History

Matthew Honnibal 056b08c0df Delete obsolete nn_text_class example		2017-10-05 18:27:10 +02:00
..
inventory_count	Rename inventory count example	2016-11-01 02:30:22 +01:00
keras_parikh_entailment	Corretions for model test example	2017-05-03 22:41:23 +08:00
training	Update textcat example	2017-10-04 15:12:28 +02:00
README.md	Add README.md to examples	2016-11-01 01:14:04 +01:00
_handler.py	* Add _handler to resolve Issue #123	2015-10-15 02:44:23 +11:00
deep_learning_keras.py	Fix use of dropout in sentiment analysis LSTM example	2016-12-20 16:26:38 -06:00
get_parse_subregions.py	move displacy to its own subdomain	2016-02-19 14:03:52 +01:00
information_extraction.py	* Tweak information extraction example	2015-10-06 10:35:49 +11:00
matcher_example.py	* Add clarifying comment	2015-09-27 18:17:41 +10:00
parallel_parse.py	added batch_size as keyword argument	2016-03-10 14:16:34 -08:00
phrase_matcher.py	Rename phrase matcher example	2017-09-20 22:51:58 +02:00
pos_tag.py	Fix formatting and typo (closes #967 )	2017-04-16 23:56:12 +02:00
twitter_filter.py	* Begin rewriting twitter_filter examples	2015-08-22 22:12:26 +02:00
vectors_fast_text.py	Add example loadig Fast Text vectors	2017-10-01 23:40:02 +02:00

README.md

spaCy examples

The examples are Python scripts with well-behaved command line interfaces. For a full list of spaCy tutorials and code snippets, see the documentation.

How to run an example

For example, to run the nn_text_class.py script, do:

$ python examples/nn_text_class.py
usage: nn_text_class.py [-h] [-d 3] [-H 300] [-i 5] [-w 40000] [-b 24]
                        [-r 0.3] [-p 1e-05] [-e 0.005]
                        data_dir
nn_text_class.py: error: too few arguments

You can print detailed help with the -h argument.

While we try to keep the examples up to date, they are not currently exercised by the test suite, as some of them require significant data downloads or take time to train. If you find that an example is no longer running, please tell us! We know there's nothing worse than trying to figure out what you're doing wrong, and it turns out your code was never the problem.