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learning rate 2e-5,
batch size 64,
num_train_epochs=8,| 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 |
1from transformers import pipeline
2classifier = pipeline("text-classification",model='bhadresh-savani/roberta-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': 0.002281982684507966},
10{'label': 'joy', 'score': 0.9726489186286926},
11{'label': 'love', 'score': 0.021365027874708176},
12{'label': 'anger', 'score': 0.0026395076420158148},
13{'label': 'fear', 'score': 0.0007162453257478774},
14{'label': 'surprise', 'score': 0.0003483477921690792}
15]]
16"""1{
2 'test_accuracy': 0.9395,
3 'test_f1': 0.9397328860104454,
4 'test_loss': 0.14367154240608215,
5 'test_runtime': 10.2229,
6 'test_samples_per_second': 195.639,
7 'test_steps_per_second': 3.13
8 }