# from textcat.manual to textcat.teach

**URL:** <https://support.prodi.gy/t/from-textcat-manual-to-textcat-teach/5304>\
**Category:** Uncategorized\
**Tags:** usage, textcat, best-practices\
**Created:** [February 10, 2022, 5:21pm UTC](https://support.prodi.gy/t/from-textcat-manual-to-textcat-teach/5304 "2022-02-10T17:21:41Z")\
**Posts on this page:** 2\
**Page:** 1

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**Author:** ![skander](https://sea2.discourse-cdn.com/flex020/user_avatar/support.prodi.gy/skander/32/2775_2.png) [@skander](https://support.prodi.gy/u/skander)\
**Post date:** [February 10, 2022, 5:21pm UTC](https://support.prodi.gy/t/from-textcat-manual-to-textcat-teach/5304/1 "2022-02-10T17:21:41Z")

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Hi community,  
I am new to prodigy and a bit confused about the process.  
I am working on a project to classify sentiment on some news and journal articles with the following labels: positive, negative and neutral. I started the annotation process by using a manual recipe and as of now we have collected about 500 samples. I run an experiment and trained a model on that data and got an accuracy of 70%.

The manual process is still running but I currently would like to switch to the active learning mode using the model I have trained and a bit confused on the steps I need to take.

Could anyone shed light a bit on that.

Much appreciated!

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<div class="post-metadata">

**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:** [February 13, 2022, 9:44am UTC](https://support.prodi.gy/t/from-textcat-manual-to-textcat-teach/5304/2 "2022-02-13T09:44:59Z")

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> [@skander](#):
>
> The manual process is still running but I currently would like to switch to the active learning mode using the model I have trained and a bit confused on the steps I need to take.

Hi! In that case, you could use the model you trained on your previously collected 500 annotations and use that as the input model when you run `textcat.teach` and save the annotations to a new dataset. Based on the model's predictions, the recipe will then select the most uncertain predictions and will let you accept/reject them.

Depending on the number of labels you have, it might make sense to start with a subset of labels, especially those that may need the most improvement. If you run the training with `--label-stats`, it will give you a breakdown of the accuracy per label, so you can see if there's one label in particular that stands out.

After you've annotated with `textcat.teach`, you can then train a new model from scratch using the previously collected annotations + the new annotations.