File size: 3,573 Bytes
fdffa5f f168954 fdffa5f 5bd76bd fdffa5f ef40418 1810b5d ef40418 fb56dd6 ef40418 b380cb9 ec92673 b380cb9 ef40418 1376523 ef40418 1810b5d fb56dd6 69a18fe ef40418 fdffa5f |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 |
---
inference: false
license: mit
language:
- en
metrics:
- exact_match
- f1
- bertscore
pipeline_tag: Text Classification
---
# QA-Evaluation-Metrics
[](https://pypi.org/project/qa-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 [**CFMatcher**](https://arxiv.org/abs/2401.13170), 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 = ["Charles , Prince of Wales"]
candidate_answer = "Prince Charles"
match_result = em_match(reference_answer, candidate_answer)
print("Exact Match: ", match_result)
```
#### Transformer Match
Our fine-tuned BERT model is this repository. Our Package also supports downloading and matching directly. More Matching transformer models will be available 🔥🔥🔥
```python
from qa_metrics.transformerMatcher 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))
```
#### 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)
```
#### CFMatch
```python
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; bert Match: %s" % (scores, match_result))
```
If you find this repo avialable, please cite our paper:
```bibtex
@misc{li2024cfmatch,
title={CFMatch: Aligning Automated Answer Equivalence Evaluation with Expert Judgments For Open-Domain Question Answering},
author={Zongxia Li and Ishani Mondal and Yijun Liang and Huy Nghiem and Jordan Boyd-Graber},
year={2024},
eprint={2401.13170},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
## Updates
- [01/24/24] 🔥 The full paper is uploaded and can be accessed [here]([https://arxiv.org/abs/2310.14566](https://arxiv.org/abs/2401.13170)). 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, 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]]. |