---
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

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

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

#[{'label': 'LABEL_2', 'score': 0.7510055303573608}]
```
# Load model directly
```markdown
from transformers import AutoTokenizer, AutoModelForSequenceClassification

tokenizer = AutoTokenizer.from_pretrained("saribasmetehan/bert-base-turkish-sentiment-analysis")
model = AutoModelForSequenceClassification.from_pretrained("saribasmetehan/bert-base-turkish-sentiment-analysis")
```
## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.1902        | 1.0   | 625  | 0.1629          | 0.9575   |
| 0.1064        | 2.0   | 1250 | 0.1790          | 0.96     |
| 0.0631        | 3.0   | 1875 | 0.2358          | 0.96     |
| 0.0146        | 4.0   | 2500 | 0.2458          | 0.962    |


### Framework versions

- Transformers 4.40.2
- Pytorch 2.2.1+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1
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