WIP on removing goldparse
Get ArcEager compiling after GoldParse excise
Update setup.py
Get spacy.syntax compiling after removing GoldParse
Rename NewExample -> Example and clean up
Clean html files
Start updating tests
Update Morphologizer
* verbose and tag_map options
* adding init_tok2vec option and only changing the tok2vec that is specified
* adding omit_extra_lookups and verifying textcat config
* wip
* pretrain bugfix
* add replace and resume options
* train_textcat fix
* raw text functionality
* improve UX when KeyError or when input data can't be parsed
* avoid unnecessary access to goldparse in TextCat pipe
* save performance information in nlp.meta
* add noise_level to config
* move nn_parser's defaults to config file
* multitask in config - doesn't work yet
* scorer offering both F and AUC options, need to be specified in config
* add textcat verification code from old train script
* small fixes to config files
* clean up
* set default config for ner/parser to allow create_pipe to work as before
* two more test fixes
* small fixes
* cleanup
* fix NER pickling + additional unit test
* create_pipe as before
* Make Example require a Doc object (previously optional)
Clarify methods in GoldCorpus
WIP refactor Example
Refactor Example.split_sents
Fix test
Fix augment
Update test
Update test
Fix import
Update test_scorer
Update Example
* Move get_parses_from_example to spacy.syntax
* Get GoldParse out of Example
* Avoid expecting GoldParse input in parser
* Add Alignment to spacy.gold.align
* Update Example object
* Add comment
* Update pipeline
* Fix imports
* Simplify gold_io
* WIP on GoldCorpus
* Update test
* Xfail some gold tests
* Remove ignore_misaligned option from GoldCorpus
* Fix Example constructor
* Update test
* Fix usage of Example
* Add deprecated_get_gold method on Example
* Patch scorer
* Fix test
* Fix test
* Update tests
* Xfail a test
* Fix passing of make_projective
* Pass make_projective by default
* Hack data format in Example.from_dict
* Update tests
* Fix example.from_dict
* Update morphologizer
* Fix entity linker
* Add get_field to TokenAnnotation
* Fix Example.get_aligned
* Update test
* Fix alignment
* Fix corpus
* Fix GoldCorpus
* Handle misaligned
* Format
* Fix missing import
During `nlp.update`, components can be passed a boolean set_annotations
to indicate whether they should assign annotations to the `Doc`. This
needs to be called if downstream components expect to use the
annotations during training, e.g. if we wanted to use tagger features in
the parser.
Components can specify their assignments and requirements, so we can
figure out which components have these inter-dependencies. After
figuring this out, we can guess whether to pass set_annotations=True.
We could also call set_annotations=True always, or even just have this
as the only behaviour. The downside of this is that it would require the
`Doc` objects to be created afresh to avoid problematic modifications.
One approach would be to make a fresh copy of the `Doc` objects within
`nlp.update()`, so that we can write to the objects without any
problems. If we do that, we can drop this logic and also drop the
`set_annotations` mechanism. I would be fine with that approach,
although it runs the risk of introducing some performance overhead, and
we'll have to take care to copy all extension attributes etc.
