Cool

New stuff

Paper: https://arxiv.org/abs/2303.03032

Takes only a few seconds to calculate.

Most similiar suffix tokens : "vfx cleanup |warcraft |defend |avatar |wall |blu |indigo |dfs |bluetooth |orian |alliance |defence |defenses |defense |guardians |descendants |navis |raid |avengersendgame "

most similiar prefix tokens : “imperi-blue-|bluec-|war-|blau-|veer-|blu-|vau-|bloo-|taun-|kavan-|kair-|storm-|anarch-|purple-|honor-|spartan-|swar-|raun-|andor-”

Simple and cool.

Florence 2 image captioning sounds interesting to use.

Do people know of any other image-to-text models (apart from CLIP) ?

I count casualty_rate = number_shot / (number_shot + number_subdued)

Which in this case is 22/64 = 34% casualty rate for civilians

and 98/131 = 75% casualty rate for police

Hmm. I mean the FLUX model looks good

, so there must maybe be some magic with the T5 ?

I have no clue, so any insights are welcome.

T5 Huggingface: https://huggingface.co/docs/transformers/model_doc/t5

T5 paper : https://arxiv.org/pdf/1910.10683

Any suggestions on what LLM i ought to use instead of T5?

Wow , yeah I found a demo here: https://huggingface.co/spaces/Qwen/Qwen2.5

A whole host of LLM models seems to be released. Thanks for the tip!

I’ll see if I can turn them into something useful 👍

That’s good to know. I’ll try them out. Thanks.

So its 64-131 between work done by bystanders vs. work done by police?

And casualty rate is actually lower for bystanders doing the work (with their guns) than the police?