metadata
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 Super_small_model |
7.5M | 28.3M | CLUECorpus2020 | CLUEVocab |
RoBERTa-tiny-pair Super_small_sentence_pair_model |
7.5M | 28.3M | CLUECorpus2020 | CLUEVocab |
RoBERTa-tiny3L768-clue small_model |
38M | 110M | CLUECorpus2020 | CLUEVocab |
RoBERTa-tiny3L312-clue small_model |
<7.5M | 24M | CLUECorpus2020 | CLUEVocab |
RoBERTa-large-clue Large_model |
290M | 1.20G | CLUECorpus2020 | CLUEVocab |
RoBERTa-large-pair Large_sentence_pair_model |
290M | 1.20G | CLUECorpus2020 | CLUEVocab |
Usage
With the help ofHuggingface-Transformers 2.5.1, 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 |
| RoBERTa-tiny-pair | clue/roberta_chinese_pair_tiny |
| RoBERTa-tiny3L768-clue | clue/roberta_chinese_3L768_clue_tiny |
| RoBERTa-tiny3L312-clue | clue/roberta_chinese_3L312_clue_tiny |
| RoBERTa-large-clue | clue/roberta_chinese_clue_large |
| RoBERTa-large-pair | clue/roberta_chinese_pair_large |
Details
Please read https://arxiv.org/pdf/2003.01355.
Please visit our repository: https://github.com/CLUEbenchmark/CLUEPretrainedModels.git