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README.md
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# Clay documentation
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## Overview
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generating semantic embeddings for tasks like similarity searches, and
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second, through fine-tuning its outputs with additional data labels.
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This fine-tuning supports various tasks, including classification
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(e.g.
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(e.g.
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as creating RGB imagery from SAR data. Moreover, users can further
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enhance model performance by incorporating higher-resolution data.
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This documentation uses
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samples and an SDK. This means that every page is also a python notebook
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anyone can use, with practical code examples for each functionality, and
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use case. Moreover, you can install `pip install clay` and use the same
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- The Clay model **code** lives on
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[Github](https://github.com/Clay-foundation/model). License:
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[Apache](https://github.com/Clay-foundation/model/LICENSE).
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- The Clay model **weights** live on [Huggin
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[OpenRAIL-M](https://github.com/Clay-foundation/model/blob/main/LICENSE-MODEL.md).
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- The Clay **documentation** [lives on this
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site](https://clay-foundation.github.io/documentation/). License:
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The model can be used in two main ways:
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1. Directly, use it to make inference. See [Model](
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1. Check and run Benchmarks on the model. See
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[Benchmarks](Benchmarks.html)
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2. Generating semantic **embeddings**. E.g. for Similarity search. See
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[Embeddings](
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3. **Fine-tunning** the model for other tasks, or for other input data.
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E.g. flood detection, crop yields, … See
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[Fine-tunning](
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## How to contribute
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> Note: If you want to contribute to the model code, please check the
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> [model repository](https://github.com/Clay-foundation/model).
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–
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Clay is a fiscally sponsored project of [Radiant Earth](), a USA
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# Clay documentation
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<!-- WARNING: THIS FILE WAS AUTOGENERATED! DO NOT EDIT! -->
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## Overview
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generating semantic embeddings for tasks like similarity searches, and
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second, through fine-tuning its outputs with additional data labels.
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This fine-tuning supports various tasks, including classification
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(e.g. flood detection and deforestation monitoring), regression
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(e.g. estimating carbon stock or crop yields), and generative tasks such
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as creating RGB imagery from SAR data. Moreover, users can further
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enhance model performance by incorporating higher-resolution data.
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This documentation uses `nbdev`, which combines documentation, code
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samples and an SDK. This means that every page is also a python notebook
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anyone can use, with practical code examples for each functionality, and
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use case. Moreover, you can install `pip install clay` and use the same
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- The Clay model **code** lives on
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[Github](https://github.com/Clay-foundation/model). License:
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[Apache](https://github.com/Clay-foundation/model/LICENSE).
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- The Clay model **weights** live on [Huggin
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Face](https://huggingface.co/made-with-clay/Clay/). License:
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[OpenRAIL-M](https://github.com/Clay-foundation/model/blob/main/LICENSE-MODEL.md).
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- The Clay **documentation** [lives on this
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site](https://clay-foundation.github.io/documentation/). License:
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The model can be used in two main ways:
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1. Directly, use it to make inference. See [Model](https://clay-foundation.github.io/documentation/model.html)
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2. Generating semantic **embeddings**. E.g. for Similarity search. See
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[Embeddings](https://clay-foundation.github.io/documentation/embeddings.html).
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3. **Fine-tunning** the model for other tasks, or for other input data.
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E.g. flood detection, crop yields, … See
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[Fine-tunning](https://clay-foundation.github.io/documentation/finetunning.html).
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See Model v0 [Release notes](https://clay-foundation.github.io/documentation/Clay%20Model%20releases/clay%20v0%20release.html)
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## How to contribute
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> Note: If you want to contribute to the model code, please check the
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> [model repository](https://github.com/Clay-foundation/model).
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>
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–
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Clay is a fiscally sponsored project of [Radiant Earth](), a USA
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