Here is a dataset for abstractive summarization created from Reddit .
Dataset https://zenodo.org/record/1168855#.WyJG3I7pdhE Paper http://aclweb.org/anthology/W17-4508
Datasets for Machine Learning
21–30 of 41 posts
Re: Datasets for Machine Learning
#22Does anyone know of good datasets for Concept Drift analysis?
Re: Datasets for Machine Learning
#23Ben from Kaggle. Open up the ~50 different individual datasets linked in separate tabs, and then quickly flip through all of them trying to get a sense of what each one is. That experience will demonstrate one of the main challenges we're aiming to solve by making Kaggle Datasets your default place to publish data online ( https://www.kaggle.com/datasets )
Great, thanks for the link (and to the blog author for her links). I do machine learning at work, but just two very specific use cases involving GANs and RNNs. I appreciate resources to use in my own time to explore other architectures.
Re: Datasets for Machine Learning
#24Earlier quoted context omitted.
I've heard that Kaggle data sets encourage people to do "supervised" ML only. Is that true?
(Not Ben, but - ) outside of academia, the main thing that seems to encourage people to do supervised ML is that it's the only thing that seems to work. I haven't really heard of any success stories with using unsupervised techniques for most common ML applications.
I'm not an expert, but I feel that:
Unsupervised techniques work really well for language modelling.
There is also weakly supervised and distant-supervision, where the labels are "noisy" or not exactly what you want.
You're right in that strong supervision, where you basically trust your class label, works really well, because it's probably the easiest case.
Combining unsupervised (e.g. pre-trained language models) with a very small set of strongly labeled data, or a larger set of weakly labeled data, seems to work pretty well too.
Re: Datasets for Machine Learning
#25Earlier quoted context omitted.
I've heard that Kaggle data sets encourage people to do "supervised" ML only. Is that true?
(Not Ben, but - ) outside of academia, the main thing that seems to encourage people to do supervised ML is that it's the only thing that seems to work. I haven't really heard of any success stories with using unsupervised techniques for most common ML applications.
I think it's more that supervised ML is sufficient for most of the low hanging fruit. It's relatively easy and well-understood, and there are a lot of things out there where we have copious data that we just need to digest into a model to make it useful.
Re: Datasets for Machine Learning
#26Can't open this website.
Re: Datasets for Machine Learning
#27Ben from Kaggle. Open up the ~50 different individual datasets linked in separate tabs, and then quickly flip through all of them trying to get a sense of what each one is. That experience will demonstrate one of the main challenges we're aiming to solve by making Kaggle Datasets your default place to publish data online ( https://www.kaggle.com/datasets )
This is great! Thanks for sharing. Would be awesome if your license filter had a "not for commercial use" vs. "for commercial use" or similar.
Thanks for the feedback! Totally agree this could be clearer
Re: Datasets for Machine Learning
#28Earlier quoted context omitted.
(Not Ben, but - ) outside of academia, the main thing that seems to encourage people to do supervised ML is that it's the only thing that seems to work. I haven't really heard of any success stories with using unsupervised techniques for most common ML applications.
Unsupervised works, but your ability to measure "does it work or not" is much more dependent on a case by case evaluation rather than a score. (Because if you know a priori what is it that you want to measure - it's supervised)
Yeah this my experience too, evaluation ends being almost endless time sink.
Re: Datasets for Machine Learning
#29Can't open this website.
Click on the link.
Re: Datasets for Machine Learning
#30Ben from Kaggle. Open up the ~50 different individual datasets linked in separate tabs, and then quickly flip through all of them trying to get a sense of what each one is. That experience will demonstrate one of the main challenges we're aiming to solve by making Kaggle Datasets your default place to publish data online ( https://www.kaggle.com/datasets )
This is a great idea Ben, and I appreciate the work you do. Do you see Kaggle datasets as a tool to encourage better data formatting, or are you also thinking about building tools for automatically visualizing, cleaning, and organising data?
All of the above, and more! One thing I'm really excited about that we're about to release is a much better explorer for tabular data (automated histograms, sorting/filtering/showing the data, and the like).
We also encourage sharing analytics code and visualizations that users create on the data back to the community. For example, see all these visualizations and insights in StackOverflow's developer survey data linked from https://www.kaggle.com/stackoverflow/stack-overflow-2018-dev...