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Prodigy: A new tool for radically efficient machine teaching

explosion.ai

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Re: Prodigy: A new tool for radically efficient machine teaching

#41

Post by transcranial »

plusepsilon wrote: post #30

I don't think (some) people understand; a slick data annotation tool like this is vastly more useful than the 20th variant of GAN that DeepMind produces :)

Totally, I think people have this weird sense of entitlement when it comes to high-quality datasets without the commensurate respect for how they're created or the level of effort that goes into them.

Fei-Fei Li gives a good sense for this in her history of ImageNet [1][2].

[1] https://qz.com/1034972/the-data-that-changed-the-direction-o...

[2] http://image-net.org/challenges/talks_2017/imagenet_ilsvrc20...

Re: Prodigy: A new tool for radically efficient machine teaching

#43

Post by abecode »

blueyes wrote: post #24
theincredulousk wrote: post #17

Guess the radical efficiency didn't carry over to their web server

> spaCY, the leading open-source NLP tool? Sounds like marketing BS. what about OpenNLP and Stanford's for NLP?

Anecdotally, I got better NER results with spaCY than OpenNLP and CoreNLP with their respective default models, and spaCY was easier to install (though I'm biased, being more familiar with Python tooling and documentation style). I was eventually implementing in Java so I did use OpenNLP for sentence splitting, but I retrained the NER with data bootstrapped from spaCY, in a way similar to what the Prodigy tool is aiming to facilitate, by first classifying using the default/vanilla model and then manually correcting labels where they were incorrect.

Re: Prodigy: A new tool for radically efficient machine teaching

#44

Post by Gimpei »

rayuela wrote: post #3

That's a nice UX but the flurry of initial upvotes on this looks kinda fishy, especially given that it's just annotation software.

I'm a data scientist and getting annotations for our data is one of our most onerous issues. I upvoted this. If it works well, I could see myself using it all the time. Making a model that gets you most of the way there is the easy part; getting clean, annotated data. Uggh.

Re: Prodigy: A new tool for radically efficient machine teaching

#46

Post by stephengillie »

SubiculumCode wrote: post #45

I work with data as a neuroscientist, but I haven't used ML. What is an an annotation in this context?

I have an NLP bot as a hobby, but use old-fashioned statistics instead of ML. It looks like annotations here mean manually training the bot by seeding the learning data with hints.

Re: Prodigy: A new tool for radically efficient machine teaching

#47

Post by SubiculumCode »

stephengillie wrote: post #46
SubiculumCode wrote: post #45

I work with data as a neuroscientist, but I haven't used ML. What is an an annotation in this context?

I have an NLP bot as a hobby, but use old-fashioned statistics instead of ML. It looks like annotations here mean manually training the bot by seeding the learning data with hints.

A utility for labeling training data for supervised learning then?

Re: Prodigy: A new tool for radically efficient machine teaching

#48

Post by michaelbarton »

This looks interesting because it add the ability to put the user in the loop of fixing/annotating the problematic observations relatively easily. I like the example of Tinder for data.

Are the examples picked those that have the highest objective function error rate, or something similar?

Does this apply only to text classification problems? Are there examples where this could be applied to tabular data?

Re: Prodigy: A new tool for radically efficient machine teaching

#50

Post by 38kkdiu »

Looks very nice, although it always takes me a bit to figure out what they're talking about with these sorts of things because I have to remind myself that most ML/DL stuff is supervised. What I research is unsupervised.

They kind of have this weird dissing of unsupervised scenarios, though. It's not like supervised or unsupervised is better or worse, they're just surrounding different problems. They can talk up their product without needing to criticize a problem domain.

It's like if you were making motors for boats, and then started talking about "these crazy people who think it's better to fly." ???

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