# Multilabel JSONL format & active learning **URL:** \ **Category:** Getting Started\ **Tags:** textcat, solved\ **Created:** [August 4, 2022, 5:27am UTC](https://support.prodi.gy/t/multilabel-jsonl-format-active-learning/5843 "2022-08-04T05:27:07Z")\ **Posts on this page:** 1\ **Showing post:** 4
**Author:** ![ryanwesslen](https://sea2.discourse-cdn.com/flex020/user_avatar/support.prodi.gy/ryanwesslen/32/2969_2.png) [@ryanwesslen](https://support.prodi.gy/u/ryanwesslen)\ **Post date:** [August 5, 2022, 1:49pm UTC](https://support.prodi.gy/t/multilabel-jsonl-format-active-learning/5843/4 "2022-08-05T13:49:28Z")
hi @suryaiitkgp ! My colleague @Jette16 reminded me there's an alternative data format for multilabel. This includes each tag as the `"options"` and then whichever are the accepted (selected) labels are in the `"accept"` ```python {"options": [{"id": "OTHER"}, {"id": "baking"}, {"id":"bread"}, {"id":"chicken"}, {"id":"eggs"}, {"id":"equipment"}, {"id": "food-safety"},{"id":"meat"}, {"id":"sauce"}, {"id":"storage-method"}, {"id":"substitutions"}], "accept":["OTHER","baking"], "text": "How can I get chewy chocolate chip cookies?\n

My chocolate chips cookies are always too crisp. How can I get chewy cookies, like those of Starbucks?

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Thank you to everyone who has answered. So far the tip that had the biggest impact was to chill and rest the dough, however I also increased the brown sugar ratio and increased a bit the butter. Also adding maple syrup helped.

\n"} ``` Perhaps to test out, can you try to use the example above as `test.jsonl` and run: ```bash prodigy db-in import_data ./test.jsonl --rehash prodigy train ./model --textcat-multilabel import_data --eval-split 0.2 --base-model blank:en ``` This will give us a reproducible use case to ensure there aren't other issues. If this works, then try to convert your data to this format. --- _[View the full topic](https://support.prodi.gy/t/multilabel-jsonl-format-active-learning/5843)._