# T5 multilabel classification using tf **URL:** \ **Category:** 🤗Transformers\ **Created:** [March 28, 2023, 4:00am UTC](https://discuss.huggingface.co/t/t5-multilabel-classification-using-tf/34858 "2023-03-28T04:00:34Z")\ **Posts on this page:** 1\ **Page:** 1
**Author:** ![sreraku](https://avatars.discourse-cdn.com/v4/letter/s/e9bcb4/32.png) [@sreraku](https://discuss.huggingface.co/u/sreraku)\ **Post date:** [March 28, 2023, 4:00am UTC](https://discuss.huggingface.co/t/t5-multilabel-classification-using-tf/34858/1 "2023-03-28T04:00:34Z")
I am trying to do multilabel classification on a corpus of data which has labels too. When The data looks like this after adding the tag in the front for each row: print(texts) 0 multilabel classification: how time changes th… 1 multilabel classification: hawaii has been in … 2 multilabel classification: not all alaskans ar… 3 multilabel classification: you should read rap… 4 multilabel classification: giving stupid kids … I am trying to tokenize above and since there are multiple rows, I am guessing i have to go in a loop. What I am trying understand is, how do I get the input\_ids, attention\_mask? should I go in a loop to get for each row or for the entire text? am I doing it right above by adding the tag multilabel classification: for each row? am totally confused whether my assumption is wrong or whether this is the way to do it. my code is: src\_tokenized = TOKENIZER.encode\_plus( texts[0], max\_length=SRC\_MAX\_LENGTH, pad\_to\_max\_length=True, truncation=True, return\_attention\_mask=True, return\_token\_type\_ids=False, return\_tensors=‘tf’ ) src\_input\_ids = src\_tokenized[‘input\_ids’] src\_attention\_mask = src\_tokenized[‘attention\_mask’] t5\_summary\_ids = t5\_model.generate(src\_input\_ids) am feeling am doing wrong by running it row by row. but am not sure. I googled for it and all i see multilabel classification example is using pytorch not tf. Appreciate all the help. TIA