metadata

license: mit
base_model: dbmdz/bert-base-turkish-cased
tags:
  - generated_from_trainer
metrics:
  - accuracy
model-index:
  - name: bert-base-turkish-sentiment-analysis
    results: []
language:
  - tr
datasets:
  - winvoker/turkish-sentiment-analysis-dataset
widget:
  - text: Sana aşığım

bert-base-turkish-sentiment-analysis

This model is a fine-tuned version of dbmdz/bert-base-turkish-cased on an winvoker/turkish-sentiment-analysis-dataset (The shuffle function was used with a training dataset of 10,000 data points and a test dataset of 2,000 points.). It achieves the following results on the evaluation set:

Loss: 0.2458
Accuracy: 0.962

Model description

"Positive" : LABEL_1
"Notr" : LABEL_0
"Negative" : LABEL_2
"Fine-Tuning Process" : https://github.com/saribasmetehan/Transformers-Library/blob/main/Turkish_Text_Classifiaction_Fine_Tuning_PyTorch.ipynb

Example

from transformers import pipeline
text = "senden nefret ediyorum"
model_id = "saribasmetehan/bert-base-turkish-sentiment-analysis"
classifier = pipeline("text-classification", model=model_id)
preds = classifier(text)
print(preds)

#[{'label': 'LABEL_2', 'score': 0.7510055303573608}]