turkish-sentiment-modern-bert

# Turkish Sentiment Modern BERT

This model is a fine-tuned ModernBERT for Turkish Sentiment Analysis. It was trained on the winvoker/turkish-sentiment-analysis-dataset and is designed to classify Turkish text into sentiment categories, such as Positive, Negative, and Neutral.

Model Overview

• Model Type: ModernBERT (BERT variant)
• Task: Sentiment Analysis
• Languages: Turkish
• Dataset: winvoker/turkish-sentiment-analysis-dataset
• Labels: Positive, Negative, Neutral
• Fine-Tuning: Fine-tuned for sentiment classification.

Performance Metrics

The model was trained for 2 epochs with the following results:

• Training Loss: Measures the model's fit to the training data.
• Validation Loss: Measures the model's generalization to unseen data.
• Accuracy: The percentage of correct predictions over all examples.
• F1 Score: A balanced metric between precision and recall.

Model Inference Example

Here’s an example of how to use the model for sentiment analysis of Turkish text:

from transformers import AutoModelForSequenceClassification, AutoTokenizer
import torch

# Load the pre-trained model and tokenizer
model_name = "bayrameker/turkish-sentiment-modern-bert"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)

# Example texts for prediction
texts = ["bu ürün çok iyi", "bu ürün berbat"]

# Tokenize the inputs
inputs = tokenizer(texts, padding=True, truncation=True, return_tensors="pt")

# Make predictions
with torch.no_grad():
logits = model(**inputs).logits

# Get the predicted sentiment labels
predictions = torch.argmax(logits, dim=-1)
labels = ["Negative", "Neutral", "Positive"] # Adjust based on your label mapping
for text, pred in zip(texts, predictions):
print(f"Text: {text} -> Sentiment: {labels[pred.item()]}")

Example Output:

Text: bu ürün çok iyi -> Sentiment: Positive
Text: bu ürün berbat -> Sentiment: Negative

Installation

To use this model, first install the required dependencies:

pip install transformers
pip install torch
pip install datasets

Model Card

• Model Name: turkish-sentiment-modern-bert
• Hugging Face Repo: Link to Model Repository
• License: MIT (or another applicable license)
• Author: Bayram Eker
• Date: 2024-12-21

Training Details

• Model: ModernBERT (Base variant)
• Framework: PyTorch
• Training Time: 34 minutes (2 epochs)
• Batch Size: 32
• Learning Rate: 8e-5
• Optimizer: AdamW

Acknowledgments

• The model was trained on the winvoker/turkish-sentiment-analysis-dataset.
• Special thanks to the Hugging Face community and all contributors to the transformers library.

Future Work

• Expand the model with more complex sentiment labels (e.g., multi-class sentiment, aspect-based sentiment analysis).
• Fine-tune the model on a larger, more diverse dataset for better generalization across various domains.

License

This model is licensed under the MIT License. See the LICENSE file for more details.