ines
c3b681e5fb
Use plac annotations for arguments and add n_iter
2017-10-26 16:11:05 +02:00
ines
bc2c92f22d
Use plac annotations for arguments
2017-10-26 16:10:56 +02:00
ines
b5c74dbb34
Update parser training example
2017-10-26 15:15:37 +02:00
ines
586b9047fd
Use create_pipe instead of importing the entity recognizer
2017-10-26 15:15:26 +02:00
ines
d425ede7e9
Fix example
2017-10-26 15:15:08 +02:00
ines
9d58673aaf
Update train_ner example for spaCy v2.0
2017-10-26 14:24:12 +02:00
ines
e904075f35
Remove stray print statements
2017-10-26 14:24:00 +02:00
ines
c30258c3a2
Remove old example
2017-10-26 14:23:52 +02:00
ines
615c315d70
Update train_new_entity_type example to use disable_pipes
2017-10-25 14:56:53 +02:00
ines
2b8e7c45e0
Use better training data JSON example
2017-10-24 16:00:56 +02:00
ines
9bf5751064
Pretty-print JSON
2017-10-24 12:22:17 +02:00
ines
6675755005
Add training data JSON example
2017-10-24 12:05:10 +02:00
Jeroen Bobbeldijk
84c6c20d1c
Fix #1444 : fix pipeline logic and wrong paramater in update call
2017-10-22 15:18:36 +02:00
Jeffrey Gerard
5ba970b495
minor cleanup
2017-10-12 12:34:46 -07:00
Jeffrey Gerard
39d3cbfdba
Bugfix example script train_ner_standalone.py, fails after training
2017-10-12 11:39:12 -07:00
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
cbb1fbef80
Update train_ner_standalone example
2017-10-03 18:49:38 +02:00
Matthew Honnibal
027a5d8b75
Update train_ner_standalone example
2017-09-15 10:36:46 +02:00
Matthew Honnibal
683d81bb49
Update example for adding entity type
2017-09-14 16:15:59 +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
ines
992559bf9a
Fix formatting and remove unused imports
2017-06-01 12:47:18 +02:00
Matthew Honnibal
5c30466c95
Update NER training example
2017-05-31 13:42:12 +02:00
Matthew Honnibal
2da16adcc2
Add dropout optin for parser and NER
...
Dropout can now be specified in the `Parser.update()` method via
the `drop` keyword argument, e.g.
nlp.entity.update(doc, gold, drop=0.4)
This will randomly drop 40% of features, and multiply the value of the
others by 1. / 0.4. This may be useful for generalising from small data
sets.
This commit also patches the examples/training/train_new_entity_type.py
example, to use dropout and fix the output (previously it did not output
the learned entity).
2017-04-27 13:18:39 +02:00
Matthew Honnibal
0605b95f2e
Merge branch 'master' of https://github.com/explosion/spaCy
2017-04-18 13:48:00 +02:00
Matthew Honnibal
2f84626417
Fix train_new_entity_type example
2017-04-18 13:47:36 +02:00
Ines Montani
734b0a4e4a
Update train_new_entity_type.py
2017-04-16 23:42:16 +02:00
ines
264af6cd17
Add documentation
2017-04-16 20:37:46 +02:00
ines
c7adca58a9
Tidy up example and only save/test if output_directory is not None
2017-04-16 16:55:01 +02:00
Matthew Honnibal
40e3024241
Move standalone NER training script into examples directory
2017-04-15 16:13:42 +02:00
Matthew Honnibal
c729d72fc6
Add new example for training new entity types
2017-04-15 16:11:06 +02:00
Matthew Honnibal
97b83c74dc
WIP on training example
2017-04-14 23:54:27 +02:00
Matthew Honnibal
ab70f6e18d
Update NER training example
2017-01-27 12:27:10 +01:00
Christos Savvopoulos
c19b83f6ae
use model_dir inside of load_model
2016-12-12 20:23:24 +00:00
Christos Savvopoulos
93cf4af701
actually commit load_ner.py
2016-12-12 20:13:33 +00:00
Christos Savvopoulos
ad54a929f8
train_ner should save vocab; add load_ner example
2016-12-12 20:09:49 +00:00
kendricktan
ba8841234a
Fixed training examples
...
Changes:
1. train_ner won't crash if no data directory is not found
2. Fixed train_tagger expected spacy.gold.GoldParse, got list
2016-10-24 16:09:23 +10:00
kendricktan
9877f3298f
updated training examples to v1.1.2
2016-10-24 11:53:33 +10:00
kendricktan
f77b3dc677
Fixed train_parser examples when model_dir isn't None
2016-10-20 23:40:51 +10:00
kendricktan
d817d57219
Fixed train_ner examples when model_dir isn't None
2016-10-20 21:09:07 +10:00
Matthew Honnibal
3fba897e0f
Update train_parser example
2016-10-16 21:41:14 +02:00
Matthew Honnibal
f787cd29fe
Refactor the pipeline classes to make them more consistent, and remove the redundant blank() constructor.
2016-10-16 21:34:57 +02:00
Matthew Honnibal
4e9727b474
Use new words keyword argument in Doc.
2016-10-16 18:16:25 +02:00
Matthew Honnibal
2508117553
Make train_parser example a bit simpler.
2016-10-16 17:58:37 +02:00
Matthew Honnibal
4574fe87c6
Add example for training parser
2016-10-16 17:05:55 +02:00
Matthew Honnibal
01b42c531f
Update train_tagger script
2016-10-16 16:10:23 +02:00
