README.md

---
inference: false
license: mit
language:
- en
metrics:
- exact_match
- f1
- bertscore
pipeline_tag: text-classification
---
# QA-Evaluation-Metrics

[![PyPI version qa-metrics](https://img.shields.io/pypi/v/qa-metrics.svg)](https://pypi.org/project/qa-metrics/) [![Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/17b7vrZqH0Yun2AJaOXydYZxr3cw20Ga6?usp=sharing)

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**](https://arxiv.org/abs/2402.11161), 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:

```bash
pip install qa-metrics
```

## Usage

The python package currently provides four QA evaluation metrics.

#### Exact Match
```python
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! 🔥🔥🔥

```python
from qa_metrics.transformerMatcher import TransformerMatcher

question = "Which movie is loosley based off the Brother Grimm's Iron Henry?"
tm = TransformerMatcher("roberta")
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
```python
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 Match
```python
from qa_metrics.pedant import PEDANT

question = "Which movie is loosley based off the Brother Grimm's Iron Henry?"
pedant = PEDANT()
scores = pedant.get_scores(reference_answer, candidate_answer, question)
max_pair, highest_scores = pedant.get_highest_score(reference_answer, candidate_answer, question)
match_result = pedant.evaluate(reference_answer, candidate_answer, question)
print("Max Pair: %s; Highest Score: %s" % (max_pair, highest_scores))
print("Score: %s; PANDA Match: %s" % (scores, match_result))
'''
Max Pair: ('the princess and the frog', 'The movie "The Princess and the Frog" is loosely based off the Brother Grimm\'s "Iron Henry"'); Highest Score: 0.854451712151719
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}}; PANDA Match: True
'''
```

```python
print(pedant.get_score(reference_answer[1], candidate_answer, question))
'''
0.7122460127464126
'''
```

If you find this repo avialable, please cite our paper:
```bibtex
@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](https://arxiv.org/abs/2402.11161). The dataset is expanded and leaderboard is updated.
- Our Training Dataset is adapted and augmented from [Bulian et al](https://github.com/google-research-datasets/answer-equivalence-dataset). Our [dataset repo](https://github.com/zli12321/Answer_Equivalence_Dataset.git) includes the augmented training set and QA evaluation testing sets discussed in our paper. 
- Now our model supports [distilroberta](https://huggingface.co/Zongxia/answer_equivalence_distilroberta), [distilbert](https://huggingface.co/Zongxia/answer_equivalence_distilbert), a smaller and more robust matching model than Bert!

## License

This project is licensed under the [MIT License](LICENSE.md) - see the LICENSE file for details.

## Contact

For any additional questions or comments, please contact [[email protected]].