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, and later finetuned on a dataset containing 10 labels of emotional blog posts.
The dataset was composed of about 1,000 sentences, with about 100 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: '้ฉใ', 1: 'ๆใ', 2: 'ใใ', 3: 'ๆใ', 4: 'ๆใใ', 5: 'ๅใณ', 6: 'ๅฅฝใ', 7: 'ๅฎใใ', 8: 'ๆฒใใฟ', 9: 'ๆฅใใใใ' }
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