nkadoor/sentiment-classifier-roberta

Fine-tuned Sentiment Classification Model

This model is a fine-tuned version of roberta-base for sentiment analysis on movie reviews.

Model Details

• Model type: Text Classification (Sentiment Analysis)
• Base model: roberta-base
• Language: English
• Task: Binary sentiment classification (positive/negative)
• Training dataset: IMDB Movie Reviews Dataset
• Training samples: 5000 samples
• Validation samples: 1000 samples
• Test samples: 1000 samples

Performance

Metric	Value
Test Accuracy	0.9590
Test F1 Score	0.9791
Test Precision	1.0000
Test Recall	0.9590

Training Details

Parameter	Value
Training epochs	3
Batch size	16
Learning rate	5e-05
Warmup steps	500
Weight decay	0.01
Max sequence length	512

Usage

Quick Start

from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline

# Using pipeline (recommended for quick inference)
classifier = pipeline("sentiment-analysis", 
                     model="nkadoor/sentiment-classifier-roberta",
                     tokenizer="nkadoor/sentiment-classifier-roberta")

result = classifier("This movie was amazing!")
print(result)  # [{'label': 'POSITIVE', 'score': 0.99}]

Manual Usage

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

# Load model and tokenizer
tokenizer = AutoTokenizer.from_pretrained("nkadoor/sentiment-classifier-roberta")
model = AutoModelForSequenceClassification.from_pretrained("nkadoor/sentiment-classifier-roberta")

def predict_sentiment(text):
    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True, max_length=512)
    
    with torch.no_grad():
        outputs = model(**inputs)
        predictions = torch.nn.functional.softmax(outputs.logits, dim=-1)
        predicted_class = torch.argmax(predictions, dim=-1).item()
        confidence = predictions[0][predicted_class].item()
    
    sentiment = "positive" if predicted_class == 1 else "negative"
    return sentiment, confidence

# Example usage
text = "This movie was absolutely fantastic!"
sentiment, confidence = predict_sentiment(text)
print(f"Sentiment: {sentiment} (Confidence: {confidence:.4f})")

Dataset

The model was trained on the IMDB Movie Reviews Dataset, which contains movie reviews labeled as positive or negative sentiment. The dataset consists of:

• 25,000 training reviews
• 25,000 test reviews
• Balanced distribution of positive and negative sentiments

Intended Use

This model is intended for sentiment analysis of English movie reviews or similar text. It can be used to:

• Analyze sentiment in movie reviews
• Classify text as positive or negative
• Build sentiment analysis applications
• Research in sentiment analysis

Limitations

• Trained specifically on movie reviews, may not generalize well to other domains
• Limited to English language
• Binary classification only (positive/negative)
• May reflect biases present in the training data

Citation

If you use this model, please cite:

@misc{sentiment-classifier-roberta,
  title={Fine-tuned RoBERTa for Sentiment Analysis},
  author={Narayana Kadoor},
  year={2025},
  url={https://huggingface.co/nkadoor/sentiment-classifier-roberta}
}

Training Logs

Final training metrics:

• Final training loss: N/A
• Best validation F1: 0.9791
• Total training time: 3.0 epochs completed

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Model trained using Transformers library by Hugging Face