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--- |
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language: zh |
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datasets: CLUECorpusSmall |
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widget: |
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- text: "最近一趟去北京的[MASK]几点发车" |
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--- |
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# Chinese word-based RoBERTa Miniatures |
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## Model description |
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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. |
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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. |
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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: |
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| | Link | |
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| -------- | :-----------------------: | |
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| **word-based RoBERTa-Tiny** | [**L=2/H=128 (Tiny)**][2_128] | |
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| **word-based RoBERTa-Mini** | [**L=4/H=256 (Mini)**][4_256] | |
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| **word-based RoBERTa-Small** | [**L=4/H=512 (Small)**][4_512] | |
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| **word-based RoBERTa-Medium** | [**L=8/H=512 (Medium)**][8_512] | |
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| **word-based RoBERTa-Base** | [**L=12/H=768 (Base)**][12_768] | |
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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: |
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| Model | Score | book_review | chnsenticorp | lcqmc | tnews(CLUE) | iflytek(CLUE) | ocnli(CLUE) | |
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| -------------- | :---: | :----: | :----------: | :---: | :---------: | :-----------: | :---------: | |
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| RoBERTa-Tiny(char) | 72.3 | 83.4 | 91.4 | 81.8 | 62.0 | 55.0 | 60.3 | |
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| **RoBERTa-Tiny(word)** | **74.4(+2.1)** | **86.7** | **93.2** | **82.0** | **66.4** | **58.2** | **59.6** | |
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| RoBERTa-Mini(char) | 75.9 | 85.7 | 93.7 | 86.1 | 63.9 | 58.3 | 67.4 | |
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| **RoBERTa-Mini(word)** | **76.9(+1.0)** | **88.5** | **94.1** | **85.4** | **66.9** | **59.2** | **67.3** | |
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| RoBERTa-Small(char) | 76.9 | 87.5 | 93.4 | 86.5 | 65.1 | 59.4 | 69.7 | |
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| **RoBERTa-Small(word)** | **78.4(+1.5)** | **89.7** | **94.7** | **87.4** | **67.6** | **60.9** | **69.8** | |
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| RoBERTa-Medium(char) | 78.0 | 88.7 | 94.8 | 88.1 | 65.6 | 59.5 | 71.2 | |
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| **RoBERTa-Medium(word)** | **79.1(+1.1)** | **90.0** | **95.1** | **88.0** | **67.8** | **60.6** | **73.0** | |
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| RoBERTa-Base(char) | 79.7 | 90.1 | 95.2 | 89.2 | 67.0 | 60.9 | 75.5 | |
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| **RoBERTa-Base(word)** | **80.4(+0.7)** | **91.1** | **95.7** | **89.4** | **68.0** | **61.5** | **76.8** | |
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For each task, we selected the best fine-tuning hyperparameters from the lists below, and trained with the sequence length of 128: |
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- epochs: 3, 5, 8 |
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- batch sizes: 32, 64 |
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- learning rates: 3e-5, 1e-4, 3e-4 |
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## How to use |
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You can use this model directly with a pipeline for masked language modeling (take the case of word-based RoBERTa-Medium): |
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```python |
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>>> from transformers import pipeline |
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>>> unmasker = pipeline('fill-mask', model='uer/roberta-medium-word-chinese-cluecorpussmall') |
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>>> unmasker("[MASK]的首都是北京。") |
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[ |
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{'sequence': '中国 的首都是北京。', |
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'score': 0.21525809168815613, |
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'token': 2873, |
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'token_str': '中国'}, |
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{'sequence': '北京 的首都是北京。', |
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'score': 0.15194718539714813, |
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'token': 9502, |
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'token_str': '北京'}, |
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{'sequence': '我们 的首都是北京。', |
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'score': 0.08854265511035919, |
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'token': 4215, |
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'token_str': '我们'}, |
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{'sequence': '美国 的首都是北京。', |
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'score': 0.06808705627918243, |
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'token': 7810, |
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'token_str': '美国'}, |
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{'sequence': '日本 的首都是北京。', |
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'score': 0.06071401759982109, |
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'token': 7788, |
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'token_str': '日本'} |
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] |
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``` |
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Here is how to use this model to get the features of a given text in PyTorch: |
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```python |
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from transformers import AlbertTokenizer, BertModel |
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tokenizer = AlbertTokenizer.from_pretrained('uer/roberta-medium-word-chinese-cluecorpussmall') |
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model = BertModel.from_pretrained("uer/roberta-medium-word-chinese-cluecorpussmall") |
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text = "用你喜欢的任何文本替换我。" |
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encoded_input = tokenizer(text, return_tensors='pt') |
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output = model(**encoded_input) |
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``` |
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and in TensorFlow: |
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```python |
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from transformers import AlbertTokenizer, TFBertModel |
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tokenizer = AlbertTokenizer.from_pretrained('uer/roberta-medium-word-chinese-cluecorpussmall') |
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model = TFBertModel.from_pretrained("uer/roberta-medium-word-chinese-cluecorpussmall") |
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text = "用你喜欢的任何文本替换我。" |
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encoded_input = tokenizer(text, return_tensors='tf') |
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output = model(encoded_input) |
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``` |
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Since BertTokenizer does not support sentencepiece, AlbertTokenizer is used here. |
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## Training data |
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[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: |
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``` |
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>>> import sentencepiece as spm |
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>>> spm.SentencePieceTrainer.train(input='cluecorpussmall.txt', |
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model_prefix='cluecorpussmall_spm', |
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vocab_size=100000, |
