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--- |
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license: mit |
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language: |
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- tr |
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metrics: |
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- accuracy |
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pipeline_tag: text-classification |
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--- |
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language: |
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- tr |
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tags: |
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- text-classification |
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- emotion |
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- pytorch |
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datasets: |
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- emotion |
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metrics: |
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- Accuracy, F1 Score |
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--- |
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# bert-base-turkish-cased-emotion |
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## Model description: |
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[bert-base-turkish-cased](https://huggingface.co/dbmdz/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. |
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``` |
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learning rate 2e-5, |
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batch size 32, |
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num_train_epochs=5, |
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optimizer=AdamW |
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``` |
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## Model Performance |
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precision recall f1-score support |
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0 0.93 0.93 0.93 1333 |
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1 0.93 0.93 0.93 1333 |
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accuracy 0.93 2666 |
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macro avg 0.93 0.93 0.93 2666 |
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weighted avg 0.93 0.93 0.93 2666 |
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## How to Use the model: |
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```python |
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from transformers import pipeline |
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classifier = pipeline("text-classification", |
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model='zafercavdar/distilbert-base-turkish-cased-emotion', |
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return_all_scores=True) |
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prediction = classifier("Bu kütüphaneyi seviyorum, en iyi yanı kolay kullanımı.", ) |
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print(prediction) |
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""" |
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Output: |
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[ |
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[ |
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{'label': 'sadness', 'score': 0.0026786490343511105}, |
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{'label': 'joy', 'score': 0.6600754261016846}, |
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{'label': 'love', 'score': 0.3203163146972656}, |
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{'label': 'anger', 'score': 0.004358913749456406}, |
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{'label': 'fear', 'score': 0.002354539930820465}, |
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{'label': 'surprise', 'score': 0.010216088965535164} |
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] |
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] |
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""" |
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``` |
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## Dataset: |
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[Beyazoerde.com reviews](https://huggingface.co/datasets/sinanyuksel/beyazperde). |
