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--- |
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language: ja |
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license: cc-by-sa-4.0 |
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datasets: |
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- wikipedia |
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- cc100 |
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mask_token: "[MASK]" |
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widget: |
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- text: "早稲田 大学 で 自然 言語 処理 を [MASK] する 。" |
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--- |
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# nlp-waseda/roberta-large-japanese-seq512 |
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## Model description |
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This is a Japanese RoBERTa large model pretrained on Japanese Wikipedia and the Japanese portion of CC-100 with the maximum sequence length of 512. |
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## How to use |
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You can use this model for masked language modeling as follows: |
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```python |
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from transformers import AutoTokenizer, AutoModelForMaskedLM |
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tokenizer = AutoTokenizer.from_pretrained("nlp-waseda/roberta-large-japanese-seq512") |
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model = AutoModelForMaskedLM.from_pretrained("nlp-waseda/roberta-large-japanese-seq512") |
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sentence = '早稲田 大学 で 自然 言語 処理 を [MASK] する 。' # input should be segmented into words by Juman++ in advance |
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encoding = tokenizer(sentence, return_tensors='pt') |
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... |
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``` |
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You can fine-tune this model on downstream tasks. |
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## Tokenization |
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The input text should be segmented into words by [Juman++](https://github.com/ku-nlp/jumanpp) in advance. Juman++ 2.0.0-rc3 was used for pretraining. Each word is tokenized into tokens by [sentencepiece](https://github.com/google/sentencepiece). |
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`BertJapaneseTokenizer` now supports automatic `JumanppTokenizer` and `SentencepieceTokenizer`. You can use [this model](https://huggingface.co/nlp-waseda/roberta-large-japanese-seq512-with-auto-jumanpp) without any data preprocessing. |
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## Vocabulary |
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The vocabulary consists of 32000 tokens including words ([JumanDIC](https://github.com/ku-nlp/JumanDIC)) and subwords induced by the unigram language model of [sentencepiece](https://github.com/google/sentencepiece). |
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## Training procedure |
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This model was trained on Japanese Wikipedia (as of 20210920) and the Japanese portion of CC-100 from the checkpoint of [nlp-waseda/roberta-large-japanese](https://huggingface.co/nlp-waseda/roberta-large-japanese). It took a week using eight NVIDIA A100 GPUs. |
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The following hyperparameters were used during pretraining: |
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- learning_rate: 6e-5 |
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- distributed_type: multi-GPU |
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- num_devices: 8 |
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- total_train_batch_size: 4120 (max_seq_length=128), 4032 (max_seq_length=512) |
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- max_seq_length: 512 |
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- optimizer: Adam with betas=(0.9,0.98) and epsilon=1e-6 |
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- lr_scheduler_type: linear |
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- training_steps: 670000 (max_seq_length=128) + 70000 (max_seq_length=512) |
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- warmup_steps: 10000 |
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- mixed_precision_training: Native AMP |
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