# Text classification of RSS articles

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**Category:** Beginners\
**Created:** [June 28, 2025, 8:03am UTC](/static-proxy?url=https%3A%2F%2Fdiscuss.huggingface.co%2Ft%2Ftext-classification-of-rss-articles%2F160986 "2025-06-28T08:03:30Z")\
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**Author:** ![John6666](https://sea2.discourse-cdn.com/hellohellohello/user_avatar/discuss.huggingface.co/john6666/32/27664_2.png) [@John6666](/static-proxy?url=https%3A%2F%2Fdiscuss.huggingface.co%2Fu%2FJohn6666)%5C
**Post date:** [June 28, 2025, 12:37pm UTC](/static-proxy?url=https%3A%2F%2Fdiscuss.huggingface.co%2Ft%2Ftext-classification-of-rss-articles%2F160986%2F2 "2025-06-28T12:37:35Z")

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Hello.

Given that it works reasonably well in practice, I think the approach is correct. There are many [successor models to BERT](https://huggingface.co/blog/modernbert), so it should be possible to improve accuracy using those.

Another approach that can be taken when there is little labeled data is something called [Positive Unlabeled Learning](https://github.com/JointEntropy/awesome-ml-pu-learning)…

Another common approach is to use commercial AI to create a training dataset using your own data. This is almost always effective if the budget allows. However, in this case, there is already a considerable amount of data available, so it may be sufficient to process the data using Python.

Resources:

> **[GitHub - UKPLab/sentence-transformers: State-of-the-Art Text Embeddings](https://github.com/UKPLab/sentence-transformers)**
>
> State-of-the-Art Text Embeddings

> [@Continue pre-training BERT](/static-proxy?url=https%3A%2F%2Fdiscuss.huggingface.co%2Ft%2Fcontinue-pre-training-bert%2F62053)%3A
>
> Hello, I have a small portion of label data, and a much bigger set of unlabeled observations. I want to use the unlabeled samples in order to continue the pre-training of BERT, and then built a classifier on top of it. Following this post I tried to use BertModel.from\_pretrained(‘bert-base-uncased’), and specifically model = BertModel.from\_pretrained(HF\_BERT\_MODEL) model.cuda() optimizer = AdamW(model.parameters(), lr = 2e-5, eps = 1e-8 …

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