## NER_eval
A simple implementation of strict/lenient matching to evaluate NER performance (precision, recall, F1-score) in 60 lines!
This script currently only supports the IOB2 format with both strict and lenient modes.
## Installation
```bash
pip install ner_metrics
```
or
```bash
pip install git+https://github.com/PL97/NER_eval.git
```
## Usage
```python
from ner_metrics import classification_report
y_true = ['B-PER', 'I-PER', 'O', 'B-ORG', 'B-ORG', 'O', 'O', 'B-PER', 'I-PER', 'O']
y_pred = ['O', 'B-PER', 'O', 'B-ORG', 'B-ORG', 'I-ORG', 'O', 'B-PER', 'I-PER', 'O']
classification_report(tags_true=y_true, tags_pred=y_pred, mode="lenient") # for lenient match
classification_report(tags_true=y_true, tags_pred=y_pred, mode="strict") # for strict match
```
Expected output
```
tag(lenient): PER precision:1.0 recall:1.0 f1-score:1.0
tag(strict): PER precision:0.5 recall:0.5 f1-score:0.5
tag(lenient): ORG precision:1.0 recall:1.0 f1-score:1.0
tag(strict): ORG precision:0.5 recall:0.5 f1-score:0.5
```
The results are also saved to *evaluation.json*
## How to cite this work
If you find this git repo useful, please consider citing it using the snippet below:
```bibtex
@misc{ner_eval,
author={Le Peng},
title={ner_metrics: A Simple Python Snippets for NER Evaluation},
howpublished={\url{https://github.com/PL97/NER_eval}},
year={2022}
}
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"description": "## NER_eval\nA simple implementation of strict/lenient matching to evaluate NER performance (precision, recall, F1-score) in 60 lines!\n\nThis script currently only supports the IOB2 format with both strict and lenient modes.\n\n## Installation\n```bash\npip install ner_metrics\n```\nor\n```bash\npip install git+https://github.com/PL97/NER_eval.git\n```\n\n\n## Usage\n\n```python\nfrom ner_metrics import classification_report\n\ny_true = ['B-PER', 'I-PER', 'O', 'B-ORG', 'B-ORG', 'O', 'O', 'B-PER', 'I-PER', 'O']\ny_pred = ['O', 'B-PER', 'O', 'B-ORG', 'B-ORG', 'I-ORG', 'O', 'B-PER', 'I-PER', 'O']\nclassification_report(tags_true=y_true, tags_pred=y_pred, mode=\"lenient\") # for lenient match\n\nclassification_report(tags_true=y_true, tags_pred=y_pred, mode=\"strict\") # for strict match\n\n```\nExpected output\n```\ntag(lenient): PER precision:1.0 recall:1.0 f1-score:1.0\ntag(strict): PER precision:0.5 recall:0.5 f1-score:0.5\ntag(lenient): ORG precision:1.0 recall:1.0 f1-score:1.0\ntag(strict): ORG precision:0.5 recall:0.5 f1-score:0.5\n```\nThe results are also saved to *evaluation.json*\n\n## How to cite this work\n\nIf you find this git repo useful, please consider citing it using the snippet below:\n```bibtex\n@misc{ner_eval,\n author={Le Peng},\n title={ner_metrics: A Simple Python Snippets for NER Evaluation},\n howpublished={\\url{https://github.com/PL97/NER_eval}},\n year={2022}\n}\n",
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