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max_sentence_length=1024, |
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max_sentencepiece_length=6, |
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user_defined_symbols=['[MASK]','[unused1]','[unused2]', |
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'[unused3]','[unused4]','[unused5]','[unused6]', |
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'[unused7]','[unused8]','[unused9]','[unused10]'], |
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pad_id=0, |
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pad_piece='[PAD]', |
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unk_id=1, |
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unk_piece='[UNK]', |
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bos_id=2, |
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bos_piece='[CLS]', |
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eos_id=3, |
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eos_piece='[SEP]', |
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train_extremely_large_corpus=True |
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) |
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``` |
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## Training procedure |
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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. |
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Taking the case of word-based RoBERTa-Medium |
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Stage1: |
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``` |
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python3 preprocess.py --corpus_path corpora/cluecorpussmall.txt \ |
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--spm_model_path models/cluecorpussmall_spm.model \ |
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--dataset_path cluecorpussmall_word_seq128_dataset.pt \ |
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--processes_num 32 --seq_length 128 \ |
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--dynamic_masking --data_processor mlm |
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``` |
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``` |
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python3 pretrain.py --dataset_path cluecorpussmall_word_seq128_dataset.pt \ |
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--spm_model_path models/cluecorpussmall_spm.model \ |
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--config_path models/bert/medium_config.json \ |
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--output_model_path models/cluecorpussmall_word_roberta_medium_seq128_model.bin \ |
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--world_size 8 --gpu_ranks 0 1 2 3 4 5 6 7 \ |
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--total_steps 1000000 --save_checkpoint_steps 100000 --report_steps 50000 \ |
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--learning_rate 1e-4 --batch_size 64 \ |
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--data_processor mlm --target mlm |
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``` |
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Stage2: |
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``` |
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python3 preprocess.py --corpus_path corpora/cluecorpussmall.txt \ |
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--spm_model_path models/cluecorpussmall_spm.model \ |
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--dataset_path cluecorpussmall_word_seq512_dataset.pt \ |
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--processes_num 32 --seq_length 512 \ |
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--dynamic_masking --data_processor mlm |
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``` |
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``` |
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python3 pretrain.py --dataset_path cluecorpussmall_word_seq512_dataset.pt \ |
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--spm_model_path models/cluecorpussmall_spm.model \ |
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--pretrained_model_path models/cluecorpussmall_word_roberta_medium_seq128_model.bin-1000000 \ |
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--config_path models/bert/medium_config.json \ |
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--output_model_path models/cluecorpussmall_word_roberta_medium_seq512_model.bin \ |
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--world_size 8 --gpu_ranks 0 1 2 3 4 5 6 7 \ |
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--total_steps 250000 --save_checkpoint_steps 50000 --report_steps 10000 \ |
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--learning_rate 5e-5 --batch_size 16 \ |
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--data_processor mlm --target mlm |
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``` |
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Finally, we convert the pre-trained model into Huggingface's format: |
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``` |
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python3 scripts/convert_bert_from_uer_to_huggingface.py --input_model_path models/cluecorpussmall_word_roberta_medium_seq512_model.bin-250000 \ |
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--output_model_path pytorch_model.bin \ |
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--layers_num 8 --type mlm |
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``` |
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### BibTeX entry and citation info |
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``` |
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@article{devlin2018bert, |
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title={BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding}, |
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author={Devlin, Jacob and Chang, Ming-Wei and Lee, Kenton and Toutanova, Kristina}, |
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journal={arXiv preprint arXiv:1810.04805}, |
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year={2018} |
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} |
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@article{turc2019, |
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title={Well-Read Students Learn Better: On the Importance of Pre-training Compact Models}, |
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author={Turc, Iulia and Chang, Ming-Wei and Lee, Kenton and Toutanova, Kristina}, |
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journal={arXiv preprint arXiv:1908.08962v2 }, |
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year={2019} |
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} |
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@article{zhao2019uer, |
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title={UER: An Open-Source Toolkit for Pre-training Models}, |
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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}, |
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journal={EMNLP-IJCNLP 2019}, |
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pages={241}, |
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year={2019} |
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} |
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@article{zhao2023tencentpretrain, |
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title={TencentPretrain: A Scalable and Flexible Toolkit for Pre-training Models of Different Modalities}, |
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author={Zhao, Zhe and Li, Yudong and Hou, Cheng and Zhao, Jing and others}, |
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journal={ACL 2023}, |
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pages={217}, |
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year={2023} |
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``` |
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[2_128]:https://huggingface.co/uer/roberta-tiny-word-chinese-cluecorpussmall |
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[4_256]:https://huggingface.co/uer/roberta-mini-word-chinese-cluecorpussmall |
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[4_512]:https://huggingface.co/uer/roberta-small-word-chinese-cluecorpussmall |
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[8_512]:https://huggingface.co/uer/roberta-medium-word-chinese-cluecorpussmall |
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[12_768]:https://huggingface.co/uer/roberta-base-word-chinese-cluecorpussmall |
