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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. |
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Training The fine-tuned version of the original bert-base-cased model (bert-base-cased), trained on HunEmPoli corpus, translated with Google Translate API. |
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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: |
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'Label_0': Neutral |
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'Label_1': Fear |
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'Label_2': Sadness |
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'Label_3': Anger |
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'Label_4': Disgust |
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'Label_5': Success |
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'Label_6': Joy |
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'Label_7': Trust |
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Eval results |
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precision recall f1-score support |
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0 1.00 0.50 0.67 46 |
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1 0.00 0.00 0.00 4 |
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2 0.70 0.85 0.76 188 |
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3 0.50 0.09 0.15 11 |
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4 0.85 0.75 0.80 375 |
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5 0.78 0.93 0.84 335 |
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6 0.67 0.28 0.39 36 |
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7 0.00 0.00 0.00 4 |
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accuracy 0.79 999 |
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macro avg 0.56 0.42 0.45 999 |
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weighted avg 0.79 0.79 0.77 999 |
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