DistilBERT Sentiment Classifier (IMDb) — saibapanku/distilbert-sentiment

This is a fine-tuned DistilBERT model for binary sentiment classification trained on the IMDb dataset. The model classifies movie reviews as either positive or negative.

Model Details

• Model name: saibapanku/distilbert-sentiment
• Base model: distilbert-base-uncased
• Task: Sequence Classification (Sentiment Analysis)
• Dataset: IMDb
• Labels:
  • 0: Negative
  • 1: Positive

How to Use

You can load and use the model directly with 🤗 Transformers:

from transformers import pipeline

classifier = pipeline("text-classification", model="saibapanku/distilbert-sentiment")
print(classifier("This movie was absolutely amazing!"))

Training Configuration

• Training method: Hugging Face Trainer
• Epochs: 3
• Batch size: 16
• Max sequence length: 256 tokens
• Learning rate: default
• Weight decay: 0.01
• Evaluation strategy: per epoch
• Metric used: Accuracy
• Subset used: 2,000 train / 1,000 test samples (for demo purposes)

Example Output: [{'label': 'positive', 'score': 0.9843}]

Limitations

This model was trained on a small subset of the IMDb dataset and may not generalize well to all types of reviews.

Performance on domain-specific or multi-lingual content is not guaranteed.

License

This model is distributed under the MIT License.

Feel free to fine-tune further or adapt it for your specific sentiment analysis tasks!