Update README.md

README.md

@@ -26,6 +26,26 @@ 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   | 72.3  |  83.0  |     91.4     | 81.8  |    62.0     |     55.0      |    60.3     |
+| -------------- | :---: | :----: | :----------: | :---: | :---------: | :-----------: | :---------: |
+| RoBERTa-Mini   | 75.7  |  84.8  |     93.7     | 86.1  |    63.9     |     58.3      |    67.4     |
+| -------------- | :---: | :----: | :----------: | :---: | :---------: | :-----------: | :---------: |
+| RoBERTa-Small  | 76.8  |  86.5  |     93.4     | 86.5  |    65.1     |     59.4      |    69.7     |
+| -------------- | :---: | :----: | :----------: | :---: | :---------: | :-----------: | :---------: |
+| RoBERTa-Medium | 77.8  |  87.6  |     94.8     | 88.1  |    65.6     |     59.5      |    71.2     |
+| -------------- | :---: | :----: | :----------: | :---: | :---------: | :-----------: | :---------: |
+| RoBERTa-Base   | 79.5  |  89.1  |     95.2     | 89.2  |    67.0     |     60.9      |    75.5     |
+
+For each task, we selected the best fine-tuning hyperparameters from the lists below, and trained with the sequence length of 128:
+
+- epochs: 3, 5, 8
+- batch sizes: 32, 64
+- learning rates: 3e-5, 1e-4, 3e-4
+
 ## How to use
 
 You can use this model directly with a pipeline for masked language modeling (take the case of word-based RoBERTa-Medium):
@@ -117,51 +137,51 @@ 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 \
+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 \
+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 \
+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 \
+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 \
+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
 ```