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README.md
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* Precision: 0.7731
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# Description
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This model is a finetuned BERT (bert-base-uncased)
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* It is using combinded features of sentiments and emotions (distilbert-base-uncased-finetuned-sst-2-english and roberta-base-go_emotions).
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* It is trained on a costume dataset of texts or posts (from Reddit) about general experiences of users with mental health problems.
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* Dataset was cleaned and all direct mentions of the disorder names in the texts were removed.
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model-finetuning: bert-base-uncased
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additional features (GoEmotions - SamLowe/roberta-base-go_emotions + SST2 - distilbert/distilbert-base-uncased-finetuned-sst-2-english):
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negative, positive, admiration, amusement, anger, annoyance, approval, caring, confusion, curiosity,
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desire, disappointment, disapproval, disgust, embarrassment, excitement, fear, gratitude, grief,
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joy, love, nervousness, optimism, pride, realization, relief, remorse, sadness, surprise, neutral
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The following hyperparameters were used during training:
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* Precision: 0.7731
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# Description
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This model is a finetuned BERT (bert-base-uncased) model that predict different mental disorders.
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* It is trained on a costume dataset of texts or posts (from Reddit) about general experiences of users with mental health problems.
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* Dataset was cleaned and all direct mentions of the disorder names in the texts were removed.
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model-finetuning: bert-base-uncased
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The following hyperparameters were used during training:
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