# Multi-input tag and ,multi-label output for token classification using Bert pretrained model

**URL:** <https://discuss.huggingface.co/t/multi-input-tag-and-multi-label-output-for-token-classification-using-bert-pretrained-model/134978>\
**Category:** 🤗Transformers\
**Created:** [January 9, 2025, 9:03am UTC](https://discuss.huggingface.co/t/multi-input-tag-and-multi-label-output-for-token-classification-using-bert-pretrained-model/134978 "2025-01-09T09:03:06Z")\
**Posts on this page:** 2\
**Page:** 1

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**Author:** ![nanapc17](https://avatars.discourse-cdn.com/v4/letter/n/49beb7/32.png) [@nanapc17](https://discuss.huggingface.co/u/nanapc17)\
**Post date:** [January 9, 2025, 9:03am UTC](https://discuss.huggingface.co/t/multi-input-tag-and-multi-label-output-for-token-classification-using-bert-pretrained-model/134978/1 "2025-01-09T09:03:06Z")

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Hi, if I have a huggingface dataset where tokens are tagged with pos and possibly so that I have (token, pos, lemma, fine\_pos, wordnet.word) and I want to produce for each token t labels like t1,t2, is there any tutorial or example of how to do it?  
I apologize if this question is answered before, but I’ve been searching for an answer using transformers and pretrained bert and have not managed to find one.  
Thank you.

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**Author:** ![Alanturner2](https://sea2.discourse-cdn.com/hellohellohello/user_avatar/discuss.huggingface.co/alanturner2/32/37542_2.png) [@Alanturner2](https://discuss.huggingface.co/u/Alanturner2)\
**Post date:** [January 9, 2025, 9:26am UTC](https://discuss.huggingface.co/t/multi-input-tag-and-multi-label-output-for-token-classification-using-bert-pretrained-model/134978/2 "2025-01-09T09:26:51Z")

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**Hey!**

It looks like you’re trying to label tokens in a Hugging Face dataset, such as tagging each token with multiple labels like `t1`, `t2`, alongside POS tags. You can achieve this using Hugging Face’s `transformers` library with a pre-trained model like BERT. Here’s an approach to guide you:

1. **Dataset Preparation** :

2. **Tokenization** :

3. **Using Pretrained BERT** :

4. **Multi-Labeling** :

5. **Resources** :

With this approach, you can train a pre-trained BERT model to output multiple labels per token and fine-tune it on your specific task. If you’re still having trouble, let me know, and I can help clarify further!