# Using time series for SequenceClassification models

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**Category:** 🤗Transformers\
**Created:** [January 19, 2021, 3:17pm UTC](/static-proxy?url=https%3A%2F%2Fdiscuss.huggingface.co%2Ft%2Fusing-time-series-for-sequenceclassification-models%2F3289 "2021-01-19T15:17:59Z")\
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**Author:** ![valhalla](https://sea2.discourse-cdn.com/hellohellohello/user_avatar/discuss.huggingface.co/valhalla/32/4179_2.png) [@valhalla](/static-proxy?url=https%3A%2F%2Fdiscuss.huggingface.co%2Fu%2Fvalhalla)%5C
**Post date:** [January 20, 2021, 8:02am UTC](/static-proxy?url=https%3A%2F%2Fdiscuss.huggingface.co%2Ft%2Fusing-time-series-for-sequenceclassification-models%2F3289%2F2 "2021-01-20T08:02:38Z")

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you might find some pointers here

> [@Using transformers (BERT, RoBERTa) without embedding layer](/static-proxy?url=https%3A%2F%2Fdiscuss.huggingface.co%2Ft%2Fusing-transformers-bert-roberta-without-embedding-layer%2F2807)%3A
>
> I’m looking to train a RoBERTa model on protein sequences, which is in many ways similar to normal nlp training, but in others quite different. In the language of proteins, I have 20 characters instead of the normal 26 characters used in english (it is 26 right? :D), so that is rather similar. The big difference is that you don’t really combine the characters in proteins to actual words, but rather just keep each character as a distinct token or class. Hence essentially my input to the transfo…

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