Update README.md

README.md

@@ -41,13 +41,13 @@ You can use this model directly with a pipeline for token classification :
 The model is fine-tuned by [UER-py](https://github.com/dbiir/UER-py/) on [Tencent Cloud](https://cloud.tencent.com/). We fine-tune five 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_ner.py --pretrained_model_path models/cluecorpussmall_roberta_base_seq512_model.bin-250000 \
-                   --vocab_path models/google_zh_vocab.txt \
-                   --train_path datasets/cluener2020/train.tsv \
-                   --dev_path datasets/cluener2020/dev.tsv \
-                   --label2id_path datasets/cluener2020/label2id.json \
-                   --output_model_path models/cluener2020_ner_model.bin \
-                   --learning_rate 3e-5 --epochs_num 5 --batch_size 32 --seq_length 512
+python3 finetune/run_ner.py --pretrained_model_path models/cluecorpussmall_roberta_base_seq512_model.bin-250000 \
+                            --vocab_path models/google_zh_vocab.txt \
+                            --train_path datasets/cluener2020/train.tsv \
+                            --dev_path datasets/cluener2020/dev.tsv \
+                            --label2id_path datasets/cluener2020/label2id.json \
+                            --output_model_path models/cluener2020_ner_model.bin \
+                            --learning_rate 3e-5 --epochs_num 5 --batch_size 32 --seq_length 512
 ```
 
 Finally, we convert the pre-trained model into Huggingface's format: