inference: false
license: mit
language:
- en
metrics:
- exact_match
- f1
- bertscore
pipeline_tag: text-classification
QA-Evaluation-Metrics
QA-Evaluation-Metrics is a fast and lightweight Python package for evaluating question-answering models. It provides various basic metrics to assess the performance of QA models. Check out our paper PANDA, a matching method going beyond token-level matching and is more efficient than LLM matchings but still retains competitive evaluation performance of transformer LLM models.
Installation
To install the package, run the following command:
pip install qa-metrics
Usage
The python package currently provides four QA evaluation metrics.
Exact Match
from qa_metrics.em import em_match
reference_answer = ["The Frog Prince", "The Princess and the Frog"]
candidate_answer = "The movie \"The Princess and the Frog\" is loosely based off the Brother Grimm's \"Iron Henry\""
match_result = em_match(reference_answer, candidate_answer)
print("Exact Match: ", match_result)
'''
Exact Match: False
'''
Transformer Match
Our fine-tuned BERT model is this repository. Our Package also supports downloading and matching directly. distilroberta, distilbert, and roberta are also supported now! 🔥🔥🔥
from qa_metrics.transformerMatcher import TransformerMatcher
question = "Which movie is loosley based off the Brother Grimm's Iron Henry?"
tm = TransformerMatcher("distilbert")
scores = tm.get_scores(reference_answer, candidate_answer, question)
match_result = tm.transformer_match(reference_answer, candidate_answer, question)
print("Score: %s; TM Match: %s" % (scores, match_result))
'''
Score: {'The Frog Prince': {'The movie "The Princess and the Frog" is loosely based off the Brother Grimm\'s "Iron Henry"': 0.88954514}, 'The Princess and the Frog': {'The movie "The Princess and the Frog" is loosely based off the Brother Grimm\'s "Iron Henry"': 0.9381995}}; TM Match: True
'''
F1 Score
from qa_metrics.f1 import f1_match,f1_score_with_precision_recall
f1_stats = f1_score_with_precision_recall(reference_answer[0], candidate_answer)
print("F1 stats: ", f1_stats)
match_result = f1_match(reference_answer, candidate_answer, threshold=0.5)
print("F1 Match: ", match_result)
'''
F1 stats: {'f1': 0.25, 'precision': 0.6666666666666666, 'recall': 0.15384615384615385}
F1 Match: False
'''
PANDA
from qa_metrics.cfm import CFMatcher
question = "Which movie is loosley based off the Brother Grimm's Iron Henry?"
cfm = CFMatcher()
scores = cfm.get_scores(reference_answer, candidate_answer, question)
match_result = cfm.cf_match(reference_answer, candidate_answer, question)
print("Score: %s; bert Match: %s" % (scores, match_result))
'''
Score: {'the frog prince': {'The movie "The Princess and the Frog" is loosely based off the Brother Grimm\'s "Iron Henry"': 0.7131625951317375}, 'the princess and the frog': {'The movie "The Princess and the Frog" is loosely based off the Brother Grimm\'s "Iron Henry"': 0.854451712151719}}; CF Match: True
'''
If you find this repo avialable, please cite our paper:
@misc{li2024panda,
title={PANDA (Pedantic ANswer-correctness Determination and Adjudication):Improving Automatic Evaluation for Question Answering and Text Generation},
author={Zongxia Li and Ishani Mondal and Yijun Liang and Huy Nghiem and Jordan Lee Boyd-Graber},
year={2024},
eprint={2402.11161},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
Updates
- [01/24/24] 🔥 The full paper is uploaded and can be accessed here. The dataset is expanded and leaderboard is updated.
- Our Training Dataset is adapted and augmented from Bulian et al. Our dataset repo includes the augmented training set and QA evaluation testing sets discussed in our paper.
- Now our model supports distilroberta, distilbert, a smaller and more robust matching model than Bert!
License
This project is licensed under the MIT License - see the LICENSE file for details.
Contact
For any additional questions or comments, please contact [[email protected]].