A minimal PyTorch & Transformers example for joint intent classification & slot tagging on the ATIS dataset.
This repo show cases comparison between multiple strategies of training multi task learning
See requirements.txt for full list. At minimum:
- Python 3.9+
- torch >= 1.12.0
- transformers >= 4.18.0
- scikit-learn >= 1.0.0
- pandas >= 1.3.0
- matplotlib >= 3.4.0
- tqdm >= 4.60.0
git clone https://github.com/Biswajit-Banerjee/multi_task_nlp.git
cd multi_task_nlp
pip install -r requirements.txt
This project uses a curated subset of the Airline Travel Information System (ATIS) corpus. Each example consists of a user’s flight-related utterance, annotated with:
- Intent: the user’s overall goal (e.g. searching for flights, checking fares).
- Entities: token-level labels indicating semantic categories (e.g. cities, times).
{
"text": "i need a flight tomorrow from columbus to minneapolis",
"entities":["X","X","X","X","DateTime","X","Location","X","Location"],
"intent": "Flight"
}