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---
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"
```
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