saibapanku/distilbert-sentiment

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!