fundaylnci
/

TurkReviewSentiment-RoBERTa

@@ -1,199 +1,112 @@
 ---
 library_name: transformers
-tags: []
 ---
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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
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+model_name = "fundaylnci/TurkReviewSentiment-RoBERTa"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForSequenceClassification.from_pretrained(model_name)
+text = "Bu ürün harika!"  # Example Turkish text
+inputs = tokenizer(text, return_tensors="pt")
+outputs = model(**inputs)
+print(outputs.logits)  # Model prediction
 ## Training Details
 ### Training Data
+Dataset: E-commerce product reviews + manually labeled sentiment data
 ### Training Procedure
+Epochs: 3
+Batch Size: 16
+Optimizer: AdamW
 #### 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
+Accuracy = 92.3%
+F1-score = 90.8%
 ## 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}
+}