qa-metrics 0.1.19

This package provides standard and classifier-based short form QA evaluation methods

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.

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 = ["Charles , Prince of Wales"]
candidate_answer = "Prince Charles"
match_result = ExactMatch(reference_answer, candidate_answer)
print("Exact Match: ", match_result)

F1 Score

from qa_metrics.f1 import f1_match

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)

CFMatch

from qa_metrics.cfm import CFMatcher

question = "who will take the throne after the queen dies"
cfm = CFMatcher()
scores = cfm.get_scores(reference_answer, candidate_answer, question)
match_result = cfm.cf_match(reference_answer, candidate_answer, question)
print("Score: %s; CF Match: %s" % (scores, match_result))

Transformer Match

Our fine-tuned BERT model is on 🤗 Huggingface. Our Package also supports downloading and matching directly. More Matching transformer models will be available 🔥🔥🔥

from qa_metrics.transformerMacher import TransformerMatcher

question = "who will take the throne after the queen dies"
tm = TransformerMatcher("bert")
scores = tm.get_scores(reference_answer, candidate_answer, question)
match_result = tm.transformer_match(reference_answer, candidate_answer, question)
print("Score: %s; CF Match: %s" % (scores, match_result))

License

This project is licensed under the MIT License - see the LICENSE file for details.

Contact

For any additional questions or comments, please contact [[e-mail]].