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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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- gpt2 |
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datasets: |
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- wikipedia |
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- cc100 |
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- oscar |
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widget: |
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- text: "<s>昨日私は京都で" |
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--- |
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# Model Card for Japanese character-level GPT-2 Large |
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## Model description |
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This is a Japanese character-level GPT-2 Large (717M parameters) language model pre-trained on Japanese Wikipedia, the Japanese portion of CC-100, and the Japanese portion of OSCAR. |
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## How to use |
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You can use this model directly with a pipeline for text generation. |
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```python |
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>>> from transformers import pipeline, set_seed |
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>>> generator = pipeline('text-generation', model='ku-nlp/gpt2-large-japanese-char') |
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>>> set_seed(5) |
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>>> generator("<s>昨日私は京都で", max_length=30, do_sample=True, num_return_sequences=5) |
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[{'generated_text': '<s>昨日私は京都で仕事だったのですが、帰りは車を信号で止めて、'}, |
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{'generated_text': '<s>昨日私は京都で開かれた大阪市都市戦略会議に出席しました。そ'}, |
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{'generated_text': '<s>昨日私は京都で行われました関西の教育者・学校事例が集まるイ'}, |
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{'generated_text': '<s>昨日私は京都では初雪を見ました。朝は少しパッとしない天気で'}, |
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{'generated_text': '<s>昨日私は京都でこみっくトレジャーさんの撮影を見学させていた'}] |
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``` |
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You can also use this model to get the features of a given text. |
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## Vocabulary |
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A character-level vocabulary of size 6K is used. To be precise, rare characters may be split into bytes because byte-level byte-pair encoding (BPE) is used. The BPE tokenizer was trained on a small subset of the training data. Since the data were converted into a one-character-per-line format, merge operations never go beyond character boundaries. |
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Note that the tokenizer maps U+0020 to `[UNK]` because preprocessing eliminated whitespace characters (U+0020) from training data. Use U+3000 (Ideographic Space) instead. |
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## Training data |
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We used the following corpora for pre-training: |
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- Japanese Wikipedia (as of 20221020, 3.2GB, 27M sentences, 1.3M documents) |
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- Japanese portion of CC-100 (85GB, 619M sentences, 66M documents) |
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- Japanese portion of OSCAR (54GB, 326M sentences, 25M documents) |
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Note that we filtered out documents annotated with "header", "footer", or "noisy" tags in OSCAR. |
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Also note that Japanese Wikipedia was duplicated 10 times to make the total size of the corpus comparable to that of CC-100 and OSCAR. As a result, the total size of the training data is 171GB. |
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## Training procedure |
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The training took about 8 months (with 7 interruptions) with a single NVIDIA A100 80GB GPU. |
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The following hyperparameters were used during pre-training: |
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- learning_rate: 2e-4 |
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- per_device_train_batch_size: 6 |
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- gradient_accumulation_steps: 98 |
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- optimizer: AdamW with betas=(0.9, 0.999) and epsilon=1e-06 |
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- weight_decay: 0.01 |
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- lr_scheduler_type: linear |
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- max_grad_norm: 1.0 |
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- max_steps: 500,000 (but terminated at 186,000 steps ~= 2.0 epochs) |
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- warmup_steps: 10,000 |
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The eval loss was 1.309 while the eval accuracy was 0.6890. The evaluation set consists of 5,000 randomly sampled documents from each of the training corpora. |
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