# Nested named entity recognition

**URL:** <https://discuss.huggingface.co/t/nested-named-entity-recognition/75376>\
**Category:** Intermediate\
**Created:** [February 29, 2024, 6:52pm UTC](https://discuss.huggingface.co/t/nested-named-entity-recognition/75376 "2024-02-29T18:52:25Z")\
**Posts on this page:** 1\
**Showing post:** 2

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**Author:** ![stulcrad](https://avatars.discourse-cdn.com/v4/letter/s/4bbf92/32.png) [@stulcrad](https://discuss.huggingface.co/u/stulcrad)\
**Post date:** [March 19, 2024, 1:39am UTC](https://discuss.huggingface.co/t/nested-named-entity-recognition/75376/2 "2024-03-19T01:39:52Z")

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If anyone reading this would be interested, I solved it using this discussion

> [@Multi-label token classification](https://discuss.huggingface.co/t/multi-label-token-classification/16509):
>
> Hi! I am trying to solve a token classification problem in a multi-label setup. So far I haven’t found the best path to do it. Unlike AutoModelForSequenceClassification, AutoModelForTokenClassification lacks the problem\_type parameter, where you can specify that you’re working with multi-label. Should I deepen into logits and create a custom loss or is there a more straightforward solution? Thanks!

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