* Support data augmentation in Corpus
* Note initial docs for data augmentation
* Add augmenter to quickstart
* Fix flake8
* Format
* Fix test
* Update spacy/tests/training/test_training.py
* Improve data augmentation arguments
* Update templates
* Move randomization out into caller
* Refactor
* Update spacy/training/augment.py
* Update spacy/tests/training/test_training.py
* Fix augment
* Fix test
* make disable_pipes deprecated in favour of the new toggle_pipes
* rewrite disable_pipes statements
* update documentation
* remove bin/wiki_entity_linking folder
* one more fix
* remove deprecated link to documentation
* few more doc fixes
* add note about name change to the docs
* restore original disable_pipes
* small fixes
* fix typo
* fix error number to W096
* rename to select_pipes
* also make changes to the documentation
Co-authored-by: Matthew Honnibal <honnibal+gh@gmail.com>
* simplify creation of KB by skipping dim reduction
* small fixes to train EL example script
* add KB creation and NEL training example scripts to example section
* update descriptions of example scripts in the documentation
* moving wiki_entity_linking folder from bin to projects
* remove test for wiki NEL functionality that is being moved
* document token ent_kb_id
* document span kb_id
* update pipeline documentation
* prior and context weights as bool's instead
* entitylinker api documentation
* drop for both models
* finish entitylinker documentation
* small fixes
* documentation for KB
* candidate documentation
* links to api pages in code
* small fix
* frequency examples as counts for consistency
* consistent documentation about tensors returned by predict
* add entity linking to usage 101
* add entity linking infobox and KB section to 101
* entity-linking in linguistic features
* small typo corrections
* training example and docs for entity_linker
* predefined nlp and kb
* revert back to similarity encodings for simplicity (for now)
* set prior probabilities to 0 when excluded
* code clean up
* bugfix: deleting kb ID from tokens when entities were removed
* refactor train el example to use either model or vocab
* pretrain_kb example for example kb generation
* add to training docs for KB + EL example scripts
* small fixes
* error numbering
* ensure the language of vocab and nlp stay consistent across serialization
* equality with =
* avoid conflict in errors file
* add error 151
* final adjustements to the train scripts - consistency
* update of goldparse documentation
* small corrections
* push commit
* typo fix
* add candidate API to kb documentation
* update API sidebar with EntityLinker and KnowledgeBase
* remove EL from 101 docs
* remove entity linker from 101 pipelines / rephrase
* custom el model instead of existing model
* set version to 2.2 for EL functionality
* update documentation for 2 CLI scripts
* Changed learning rate by its param name.
I've been searching for a while how the parameter learning rate was named, with `beta1` and `beta2` its easy as they are marked as code, but learning rate wasn't. I think writing the actual parameter name would be helpful.
* Signing SCA
