Pile-T5

I figured it would be interesting to see scale and for the image models

my group is currently working on a T5 model (and tokenizer) for html, as there are very few (if any) tokenizers that work well with HTML!

You can try using GPT4's tokenizer on your own HTML inputs below [1] ... there's definitely room for improvement!

[1] https://tiktokenizer.vercel.app

Blast from the past? Like 2 years ago?

A lot of embedding models are built on top of T5's encoder, this offers a new option

The modularity of the enc-dec approach is useful - you can insert additional models in between (e.g. A diffusion model), you can use different encoders for different modalities, etc

I've downloaded the data for every model on Huggingface and, if nothing else, flan-t5-xxl is the largest encoder (discriminant) model in terms of total file sizes. To be clear T5 is an encoder-decoder model. But the shift in focus to decoder-only has made it so encoder and encoder-decoder models have been, for lack of a better word, neglected, in terms of training focus.