update README

README.md

@@ -1,3 +1,41 @@
 ---
+language: ja
 license: cc-by-sa-4.0
+datasets:
+- wikipedia
+- cc100
+- 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.
+
+```
+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
+```