Every major AI shop has a ton of in-house classifiers already, big, small, generalist, specialized. Some are used in inference pipelines (e.g. safeguards), some are used in data preparation, training, analysis and investigation, research, various one-off and intermediate tasks etc. Offering them on a public API doesn't always make business sense. I don't see much substance to this buzz, looks like people that are new to all this are discovering that classifiers exist, they are more efficient at classification, and many tasks commonly done with generative models are classification in disguise. Which is not bad at all, a fresh look at their use is great to have.
People in my lab (sklearn people) developped something that I feel close to jev but focused on tabular data : <a href="https://tabicl.readthedocs.io/en/latest/" rel="nofollow">https://tabicl.readthedocs.io/en/latest/
This is a transformer based classifier with massive pretraining on synthetic datasets and it outperforms boosting classifiers on many benchmarks without the need of more gradient descent steps (the forward pass on X_train, y_train IS the training).
I understand that jev focus on text entry. But I feel that it is a similar kind of model but trained on text. Did someone test it on tabular data as well ?
orbital-decay · · focus · HN ↗
d2ou · · focus · HN ↗
This is a transformer based classifier with massive pretraining on synthetic datasets and it outperforms boosting classifiers on many benchmarks without the need of more gradient descent steps (the forward pass on X_train, y_train IS the training).
I understand that jev focus on text entry. But I feel that it is a similar kind of model but trained on text. Did someone test it on tabular data as well ?