# Understanding multi-label classification training **URL:** \ **Category:** Beginners\ **Created:** [February 14, 2023, 6:18am UTC](https://discuss.huggingface.co/t/understanding-multi-label-classification-training/31675 "2023-02-14T06:18:41Z")\ **Posts on this page:** 1\ **Page:** 1
**Author:** ![msafar](https://avatars.discourse-cdn.com/v4/letter/m/ec9cab/32.png) [@msafar](https://discuss.huggingface.co/u/msafar)\ **Post date:** [February 14, 2023, 6:18am UTC](https://discuss.huggingface.co/t/understanding-multi-label-classification-training/31675/1 "2023-02-14T06:18:41Z")
I have looked at all kinds of examples for multi-label classification and I still don’t quite get how I’m supposed to be tokenizing the data. I have a number of questions. The scenario is my input data has these columns: ``` Text. LabelA. LabelB ``` LabelA and LabelB EACH have multiple possible values, but every row in the data has a label…there are no missing labels. Questions: I saw examples building a matrix, 2xN. I did that, and I successfully created a tokenized dataset. But I’m not confident I did it correctly. I assume it’s just a column each with an integer for each indicating which label I’m putting in that column. 1. Do I even need to pre-tokenize the data before I train, or will the trainer somehow do that for me? If so, then what the heck is the point of passing in label2ID? 2. label2id looks like it’s a linear input, so does that mean I should somehow have continuously linear IDs? 3. Essentially, what I tried to do was create a matrix of integers and then pre-tokenize my labels. But I see examples that make me think that it’s not necessary to tokenize, I can pass in a matrix of STRINGS. 4. Is there a difference in training outcomes if I go with the linear IDs, or somehow am able to make the overlapping IDs work? 5. If I can use overlapping IDs, then how in the world do I get the label2id/id2label parameters passed in correctly in .from\_pretrained()?