Update README.md

README.md

@@ -37,18 +37,18 @@ Training data comes from three sources: [cmrc2018](https://github.com/ymcui/cmrc
 The model is fine-tuned by [UER-py](https://github.com/dbiir/UER-py/) on [Tencent Cloud](https://cloud.tencent.com/). 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). At the end of each epoch, the model is saved when the best performance on development set is achieved.
 
 ```
-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 --epochs_num 3 --batch_size 32 --seq_length 512
+python3 finetune/run_cmrc.py --pretrained_model_path models/cluecorpussmall_roberta_base_seq512_model.bin-250000 \
+                             --vocab_path models/google_zh_vocab.txt \
+                             --train_path datasets/extractive_qa.json \
+                             --dev_path datasets/cmrc2018/dev.json \
+                             --output_model_path models/extractive_qa_model.bin \
+                             --learning_rate 3e-5 --epochs_num 3 --batch_size 32 --seq_length 512
 ```
 
 Finally, we convert the fine-tuned model into Huggingface's format:
 
 ```
-python3 scripts/convert_bert_extractive_qa_from_uer_to_huggingface.py --input_model_path extractive_qa_model.bin \
+python3 scripts/convert_bert_extractive_qa_from_uer_to_huggingface.py --input_model_path models/extractive_qa_model.bin \
                                                                       --output_model_path pytorch_model.bin \
                                                                       --layers_num 12
 ```