---
language: en
license: mit
library_name: transformers
pipeline_tag: question-answering
tags:
- question-answering
- roberta
- squad2
- deepset
dataset:
- squad2
---

# CTION-QA: Question Answering Model

## Model Description
A Question Answering (Q&A) model is a transformer-based NLP model trained to understand a given context and accurately extract or generate answers to user questions from that text.
It is fine-tuned on the **SQuAD 2.0** dataset for extractive question answering.

## Performance
| Metric | Score |
|------|------|
| Exact Match | 76.9 |
| F1 Score | 79.8 |
| Context Length | 512 |

## Usage
```python
from transformers import pipeline

qa = pipeline(
    "question-answering",
    model="hariprabhakaran45/CTION-QA"
)

result = qa(
    question="Who is the Eiffel Tower named after?",
    context="The Eiffel Tower is named after Gustave Eiffel."
)

print(result["answer"])