Create README.md

README.md

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+# 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}]