# Text AI resources
URL: https://docs.datarobot.com/en/docs/classic-ui/modeling/special-workflows/textai-resources.html

> Provides links to Text AI resources available in DataRobot.

# Text AI resources

Text AI in DataRobot allows you to seamlessly incorporate text data into your model without being a Natural Language Processing (NLP) expert and without injecting extra steps in the model building process. With models and preprocessing steps designed specifically for NLP, DataRobot supports all languages from [ISO 639](https://en.wikipedia.org/wiki/List_of_ISO_639-2_codes), the set of standards for representing names for languages and language groups.

The tools available for working with text are described in the following sections.

| Topic | Description |
| --- | --- |
| Working with text |  |
| Automated transformations | Learn about automated feature engineering for text, built to enhance model accuracy. |
| Clustering based on text collections | Use clustering for detecting topics, types, taxonomies, and languages in a text collection. |
| Aggregation and imputation in time series projects | Set handling for text features in time series projects. |
| Composable ML transformers | Edit model blueprints, including pre-trained transformers, to best represent text features. |
| Model insights |  |
| Coefficients | See how text-preprocessing transforms text found in a dataset into a form that can be used by a DataRobot model. |
| Text Mining | Display the most relevant words and short phrases in any variables detected as text. |
| Word cloud | Display the most relevant words and short phrases found in your dataset in word cloud format. |
| Text Explanations | Visualize not only the text feature that is impactful, but also which specific words within a feature are impactful. |
| Multilabel modeling for text categorization | Use multilabel classification for text categorization. |
| Example: Capturing sentiment in text | See an example of uplifting a model by capturing sentiment in the text. |
| Text-related feature announcements |  |
| NLP Fine-Tuner blueprints | Read about NLP Fine-Tuner blueprints. |
| FastText for language detection | Read about FastText for language detection at data ingest. |
| TinyBERT featurizer | Read about using Google's Bidirectional Encoder Representations from Transformers (distilled version). |

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