my-sentiment-analyzer

Fine-tuned distilbert-base-uncased for binary sentiment classification (0 = negative, 1 = positive).

Intended Use

Classify English-language text (reviews, comments, feedback) as positive or negative sentiment.

Training Data

• IMDB movie reviews (25,000 train / 2,500 validation / 25,000 test)

Training Procedure

• Base model: distilbert-base-uncased
• Epochs: 2, Learning rate: 2e-5, Batch size: 32, Max sequence length: 128

Evaluation Results

Metric	Score
Accuracy	0.876
Precision	0.871
Recall	0.883
F1	0.877

Limitations

• Trained on movie reviews; may not generalize well to other domains.
• Binary classification only — no "neutral" category.
• English only.

Usage

from transformers import pipeline
classifier = pipeline("sentiment-analysis", model="MahadKhannn/my-sentiment-analyzer")
result = classifier("This was a great experience!")
print(result)