# Fine tune Zero-shot classification on multi-label dataset

**URL:** <https://discuss.huggingface.co/t/fine-tune-zero-shot-classification-on-multi-label-dataset/63736>\
**Category:** Models\
**Created:** [November 27, 2023, 4:16pm UTC](https://discuss.huggingface.co/t/fine-tune-zero-shot-classification-on-multi-label-dataset/63736 "2023-11-27T16:16:58Z")\
**Posts on this page:** 1\
**Showing post:** 5

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**Author:** ![panigrah](https://avatars.discourse-cdn.com/v4/letter/p/bb73d2/32.png) [@panigrah](https://discuss.huggingface.co/u/panigrah)\
**Post date:** [November 30, 2023, 2:32pm UTC](https://discuss.huggingface.co/t/fine-tune-zero-shot-classification-on-multi-label-dataset/63736/5 "2023-11-30T14:32:13Z")

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According to this here

> [@New pipeline for zero-shot text classification](https://discuss.huggingface.co/t/new-pipeline-for-zero-shot-text-classification/681/15):
>
> Thanks for the helpful answer, @rsk97. Let me just add a bit: I discuss this briefly in my blog post under Classification as Natural Language Inference -\> When Some Annotated Data is Available. In short, if you have a limited amount of labeled data, you can further fine-tune the pre-trained NLI model. Pass the true label for a given sequence in the same way as you would during inference, e.g. \<cls\> Who are you voting for in 2020 ? \<sep\> This text is about politics . \<sep\>, and calculate the l…

The recommend approach is what you are suggesting in option 1. Basically present each of your multiple labels as entailments separately, but the author also suggests presenting an equal number of contradictions.

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