Update README.md

README.md

@@ -3,30 +3,23 @@ language: Chinese
 widget: 
 - text: "北京上个月召开了两会"
 
-
-
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 ---
 
 # Chinese RoBERTa-Base Models for Text Classification
 
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-
 ## Model description
 
-This is the set of 5 Chinese RoBERTa base models fine-tuned by [UER-py](https://arxiv.org/abs/1909.05658).
+This is the set of 5 Chinese RoBERTa-Base classification models fine-tuned by [UER-py](https://arxiv.org/abs/1909.05658). You can download the 5 Chinese RoBERTa-Base classification models either from the [UER-py Modelzoo page](https://github.com/dbiir/UER-py/wiki/Modelzoo) (in UER-py format), or via HuggingFace from the links below:
 
 You can download the 5 Chinese RoBERTa base models either from the links below:
 
 |    corpus     |                           Link                            |
 | :-----------: | :-------------------------------------------------------: |
-|  **JD full**  |   [**roberta-base-finetuned-jd-full-chinese**][JD_full]   |
-| **JD binary** | [**roberta-base-finetuned-jd-binary-chinese**][JD_binary] |
-| **Dianping**  |  [**roberta-base-finetuned-dianping-chinese**][Dianping]  |
-|   **Ifeng**   |     [**roberta-base-finetuned-ifeng-chinese**][Ifeng]     |
-| **Chinanews** | [**roberta-base-finetuned-chinanews-chinese**][Chinanews] |
-
-
+|  **JD full**  |   [**roberta-base-finetuned-jd-full-chinese**][jd_full]   |
+| **JD binary** | [**roberta-base-finetuned-jd-binary-chinese**][jd_binary] |
+| **Dianping**  |  [**roberta-base-finetuned-dianping-chinese**][dianping]  |
+|   **Ifeng**   |     [**roberta-base-finetuned-ifeng-chinese**][ifeng]     |
+| **Chinanews** | [**roberta-base-finetuned-chinanews-chinese**][chinanews] |
 
 ## How to use
 
@@ -41,34 +34,30 @@ You can use this model directly with a pipeline for text classification (take th
     [{'label': 'mainland China politics', 'score': 0.7211663722991943}]
 ```
 
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-
 ## Training data
 
 We use 5 Chinese text classification datasets which are collected by [Glyph](https://github.com/zhangxiangxiao/glyph) project.
 
-
-
 ## Training procedure
 
-Models are fine-tuned by [UER-py](https://github.com/dbiir/UER-py/) on [Tencent Cloud](https://cloud.tencent.com/). We fine-tune three epochs with a sequence length of 512 on the basis of the pre-trained model [chinese_roberta_L-12_H-768](https://huggingface.co/uer/chinese_roberta_L-12_H-768). At the end of each epoch, the model is saved when the best performance on development set is achieved.
+Models are fine-tuned by [UER-py](https://github.com/dbiir/UER-py/) on [Tencent Cloud](https://cloud.tencent.com/). We fine-tune three epochs with a sequence length of 512 on the basis of the pre-trained model [chinese_roberta_L-12_H-768](https://huggingface.co/uer/chinese_roberta_L-12_H-768). At the end of each epoch, the model is saved when the best performance on development set is achieved. We use the same hyper-parameters on different models.
 
 Taking the case of roberta-base-finetuned-chinanews-chinese
 
 ```
-python3 run_classifier.py --pretrained_model_path models/cluecorpussmall_roberta_base_seq512_model.bin-250000 \ 
+python3 run_classifier.py --pretrained_model_path models/cluecorpussmall_roberta_base_seq512_model.bin-250000 \
                           --vocab_path models/google_zh_vocab.txt \
-                          --train_path Glyph/Chinanews_train.txt \
-                          --dev_path Glypg/Chinanews_test.txt \
-                          --output_model_path models/Chinanews_model.bin \
-                          --learning_rate 3e-5 --batch_size 32 --epochs_num 3 \
-                          --seq_length 512 --embedding word_pos_seg --encoder transformer --mask fully_visible
+                          --train_path datasets/glyph/chinanews/train.tsv \
+                          --dev_path datasets/glyph/chinanews/dev.tsv \
+                          --output_model_path models/chinanews_classifier_model.bin \
+                          --learning_rate 3e-5 --batch_size 32 --epochs_num 3 --seq_length 512 \
+                          --embedding word_pos_seg --encoder transformer --mask fully_visible
 ```
 
 Finally, we convert the pre-trained model into Huggingface's format:
 
 ```
-python3 scripts/convert_bert_text_classification_from_uer_to_huggingface.py --input_model_path models/Chinanews_model.bin \
+python3 scripts/convert_bert_text_classification_from_uer_to_huggingface.py --input_model_path models/chinanews_classifier_model.bin \
                                                                             --output_model_path pytorch_model.bin \
                                                                             --layers_num 12
 ```
@@ -85,8 +74,8 @@ python3 scripts/convert_bert_text_classification_from_uer_to_huggingface.py --in
 }
 ```
 
-[JD_full]:https://huggingface.co/uer/roberta-base-finetuned-jd-full-chinese
-[JD_binary]:https://huggingface.co/uer/roberta-base-finetuned-jd-binary-chinese
-[Dianping]:https://huggingface.co/uer/roberta-base-finetuned-dianping-chinese
-[Ifeng]:https://huggingface.co/uer/roberta-base-finetuned-ifeng-chinese
-[Chinanews]:https://huggingface.co/uer/roberta-base-finetuned-chinanews-chinese
+[jd_full]:https://huggingface.co/uer/roberta-base-finetuned-jd-full-chinese
+[jd_binary]:https://huggingface.co/uer/roberta-base-finetuned-jd-binary-chinese
+[dianping]:https://huggingface.co/uer/roberta-base-finetuned-dianping-chinese
+[ifeng]:https://huggingface.co/uer/roberta-base-finetuned-ifeng-chinese
+[chinanews]:https://huggingface.co/uer/roberta-base-finetuned-chinanews-chinese