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--- |
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language: ja |
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license: MIT |
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datasets: |
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- mC4 Japanese |
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--- |
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# transformers-ud-japanese-electra-ginza (sudachitra-wordpiece, mC4 Japanese) |
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This is an [ELECTRA](https://github.com/google-research/electra) model pretrained on approximately 200M Japanese sentences. |
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The input text is tokenized by [SudachiTra](https://github.com/WorksApplications/SudachiTra) with the WordPiece subword tokenizer. |
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See `tokenizer_config.json` for the setting details. |
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## Model architecture |
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The model architecture is the same as the original ELECTRA base model; 12 layers, 768 dimensions of hidden states, and 12 attention heads. |
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## Training Data |
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This model is trained on the Japanese texts extracted from the [mC4](https://huggingface.co/datasets/mc4) Common Crawl's multilingual web crawl corpus. |
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We used the [Sudachi](https://github.com/WorksApplications/Sudachi) to split texts into sentences, and also applied a simple rule-based filter to remove nonlinguistic segments of mC4 multilingual corpus. |
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The extracted texts contains over 600M sentences in total, and we used approximately 200M sentences for pretraining. |
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## Licenses |
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The pretrained models are distributed under the terms of the [MIT License](https://opensource.org/licenses/mit-license.php). |
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## Citations |
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- mC4 |
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Contains information from `mC4` which is made available under the [ODC Attribution License](https://opendatacommons.org/licenses/by/1-0/). |
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``` |
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@article{2019t5, |
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author = {Colin Raffel and Noam Shazeer and Adam Roberts and Katherine Lee and Sharan Narang and Michael Matena and Yanqi Zhou and Wei Li and Peter J. Liu}, |
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title = {Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer}, |
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journal = {arXiv e-prints}, |
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year = {2019}, |
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archivePrefix = {arXiv}, |
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eprint = {1910.10683}, |
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} |
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``` |
