[cygn]

I built a browser extension that does this, well for posts on twitter, hackernews, reddit etc. If you want it for all text, it would also be feasible. I use a quantized mini-LM model that runs very fast and classifies eg your whole twitter feed in a couple of seconds.

Check it out: https://slopsieve.com/extension

Accuracy is also much higher than this approach here. 0.9944 AUC, 0.966 acc@.5, 0.971 F1@.5

[Anoian]

Went to the website and inserted the blogposts I wrote in the last year. I had a pretty good understanding which of my blogposts had more reworks and which ones had entire passages being generated using AI and then left as is because I was happy with them. None of my articles were over 50% according to your model but the ones that I know took me a long time to write, even though I used lots of AI in the creation of them, hit below 10%, probably because I hand-edited them a lot. Overall a nice website, thanks for sharing :)

[lcnhk42]

Hey this is really cool. Do you mind sharing a little bit about your approach? Is it similar SVM like algorithm or something else entirely?

[joouha]

Not GP, but I too was interested in this slopdetect-minilm-v3 model.

The model appears to be similar to MiniLM-L6 (384 dimension, 6 transformer layers) but uses RoBERTa/GPT-2 style embedding/tokenization (50265 vocab size), used for binary classification, and quantized to INT8.

My guess is that is was distilled from roberta-base, then fine-tuned on freely available pile like artem9k/ai-text-detection-pile or similar.

It's a nice model - I've just used it to create a browser extension which highlights text based on how likely it is to be LLM genereated.

Edit - a quick google search reveals ibm-granite/granite-embedding-30m-english with the same architecture: slap a binary classification head on, fine tune, job done.

[joouha]

Just noticed in the model's metadata:

> MiniLMv2 L6-H384 (30M params) progressively distilled L24→L12→L6 from RoBERTa-large-v8 teacher (neobert-v2 recipe, min_words=0). Byte-level BPE tokenizer. INT8 quantized.