I am very curious about a very large classification dataset approx. 1e9 samples and 1e4 classes. However, I cannot find any. Could you recommend me some?
1 Answer
1B rows is easy to reach. 10K labels can be achieved with concatenations.
For example, take Wikipedia for 100 languages, and split it into rows - by sentence, by n-gram
You now have enough rows, but still only 100 labels.
Then run some lib over each, and concatenate the output to the labels, yielding a new set of labels.
The lib could be a parsing lib like spaCy (POS tag sequence), it could be the language identification like langid.py, it could be sentiment, or combinations of the above. But somehow there should also be ~100 possible outputs (100^2 == 10^4).
For example you might have a row from the 3-gram va a Milano
from the Corsican Wikipedia (co
). For that sequence langid.py returns [['es': 0.5, 'it': 0.4, ...]
. So the generated row can be:
va a Milano \t co_es_it
Note the classes will not be balanced, although you could balance them by doing it at greater scale and then discarding as needed.