8 Commits

Author	SHA1	Message	Date
ines	a7b9074b4c	Update textcat training example and docs	2017-10-27 00:48:45 +02:00
ines	b61866a2e4	Update textcat example	2017-10-27 00:32:19 +02:00
Matthew Honnibal	563f46f026	Fix multi-label support for text classification ... The TextCategorizer class is supposed to support multi-label text classification, and allow training data to contain missing values. For this to work, the gradient of the loss should be 0 when labels are missing. Instead, there was no way to actually denote "missing" in the GoldParse class, and so the TextCategorizer class treated the label set within gold.cats as complete. To fix this, we change GoldParse.cats to be a dict instead of a list. The GoldParse.cats dict should map to floats, with 1. denoting 'present' and 0. denoting 'absent'. Gradients are zeroed for categories absent from the gold.cats dict. A nice bonus is that you can also set values between 0 and 1 for partial membership. You can also set numeric values, if you're using a text classification model that uses an appropriate loss function. Unfortunately this is a breaking change; although the functionality was only recently introduced and hasn't been properly documented yet. I've updated the example script accordingly.	2017-10-05 18:43:02 -05:00
Matthew Honnibal	f1b86dff8c	Update textcat example	2017-10-04 15:12:28 +02:00
Matthew Honnibal	79a94bc166	Update textcat exampe	2017-10-04 14:55:30 +02:00
Matthew Honnibal	c16ef0a85c	Clarify train textcat example	2017-07-29 21:59:27 +02:00
Matthew Honnibal	54a539a113	Finish text classifier example	2017-07-23 00:34:12 +02:00
Matthew Honnibal	2bc7d87c70	Add example for training text classifier	2017-07-22 20:15:32 +02:00