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---
library_name: transformers
tags:
- sentiment-analysis
- text-classification
- turkish
license: mit
base_model:
- FacebookAI/xlm-roberta-base
---
# 🏆 TurkReviewSentiment-RoBERTa: Sentiment Analysis Model for Turkish Texts
📢 **A fine-tuned XLM-RoBERTa model for sentiment analysis in Turkish texts!**
This model can classify user reviews and texts as **positive** or **negative** sentiment.
---
## 🔍 **Model Details**
### Model Description
✅ **Base Model:** XLM-RoBERTa
✅ **Finetuned from:** `xlm-roberta-base`
✅ **Training Data:** User reviews from e-commerce platforms
✅ **Task:** Sentiment Analysis (Binary Classification: Positive / Negative)
✅ **Language:** Turkish
✅ **Use Cases:** Customer feedback analysis, social media monitoring, market research
✅ **License:** MIT
### Model Sources
- **Repository:** [Hugging Face Model Page](https://huggingface.co/fundaylnci/TurkReviewSentiment-RoBERTa)
---
## 📌 **How to Use the Model**
```python
import torch
from transformers import AutoModelForSequenceClassification, AutoTokenizer
def load_model(model_path):
model = AutoModelForSequenceClassification.from_pretrained(model_path)
tokenizer = AutoTokenizer.from_pretrained(model_path)
return model, tokenizer
def get_sentiment(text, model, tokenizer):
inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True, max_length=512)
device = next(model.parameters()).device
inputs = {k: v.to(device) for k, v in inputs.items()}
outputs = model(**inputs)
logits = outputs.logits
predicted_class = torch.argmax(logits, dim=1).item()
return "positive" if predicted_class == 1 else "negative"
model_path = "fundaylnci/TurkReviewSentiment-RoBERTa"
model, tokenizer = load_model(model_path)
text ="Bu ürün harika!"
get_sentiment(text,model, tokenizer)
## Training Details
### Training Data
Dataset: E-commerce product reviews + manually labeled sentiment data
#### Preprocessing [optional]
Tokenization with xlm-roberta-base tokenizer
#### Training Hyperparameters
training_args = TrainingArguments(
output_dir="./results",
evaluation_strategy="epoch",
learning_rate=learning_rate,
per_device_train_batch_size=batch_size,
num_train_epochs=3,
weight_decay=0.01,
logging_dir="./logs",
logging_steps=10,
)
#### Speeds, Sizes, Times [optional]
Checkpoint Size: ~500MB
Training Time: ~6 hours on NVIDIA T4 GPU
## Evaluation
Evaluation Metrics: Accuracy, F1-score
# Version 149608e489c0979a661489d53245a30a48d3d38e
Accuracy: 0.961
Precision: 0.977
Recall: 0.980
F1 Score: 0.979
## Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator.
Hardware: NVIDIA T4 GPU
Training Hours: ~6 hours
Cloud Provider: Google Cloud
Estimated CO2 Emitted: ~3.2 kg
## Citation [optional]
**BibTeX:**
@article{turkreviewsentiment2025,
title={TurkReviewSentiment-RoBERTa: Sentiment Analysis for Turkish Reviews},
author={Your Name},
journal={Hugging Face Model Hub},
year={2025}
}
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