uer
/

roberta-base-word-chinese-cluecorpussmall

@@ -26,20 +26,20 @@ You can download the 5 Chinese RoBERTa miniatures either from the [UER-py Github
 | **word-based RoBERTa-Medium**  | [**L=8/H=512 (Medium)**][8_512] |
 | **word-based RoBERTa-Base** | [**L=12/H=768 (Base)**][12_768] |
-Here are scores on the devlopment set of six Chinese tasks:
 | Model          | Score | douban | chnsenticorp | lcqmc | tnews(CLUE) | iflytek(CLUE) | ocnli(CLUE) |
 | -------------- | :---: | :----: | :----------: | :---: | :---------: | :-----------: | :---------: |
-| RoBERTa-Tiny(char)   | 72.3        |  83.0  |     91.4     | 81.8  |    62.0     |     55.0      |    60.3     |
-| RoBERTa-Tiny(word)   | 74.3(+2.0)  |  86.4  |     93.2     | 82.0  |    66.4     |     58.2      |    59.6     |
-| RoBERTa-Mini(char)   | 75.7        |  84.8  |     93.7     | 86.1  |    63.9     |     58.3      |    67.4     |
-| RoBERTa-Mini(word)   | 76.7(+1.0)  |  87.6  |     94.1     | 85.4  |    66.9     |     59.2      |    67.3     |
-| RoBERTa-Small(char)  | 76.8        |  86.5  |     93.4     | 86.5  |    65.1     |     59.4      |    69.7     |
-| RoBERTa-Small(word)  | 78.1(+1.3)  |  88.5  |     94.7     | 87.4  |    67.6     |     60.9      |    69.8     |
-| RoBERTa-Medium(char) | 77.8        |  87.6  |     94.8     | 88.1  |    65.6     |     59.5      |    71.2     |
-| RoBERTa-Medium(word) | 78.9(+1.1)  |  89.2  |     95.1     | 88.0  |    67.8     |     60.6      |    73.0     |
-| RoBERTa-Base(char)   | 79.5        |  89.1  |     95.2     | 89.2  |    67.0     |     60.9      |    75.5     |
-| RoBERTa-Base(word)   | 80.2(+0.7)  |  90.3  |     95.7     | 89.4  |    68.0     |     61.5      |    76.8     |
 For each task, we selected the best fine-tuning hyperparameters from the lists below, and trained with the sequence length of 128:
@@ -138,51 +138,51 @@ 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 12 --target mlm
 ```

 | **word-based RoBERTa-Medium**  | [**L=8/H=512 (Medium)**][8_512] |
 | **word-based RoBERTa-Base** | [**L=12/H=768 (Base)**][12_768] |
+Compared with [char-based models](https://huggingface.co/uer/chinese_roberta_L-2_H-128), word-based models achieve better results in most cases. Here are scores on the devlopment set of six Chinese tasks:
 | Model          | Score | douban | chnsenticorp | lcqmc | tnews(CLUE) | iflytek(CLUE) | ocnli(CLUE) |
 | -------------- | :---: | :----: | :----------: | :---: | :---------: | :-----------: | :---------: |
+| RoBERTa-Tiny(char)       | 72.3            |  83.0      |     91.4         | 81.8      |    62.0         |     55.0          |    60.3     |
+| **RoBERTa-Tiny(word)**   | **74.3(+2.0)**  |  **86.4**  |     **93.2**     | **82.0**  |    **66.4**     |     **58.2**      |    **59.6**     |
+| RoBERTa-Mini(char)       | 75.7            |  84.8      |     93.7         | 86.1      |    63.9         |     58.3          |    67.4     |
+| **RoBERTa-Mini(word)**   | **76.7(+1.0)**  |  **87.6**  |     **94.1**     | **85.4**  |    **66.9**     |     **59.2**      |    **67.3**     |
+| RoBERTa-Small(char)      | 76.8            |  86.5      |     93.4         | 86.5      |    65.1         |     59.4          |    69.7     |
+| **RoBERTa-Small(word)**  | **78.1(+1.3)**  |  **88.5**  |     **94.7**     | **87.4**  |    **67.6**     |     **60.9**      |    **69.8**     |
+| RoBERTa-Medium(char)     | 77.8            |  87.6      |     94.8         | 88.1      |    65.6         |     59.5          |    71.2     |
+| **RoBERTa-Medium(word)** | **78.9(+1.1)**  |  **89.2**  |     **95.1**     | **88.0**  |    **67.8**     |     **60.6**      |    **73.0**     |
+| RoBERTa-Base(char)       | 79.5            |  89.1      |     95.2         | 89.2      |    67.0         |     60.9          |    75.5     |
+| **RoBERTa-Base(word)**   | **80.2(+0.7)**  |  **90.3**  |     **95.7**     | **89.4**  |    **68.0**     |     **61.5**      |    **76.8**     |
 For each task, we selected the best fine-tuning hyperparameters from the lists below, and trained with the sequence length of 128:
 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
 ```