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README.md

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+---
+language: Chinese
+datasets: CLUECorpusSmall
+widget: 
+- text: "中国的首都是[MASK]"
+
+
+
+---
+# Chinese word-based RoBERTa Miniatures
+
+## Model description
+
+This is the set of 5 Chinese word-based 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 5 Chinese word-based 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 5 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] |
+
+## 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-base-word-chinese-cluecorpussmall')
+>>> unmasker("[MASK]的首都是北京。")
+[
+    {'sequence': '中国 的首都是北京。',
+     'score': 0.21525809168815613, 
+     'token': 2873, 
+     'token_str': '中国'}, 
+    {'sequence': '北京 的首都是北京。', 
+     'score': 0.15194718539714813, 
+     'token': 9502, 
+     'token_str': '北京'}, 
+    {'sequence': '我们 的首都是北京。', 
+     'score': 0.08854265511035919, 
+     'token': 4215, 
+     'token_str': '我们'},
+    {'sequence': '美国 的首都是北京。', 
+     'score': 0.06808705627918243, 
+     'token': 7810, 
+     'token_str': '美国'}, 
+    {'sequence': '日本 的首都是北京。', 
+     'score': 0.06071401759982109, 
+     'token': 7788, 
+     'token_str': '日本'}
+]
+```
+
+BertTokenizer does not support sentencepiece, so we use AlbertTokenizer here.
+
+Here is how to use this model to get the features of a given text in PyTorch:
+
+```python
+from transformers import AlbertTokenizer, BertModel
+tokenizer = AlbertTokenizer.from_pretrained('uer/roberta-base-word-chinese-cluecorpussmall')
+model = BertModel.from_pretrained("uer/roberta-base-word-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 AlbertTokenizer, TFBertModel
+tokenizer = AlbertTokenizer.from_pretrained('uer/roberta-base-word-chinese-cluecorpussmall')
+model = TFBertModel.from_pretrained("uer/roberta-base-word-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. Google's [sentencepiece](https://github.com/google/sentencepiece) is used for word segmentation. The sentencepiece model is trained on CLUECorpusSmall corpus:
+
+```
+>>> import sentencepiece as spm
+>>> spm.SentencePieceTrainer.train(input='cluecorpussmall.txt',
+             model_prefix='cluecorpussmall_spm',
+             vocab_size=100000,
+             max_sentence_length=1024,
+             max_sentencepiece_length=6,
+             user_defined_symbols=['[MASK]','[unused1]','[unused2]',
+                '[unused3]','[unused4]','[unused5]','[unused6]',
+                '[unused7]','[unused8]','[unused9]','[unused10]'],
+             pad_id=0,
+             pad_piece='[PAD]',
+             unk_id=1,
+             unk_piece='[UNK]',
+             bos_id=2,
+             bos_piece='[CLS]',
+             eos_id=3,
+             eos_piece='[SEP]',
+             train_extremely_large_corpus=True
+            )
+```
+
+## 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
+```
+
+### 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-word-chinese-cluecorpussmall
+[4_256]:https://huggingface.co/uer/roberta-mini-word-chinese-cluecorpussmall
+[4_512]:https://huggingface.co/uer/roberta-small-word-chinese-cluecorpussmall
+[8_512]:https://huggingface.co/uer/roberta-medium-word-chinese-cluecorpussmall
+[12_768]:https://huggingface.co/uer/roberta-base-word-chinese-cluecorpussmall

config.json

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+{
+  "architectures": [
+    "BertForMaskedLM"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "gradient_checkpointing": false,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 512,
+  "initializer_range": 0.02,
+  "intermediate_size": 2048,
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 512,
+  "model_type": "bert",
+  "num_attention_heads": 8,
+  "num_hidden_layers": 4,
+  "pad_token_id": 0,
+  "tokenizer_class": "AlbertTokenizer",
+  "vocab_size": 100000
+}

pytorch_model.bin

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+size 257784263

special_tokens_map.json

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spiece.model

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tf_model.h5

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tokenizer_config.json

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+{"do_lower_case": false, "remove_space": true, "keep_accents": false, "bos_token": "[CLS]", "eos_token": "[SEP]", "unk_token": "[UNK]", "sep_token": "[SEP]", "pad_token": "[PAD]", "cls_token": "[CLS]", "mask_token": "[MASK]", "tokenize_chinese_chars": true, "strip_accents": null, "model_max_length": 512}