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---
title: Intent Classifier Chatbot
emoji: 🤖
colorFrom: blue
colorTo: purple
sdk: docker
pinned: false
app_port: 7860
license: apache-2.0
short_description: Intent Detection API using BERT and Flask
---
# 🤖 Bert Intent Chatbot (Encoder-Only)
**Intent Detection API using BERT and Flask**
This project uses a fine-tuned BERT model that is an encoder only model without a decoder used to detect user intent from text inputs (e.g., chatbot queries). It provides a lightweight Flask API to classify input sentences into predefined intent categories.
---
## 🔧 Features
- ✅ Pretrained BERT fine-tuned on [CLINC150](https://huggingface.co/datasets/clinc_oos)
- 🧠 Real-time intent classification from natural text
- 🌐 REST API using Flask
- 🤖 Easy to integrate with chatbots, voice assistants, or NLP systems
---
## 📊 Dataset: CLINC150
The project uses the **CLINC150 dataset**, a benchmark dataset for intent classification in task-oriented dialogue systems.
### 🧾 Overview
- **Total intents**: 150 unique user intents
- **Domains**: 10 real-world domains (e.g., banking, travel, weather, small talk)
- **Examples**: ~22,500 utterances
- **Language**: English
- **Out-of-scope (OOS)**: Includes OOS examples to test robustness
### 📁 Dataset Splits
| Split | Examples |
|-------------|----------|
| Train | 7,000 |
| Validation | 3,000 |
| Test | 5,500 |
### 📦 Source
- Official repo: [clinc/oos-eval](https://github.com/clinc/oos-eval)
- Hugging Face: [`clinc_oos`](https://huggingface.co/datasets/clinc_oos)
---
## 🚀 Example
### Request
```bash
{
"text": "I want to book a flight"
}
```
### Response
```bash
{
"intent": "book_flight"
}
```
---
# Intent Classifier Chatbot
A sophisticated intent classification system built with BERT and FastAPI that can predict user intents from natural language text.
## Features
- **Advanced NLP**: Uses BERT-based transformer model for accurate intent classification
- **150+ Intent Classes**: Trained on the CLINC150 dataset with comprehensive intent coverage
- **Real-time Prediction**: FastAPI backend for fast inference
- **Clean UI**: Simple and intuitive web interface
- **Production Ready**: Dockerized for easy deployment
## How to Use
1. Enter your message in the text area
2. Click "Predict Intent"
3. See the AI's prediction of your intent
Try examples like:
- "Set an alarm for 7am" → Alarm
- "Transfer money to John" → Transfer
- "What's the weather like?" → Weather
- "Book a flight to Paris" → Book Flight
## Model Details
- **Architecture**: BERT for Sequence Classification
- **Dataset**: CLINC150 (151 intent classes including out-of-scope)
- **Accuracy**: High performance on intent classification tasks
- **Preprocessing**: Advanced tokenization and text normalization
## Tech Stack
- **Backend**: FastAPI, PyTorch, Transformers
- **Frontend**: HTML, CSS, JavaScript
- **Model**: BERT-base fine-tuned on CLINC150
- **Deployment**: Docker, Hugging Face Spaces
## Author
**Saher Muhamed**
- GitHub: [@sahermuhamed1](https://github.com/sahermuhamed1)
- Email: sahermuhamed176@gmail.com
## 🤗 Hugging Face Spaces Configuration
To deploy this project on [Hugging Face Spaces](https://huggingface.co/spaces), you can use a `README.md` and a `config.json` file to configure your Space for inference.
Check out the configuration reference at:
https://huggingface.co/docs/hub/spaces-config-reference
Example `config.json` for inference API:
```json
{
"hf_space_type": "inference",
"python_version": "3.10",
"sdk": "gradio",
"entrypoint": "app.py"
}
```
- Make sure your `requirements.txt` lists all dependencies.
- The `entrypoint` should point to your main app file (e.g., `app.py` or `main.py`).
- For more details and advanced configuration, see the [Spaces config reference](https://huggingface.co/docs/hub/spaces-config-reference). |