# Multi-label classification with non-binary label

**URL:** <https://discuss.huggingface.co/t/multi-label-classification-with-non-binary-label/33874>\
**Category:** Beginners\
**Created:** [March 15, 2023, 5:00pm UTC](https://discuss.huggingface.co/t/multi-label-classification-with-non-binary-label/33874 "2023-03-15T17:00:53Z")\
**Posts on this page:** 1\
**Page:** 1

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**Author:** ![BaroneRampante](https://avatars.discourse-cdn.com/v4/letter/b/3e96dc/32.png) [@BaroneRampante](https://discuss.huggingface.co/u/BaroneRampante)\
**Post date:** [March 15, 2023, 5:00pm UTC](https://discuss.huggingface.co/t/multi-label-classification-with-non-binary-label/33874/1 "2023-03-15T17:00:54Z")

</div>

Hello, I’m working on a classification problem where, for each text sample in my dataset, I have to predict three labels. Two of these labels are binary, while the remaining one is not and can take up one of four different values:

```auto
text_sample {
    label_1: # binary -> A or B
    label_2: # binary -> A or B
    label_3: # non-binary -> A, B, C or D
}

```

I’d like to try out BERT and from the docs `BertForSequenceClassification` with “multi\_label\_classification” as problem type looks almost like what I need. The issue is the third label.

Is there any way I can rely on or customize what the Hub already provides to solve this task? If so, I’d really appreciate a nudge in the right direction.