Create README.md

README.md

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+---
+language: en
+license: mit
+tags:
+- sentiment-analysis
+- text-classification
+- yelp
+- transformers
+- distilbert
+---
+
+# Yelp Reviews Sentiment Analyzer
+
+## Model Overview
+This is a DistilBERT-based sentiment analysis model fine-tuned on a subset of the Yelp Open Dataset. It classifies restaurant reviews into three categories: Negative, Neutral, and Positive.
+
+## Intended Use
+- Sentiment classification of restaurant reviews for business insights, customer feedback analysis, or academic research.
+- Can be integrated into applications to provide real-time sentiment detection.
+
+## Training Data
+- Yelp Open Dataset (restaurant reviews subset).
+- Labels derived from star ratings converted into sentiment classes.
+
+## Model Architecture
+- Based on `distilbert-base-uncased`.
+- Fine-tuned using Hugging Face's `AutoModelForSequenceClassification`.
+
+## Performance
+- Accuracy: ~78.5%
+- F1 Score: ~78.4%
+- Precision: ~78.3%
+- Recall: ~78.5%
+
+## Limitations
+- Performance may vary on reviews from domains outside Yelp restaurants.
+- Model is trained only on English-language reviews.
+- Neutral class can be subjective, and borderline cases may be misclassified.
+
+## How to Use
+Use Hugging Face Transformers pipeline:
+
+```python
+from transformers import pipeline
+
+sentiment_analyzer = pipeline("sentiment-analysis", model="fitsblb/YelpReviewsAnalyzer")
+
+result = sentiment_analyzer("The food was amazing but the service was slow.")
+print(result)