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README.md

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+---
+language: zh
+datasets: CLUECorpusSmall
+widget: 
+- text: "北京是[MASK]国的首都。"
+
+
+
+---
+# Chinese Whole Word Masking RoBERTa Miniatures
+
+## Model description
+
+This is the set of 6 Chinese Whole Word Masking RoBERTa models pre-trained by [UER-py](https://arxiv.org/abs/1909.05658).
+
+[Turc et al.](https://arxiv.org/abs/1908.08962) have shown that the standard BERT recipe is effective on a wide range of model sizes. Following their paper, we released the 6 Chinese Whole Word Masking RoBERTa models. In order to facilitate users to reproduce the results, we used the publicly available corpus and word segmentation tool, and provided all training details.
+
+You can download the 6 Chinese RoBERTa miniatures either from the [UER-py Github page](https://github.com/dbiir/UER-py/), or via HuggingFace from the links below:
+
+|          |           Link           |
+| -------- | :-----------------------: |
+| **Tiny**  | [**2/128 (Tiny)**][2_128] |
+| **Mini**  | [**4/256 (Mini)**][4_256] |
+| **Small**  | [**4/512 (Small)**][4_512] |
+| **Medium**  | [**8/512 (Medium)**][8_512] |
+| **Base** | [**12/768 (Base)**][12_768] |
+| **Large** | [**24/1024 (Large)**][24_1024] |
+
+Here are scores on the devlopment set of six Chinese tasks:
+
+| Model              | Score | douban | chnsenticorp | lcqmc | tnews(CLUE) | iflytek(CLUE) | ocnli(CLUE) |
+| ------------------ | :---: | :----: | :----------: | :---: | :---------: | :-----------: | :---------: |
+| RoBERTa-Tiny-WWM   | 72.1  |  82.8  |     91.8     | 81.8  |    62.1     |     55.4      |    58.6     |
+| RoBERTa-Mini-WWM   | 76.1  |  84.9  |     93.0     | 86.8  |    64.4     |     58.7      |    68.8     |
+| RoBERTa-Small-WWM  | 77.3  |  86.8  |     93.8     | 87.2  |    65.2     |     59.6      |    71.4     |
+| RoBERTa-Medium-WWM | 78.4  |  88.2  |     94.4     | 88.8  |    66.0     |     59.9      |    73.2     |
+| RoBERTa-Base-WWM   | 80.1  |  90.0  |     95.8     | 89.4  |    67.5     |     61.8      |    76.2     |
+| RoBERTa-Large-WWM  | 81.0  |  90.4  |     95.8     | 90.0  |    68.5     |     62.1      |    79.1     |
+
+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:
+
+```python
+>>> from transformers import pipeline
+>>> unmasker = pipeline('fill-mask', model='uer/roberta-tiny-wwm-chinese-cluecorpussmall')
+>>> unmasker("北京是[MASK]国的首都。")
+[
+    {'score': 0.294228732585907, 
+     'token': 704, 
+     'token_str': '中', 
+     'sequence': '北 京 是 中 国 的 首 都 。'},
+    {'score': 0.19691626727581024, 
+     'token': 1266, 
+     'token_str': '北', 
+     'sequence': '北 京 是 北 国 的 首 都 。'},
+    {'score': 0.1070084273815155, 
+     'token': 7506, 
+     'token_str': '韩', 
+     'sequence': '北 京 是 韩 国 的 首 都 。'},
+    {'score': 0.031527262181043625, 
+     'token': 2769, 
+     'token_str': '我', 
+     'sequence': '北 京 是 我 国 的 首 都 。'},
+    {'score': 0.023054633289575577, 
+     'token': 1298, 
+     'token_str': '南', 
+     'sequence': '北 京 是 南 国 的 首 都 。'}
+]
+    
+
+
+```
+
+Here is how to use this model to get the features of a given text in PyTorch:
+
+```python
+from transformers import BertTokenizer, BertModel
+tokenizer = BertTokenizer.from_pretrained('uer/roberta-base-wwm-chinese-cluecorpussmall')
+model = BertModel.from_pretrained("uer/roberta-base-wwm-chinese-cluecorpussmall")
+text = "用你喜欢的任何文本替换我。"
+encoded_input = tokenizer(text, return_tensors='pt')
+output = model(**encoded_input)
+```
+
+and in TensorFlow:
+
+```python
+from transformers import BertTokenizer, TFBertModel
+tokenizer = BertTokenizer.from_pretrained('uer/roberta-base-wwm-chinese-cluecorpussmall')
+model = TFBertModel.from_pretrained("uer/roberta-base-wwm-chinese-cluecorpussmall")
+text = "用你喜欢的任何文本替换我。"
+encoded_input = tokenizer(text, return_tensors='tf')
+output = model(encoded_input)
+```
+
+## Training data
+
+[CLUECorpusSmall](https://github.com/CLUEbenchmark/CLUECorpus2020/) is used as training data. 
+
+## 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.
+
+[jieba](https://github.com/fxsjy/jieba) is used as word segmentation tool.
+
+Taking the case of Whole Word Masking RoBERTa-Medium
+
+Stage1:
+
+```
+python3 preprocess.py --corpus_path corpora/cluecorpussmall.txt \
+                      --vocab_path models/google_zh_vocab.txt \
+                      --dataset_path cluecorpussmall_seq128_dataset.pt \
+                      --processes_num 32 --seq_length 128 \
+                      --dynamic_masking --data_processor mlm
+```
+
+```
+python3 pretrain.py --dataset_path cluecorpussmall_word_seq128_dataset.pt \
+                    --vocab_path models/google_zh_vocab.txt \
+                    --config_path models/bert/medium_config.json \
+                    --output_model_path models/cluecorpussmall_wwm_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 \
+                    --whole_word_masking \
+                    --data_processor mlm --target mlm
+```
+
+Stage2:
+
+```
+python3 preprocess.py --corpus_path corpora/cluecorpussmall.txt \
+                      --vocab_path models/google_zh_vocab.txt \
+                      --dataset_path cluecorpussmall_seq512_dataset.pt \
+                      --processes_num 32 --seq_length 512 \
+                      --dynamic_masking --data_processor mlm
+```
+
+```
+python3 pretrain.py --dataset_path cluecorpussmall_seq512_dataset.pt \
+                    --vocab_path models/google_zh_vocab.txt \
+                    --pretrained_model_path models/cluecorpussmall_wwm_roberta_medium_seq128_model.bin-1000000 \
+                    --config_path models/bert/medium_config.json \
+                    --output_model_path models/cluecorpussmall_wwm_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 \
+                    --whole_word_masking \
+                    --data_processor mlm --target mlm
+```
+
+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_wwm_roberta_medium_seq512_model.bin \
+                                                        --output_model_path pytorch_model.bin \
+                                                        --layers_num 8 --type mlm
+```
+
+### BibTeX entry and citation info
+
+```
+@article{zhao2019uer,
+  title={UER: An Open-Source Toolkit for Pre-training Models},
+  author={Zhao, Zhe and Chen, Hui and Zhang, Jinbin and Zhao, Xin and Liu, Tao and Lu, Wei and Chen, Xi and Deng, Haotang and Ju, Qi and Du, Xiaoyong},
+  journal={EMNLP-IJCNLP 2019},
+  pages={241},
+  year={2019}
+}
+```
+
+[2_128]:https://huggingface.co/uer/roberta-tiny-wwm-chinese-cluecorpussmall
+[4_256]:https://huggingface.co/uer/roberta-mini-wwm-chinese-cluecorpussmall
+[4_512]:https://huggingface.co/uer/roberta-small-wwm-chinese-cluecorpussmall
+[8_512]:https://huggingface.co/uer/roberta-medium-wwm-chinese-cluecorpussmall
+[12_768]:https://huggingface.co/uer/roberta-base-wwm-chinese-cluecorpussmall
+[24_1024]:https://huggingface.co/uer/roberta-large-wwm-chinese-cluecorpussmall

