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| 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 |
1from transformers import pipeline
2classifier = pipeline("text-classification",model='bhadresh-savani/electra-base-emotion', return_all_scores=True)
3prediction = classifier("I love using transformers. The best part is wide range of support and its easy to use", )
4print(prediction)
5
6"""
7Output:
8[[
9{'label': 'sadness', 'score': 0.0006792712374590337},
10{'label': 'joy', 'score': 0.9959300756454468},
11{'label': 'love', 'score': 0.0009452480007894337},
12{'label': 'anger', 'score': 0.0018055217806249857},
13{'label': 'fear', 'score': 0.00041110432357527316},
14{'label': 'surprise', 'score': 0.0002288572577526793}
15]]
16"""1{
2 'epoch': 8.0,
3 'eval_accuracy': 0.9195,
4 'eval_f1': 0.918975455617076,
5 'eval_loss': 0.3486028015613556,
6 'eval_runtime': 4.2308,
7 'eval_samples_per_second': 472.726,
8 'eval_steps_per_second': 7.564
9 }