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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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- Hazumi
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---
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# ouktlab/Hazumi-AffNeg-Classifier
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## Model description
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This is a Japanese fine-tuned [BERT](https://github.com/google-research/bert) model trained on exchange data
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(Yes/No questions from the system and corresponding user responses)
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extracted from the multimodal dialogue corpus Hazumi.
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The pre-trained BERT model used is [cl-tohoku/bert-base-japanese-v3](https://huggingface.co/tohoku-nlp/bert-base-japanese-v3), released by Tohoku University.
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For fine-tuning, the JNLI script from [JGLUE](https://github.com/yahoojapan/JGLUE) was employed.
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## Training procedure
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This model was fine-tuned using the following script, which was borrowed from the JNLI script in [JGLUE](https://github.com/yahoojapan/JGLUE).
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```
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python transformers-4.9.2/examples/pytorch/text-classification/run_glue.py \
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--model_name_or_path tohoku-nlp/bert-base-japanese-v3 \
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--metric_name wnli \
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--do_train --do_eval --do_predict \
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--max_seq_length 128 \
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--per_device_train_batch_size 8 \
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--learning_rate 5e-05 \
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--num_train_epochs 4 \
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--output_dir <output_dir> \
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--train_file <train json file> \
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--validation_file <train json file> \
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--test_file <train json file> \
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--use_fast_tokenizer False \
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--evaluation_strategy epoch \
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--save_steps 5000 \
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--warmup_ratio 0.1
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```
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