uer
/

roberta-tiny-word-chinese-cluecorpussmall

@@ -140,51 +140,51 @@ Taking the case of word-based RoBERTa-Medium
 Stage1:
 ```
-python3 preprocess.py --corpus_path corpora/cluecorpussmall.txt \\\\
-                      --spm_model_path models/cluecorpussmall_spm.model \\\\
-                      --dataset_path cluecorpussmall_word_seq128_dataset.pt \\\\
-                      --processes_num 32 --seq_length 128 \\\\
                       --dynamic_masking --target mlm
 ```
 ```
-python3 pretrain.py --dataset_path cluecorpussmall_word_seq128_dataset.pt \\\\
-                    --spm_model_path models/cluecorpussmall_spm.model \\\\
-                    --config_path models/bert/medium_config.json \\\\
-                    --output_model_path models/cluecorpussmall_word_roberta_medium_seq128_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 64 \\\\
                     --embedding word_pos_seg --encoder transformer --mask fully_visible --target mlm --tie_weights
 ```
 Stage2:
 ```
-python3 preprocess.py --corpus_path corpora/cluecorpussmall.txt \\\\
-                      --spm_model_path models/cluecorpussmall_spm.model \\\\
-                      --dataset_path cluecorpussmall_word_seq512_dataset.pt \\\\
-                      --processes_num 32 --seq_length 512 \\\\
                       --dynamic_masking --target mlm
 ```
 ```
-python3 pretrain.py --dataset_path cluecorpussmall_word_seq512_dataset.pt \\\\
-                    --pretrained_model_path models/cluecorpussmall_word_roberta_medium_seq128_model.bin-1000000 \\\\
-                    --spm_model_path models/cluecorpussmall_spm.model \\\\
-                    --config_path models/bert/medium_config.json \\\\
-                    --output_model_path models/cluecorpussmall_word_roberta_medium_seq512_model.bin \\\\
-                    --world_size 8 --gpu_ranks 0 1 2 3 4 5 6 7 \\\\
-                    --total_steps 250000 --save_checkpoint_steps 50000 --report_steps 10000 \\\\
-                    --learning_rate 5e-5 --batch_size 16 \\\\
                     --embedding word_pos_seg --encoder transformer --mask fully_visible --target mlm --tie_weights
 ```
 Finally, we convert the pre-trained model into Huggingface's format:
 ```
-python3 scripts/convert_bert_from_uer_to_huggingface.py --input_model_path models/cluecorpussmall_word_roberta_medium_seq128_model.bin-250000 \\\\
-                                                        --output_model_path pytorch_model.bin \\\\
                                                         --layers_num 8 --target mlm
 ```

 Stage1:
 ```
+python3 preprocess.py --corpus_path corpora/cluecorpussmall.txt \
+                      --spm_model_path models/cluecorpussmall_spm.model \
+                      --dataset_path cluecorpussmall_word_seq128_dataset.pt \
+                      --processes_num 32 --seq_length 128 \
                       --dynamic_masking --target mlm
 ```
 ```
+python3 pretrain.py --dataset_path cluecorpussmall_word_seq128_dataset.pt \
+                    --spm_model_path models/cluecorpussmall_spm.model \
+                    --config_path models/bert/medium_config.json \
+                    --output_model_path models/cluecorpussmall_word_roberta_medium_seq128_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 64 \
                     --embedding word_pos_seg --encoder transformer --mask fully_visible --target mlm --tie_weights
 ```
 Stage2:
 ```
+python3 preprocess.py --corpus_path corpora/cluecorpussmall.txt \
+                      --spm_model_path models/cluecorpussmall_spm.model \
+                      --dataset_path cluecorpussmall_word_seq512_dataset.pt \
+                      --processes_num 32 --seq_length 512 \
                       --dynamic_masking --target mlm
 ```
 ```
+python3 pretrain.py --dataset_path cluecorpussmall_word_seq512_dataset.pt \
+                    --pretrained_model_path models/cluecorpussmall_word_roberta_medium_seq128_model.bin-1000000 \
+                    --spm_model_path models/cluecorpussmall_spm.model \
+                    --config_path models/bert/medium_config.json \
+                    --output_model_path models/cluecorpussmall_word_roberta_medium_seq512_model.bin \
+                    --world_size 8 --gpu_ranks 0 1 2 3 4 5 6 7 \
+                    --total_steps 250000 --save_checkpoint_steps 50000 --report_steps 10000 \
+                    --learning_rate 5e-5 --batch_size 16 \
                     --embedding word_pos_seg --encoder transformer --mask fully_visible --target mlm --tie_weights
 ```
 Finally, we convert the pre-trained model into Huggingface's format:
 ```
+python3 scripts/convert_bert_from_uer_to_huggingface.py --input_model_path models/cluecorpussmall_word_roberta_medium_seq128_model.bin-250000 \
+                                                        --output_model_path pytorch_model.bin \
                                                         --layers_num 8 --target mlm
 ```