opinion-mining

🛍️ Product Review Sentiment Classifier

A fine-tuned DistilBERT model for classifying product reviews as Positive or Negative.

Fine-tuned on the Amazon Polarity dataset with 5,000 training samples.

📊 Model Performance

🚀 Usage

from transformers import pipeline

classifier = pipeline("text-classification", model="JuhiSaxena2002/opinion-mining")

result = classifier("This product is absolutely amazing! Best purchase ever.")
print(result)  # [{'label': 'POSITIVE', 'score': 0.99}]

result = classifier("Terrible quality. Broke after one day. Very disappointed.")
print(result)  # [{'label': 'NEGATIVE', 'score': 0.98}]

🏗️ Model Architecture

• Base Model: distilbert-base-uncased
• Task: Binary Sequence Classification
• Labels: POSITIVE, NEGATIVE
• Max Sequence Length: 128 tokens
• Training Epochs: 3

📦 Training Details

• Dataset: Amazon Polarity
• Train samples: 5,000
• Test samples: 1,000
• Learning Rate: 2e-5
• Batch Size: 32
• Optimizer: AdamW with warmup

🙋 Author

Built and fine-tuned by JuhiSaxena2002