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https://github.com/explosion/prodigy-evaluate

🔎 A Prodigy plugin for evaluating spaCy pipelines
https://github.com/explosion/prodigy-evaluate

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🔎 A Prodigy plugin for evaluating spaCy pipelines

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# 🔎 Prodigy-evaluate

This repository contains a Prodigy plugin for recipes to evaluate spaCy pipelines. It features multiple recipes:

1. `evaluate.evaluate`: Evaluate a spaCy pipeline on one or more datasets for different components. Passing flags like `--label-stats` or `--confusion-matrix` will compute a variety of evaluation metrics, including precision, recall, F1, accuracy, and more.



2. `evaluate.evaluate-example`: Evaluate a spaCy pipeline on one or more datasets for different components on a **per-example basis**. This is helpful for debugging and for understanding the hardest examples for your model.



3. `evaluate.nervaluate`: Evaluate a spaCy NER component on one or more datasets. This recipe uses the `nervaluate` library to calculate various metric for NER. You can learn more about the metrics in the [nervaluate documentation](https://github.com/MantisAI/nervaluate). This is helpful because the approach takes into account partial matches, which may be a more relevant metric for your NER use case.



You can install this plugin via `pip`.

```
pip install "prodigy-evaluate @ git+https://github.com/explosion/prodigy-evaluate"
```

To learn more about this plugin and additional functionality, you can check the [Prodigy docs](https://prodi.gy/docs/plugins/#evaluate).

## Issues?

Are you have trouble with this plugin? Let us know on our [support forum](https://support.prodi.gy/) and we'll get back to you!