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---
library_name: transformers
base_model: cardiffnlp/twitter-xlm-roberta-base-sentiment-multilingual
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: democracy-sentiment-analysis-turkish-roberta
results: []
license: mit
language:
- tr
---
<!-- 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. -->
# democracy-sentiment-analysis-turkish-roberta
This model is a fine-tuned version of [cardiffnlp/twitter-xlm-roberta-base-sentiment-multilingual](https://huggingface.co/cardiffnlp/twitter-xlm-roberta-base-sentiment-multilingual) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4469
- Accuracy: 0.8184
- F1: 0.8186
- Precision: 0.8224
- Recall: 0.8184
## Model description
This model is fine-tuned from the base model cardiffnlp/twitter-xlm-roberta-base-sentiment-multilingual for sentiment analysis in Turkish, specifically focusing on democracy-related text. The model classifies texts into three sentiment categories:
Positive
Neutral
Negative
## Intended uses & limitations
This model is well-suited for analyzing sentiments in Turkish texts that discuss democracy, governance, and related political discourse.
## Training and evaluation data
The training dataset consists of 30,000 rows gathered from various sources, including: Kaggle, Hugging Face, Ekşi Sözlük, and synthetic data generated using state-of-the-art LLMs.
The dataset is multilingual in origin, with texts in English, Russian, and Turkish. All non-Turkish texts were translated into Turkish. The data represents a broad spectrum of democratic discourse from 30 different sources.
## How to Use
To use this model for sentiment analysis, you can leverage the Hugging Face `pipeline` for text classification as shown below:
```python
from transformers import pipeline
# Load the model from Hugging Face
sentiment_model = pipeline(model="yeniguno/democracy-sentiment-analysis-turkish-roberta", task='text-classification')
# Example text input
response = sentiment_model("En iyisi devletin tüm gücünü tek bir lidere verelim")
# Print the result
print(response)
# [{'label': 'negative', 'score': 0.9617443084716797}]
# Example text input
response = sentiment_model("Birçok farklı sesin çıkması zaman alıcı ve karmaşık görünebilir, ancak demokrasinin getirdiği özgürlük ve çeşitlilik, toplumun gerçek gücüdür.")
# Print the result
print(response)
# [{'label': 'positive', 'score': 0.958978533744812}]
# Example text input
response = sentiment_model("Bugün hava yağmurlu.")
# Print the result
print(response)
# [{'label': 'neutral', 'score': 0.9915837049484253}]
```
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 0.7236 | 1.0 | 802 | 0.4797 | 0.8039 | 0.8031 | 0.8037 | 0.8039 |
| 0.424 | 2.0 | 1604 | 0.4469 | 0.8184 | 0.8186 | 0.8224 | 0.8184 |
### Framework versions
- Transformers 4.44.2
- Pytorch 2.4.0+cu121
- Datasets 2.21.0
- Tokenizers 0.19.1 |