---
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}]