SUMIN/HuggingFace_transformer
1
0
Fork 0
Code Issues Pull Requests Actions 7 Packages Projects Releases Wiki Activity

readme update

Browse Source
This commit is contained in:
thomwolf
2019-07-11 15:39:49 +02:00
parent b21d84b027
commit 6135de2fa3
1 changed files with 2 additions and 12 deletions
Download Patch File Download Diff File Expand all files Collapse all files

14
README.md
View File

@@ -1620,20 +1620,10 @@ and unpack it to some directory `$GLUE_DIR`.
```shell ```shell
export GLUE_DIR=/path/to/glue export GLUE_DIR=/path/to/glue
python run_xlnet_classifier.py \ CUDA_VISIBLE_DEVICES=0,1,2,3 python ./examples/run_glue.py --do_train --task_name=sts-b --data_dir=${GLUE_DIR}/STS-B --output_dir=./proc_data/sts-b-110 --max_seq_length=128 --per_gpu_eval_batch_size=8 --per_gpu_train_batch_size=8 --max_steps=1200 --model_name=xlnet-large-cased --overwrite_output_dir --overwrite_cache --warmup_steps=120
--task_name STS-B \
--do_train \
--do_eval \
--data_dir $GLUE_DIR/STS-B/ \
--max_seq_length 128 \
--train_batch_size 8 \
--gradient_accumulation_steps 1 \
--learning_rate 5e-5 \
--num_train_epochs 3.0 \
--output_dir /tmp/mrpc_output/
``` ```
Our test ran on a few seeds with [the original implementation hyper-parameters](https://github.com/zihangdai/xlnet#1-sts-b-sentence-pair-relevance-regression-with-gpus) gave evaluation results between 84% and 88%. This hyper-parameters give evaluation results pearsonr > 0.918.
### Distributed training ### Distributed training