# Using time series for SequenceClassification models **URL:** \ **Category:** 🤗Transformers\ **Created:** [January 19, 2021, 3:17pm UTC](https://discuss.huggingface.co/t/using-time-series-for-sequenceclassification-models/3289 "2021-01-19T15:17:59Z")\ **Posts on this page:** 1\ **Showing post:** 2
**Author:** ![valhalla](https://sea2.discourse-cdn.com/hellohellohello/user_avatar/discuss.huggingface.co/valhalla/32/4179_2.png) [@valhalla](https://discuss.huggingface.co/u/valhalla)\ **Post date:** [January 20, 2021, 8:02am UTC](https://discuss.huggingface.co/t/using-time-series-for-sequenceclassification-models/3289/2 "2021-01-20T08:02:38Z")
you might find some pointers here > [@Using transformers (BERT, RoBERTa) without embedding layer](https://discuss.huggingface.co/t/using-transformers-bert-roberta-without-embedding-layer/2807): > > 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… --- _[View the full topic](https://discuss.huggingface.co/t/using-time-series-for-sequenceclassification-models/3289)._