提交 778b9951 编写于 作者: T tianxin04

release scripts

上级 78489c4b
{
"attention_probs_dropout_prob": 0.1,
"hidden_act": "relu",
"hidden_dropout_prob": 0.1,
"hidden_size": 768,
"initializer_range": 0.02,
"max_position_embeddings": 513,
"num_attention_heads": 12,
"num_hidden_layers": 12,
"type_vocab_size": 2,
"vocab_size": 18000
}
因为 它太大了无法显示 source diff 。你可以改为 查看blob
./data/demo_train_set.gz 1.1
./data/demo_valid_set.gz 1.1
...@@ -38,7 +38,7 @@ train_g.add_arg("lr_scheduler", str, "linear_warmup_decay", ...@@ -38,7 +38,7 @@ train_g.add_arg("lr_scheduler", str, "linear_warmup_decay",
"scheduler of learning rate.", choices=['linear_warmup_decay', 'noam_decay']) "scheduler of learning rate.", choices=['linear_warmup_decay', 'noam_decay'])
train_g.add_arg("weight_decay", float, 0.01, "Weight decay rate for L2 regularizer.") train_g.add_arg("weight_decay", float, 0.01, "Weight decay rate for L2 regularizer.")
train_g.add_arg("num_train_steps", int, 1000000, "Total steps to perform pretraining.") train_g.add_arg("num_train_steps", int, 1000000, "Total steps to perform pretraining.")
train_g.add_arg("warmup_steps", int, 4000, "Total steps to perform warmup when pretraining.") train_g.add_arg("warmup_steps", int, 5000, "Total steps to perform warmup when pretraining.")
train_g.add_arg("save_steps", int, 10000, "The steps interval to save checkpoints.") train_g.add_arg("save_steps", int, 10000, "The steps interval to save checkpoints.")
train_g.add_arg("validation_steps", int, 1000, "The steps interval to evaluate model performance.") train_g.add_arg("validation_steps", int, 1000, "The steps interval to evaluate model performance.")
train_g.add_arg("use_fp16", bool, False, "Whether to use fp16 mixed precision training.") train_g.add_arg("use_fp16", bool, False, "Whether to use fp16 mixed precision training.")
......
set -eux
export FLAGS_sync_nccl_allreduce=1
export CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7
python -u ./train.py --use_cuda True \
--is_distributed False\
--use_fast_executor True \
--weight_sharing True \
--in_tokens true \
--batch_size 8192 \
--vocab_path ./config/vocab.txt \
--train_filelist ./data/train_filelist \
--valid_filelist ./data/valid_filelist \
--validation_steps 100 \
--num_train_steps 1000000 \
--checkpoints ./checkpoints \
--save_steps 10000 \
--ernie_config_path ./config/ernie_config.json \
--learning_rate 1e-4 \
--weight_decay 0.01 \
--max_seq_len 512 \
--skip_steps 10
set -eux
export FLAGS_sync_nccl_allreduce=1
export CUDA_VISIBLE_DEVICES=0
python -u run_classifier.py \
--use_cuda true \
--verbose true \
--do_train true \
--do_val true \
--do_test true \
--batch_size 24 \
--init_pretraining_params ${MODEL_PATH} \
--train_set ${TASK_DATA_PATH}/chnsenticorp/train.tsv \
--dev_set ${TASK_DATA_PATH}/chnsenticorp/dev.tsv \
--test_set ${TASK_DATA_PATH}/chnsenticorp/test.tsv \
--vocab_path config/vocab.txt \
--checkpoints ./checkpoints \
--save_steps 1000 \
--weight_decay 0.01 \
--warmup_proportion 0.0 \
--validation_steps 100 \
--epoch 10 \
--max_seq_len 256 \
--ernie_config_path config/ernie_config.json \
--learning_rate 5e-5 \
--skip_steps 10 \
--num_iteration_per_drop_scope 1 \
--num_labels 2 \
--random_seed 1
