|
--- |
|
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 |
