# Using Tokenizer for integer data

**URL:** </static-proxy?url=https%3A%2F%2Fdiscuss.huggingface.co%2Ft%2Fusing-tokenizer-for-integer-data%2F28835%3E%5C
**Category:** 🤗Tokenizers\
**Created:** [January 3, 2023, 10:42am UTC](/static-proxy?url=https%3A%2F%2Fdiscuss.huggingface.co%2Ft%2Fusing-tokenizer-for-integer-data%2F28835 "2023-01-03T10:42:20Z")\
**Posts on this page:** 1\
**Page:** 1

<div class="post-metadata">

**Author:** ![HasnainKhan](https://avatars.discourse-cdn.com/v4/letter/h/e79b87/32.png) [@HasnainKhan](/static-proxy?url=https%3A%2F%2Fdiscuss.huggingface.co%2Fu%2FHasnainKhan)%5C
**Post date:** [January 3, 2023, 10:42am UTC](/static-proxy?url=https%3A%2F%2Fdiscuss.huggingface.co%2Ft%2Fusing-tokenizer-for-integer-data%2F28835%2F1 "2023-01-03T10:42:20Z")

</div>

Hey guys, I am working on a problem where I am having tabular data having more than 90 features and one target and all the features are in integers (continuous). I want to use pre-trained BERT, GPT2 but when it comes to the tokenizer the tokenizer is expecting the input in the text format. I can change the integer data in the text format like this;

original\_data = [1,2,3,4,5,…,94]  
transformed\_data = [“1 2 3 4 5 …94”]

Now if I pass the transformed\_data to the tokenizer then surely it will work but I wanna know if someone tried to use transformers for this purpose and if yes, then what was the outcome, and how did the results look like?