> For the complete documentation index, see [llms.txt](https://learn.evidentlyai.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://learn.evidentlyai.com/ml-observability-course/module-3-ml-monitoring-for-unstructured-data/monitoring-text-data-code-practice.md).

# 3.5. Monitoring text data \[CODE PRACTICE]

{% embed url="<https://www.youtube.com/watch?v=RIultWCjYXo&list=PL9omX6impEuOpTezeRF-M04BW3VfnPBRF&index=19>" %}

**Video 5**. [Monitoring text data \[CODE PRACTICE\]](https://www.youtube.com/watch?v=RIultWCjYXo\&list=PL9omX6impEuOpTezeRF-M04BW3VfnPBRF\&index=19), by Emeli Dral

In this video, we walk you through the code example of evaluations for unstructured data using the open-source [Evidently](https://github.com/evidentlyai/evidently) Python library.

**Want to go straight to code?** Here is the [example notebook](https://github.com/evidentlyai/ml_observability_course/blob/main/module3/unstructured_data_code_practice.ipynb) to follow along.

**Links to docs:**

* [Text overview](https://docs.evidentlyai.com/presets/text-overview)
* [Embeddings](https://docs.evidentlyai.com/user-guide/customization/embeddings-drift-parameters)

**Outline:**\
[00:00](https://youtu.be/RIultWCjYXo?si=5s0_-fMduGKorqci) Import libraries and datasets\
[02:26](https://youtu.be/RIultWCjYXo?si=Vyrnq26avImqSUB6\&t=146) Prepare a multimodal dataset with raw text\
[08:15](https://youtu.be/RIultWCjYXo?si=hKrfvOBPZ3kFeisC\&t=495) Text data overview report\
[11:18](https://youtu.be/RIultWCjYXo?si=nws_RxLC2YsoiD1C\&t=678) Model-based text data drift detection\
[13:49](https://youtu.be/RIultWCjYXo?si=WlLgpbHHt2Bi-UIH\&t=829) Adding custom text descriptors to report\
[17:50](https://youtu.be/RIultWCjYXo?si=9KWXxjYqW4eaE97n\&t=1070) Custom report with descriptor drift detection\
[23:55](https://youtu.be/RIultWCjYXo?si=9lNBhLuipZrDK8zi\&t=1435) Embedding drift detection

That’s it! We covered three use cases for evaluating unstructured data – multimodal data with raw text, detecting drift in text data with descriptors, and embedding drift detection – and learned to derive actionable metrics.


---

# Agent Instructions
This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com.

## Querying This Documentation
If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.

Perform an HTTP GET request on the current page URL with the `ask` query parameter, and the optional `goal` query parameter:

```
GET https://learn.evidentlyai.com/ml-observability-course/module-3-ml-monitoring-for-unstructured-data/monitoring-text-data-code-practice.md?ask=<question>&goal=<endgoal>
```

`ask` is the immediate question: it should be specific, self-contained, and written in natural language.
`goal` is optional and describes the broader end goal you are ultimately trying to accomplish on behalf of the user. GitBook uses it to tailor the answer towards what is most useful for that goal.

The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.