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license: apache-2.0
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Model description Cased fine-tuned BERT model for English, trained on (manually annotated) Hungarian parliamentary speeches scraped from parlament.hu, and translated with Google Translate API.


Training The fine-tuned version of the original bert-base-cased model (bert-base-cased), trained on HunEmPoli corpus, translated with Google Translate API.


The model can be used as any other (cased) BERT model. It has been tested recognizing emotions at the sentence level in (parliamentary) pre-agenda speeches, where:

'Label_0': Neutral

'Label_1': Fear

'Label_2': Sadness

'Label_3': Anger

'Label_4': Disgust

'Label_5': Success

'Label_6': Joy

'Label_7': Trust


Eval results

                  precision    recall  f1-score   support

           0       1.00      0.50      0.67        46
           1       0.00      0.00      0.00         4
           2       0.70      0.85      0.76       188
           3       0.50      0.09      0.15        11
           4       0.85      0.75      0.80       375
           5       0.78      0.93      0.84       335
           6       0.67      0.28      0.39        36
           7       0.00      0.00      0.00         4

    accuracy                           0.79       999
    macro avg       0.56      0.42     0.45       999
    weighted avg    0.79      0.79     0.77       999