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prajjwal888
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roberta-finetuned-review-classifier

Text Classification
Transformers
Safetensors
roberta
sentiment-analysis
huggingface
amazon-reviews
fine-tuned
customer-feedback
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RoBERTa Fine-Tuned Review Classifier

This is a fine-tuned RoBERTa model built for binary sentiment classification of Amazon product reviews. The model classifies reviews into LABEL_0 (negative) or LABEL_1 (positive), and has been integrated into a Chrome extension that predicts numeric ratings based on review text.

Model Details

Model Description

Model Sources

  • Base Model: roberta-base
  • Fine-tuning & Deployment: Hugging Face + FastAPI (on Render)
  • Demo Frontend: Chrome extension (injects rating predictions on Amazon product pages)

Use Cases

Direct Use

You can use this model to predict sentiment or approximate ratings of customer reviews. It's ideal for:

  • Product feedback classification
  • Chrome extensions or browser tools
  • E-commerce dashboards

Downstream Use

Can be used in:

  • Recommender systems
  • Review authenticity/fraud detection
  • Customer satisfaction prediction

Out-of-Scope Use

  • Reviews in non-English languages
  • Sarcastic or ambiguous tone detection
  • Fine-grained star rating (e.g., 3 vs. 4)

Bias, Risks & Limitations

Bias

Model may inherit biases from training data—especially in underrepresented product categories or reviewer demographics.

Limitations

  • Struggles with sarcastic or short reviews.
  • Works only on English-language text.
  • Predictions may be unreliable for very long reviews (truncated at 512 tokens).

Recommendations

  • Do not use this model for making critical business decisions without human verification.
  • Fine-tune on domain-specific reviews if required.

How to Get Started 

from transformers import pipeline

classifier = pipeline("text-classification", model="prajjwal888/roberta-finetuned-review-classifier")
result = classifier("The product quality is fantastic. Loved it!")
print(result)

Example Output:

[{"label": "LABEL_1", "score": 0.9987}]

Training Details

Dataset

Custom dataset scraped and labeled from Amazon product reviews. Labeled into two categories based on review sentiment (not star ratings).

Preprocessing

  • Lowercasing
  • Removal of HTML and special characters
  • Truncated to 512 tokens

Training Hyperparameters

Hyperparameter Value
Epochs 3
Batch size 16
Max length 512
Optimizer AdamW
Learning rate 2e-5
Precision fp16

Evaluation

Metrics

Metric Value
Accuracy ~91%
F1 Score ~90.5%
Precision ~90%
Recall ~91%

Evaluation was performed on a 20% held-out validation set from the same distribution.

Environmental Impact

  • Hardware Used: NVIDIA T4 GPU
  • Platform: Google Colab + Render
  • Training Duration: ~1.5 hours
  • Estimated CO₂ Emissions: ~0.3 kg (based on ML CO2 Impact Calculator)

Technical Specifications

  • Model Type: Transformer Encoder (RoBERTa)
  • Architecture: 12-layer, 768-hidden, 12-heads, ~125M parameters
  • Framework: PyTorch (via transformers)

Citation

@misc{prajjwal888_review_classifier_2024, title={RoBERTa Fine-Tuned Review Classifier}, author={Prajjwal Chouhan}, year={2024}, howpublished={\url{https://huggingface.co/prajjwal888/roberta-finetuned-review-classifier}}, }

Contact

Acknowledgments

Thanks to the Hugging Face community and the creators of roberta-base. This project is inspired by practical applications of NLP in e-commerce.

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