Update README.md

README.md

@@ -36,11 +36,25 @@ The model is pre-trained by [UER-py](https://github.com/dbiir/UER-py/) on [Tence
 
 
 ```
-python3 preprocess.py --corpus_path corpora/cluecorpussmall.txt \\n                      --vocab_path models/google_zh_vocab.txt \\n                      --dataset_path cluecorpussmall_bart_seq512_dataset.pt \\n                      --processes_num 32 --seq_length 512 \\n                      --target bart 
+python3 preprocess.py --corpus_path corpora/cluecorpussmall.txt \
+                      --vocab_path models/google_zh_vocab.txt \
+                      --dataset_path cluecorpussmall_bart_seq512_dataset.pt \
+                      --processes_num 32 --seq_length 512 \
+                      --target bart 
 ```
 
 ```
-python3 pretrain.py --dataset_path cluecorpussmall_bart_seq512_dataset.pt \\n                    --vocab_path models/google_zh_vocab.txt \\n                    --config_path models/bart/base_config.json \\n                    --output_model_path models/cluecorpussmall_bart_base_seq512_model.bin \\n                    --world_size 8 --gpu_ranks 0 1 2 3 4 5 6 7 \\n                    --total_steps 1000000 --save_checkpoint_steps 100000 --report_steps 50000 \\n                    --learning_rate 1e-4 --batch_size 16 \\n                    --span_masking --span_max_length 3 \\n                    --embedding word_pos --tgt_embedding word_pos \\n                    --encoder transformer --mask fully_visible --decoder transformer \\n                    --target bart --tie_weights --has_lmtarget_bias
+python3 pretrain.py --dataset_path cluecorpussmall_bart_seq512_dataset.pt \
+                    --vocab_path models/google_zh_vocab.txt \
+                    --config_path models/bart/base_config.json \
+                    --output_model_path models/cluecorpussmall_bart_base_seq512_model.bin \
+                    --world_size 8 --gpu_ranks 0 1 2 3 4 5 6 7 \
+                    --total_steps 1000000 --save_checkpoint_steps 100000 --report_steps 50000 \
+                    --learning_rate 1e-4 --batch_size 16 \
+                    --span_masking --span_max_length 3 \
+                    --embedding word_pos --tgt_embedding word_pos \
+                    --encoder transformer --mask fully_visible --decoder transformer \
+                    --target bart --tie_weights --has_lmtarget_bias
 ```
 
 Finally, we convert the pre-trained model into Huggingface's format: