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druzsan committed Oct 24, 2023
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8 changes: 4 additions & 4 deletions CONTRIBUTING.md
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Expand Up @@ -11,10 +11,10 @@ Technical details on how to contribute can be found in our [documentation](https

There are several ways you can contribute to Spotlight:

* Fix outstanding issues.
* Implement new features.
* Submit issues related to bugs or desired new features.
* Share your use case
- Fix outstanding issues.
- Implement new features.
- Submit issues related to bugs or desired new features.
- Share your use case

If you don't know where to start, you might want to have a look at [hacktoberfest issues](https://github.com/Renumics/spotlight/issues?q=is%3Aissue+is%3Aopen+label%3Ahacktoberfest)
and our guide on how to create a [new Lens](https://renumics.com/docs/development/lenses).
15 changes: 7 additions & 8 deletions README.md
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<p align="center"><a href="https://spotlight.renumics.com"><img src="static/img/spotlight_video.gif" width="100%"/></a></p>

Spotlight helps you to **understand unstructured datasets** fast. You can quickly create **interactive visualizations** and leverage data enrichments (e.g. embeddings, prediction, uncertainties) to **identify critical clusters** in your data.
Spotlight helps you to **understand unstructured datasets** fast. You can quickly create **interactive visualizations** and leverage data enrichments (e.g. embeddings, prediction, uncertainties) to **identify critical clusters** in your data.

Spotlight supports most unstructured data types including **images, audio, text, videos, time-series and geometric data**. You can start from your existing dataframe:

<p align="left"><img src="static/img/dataframe_head_sample.png" width="100%"/></a></p>

And start Spotlight with just a few lines of code:
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<td rowspan="3">[Classification]</td>
<td>Find Issues in Any Image Classification Dataset</td>
<td><a href="https://www.renumics.com/next/docs/use-cases/image-classification">👨‍💻</a> <a href="https://medium.com/@daniel-klitzke/finding-problematic-data-slices-in-unstructured-data-aeec0a3b9a2a">📝</a> <a href="https://huggingface.co/spaces/renumics/sliceguard-unstructured-data">🕹️</a></td>
</tr>
</tr>
<tr>
<td>Find data issues in the CIFAR-100 image dataset</td>
<td><a href="https://huggingface.co/spaces/renumics/navigate-data-issues">🕹️</a></td>
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</tbody>
</table>


## ⏱️ Quickstart

Get started by installing Spotlight and loading your first dataset.
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layout= spotlight.layouts.debug_classification(label='gender', prediction='m1_gender_prediction', embedding='m1_embedding', features=['age', 'emotion'])
spotlight.show(ds, layout=layout)
```

Here, the data types are discovered automatically from the dataset and we use a pre-defined layout for model debugging. Custom layouts can be built programmatically or via the UI.

> The `datasets[audio]` package can be installed via pip.


#### Usage Tracking

We have added crash report and performance collection. We do NOT collect user data other than an anonymized Machine Id obtained by py-machineid, and only log our own actions. We do NOT collect folder names, dataset names, or row data of any kind only aggregate performance statistics like total time of a table_load, crash data, etc. Collecting Spotlight crashes will help us improve stability. To opt out of the crash report collection define an environment variable called `SPOTLIGHT_OPT_OUT` and set it to true. e.G.`export SPOTLIGHT_OPT_OUT=true`
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## Learn more about unstructured data workflows

- 🤗 [Huggingface](https://huggingface.co/renumics) example spaces and datasets
- 🏀 [Playbook](https://renumics.com/docs/playbook/) for data-centric AI workflows
- 🍰 [Sliceguard](https://github.com/Renumics/sliceguard) library for automatic slice detection
- 🤗 [Huggingface](https://huggingface.co/renumics) example spaces and datasets
- 🏀 [Playbook](https://renumics.com/docs/playbook/) for data-centric AI workflows
- 🍰 [Sliceguard](https://github.com/Renumics/sliceguard) library for automatic slice detection

## Contribute

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