metadata
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
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"])