<!--- Provide a general summary of your changes in the title. -->
## Description
Update the training examples in `/examples/training` to show usage of spaCy's `minibatch` and `compounding` helpers ([see here](https://spacy.io/usage/training#tips-batch-size) for details). The lack of batching in the examples has caused some confusion in the past, especially for beginners who would copy-paste the examples, update them with large training sets and experienced slow and unsatisfying results.
### Types of change
enhancements
## Checklist
<!--- Before you submit the PR, go over this checklist and make sure you can
tick off all the boxes. [] -> [x] -->
- [x] I have submitted the spaCy Contributor Agreement.
- [x] I ran the tests, and all new and existing tests passed.
- [x] My changes don't require a change to the documentation, or if they do, I've added all required information.
* Integrate Python kernel via Binder
* Add live model test for languages with examples
* Update docs and code examples
* Adjust margin (if not bootstrapped)
* Add binder version to global config
* Update terminal and executable code mixins
* Pass attributes through infobox and section
* Hide v-cloak
* Fix example
* Take out model comparison for now
* Add meta text for compat
* Remove chart.js dependency
* Tidy up and simplify JS and port big components over to Vue
* Remove chartjs example
* Add Twitter icon
* Add purple stylesheet option
* Add utility for hand cursor (special cases only)
* Add transition classes
* Add small option for section
* Add thumb object for small round thumbnail images
* Allow unset code block language via "none" value
(workaround to still allow unset language to default to DEFAULT_SYNTAX)
* Pass through attributes
* Add syntax highlighting definitions for Julia, R and Docker
* Add website icon
* Remove user survey from navigation
* Don't hide GitHub icon on small screens
* Make top navigation scrollable on small screens
* Remove old resources page and references to it
* Add Universe
* Add helper functions for better page URL and title
* Update site description
* Increment versions
* Update preview images
* Update mentions of resources
* Fix image
* Fix social images
* Fix problem with cover sizing and floats
* Add divider and move badges into heading
* Add docstrings
* Reference converting section
* Add section on converting word vectors
* Move converting section to custom section and fix formatting
* Remove old fastText example
* Move extensions content to own section
Keep weird ID to not break permalinks for now (we don't want to rewrite URLs if not absolutely necessary)
* Use better component example and add factories section
* Add note on larger model
* Use better example for non-vector
* Remove similarity in context section
Only works via small models with tensors so has always been kind of confusing
* Add note on init-model command
* Fix lightning tour examples and make excutable if possible
* Add spacy train CLI section to train
* Fix formatting and add video
* Fix formatting
* Fix textcat example description (resolves#2246)
* Add dummy file to try resolve conflict
* Delete dummy file
* Tidy up [ci skip]
* Ensure sufficient height of loading container
* Add loading animation to universe
* Update Thebelab build and use better startup message
* Fix asset versioning
* Fix typo [ci skip]
* Add note on project idea label
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.
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).
