README.md

ERNIE-GEN

ERNIE-GEN is a multi-flow language generation framework for both pre-training and fine-tuning. Only finetune strategy is illustrated in this section.

Finetune

We use Abstractive Summarization task CNN/DailyMail to illustate usage of ERNIE-GEN, you can download preprocessed finetune data from here

To starts finetuning ERNIE-GEN, run:

python3 -m paddle.distributed.launch \
    --log_dir ./log  \
    ./demo/seq2seq/finetune_seq2seq_dygraph.py \
    --from_pretrained ernie-gen-base-en \
    --data_dir ./data/cnndm \
    --save_dir ./model_cnndm \
    --label_smooth 0.1 \
    --use_random_noice \
    --noise_prob 0.7 \
    --predict_output_dir ./pred \
    --max_steps $((287113*30/64))

Note that you need more than 2 GPUs to run the finetuning. During multi-gpu finetuning, max_steps is used as stop criteria rather than epoch to prevent dead block. We simply canculate max_steps with: EPOCH * NUM_TRIAN_EXAMPLE / TOTAL_BATCH. This demo script will save a finetuned model at --save_dir, and do muti-gpu prediction every --eval_steps and save prediction results at --predict_output_dir.

Evalution

While finetuning, a serials of prediction files is generated. First you need to sort and join all files with:

sort -t$'\t' -k1n ./pred/pred.step60000.* |awk -F"\t" '{print $2}'> final_prediction

then use ./eval_cnndm/cnndm_eval.sh to calcuate all metrics (pyrouge is required to evalute CNN/Daily Mail.)

sh cnndm_eval.sh final_prediction ./data/cnndm/dev.summary

Inference

To run beam serach decode after you got a finetuned model. try:

cat one_column_source_text| python3 demo/seq2seq/decode.py \
    --from_pretrained ./ernie_gen_large \
    --save_dir ./model_cnndm \
    --bsz 8