uer
/

roberta-base-word-chinese-cluecorpussmall

@@ -131,58 +131,58 @@ Since BertTokenizer does not support sentencepiece, AlbertTokenizer is used here
 ## Training procedure
-Models are pre-trained by [UER-py](https://github.com/dbiir/UER-py/) on [Tencent Cloud TI-ONE](https://cloud.tencent.com/product/tione/). We pre-train 1,000,000 steps with a sequence length of 128 and then pre-train 250,000 additional steps with a sequence length of 512. We use the same hyper-parameters on different model sizes.
 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 12 --target mlm
 ```

 ## Training procedure
+Models are pre-trained by [UER-py](https://github.com/dbiir/UER-py/) on [Tencent Cloud](https://cloud.tencent.com/). We pre-train 1,000,000 steps with a sequence length of 128 and then pre-train 250,000 additional steps with a sequence length of 512. We use the same hyper-parameters on different model sizes.
 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 12 --target mlm
 ```