[spott]

Embedding models are frequently based on Bert style models, but Bert models can be finetuned to do a lot more than just embeddings.

So an embedding focused finetune of modern Bert should be compared to something like voyageai, but not modern Bert itself.

[KTibow]

What are the people who keep downloading Bert doing then? Are they the minority who directly use it for embeddings?

[janalsncm]

They are probably fine tuning on their own particular downstream tasks, either for embeddings or as a component of a larger model.

[spott]

I’m honestly not sure why Bert-based-uncased is so popular… the model isn’t that useful on its own. From their huggingface page:

> You can use the raw model for either masked language modeling or next sentence prediction, but it's mostly intended to be fine-tuned on a downstream task. See the model hub to look for fine-tuned versions of a task that interests you.

> Note that this model is primarily aimed at being fine-tuned on tasks that use the whole sentence (potentially masked) to make decisions, such as sequence classification, token classification or question answering. For tasks such as text generation you should look at model like GPT2.

[strangecasts]

I think this comes down to the Huggingface libraries defaulting to downloading the model from HF if they cannot locate the weights - so "make your own text classifier" tutorial notebooks default to bert-based-uncased as a "standard" pretrained encoder you can put a classification head on top of and finetune, and in turn people run them in Google Colab and just download another copy of the weights on startup, which counts towards the total

[metanonsense]

I am out of the game for a year or so (and was never completely in the game), but back then BERT was the basis for lots of interesting applications. The original Vision Transformer (ViT) was based (or at least inspired by) BERT, it was used for graph transformers, visual language understanding, etc.