Hi @dikster99,
The way I usually search for models on the Hub 185 is by selecting the task in the sidebar, followed by applying a filter on the target dataset (or querying with the search bar if I know the exact name). In both your cases, you’re interested in the Text Classification tags, which is a specific example of sequence classification:

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However, this assumes that someone has already fine-tuned a model that satisfies your needs. If not, there are two main options:

If you have your own labelled dataset, fine-tune a pretrained language model like distilbert-base-uncased (a faster variant of BERT). You can find a nice example for text classification here 277 and see here 502 for the multi-label case. In general this is similar to your second example with ConvBertForSequenceClassification and you were correct to specify num_labels in the from_pretrained function 

If you have no labelled dataset, then you could try out one of the Zero-Shot Classification models (e.g. here 43). See this 32 blog post for an explanation on how zero-shot works.

Hope that helps!
PS. the Pipeline classes are typically used for generating predictions from a fine-tuned model, so your example with bert-base-multilingual-uncased-sentiment wouldn’t work because that model was trained on 5 labels and does not know about the 25 labels you are interested in.