set -eux
export FLAGS_sync_nccl_allreduce=1
export CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7
python -u run_classifier.py \
--use_cuda true \
--verbose true \
--do_train true \
--do_val true \
--do_test true \
--batch_size 8 \
--init_pretraining_params ${MODEL_PATH} \
--train_set ${TASK_DATA_PATH}/nlpcc-dbqa/train.tsv \
--dev_set ${TASK_DATA_PATH}/nlpcc-dbqa/dev.tsv \
--test_set ${TASK_DATA_PATH}/nlpcc-dbqa/test.tsv \
--vocab_path config/vocab.txt \
--ernie_config_path config/ernie_config.json \
--checkpoints "./checkpoints" \
--save_steps 1000 \
--weight_decay 0.01 \
--warmup_proportion 0.0 \
--validation_steps 1000 \
--epoch 3 \
--max_seq_len 512 \
--learning_rate 2e-5 \
--skip_steps 10 \
--num_iteration_per_drop_scope 1 \
--num_labels 2 \
--random_seed 1
set -eux
export FLAGS_sync_nccl_allreduce=1
export CUDA_VISIBLE_DEVICES=0
python -u run_classifier.py \
--use_cuda true \
--verbose true \
--do_train true \
--do_val true \
--do_test true \
--batch_size 32 \
--init_pretraining_params ${MODEL_PATH} \
--train_set ${TASK_DATA_PATH}/lcqmc/train.tsv \
--dev_set ${TASK_DATA_PATH}/lcqmc/dev.tsv \
--test_set ${TASK_DATA_PATH}/lcqmc/test.tsv \
--vocab_path config/vocab.txt \
--checkpoints ./checkpoints \
--save_steps 1000 \
--weight_decay 0.0 \
--warmup_proportion 0.0 \
--validation_steps 100 \
--epoch 3 \
--max_seq_len 128 \
--ernie_config_path config/ernie_config.json \
--learning_rate 2e-5 \
--skip_steps 10 \
--num_iteration_per_drop_scope 1 \
--num_labels 2 \
--random_seed 1
set -eux
export FLAGS_sync_nccl_allreduce=1
export CUDA_VISIBLE_DEVICES=0
python -u run_sequence_labeling.py \
--use_cuda true \
--do_train true \
--do_val true \
--do_test true \
--batch_size 16 \
--init_pretraining_params ${MODEL_PATH} \
--num_labels 7 \
--label_map_config ${TASK_DATA_PATH}/label_map.json \
--train_set ${TASK_DATA_PATH}/msra_ner/train.tsv \
--dev_set ${TASK_DATA_PATH}/msra_ner/dev.tsv \
--test_set ${TASK_DATA_PATH}/msra_ner/test.tsv \
--vocab_path config/vocab.txt \
--ernie_config_path config/ernie_config.json \
--checkpoints ./checkpoints \
--save_steps 100000 \
--weight_decay 0.01 \
--warmup_proportion 0.0 \
--validation_steps 100 \
--epoch 3 \
--max_seq_len 256 \
--learning_rate 5e-5 \
--skip_steps 10 \
--num_iteration_per_drop_scope 1 \
--random_seed 1
set -eux
export FLAGS_sync_nccl_allreduce=1
export CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7
python -u run_classifier.py \
--use_cuda true \
--do_train true \
--do_val true \
--do_test true \
--verbose true \
--batch_size 8192 \
--in_tokens true \
--init_pretraining_params ${MODEL_PATH} \
--train_set ${TASK_DATA_PATH}/xnli/train.tsv \
--dev_set ${TASK_DATA_PATH}/xnli/dev.tsv \
--test_set ${TASK_DATA_PATH}/xnli/test.tsv \
--vocab_path config/vocab.txt \
--label_map ${TASK_DATA_PATH}/xnli/label_map.json \
--ernie_config_path config/ernie_config.json \
--checkpoints ./checkpoints \
--save_steps 1000 \
--weight_decay 0.01 \
--warmup_proportion 0.0 \
--validation_steps 25 \
--epoch 3 \
--max_seq_len 512 \
--learning_rate 1e-4 \
--skip_steps 10 \
--num_iteration_per_drop_scope 1 \
--num_labels 3 \
--random_seed 1
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