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--- |
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language: ja |
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license: cc-by-sa-4.0 |
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library_name: transformers |
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tags: |
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- roberta |
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- fill-mask |
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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: "京都 大学 で [MASK] を 専攻 する 。" |
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- text: "東京 は 日本 の [MASK] だ 。" |
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- text: "カフェ で [MASK] を 注文 する 。" |
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- text: "[MASK] 名人 が タイトル の 防衛 に 成功 する 。" |
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--- |
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# ku-accms/roberta-base-japanese-ssuw |
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## Model description |
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This is a pre-trained Japanese RoBERTa base model for super short unit words (SSUW). |
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## Pre-processing |
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The input text should be converted to full-width (zenkaku) characters and segmented into super short unit words in advance (e.g., by KyTea). |
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## How to use |
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You can use this model directly with a pipeline for masked language modeling: |
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```python |
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>>> from transformers import pipeline |
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>>> unmasker = pipeline('fill-mask', model='ku-accms/roberta-base-japanese-ssuw') |
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>>> unmasker("京都 大学 で [MASK] を 専攻 する 。") |
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[{'sequence': '京都 大学 で 文学 を 専攻 する 。', |
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'score': '0.1479644924402237', |
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'token': '17907', |
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'token_str': '文学'} |
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{'sequence': '京都 大学 で 哲学 を 専攻 する 。', |
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'score': '0.07658644765615463', |
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'token': '19302', |
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'token_str': '哲学'} |
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{'sequence': '京都 大学 で デザイン を 専攻 する 。', |
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'score': '0.06302948296070099', |
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'token': '14411', |
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'token_str': 'デザイン'} |
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{'sequence': '京都 大学 で 建築 を 専攻 する 。', |
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'score': '0.060596249997615814', |
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'token': '15478', |
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'token_str': '建築'} |
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{'sequence': '京都 大学 で 工学 を 専攻 する 。', |
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'score': '0.0574776753783226', |
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'token': '18632', |
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'token_str': '工学'} |
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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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import zenhan |
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import Mykytea |
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kytea_model_path = "somewhere" |
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kytea = Mykytea.Mykytea("-model {} -notags".format(kytea_model_path)) |
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def preprocess(text): |
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return " ".join(kytea.getWS(zenhan.h2z(text))) |
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from transformers import BertTokenizer, RobertaModel |
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tokenizer = BertTokenizer.from_pretrained('ku-accms/roberta-base-japanese-ssuw') |
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model = RobertaModel.from_pretrained("ku-accms/roberta-base-japanese-ssuw") |
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text = "京都大学で自然言語処理を専攻する。" |
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encoded_input = tokenizer(preprocess(text), return_tensors='pt') |
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output = model(**encoded_input) |
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``` |
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## Training data |
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We used a Japanese Wikipedia dump (as of 20230101, 3.3GB) and a Japanese portion of CC100 (70GB). |
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## Training procedure |
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We first segmented the texts into words by KyTea and then tokenized the words into subwords using WordPiece with a vocabulary size of 32,000. We pre-trained the RoBERTa model using [transformers](https://github.com/huggingface/transformers) library. The training took about 7 days using 4 NVIDIA A100-SXM4-80GB GPUs. |
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The following hyperparameters were used for the pre-training. |
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- learning_rate: 1e-4 |
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- weight decay: 1e-2 |
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- per_device_train_batch_size: 80 |
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- num_devices: 4 |
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- gradient_accumulation_steps: 3 |
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- total_train_batch_size: 960 |
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- max_seq_length: 512 |
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- optimizer: AdamW with betas=(0.9,0.999) and epsilon=1e-06 |
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- lr_scheduler_type: linear schedule with warmup |
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- training_steps: 500,000 |
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- warmup_steps: 10,000 |
