# Text Classification (Embed)

:::callout{intent="note" title="This Guide Uses the Embed Endpoint."}
You can find more information about the endpoint [here](/api).
:::

This notebook shows how to build a classifiers using Cohere's embeddings. You can find the code on [Github](https://github.com/cohere-ai/cohere-developer-experience/blob/main/notebooks/Text_Classification_Using_Embeddings.ipynb) or in this [colab notebook](https://colab.research.google.com/github/cohere-ai/cohere-developer-experience/blob/main/notebooks/Text_Classification_Using_Embeddings.ipynb).

<img src="../img/fern/assets/images/241a5bb-TextClassficationEmbed_Visual_1.png" alt="classifier.">

The example classification task here will be sentiment analysis of film reviews. We'll train a simple classifier to detect whether a film review is negative (class 0) or positive (class 1).

We'll go through the following steps:

1. Install Cohere
2. Get the dataset
3. Get the embeddings of the reviews (for both the training set and the test set)
4. Train a classifier using the training set
5. Evaluate the performance of the classifier on the testing set

## 1. Install Cohere and Other Dependencies

## Related pages

- [Changelog](../changelog.md)
- [Cohere](../index.md)
- [Cohere API](./cohere-api-index.md)
- [Cohere Labs](./cohere-labs-index.md)
- [Cohere Platform](./cohere-platform-index.md)
- [Cookbooks](./cookbooks-index.md)
- [Deployment Options](./deployment-options-index.md)
- [Embeddings (Vectors, Search, Retrieval)](./embeddings-vectors-search-retrieval-index.md)
- [Get Started](./get-started-index.md)
- [Going to Production](./going-to-production-index.md)

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