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	0.873
Precision	0.862
Recall	0.887
F1	0.875

Limitations

• Trained on movie reviews; may not generalize well to other domains without further fine-tuning.
• Binary classification only — does not detect neutral sentiment.
• English only.

Usage

from transformers import pipeline

classifier = pipeline("sentiment-analysis", model="kabra686/my-sentiment-analyzer")

result = classifier("This was a great experience!")

print(result)