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--- |
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language: en |
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tags: |
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- text-classification |
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- pytorch |
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- roberta |
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- emotions |
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datasets: |
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- go_emotions |
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license: mit |
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widget: |
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- text: "I am not feeling well today." |
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--- |
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## This model is trained for GoEmotions dataset which contains labeled 58k Reddit comments with 28 emotions |
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- admiration, amusement, anger, annoyance, approval, caring, confusion, curiosity, desire, disappointment, disapproval, disgust, embarrassment, excitement, fear, gratitude, grief, joy, love, nervousness, optimism, pride, realization, relief, remorse, sadness, surprise + neutral |
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## Training details: |
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- The training script is provided here: https://github.com/bsinghpratap/roberta_train_goEmotion |
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- Please feel free to start an issue in the repo if you have trouble running the model and I would try to respond as soon as possible. |
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- The model works well on most of the emotions except: 'desire', 'disgust', 'embarrassment', 'excitement', 'fear', 'grief', 'nervousness', 'pride', 'relief', 'remorse', 'surprise'] |
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- I'll try to fine-tune the model further and update here if RoBERTa achieves a better performance. |
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- Each text datapoint can have more than 1 label. Most of the training set had 1 label: Counter({1: 36308, 2: 6541, 3: 532, 4: 28, 5: 1}). So currently I just used the first label for each of the datapoint. Not ideal but it does a decent job. |
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## Model Performance |
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Emotion: admiration<br> |
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GoEmotions Paper: 0.65<br> |
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RoBERTa: 0.62<br> |
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Support: 504<br> |
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Emotion: amusement<br> |
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GoEmotions Paper: 0.80<br> |
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RoBERTa: 0.78<br> |
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Support: 252<br> |
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Emotion: anger<br> |
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GoEmotions Paper: 0.47<br> |
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RoBERTa: 0.44<br> |
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Support: 197<br> |
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Emotion: annoyance<br> |
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GoEmotions Paper: 0.34<br> |
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RoBERTa: 0.22<br> |
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Support: 286<br> |
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Emotion: approval<br> |
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GoEmotions Paper: 0.36<br> |
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RoBERTa: 0.31<br> |
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Support: 318<br> |
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Emotion: caring<br> |
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GoEmotions Paper: 0.39<br> |
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RoBERTa: 0.24<br> |
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Support: 114<br> |
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Emotion: confusion<br> |
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GoEmotions Paper: 0.37<br> |
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RoBERTa: 0.29<br> |
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Support: 139<br> |
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Emotion: curiosity<br> |
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GoEmotions Paper: 0.54<br> |
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RoBERTa: 0.48<br> |
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Support: 233<br> |
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Emotion: disappointment<br> |
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GoEmotions Paper: 0.28<br> |
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RoBERTa: 0.18<br> |
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Support: 127<br> |
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Emotion: disapproval<br> |
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GoEmotions Paper: 0.39<br> |
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RoBERTa: 0.26<br> |
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Support: 220<br> |
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Emotion: gratitude<br> |
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GoEmotions Paper: 0.86<br> |
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RoBERTa: 0.84<br> |
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Support: 288<br> |
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Emotion: joy<br> |
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GoEmotions Paper: 0.51<br> |
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RoBERTa: 0.47<br> |
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Support: 116<br> |
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Emotion: love<br> |
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GoEmotions Paper: 0.78<br> |
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RoBERTa: 0.68<br> |
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Support: 169<br> |
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Emotion: neutral<br> |
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GoEmotions Paper: 0.68<br> |
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RoBERTa: 0.61<br> |
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Support: 1606<br> |
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Emotion: optimism<br> |
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GoEmotions Paper: 0.51<br> |
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RoBERTa: 0.52<br> |
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Support: 120<br> |
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Emotion: realization<br> |
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GoEmotions Paper: 0.21<br> |
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RoBERTa: 0.15<br> |
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Support: 109<br> |
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Emotion: sadness<br> |
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GoEmotions Paper: 0.49<br> |
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RoBERTa: 0.42<br> |
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Support: 108 |
