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	---
	language: zh
	datasets: CLUECorpusSmall
	widget:
	- text: "最近一趟去北京的[MASK]几点发车"


	---


	# Chinese word-based RoBERTa Miniatures

	## Model description

	This is the set of 5 Chinese word-based RoBERTa models pre-trained by [UER-py](https://github.com/dbiir/UER-py/), which is introduced in [this paper](https://arxiv.org/abs/1909.05658). Besides, the models could also be pre-trained by [TencentPretrain](https://github.com/Tencent/TencentPretrain) introduced in [this paper](https://arxiv.org/abs/2212.06385), which inherits UER-py to support models with parameters above one billion, and extends it to a multimodal pre-training framework.

	Most Chinese pre-trained weights are based on Chinese character. Compared with character-based models, word-based models are faster (because of shorter sequence length) and have better performance according to our experimental results. To this end, we released the 5 Chinese word-based RoBERTa models of different sizes. In order to facilitate users in reproducing the results, we used a 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 Modelzoo page](https://github.com/dbiir/UER-py/wiki/Modelzoo), or via HuggingFace from the links below:

	\| \| Link \|
	\| -------- \| :-----------------------: \|
	\| word-based RoBERTa-Tiny \| [L=2/H=128 (Tiny)][2_128] \|
	\| word-based RoBERTa-Mini \| [L=4/H=256 (Mini)][4_256] \|
	\| word-based RoBERTa-Small \| [L=4/H=512 (Small)][4_512] \|
	\| word-based RoBERTa-Medium \| [L=8/H=512 (Medium)][8_512] \|
	\| word-based RoBERTa-Base \| [L=12/H=768 (Base)][12_768] \|

	Compared with [char-based models](https://huggingface.co/uer/chinese_roberta_L-2_H-128), word-based models achieve better results in most cases. Here are scores on the devlopment set of six Chinese tasks:

	\| Model \| Score \| book_review \| chnsenticorp \| lcqmc \| tnews(CLUE) \| iflytek(CLUE) \| ocnli(CLUE) \|
	\| -------------- \| :---: \| :----: \| :----------: \| :---: \| :---------: \| :-----------: \| :---------: \|
	\| RoBERTa-Tiny(char) \| 72.3 \| 83.4 \| 91.4 \| 81.8 \| 62.0 \| 55.0 \| 60.3 \|
	\| RoBERTa-Tiny(word) \| 74.4(+2.1) \| 86.7 \| 93.2 \| 82.0 \| 66.4 \| 58.2 \| 59.6 \|
	\| RoBERTa-Mini(char) \| 75.9 \| 85.7 \| 93.7 \| 86.1 \| 63.9 \| 58.3 \| 67.4 \|
	\| RoBERTa-Mini(word) \| 76.9(+1.0) \| 88.5 \| 94.1 \| 85.4 \| 66.9 \| 59.2 \| 67.3 \|
	\| RoBERTa-Small(char) \| 76.9 \| 87.5 \| 93.4 \| 86.5 \| 65.1 \| 59.4 \| 69.7 \|
	\| RoBERTa-Small(word) \| 78.4(+1.5) \| 89.7 \| 94.7 \| 87.4 \| 67.6 \| 60.9 \| 69.8 \|
	\| RoBERTa-Medium(char) \| 78.0 \| 88.7 \| 94.8 \| 88.1 \| 65.6 \| 59.5 \| 71.2 \|
	\| RoBERTa-Medium(word) \| 79.1(+1.1) \| 90.0 \| 95.1 \| 88.0 \| 67.8 \| 60.6 \| 73.0 \|
	\| RoBERTa-Base(char) \| 79.7 \| 90.1 \| 95.2 \| 89.2 \| 67.0 \| 60.9 \| 75.5 \|
	\| RoBERTa-Base(word) \| 80.4(+0.7) \| 91.1 \| 95.7 \| 89.4 \| 68.0 \| 61.5 \| 76.8 \|

	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):

	```python
	>>> from transformers import pipeline
	>>> unmasker = pipeline('fill-mask', model='uer/roberta-medium-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'}
	]
	```

	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-medium-word-chinese-cluecorpussmall')
	model = BertModel.from_pretrained("uer/roberta-medium-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-medium-word-chinese-cluecorpussmall')
	model = TFBertModel.from_pretrained("uer/roberta-medium-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)
	```

	Since BertTokenizer does not support sentencepiece, AlbertTokenizer is used here.

	## 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](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.

	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 --data_processor 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 \
	--data_processor mlm --target mlm
	```

	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 --data_processor mlm
	```

	```
	python3 pretrain.py --dataset_path cluecorpussmall_word_seq512_dataset.pt \
	--spm_model_path models/cluecorpussmall_spm.model \
	--pretrained_model_path models/cluecorpussmall_word_roberta_medium_seq128_model.bin-1000000 \
	--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 \
	--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_word_roberta_medium_seq512_model.bin-250000 \
	--output_model_path pytorch_model.bin \
	--layers_num 8 --type mlm
	```

	### BibTeX entry and citation info

	```
	@article{devlin2018bert,
	title={BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding},
	author={Devlin, Jacob and Chang, Ming-Wei and Lee, Kenton and Toutanova, Kristina},
	journal={arXiv preprint arXiv:1810.04805},
	year={2018}
	}

	@article{turc2019,
	title={Well-Read Students Learn Better: On the Importance of Pre-training Compact Models},
	author={Turc, Iulia and Chang, Ming-Wei and Lee, Kenton and Toutanova, Kristina},
	journal={arXiv preprint arXiv:1908.08962v2 },
	year={2019}
	}

	@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}
	}

	@article{zhao2023tencentpretrain,
	title={TencentPretrain: A Scalable and Flexible Toolkit for Pre-training Models of Different Modalities},
	author={Zhao, Zhe and Li, Yudong and Hou, Cheng and Zhao, Jing and others},
	journal={ACL 2023},
	pages={217},
	year={2023}
	```

	[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