This is a BERT Base model for emotion analysis in Japanese additionally fine-tuned for emotion detection and classification.
The model was based on
tohoku-nlp/bert-base-japanese-v3, and later fine-tuned on a dataset containing 10 labels of emotional texts from Japanese dictionaries, blogs and Twitter.
The dataset was composed of about 1,700 sentences, with about 100~250 sentences each for each emotion category.
emotion_mapping = {
0: 'amaze',
1: 'anger',
2: 'dislike',
3: 'excite',
4: 'fear',
5: 'joy',
6: 'like',
7: 'relief',
8: 'sad',
9: 'shame'
}
emotion_mapping = {
0: '驚き',
1: '怒り',
2: 'いや',
3: '昂り',
4: '怖がり',
5: '喜び',
6: '好き',
7: '安らぎ',
8: '悲しみ',
9: '恥ずかしい'
}
