huranokuma
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README.md
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license: mit
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tags:
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---
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It achieves the following results on the evaluation set:
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- Loss: 1.0827
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- Accuracy: 0.7436
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## Model description
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More information needed
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## Intended uses & limitations
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More information needed
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## Training and evaluation data
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More information needed
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## Training procedure
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### Training hyperparameters
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The following hyperparameters were used during training:
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- learning_rate: 5e-05
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- train_batch_size: 10
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- eval_batch_size: 10
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- seed: 42
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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- lr_scheduler_type: linear
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- num_epochs: 2.0
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### Training results
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### Framework versions
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- Transformers 4.22.0.dev0
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- Pytorch 1.12.1+cu113
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- Datasets 2.4.0
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- Tokenizers 0.12.1
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language: ja
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thumbnail: https://1.bp.blogspot.com/-pOL-P7Mvgkg/YEGQAdidksI/AAAAAAABdc0/SbD0lC_X8iY_t5xLFtQYFC3FHFgziBuzgCNcBGAsYHQ/s932/buranko_businesswoman_sad.png
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license: mit
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tags:
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- ja
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- japanese
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- gpt2
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- text-generation
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- lm
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- nlp
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widget:
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- text: "御社を志望した理由は"
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---
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# ESを書くAI
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Japanese GPT-2 modelをファインチューニングしました
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ファインチューニングには、あらゆる分野から140,000件ほどのESを用いました。
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webアプリ<br>
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http://www.eswrite.com
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The model was trained using code from Github repository [rinnakk/japanese-pretrained-models](https://github.com/rinnakk/japanese-pretrained-models) by [rinna Co., Ltd.](https://corp.rinna.co.jp/)
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