# Textcat model with multiple classes

**URL:** <https://support.prodi.gy/t/textcat-model-with-multiple-classes/2163>\
**Category:** Uncategorized\
**Tags:** usage, textcat\
**Created:** [October 31, 2019, 2:37am UTC](https://support.prodi.gy/t/textcat-model-with-multiple-classes/2163 "2019-10-31T02:37:49Z")\
**Posts on this page:** 1\
**Showing post:** 2

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**Author:** ![ines](https://sea2.discourse-cdn.com/flex020/user_avatar/support.prodi.gy/ines/32/3_2.png) [@ines](https://support.prodi.gy/u/ines)\
**Post date:** [October 31, 2019, 10:33am UTC](https://support.prodi.gy/t/textcat-model-with-multiple-classes/2163/2 "2019-10-31T10:33:58Z")

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Hi! Glad to hear things have been going well so far 😃

> [@trevorwelch](#):
>
> Is there a better flow to achieve the above? It seems like quite a lot of steps for what would seem to me to be a common approach, but I'm entirely fine with it now that I've sketched it out (although it doesn't work as is):
> 
> - tag some data with NLP rules,
> - accept/reject the tags with Prodigy
> - use the results to train a new model

Your current approach does indeed sound a bit complicated for what it is, and I'm sure there's an easier way to achieve the same result. Have you had a look at the `textcat.manual` recipe yet? It shows the text and options in a multiple-choice interface and the format you get out is directly compatible with `textcat.teach`.

A single annotation task in the choice format could look like this:

```json
{
    "text": "Some text",
    "options": [
        {"id": "LABEL1", "text": "Label 1"},
        {"id": "LABEL2", "text": "Label 2"}
    ]
}

```

When you select an option, a key `"accept"` is added to the task and it holds a list of the selected IDs. For example: `"accept": ["LABEL1", "LABEL2"]`. You can also provide those when you load in the data to pre-select certain categories – e.g. based on your rules – and then correct them if needed.

For training, you might also consider training with spaCy directly – this gives you more flexibility and you get to tweak more settings, experiment with different architectures etc. [See here](https://github.com/explosion/spaCy/blob/master/examples/training/train_textcat.py) for an example script.

> [@trevorwelch](#):
>
> I'm a little confused on how datasets work in general, as you can see I'm creating multiple different datasets but I assume this is not the right way to be thinking about this. Can I keep my annotations and my raw data in the same dataset? Etc.

Datasets in Prodigy hold the annotations you collect. There's typically no need to import raw data before you annotated – this can all be done on the command line when you start the recipe.

Datasets are append-only so you'll never lose any state or data. So if you want to manually edit examples in an existing dataset, you should export it, edit it and then import it to a new dataset. This creates more data overall – but it means you'll always be able to recover the previous dataset. We recommend creating a new dataset for every annotation experiment, annotation type etc. Merging datasets later is easy – there's a `db-merge` command and each example has hashes that let you find all annotations on the same input text. You can also think of a dataset as one unit of data you'd run a particular experiment with.

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