examples

Three quick usage examples for these scripts:

run_pre_train.py: Pre-train ZEN model from scratch or BERT model

python run_pre_train.py  \
    --pregenerated_data /path/to/pregenerated_data   \
    --bert_model /path/to/bert_model  \
    --do_lower_case  \
    --output_dir /path/to/output_dir   \
    --epochs 20  \
    --train_batch_size 128   \
    --reduce_memory  \
    --fp16  \
    --scratch  \
    --save_name ZEN_pretrain_base_

run_sequence_level_classification.py: Fine-tune on tasks for sequence classification

python run_sequence_level_classification.py \
    --task_name TASKNAME \
    --do_train \
    --do_eval \
    --do_lower_case \
    --data_dir /path/to/dataset \
    --bert_model /path/to/zen_model \
    --max_seq_length 512 \
    --train_batch_size 32 \
    --learning_rate 2e-5 \
    --num_train_epochs 30.0

where TASKNAME can be one of DC, SA, SPM and NLI

script of fine-tuning thucnews

python run_sequence_level_classification.py \
    --task_name thucnews \
    --do_train \
    --do_eval \
    --do_lower_case \
    --data_dir /path/to/dataset/thucnews \
    --bert_model /path/to/zen_model \
    --max_seq_length 512 \
    --train_batch_size 32 \
    --learning_rate 2e-5 \
    --num_train_epochs 30.0

script of fine-tuning chnsenticorp

python run_sequence_level_classification.py \
    --task_name ChnSentiCorp \
    --do_train \
    --do_eval \
    --do_lower_case \
    --data_dir /path/to/dataset/ChnSentiCorp \
    --bert_model /path/to/zen_model \
    --max_seq_length 512 \
    --train_batch_size 32 \
    --learning_rate 2e-5 \
    --num_train_epochs 30.0

script of fine-tuning LCQMC

python run_sequence_level_classification.py \
    --task_name lcqmc \
    --do_train \
    --do_eval \
    --do_lower_case \
    --data_dir /path/to/dataset/lcqmc \
    --bert_model /path/to/zen_model \
    --max_seq_length 128 \
    --train_batch_size 128 \
    --learning_rate 5e-5 \
    --num_train_epochs 30.0

script of fine-tuning XNLI

python run_sequence_level_classification.py \
    --task_name xnli \
    --do_train \
    --do_eval \
    --do_lower_case \
    --data_dir /path/to/dataset/xnli \
    --bert_model /path/to/zen_model \
    --max_seq_length 128 \
    --train_batch_size 128 \
    --learning_rate 5e-5 \
    --num_train_epochs 30.0

run_token_level_classification.py: Fine-tune on tasks for sequence classification

python run_token_level_classification.py \
    --task_name TASKNAME \
    --do_train \
    --do_eval \
    --do_lower_case \
    --data_dir /path/to/dataset \
    --bert_model /path/to/zen_model \
    --max_seq_length 128 \
    --do_train  \
    --do_eval \
    --train_batch_size 128 \
    --num_train_epochs 30 \
    --warmup_proportion 0.1

where TASKNAME can be one of CWS, POS and NER

script of fine-tuning msra

python run_token_level_classification.py \
    --task_name cwsmsra \
    --do_train \
    --do_eval \
    --do_lower_case \
    --data_dir /path/to/dataset \
    --bert_model /path/to/zen_model \
    --max_seq_length 256 \
    --do_train  \
    --do_eval \
    --train_batch_size 96 \
    --num_train_epochs 30 \
    --warmup_proportion 0.1

script of fine-tuning CTB5

python run_token_level_classification.py \
    --task_name pos \
    --do_train \
    --do_eval \
    --do_lower_case \
    --data_dir /path/to/dataset \
    --bert_model /path/to/zen_model \
    --max_seq_length 256 \
    --do_train  \
    --do_eval \
    --train_batch_size 96 \
    --num_train_epochs 30 \
    --warmup_proportion 0.1

script of fine-tuning msra_ner

python run_token_level_classification.py \
    --task_name msra \
    --do_train \
    --do_eval \
    --do_lower_case \
    --data_dir /path/to/dataset \
    --bert_model /path/to/zen_model \
    --max_seq_length 128 \
    --do_train  \
    --do_eval \
    --train_batch_size 128 \
    --num_train_epochs 30 \
    --warmup_proportion 0.1