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---
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"
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-turkish-sentiment-analysis
This model is a fine-tuned version of [dbmdz/bert-base-turkish-cased](https://huggingface.co/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
<ul>
<li>"Positive" : LABEL_1</li>
<li>"Notr" : LABEL_0 </li>
<li>"Negative" : LABEL_2</li>
<li>"Fine-Tuning Process" : https://github.com/saribasmetehan/Transformers-Library/blob/main/Turkish_Text_Classifiaction_Fine_Tuning_PyTorch.ipynb</li>
</ul>
## Example
```python
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}]
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