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

python

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:

bibtex

@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

Model trained using Transformers library by Hugging Face