config.json

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+{
+  "architectures": [
+    "BertForMaskedLM"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "gradient_checkpointing": false,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 1024,
+  "initializer_range": 0.02,
+  "intermediate_size": 4096,
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 512,
+  "model_type": "bert",
+  "num_attention_heads": 16,
+  "num_hidden_layers": 24,
+  "pad_token_id": 0,
+  "position_embedding_type": "absolute",
+  "torch_dtype": "float32",
+  "transformers_version": "4.17.0",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 21128
+}

pytorch_model.bin

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+version https://git-lfs.github.com/spec/v1
+oid sha256:7c932e4a80421de25d75cc0a6112badc01635a2aeaf359d9315e10e06e946313
+size 1302349831

special_tokens_map.json

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+{"unk_token": "[UNK]", "sep_token": "[SEP]", "pad_token": "[PAD]", "cls_token": "[CLS]", "mask_token": "[MASK]"}

tokenizer_config.json

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+{"do_lower_case": true, "do_basic_tokenize": true, "never_split": null, "unk_token": "[UNK]", "sep_token": "[SEP]", "pad_token": "[PAD]", "cls_token": "[CLS]", "mask_token": "[MASK]", "tokenize_chinese_chars": true, "strip_accents": null, "special_tokens_map_file": null, "tokenizer_class": "BertTokenizer"}