Electra-base-emotion
Model description:
Model Performance Comparision on Emotion Dataset from Twitter:
Model | Accuracy | F1 Score | Test Sample per Second |
---|---|---|---|
Distilbert-base-uncased-emotion | 93.8 | 93.79 | 398.69 |
Bert-base-uncased-emotion | 94.05 | 94.06 | 190.152 |
Roberta-base-emotion | 93.95 | 93.97 | 195.639 |
Albert-base-v2-emotion | 93.6 | 93.65 | 182.794 |
Electra-base-emotion | 91.95 | 91.90 | 472.72 |
How to Use the model:
from transformers import pipeline
classifier = pipeline("text-classification",model='bhadresh-savani/electra-base-emotion', return_all_scores=True)
prediction = classifier("I love using transformers. The best part is wide range of support and its easy to use", )
print(prediction)
"""
Output:
[[
{'label': 'sadness', 'score': 0.0006792712374590337},
{'label': 'joy', 'score': 0.9959300756454468},
{'label': 'love', 'score': 0.0009452480007894337},
{'label': 'anger', 'score': 0.0018055217806249857},
{'label': 'fear', 'score': 0.00041110432357527316},
{'label': 'surprise', 'score': 0.0002288572577526793}
]]
"""
Dataset:
Training procedure
Eval results
{
'epoch': 8.0,
'eval_accuracy': 0.9195,
'eval_f1': 0.918975455617076,
'eval_loss': 0.3486028015613556,
'eval_runtime': 4.2308,
'eval_samples_per_second': 472.726,
'eval_steps_per_second': 7.564
}
Reference:
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Dataset used to train bhadresh-savani/electra-base-emotion
Evaluation results
- Accuracy on emotiontest set verified0.926
- Precision Macro on emotiontest set verified0.912
- Precision Micro on emotiontest set verified0.926
- Precision Weighted on emotiontest set verified0.931
- Recall Macro on emotiontest set verified0.854
- Recall Micro on emotiontest set verified0.926
- Recall Weighted on emotiontest set verified0.926
- F1 Macro on emotiontest set verified0.866
- F1 Micro on emotiontest set verified0.926
- F1 Weighted on emotiontest set verified0.925