---
license: apache-2.0
base_model: google-bert/bert-base-uncased
datasets:
- clinc/clinc_oos
metrics:
- accuracy
library_name: transformers
tags:
- nlp
- intent-classification
- finance
- travel
- banking

---

## 🤖 Intent Detection using Fine-Tuned BERT

This project utilizes a fine-tuned BERT model (`bert-base-uncased`) for **intent classification** tasks. It is an **encoder-only transformer** designed to detect user intents from text inputs (e.g., chatbot queries) and classify them into predefined categories such as `banking`, `travel`, `finance`, and more.

The model is trained on the [CLINC150 (clinc_oos)](https://huggingface.co/datasets/clinc/clinc_oos) dataset and evaluated using accuracy as the primary metric.

---
## 📊 Dataset --> CLINC150

The project uses the **CLINC150 dataset**, a benchmark dataset for intent classification in task-oriented dialogue systems.

---
### 🧾 Dataset 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

---

### 📦 Source

- Official repo: [clinc/oos-eval](https://github.com/clinc/oos-eval)
- Hugging Face: [`clinc_oos`](https://huggingface.co/datasets/clinc_oos)

---

## 🚀 Example

### Request: "I want to book a flight"

### Response: "book_flight"
```