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---
language: zh
---
# Introduction
This model was trained on TPU and the details are as follows:
## Model
##
| Model_name | params | size | Training_corpus | Vocab |
| :------------------------------------------ | :----- | :------- | :----------------- | :-----------: |
| **`RoBERTa-tiny-clue`** <br/>Super_small_model | 7.5M | 28.3M | **CLUECorpus2020** | **CLUEVocab** |
| **`RoBERTa-tiny-pair`** <br/>Super_small_sentence_pair_model | 7.5M | 28.3M | **CLUECorpus2020** | **CLUEVocab** |
| **`RoBERTa-tiny3L768-clue`** <br/>small_model | 38M | 110M | **CLUECorpus2020** | **CLUEVocab** |
| **`RoBERTa-tiny3L312-clue`** <br/>small_model | <7.5M | 24M | **CLUECorpus2020** | **CLUEVocab** |
| **`RoBERTa-large-clue`** <br/> Large_model | 290M | 1.20G | **CLUECorpus2020** | **CLUEVocab** |
| **`RoBERTa-large-pair`** <br/>Large_sentence_pair_model | 290M | 1.20G | **CLUECorpus2020** | **CLUEVocab** |
### Usage
With the help of[Huggingface-Transformers 2.5.1](https://github.com/huggingface/transformers), you could use these model as follows
```
tokenizer = BertTokenizer.from_pretrained("MODEL_NAME")
model = BertModel.from_pretrained("MODEL_NAME")
```
`MODEL_NAME`：
| Model_NAME | MODEL_LINK |
| -------------------------- | ------------------------------------------------------------ |
| **RoBERTa-tiny-clue** | [`clue/roberta_chinese_clue_tiny`](https://huggingface.co/clue/roberta_chinese_clue_tiny) |
| **RoBERTa-tiny-pair** | [`clue/roberta_chinese_pair_tiny`](https://huggingface.co/clue/roberta_chinese_pair_tiny) |
| **RoBERTa-tiny3L768-clue** | [`clue/roberta_chinese_3L768_clue_tiny`](https://huggingface.co/clue/roberta_chinese_3L768_clue_tiny) |
| **RoBERTa-tiny3L312-clue** | [`clue/roberta_chinese_3L312_clue_tiny`](https://huggingface.co/clue/roberta_chinese_3L312_clue_tiny) |
| **RoBERTa-large-clue** | [`clue/roberta_chinese_clue_large`](https://huggingface.co/clue/roberta_chinese_clue_large) |
| **RoBERTa-large-pair** | [`clue/roberta_chinese_pair_large`](https://huggingface.co/clue/roberta_chinese_pair_large) |
## Details
Please read <a href='https://arxiv.org/pdf/2003.01355'>https://arxiv.org/pdf/2003.01355.
Please visit our repository: https://github.com/CLUEbenchmark/CLUEPretrainedModels.git
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