| # TinySentimentClassifier | |
| ## Overview | |
| TinySentimentClassifier is a compact BERT-based model fine-tuned for sentiment analysis on English text. It classifies input text into three categories: **positive**, **neutral**, or **negative**. Designed for efficiency, it is suitable for deployment on resource-constrained environments while maintaining strong performance on standard sentiment datasets. | |
| ## Model Architecture | |
| - **Base model**: DistilBERT (distilled version of BERT-base-uncased) | |
| - **Task head**: Sequence classification head with 3 output labels | |
| - **Hidden size**: 768 | |
| - **Number of layers**: 6 | |
| - **Parameters**: ~66M | |
| The model follows the standard `BertForSequenceClassification` architecture from the Transformers library. | |
| ## Usage | |
| ```python | |
| from transformers import pipeline | |
| classifier = pipeline( | |
| "sentiment-analysis", | |
| model="your-username/TinySentimentClassifier", | |
| return_all_scores=False | |
| ) | |
| result = classifier("I love this product!") | |
| print(result) | |
| # [{'label': 'positive', 'score': 0.99}] |