# Fintuning Transformer on CLEF dataset

**URL:** </static-proxy?url=https%3A%2F%2Fdiscuss.huggingface.co%2Ft%2Ffintuning-transformer-on-clef-dataset%2F9584%3E%5C
**Category:** 🤗Transformers\
**Created:** [August 30, 2021, 8:13am UTC](/static-proxy?url=https%3A%2F%2Fdiscuss.huggingface.co%2Ft%2Ffintuning-transformer-on-clef-dataset%2F9584 "2021-08-30T08:13:39Z")\
**Posts on this page:** 1\
**Showing post:** 2

<div class="post-metadata">

**Author:** ![lewtun](https://sea2.discourse-cdn.com/hellohellohello/user_avatar/discuss.huggingface.co/lewtun/32/30995_2.png) [@lewtun](/static-proxy?url=https%3A%2F%2Fdiscuss.huggingface.co%2Fu%2Flewtun)%5C
**Post date:** [September 1, 2021, 8:59am UTC](/static-proxy?url=https%3A%2F%2Fdiscuss.huggingface.co%2Ft%2Ffintuning-transformer-on-clef-dataset%2F9584%2F2 "2021-09-01T08:59:41Z")

</div>

Hey @rasel277144, if I understand correctly you’d like to classify whether whether a user is “depressed” based on their posts?

In this case, you could concatenate all the user posts and treat it as a standard classification problem and Sylvain has created a nice tutorial for this task [here](https://colab.research.google.com/github/huggingface/notebooks/blob/master/examples/text_classification.ipynb).

Having said that, you will probably run into limitations with the maximum context size of models like BERT (typically just a few paragraphs), so you might want to see if models like [BigBird](https://huggingface.co/models?search=bigbird) or [LongFormer](https://huggingface.co/models?search=longformer) can help as their context size is 8x that of BERT. If that’s still not sufficient, you might want to adapt some of the suggestions in [this thread](/static-proxy?url=https%3A%2F%2Fdiscuss.huggingface.co%2Ft%2Fsummarization-on-long-documents%2F920%2F2) to text classification (e.g. you could create an embedding for each user post, average the embeddings, and then use those embeddings for a simple logistic regression classifier)

PS I put “depressed” in quotes because I assume this is not a phenomenon we can hope to capture accurately from written text alone. I also suggest treading _ **very carefully** _ in this domain as there’s plenty of [public examples](https://www.nabla.com/blog/gpt-3/) where using NLP to diagnose patient well-being leads to bad outcomes.

---

_[View the full topic](/static-proxy?url=https%3A%2F%2Fdiscuss.huggingface.co%2Ft%2Ffintuning-transformer-on-clef-dataset%2F9584)._