README.md

---
language: ja
license: cc-by-sa-4.0
datasets:
- Hazumi
---

# ouktlab/Hazumi-AffNeg-Classifier 

## Model description

This is a Japanese fine-tuned [BERT](https://github.com/google-research/bert) model trained on exchange data 
(Yes/No questions from the system and corresponding user responses) 
extracted from the multimodal dialogue corpus Hazumi. 
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.
For fine-tuning, the JNLI script from [JGLUE](https://github.com/yahoojapan/JGLUE) was employed.

## Training procedure

This model was fine-tuned using the following script, which was borrowed from the JNLI script in [JGLUE](https://github.com/yahoojapan/JGLUE).

```
python transformers-4.9.2/examples/pytorch/text-classification/run_glue.py \
   --model_name_or_path tohoku-nlp/bert-base-japanese-v3 \
   --metric_name wnli \
   --do_train --do_eval --do_predict  \
   --max_seq_length 128 \
   --per_device_train_batch_size 8 \
   --learning_rate 5e-05 \
   --num_train_epochs 4  \
   --output_dir <output_dir> \
   --train_file <train json file> \
   --validation_file <train json file> \
   --test_file <train json file> \
   --use_fast_tokenizer False \
   --evaluation_strategy epoch \
   --save_steps 5000 \
   --warmup_ratio 0.1
```