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	---
	language: Chinese
	datasets: CLUECorpusSmall
	widget:
	- text: "中国的首都是[MASK]"



	---


	# Chinese RoBERTa-base-word Model

	## Model description

	We use sentencepiece model to train this roberta base model. You can download the model via HuggingFace from the link [roberta-base-word-chinese-cluecorpussmall](https://huggingface.co/uer/roberta-base-word-chinese-cluecorpussmall).

	## 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("中国的首都是[MASK]。")
	```

	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.

	## Training procedure

	We use google's [sentencepiece](https://github.com/google/sentencepiece) to train the sentencepiece model.

	```
	>>> import sentencepiece as spm
	>>> spm.SentencePieceTrainer.train(input='CLUEsmall_shuf.txt',
	model_prefix='clue_6',
	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
	)
	```

	The model is 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.

	Stage1:

	```
	python3 preprocess.py --corpus_path corpora/cluecorpussmall.txt \
	--spm_model_path models/clue_6.model \
	--dataset_path cluecorpussmall_seq128_dataset.pt \
	--processes_num 32 --seq_length 128 \
	--dynamic_masking --target mlm
	```

	```
	python3 pretrain.py --dataset_path cluecorpussmall_seq128_dataset.pt \
	--spm_model_path models/clue_6.model \
	--config_path models/bert/base_config.json \
	--output_model_path models/cluecorpussmall_word_roberta_base_128.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/clue_6.model \
	--dataset_path cluecorpussmall_seq512_dataset.pt \
	--processes_num 32 --seq_length 512 \
	--dynamic_masking --target mlm
	```

	```
	python3 pretrain.py --dataset_path cluecorpussmall_seq128_dataset.pt \
	--pretrained_model_path models/cluecorpussmall_word_roberta_base_128.bin-1000000 \
	--spm_model_path models/clue_6.model \
	--config_path models/bert/base_config.json \
	--output_model_path models/cluecorpussmall_word_roberta_base_512.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_base_512.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}
	}
	```