Update README.md

README.md

@@ -6,11 +6,11 @@ widget:
 
 ---
 
-# Chinese RoBERTa Base Model for QA
+# Chinese RoBERTa-Base Model for QA
 
 ## Model description
 
-The model is used for extractive question answering. You can download the model  from the link [roberta-base-chinese-extractive-qa](https://huggingface.co/uer/roberta-base-chinese-extractive-qa).
+The model is used for extractive question answering. You can download the model from the link [roberta-base-chinese-extractive-qa](https://huggingface.co/uer/roberta-base-chinese-extractive-qa).
 
 ## How to use
 
@@ -27,20 +27,19 @@ You can use the model directly with a pipeline for extractive question answering
 
 ## Training data
 
-Training data contains three datasets ,including [cmrc2018](https://github.com/ymcui/cmrc2018), [webqa](https://spaces.ac.cn/archives/4338) and [莱斯杯](https://www.kesci.com/home/competition/5d142d8cbb14e6002c04e14a/content/0).
+Training data comes from three sources: [cmrc2018](https://github.com/ymcui/cmrc2018), [webqa](https://spaces.ac.cn/archives/4338), and [laisi](https://www.kesci.com/home/competition/5d142d8cbb14e6002c04e14a/content/0). We only use train set of the three datasets.
 
 ## Training procedure
 
 The model is fine-tuned by [UER-py](https://github.com/dbiir/UER-py/) on [Tencent Cloud TI-ONE](https://cloud.tencent.com/product/tione/). We fine-tune three epochs with a sequence length of 512 on the basis of the pre-trained model [chinese_roberta_L-12_H-768](https://huggingface.co/uer/chinese_roberta_L-12_H-768).
 
 ```
-python3 run_cmrc.py --dataset_path lyric_dataset.pt \
-                    --pretrained_model_path models/cluecorpussmall_roberta_base_seq512_model.bin-250000 \
+python3 run_cmrc.py --pretrained_model_path models/cluecorpussmall_roberta_base_seq512_model.bin-250000 \
                     --vocab_path models/google_zh_vocab.txt \
                     --train_path extractive_qa.json \
                     --dev_path datasets/cmrc2018/dev.json \
                     --output_model_path models/extractive_qa_model.bin \
-                    --learning_rate 3e-5 --batch_size 32 --epochs_num 3 \
+                    --learning_rate 3e-5 --batch_size 32 --epochs_num 3 --seq_length 512 \
                     --embedding word_pos_seg --encoder transformer --mask fully_visible
 ```