAX_SENTENCES=16        # Batch size.
BART_PATH=/path/to/bart/model.pt

CUDA_VISIBLE_DEVICES=0,1 python train.py RTE-bin/ \
    --restore-file $BART_PATH \
    --max-sentences $MAX_SENTENCES \
    --max-tokens 4400 \
    --task sentence_prediction \
    --add-prev-output-tokens \
    --layernorm-embedding \
    --share-all-embeddings \
    --share-decoder-input-output-embed \
    --reset-optimizer --reset-dataloader --reset-meters \
    --required-batch-size-multiple 1 \
    --init-token 0 \
    --arch bart_large \
    --criterion sentence_prediction \
    --num-classes $NUM_CLASSES \
    --dropout 0.1 --attention-dropout 0.1 \
    --weight-decay 0.01 --optimizer adam --adam-betas "(0.9, 0.98)" --adam-eps 1e-08 \
    --clip-norm 0.0 \
    --lr-scheduler polynomial_decay --lr $LR --total-num-update $TOTAL_NUM_UPDATES --warmup-updates $WARMUP_UPDATES \
    --fp16 --fp16-init-scale 4 --threshold-loss-scale 1 --fp16-scale-window 128 \
    --max-epoch 10 \
    --find-unused-parameters \
    --best-checkpoint-metric accuracy --maximize-best-checkpoint-metric;

For each of the GLUE task, you will need to use following cmd-line arguments:

Model MNLI QNLI QQP RTE SST-2 MRPC CoLA STS-B
--num-classes 3 2 2 2 2 2 2 1
--lr 5e-6 1e-5 1e-5 1e-5 5e-6 2e-5 2e-5 2e-5
bsz 128 32 32 32 128 64 64 32
--total-num-update 30968 33112 113272 1018 5233 1148 1334 1799
--warmup-updates 1858 1986 6796 61 314 68 80 107

For STS-B additionally add --regression-target --best-checkpoint-metric loss and remove --maximize-best-checkpoint-metric.

Note:

a) --total-num-updates is used by --polynomial_decay scheduler and is calculated for --max-epoch=10 and --max-sentences=32/64/128 depending on the task.

b) Above cmd-args and hyperparams are tested on 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 --max-sentences.

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:

from fairseq.models.bart import BARTModel

bart = BARTModel.from_pretrained(
    'checkpoints/',
    checkpoint_file='checkpoint_best.pt',
    data_name_or_path='RTE-bin'
)

label_fn = lambda label: bart.task.label_dictionary.string(
    [label + bart.task.label_dictionary.nspecial]
)   
ncorrect, nsamples = 0, 0
bart.cuda()
bart.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 = bart.encode(sent1, sent2)
        prediction = bart.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))