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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-turkish-cased-emotion | 83.25 | 83.17 | 232.197 |
1from transformers import pipeline
2classifier = pipeline("text-classification",
3 model='zafercavdar/distilbert-base-turkish-cased-emotion',
4 return_all_scores=True)
5prediction = classifier("Bu kütüphaneyi seviyorum, en iyi yanı kolay kullanımı.", )
6print(prediction)
7
8"""
9Output:
10[
11 [
12 {'label': 'sadness', 'score': 0.0026786490343511105},
13 {'label': 'joy', 'score': 0.6600754261016846},
14 {'label': 'love', 'score': 0.3203163146972656},
15 {'label': 'anger', 'score': 0.004358913749456406},
16 {'label': 'fear', 'score': 0.002354539930820465},
17 {'label': 'surprise', 'score': 0.010216088965535164}
18 ]
19]
20
21"""1{
2 'eval_accuracy': 0.8325,
3 'eval_f1': 0.8317301441160213,
4 'eval_loss': 0.5021793842315674,
5 'eval_runtime': 8.6167,
6 'eval_samples_per_second': 232.108,
7 'eval_steps_per_second': 3.714
8}