metadata
license: mit
language:
- tr
metrics:
- accuracy
pipeline_tag: text-classification
language: - tr tags: - text-classification - emotion - pytorch datasets: - emotion metrics: - Accuracy, F1 Score
bert-base-turkish-cased-emotion
Model description:
bert-base-turkish-cased finetuned on Turkish film comments shared in beyazperde.com with the help of BERTurk pretrained language model using PyTorch and Huggingface Transformers library.
learning rate 2e-5,
batch size 32,
num_train_epochs=5,
optimizer=AdamW
Model Performance
precision recall f1-score support
0 0.93 0.93 0.93 1333
1 0.93 0.93 0.93 1333
accuracy 0.93 2666
macro avg 0.93 0.93 0.93 2666 weighted avg 0.93 0.93 0.93 2666
How to Use the model:
from transformers import pipeline
classifier = pipeline("text-classification",
model='zafercavdar/distilbert-base-turkish-cased-emotion',
return_all_scores=True)
prediction = classifier("Bu kütüphaneyi seviyorum, en iyi yanı kolay kullanımı.", )
print(prediction)
"""
Output:
[
[
{'label': 'sadness', 'score': 0.0026786490343511105},
{'label': 'joy', 'score': 0.6600754261016846},
{'label': 'love', 'score': 0.3203163146972656},
{'label': 'anger', 'score': 0.004358913749456406},
{'label': 'fear', 'score': 0.002354539930820465},
{'label': 'surprise', 'score': 0.010216088965535164}
]
]
"""