Create README.md

README.md

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+---
+tags:
+- text-classification
+- intent-classification
+- moviebot
+- distilbert
+- huggingface-pipeline
+- gradio-app
+datasets:
+- custom
+language: en
+license: mit
+widget:
+  - text: "Can you recommend a good comedy?"
+  - text: "Who directed Inception?"
+  - text: "Do you like movies?"
+---
+
+# 🎬 DistilBERT Intent Classifier for Movie & TV Assistant
+
+This model is a fine-tuned DistilBERT-based intent classifier for a conversational movie recommendation assistant. It classifies user queries into one of three intents:
+
+- **`generic`** – general small talk or casual movie mentions
+- **`recommendation`** – requests for movie or TV suggestions
+- **`factual`** – questions about movie facts, cast, streaming availability, etc.
+
+## 🧠 Model Details
+
+- **Base model:** `distilbert-base-uncased`
+- **Training data:** 6,000+ custom-labeled queries across all 3 intents
+- **Special augmentations:**
+  - Out-of-domain recommendation phrasing (e.g. restaurants, gadgets)
+  - Thematic recommendation queries (e.g. “movies for Valentine’s Day”)
+- **Use case:** Used inside a RAG-based chatbot for intent-routing and retrieval logic
+
+## ✨ Example Predictions
+
+| Input | Predicted Intent |
+|-------|------------------|
+| "Can you recommend a good horror movie?" | recommendation |
+| "Who directed Parasite?" | factual |
+| "Do you like sci-fi shows?" | generic |
+| "Suggest restaurants in Rome?" | generic (OOD) |
+
+## 🚀 Usage
+
+```python
+from transformers import pipeline
+
+classifier = pipeline("text-classification", model="your-username/intent-classifier-distilbert-moviebot")
+
+query = "Can you suggest a good action movie?"
+result = classifier(query, top_k=None)
+print(result)