* Add Lemmatizer and simplify related components
* Add `Lemmatizer` pipe with `lookup` and `rule` modes using the
`Lookups` tables.
* Reduce `Tagger` to a simple tagger that sets `Token.tag` (no pos or lemma)
* Reduce `Morphology` to only keep track of morph tags (no tag map, lemmatizer,
or morph rules)
* Remove lemmatizer from `Vocab`
* Adjust many many tests
Differences:
* No default lookup lemmas
* No special treatment of TAG in `from_array` and similar required
* Easier to modify labels in a `Tagger`
* No extra strings added from morphology / tag map
* Fix test
* Initial fix for Lemmatizer config/serialization
* Adjust init test to be more generic
* Adjust init test to force empty Lookups
* Add simple cache to rule-based lemmatizer
* Convert language-specific lemmatizers
Convert language-specific lemmatizers to component lemmatizers. Remove
previous lemmatizer class.
* Fix French and Polish lemmatizers
* Remove outdated UPOS conversions
* Update Russian lemmatizer init in tests
* Add minimal init/run tests for custom lemmatizers
* Add option to overwrite existing lemmas
* Update mode setting, lookup loading, and caching
* Make `mode` an immutable property
* Only enforce strict `load_lookups` for known supported modes
* Move caching into individual `_lemmatize` methods
* Implement strict when lang is not found in lookups
* Fix tables/lookups in make_lemmatizer
* Reallow provided lookups and allow for stricter checks
* Add lookups asset to all Lemmatizer pipe tests
* Rename lookups in lemmatizer init test
* Clean up merge
* Refactor lookup table loading
* Add helper from `load_lemmatizer_lookups` that loads required and
optional lookups tables based on settings provided by a config.
Additional slight refactor of lookups:
* Add `Lookups.set_table` to set a table from a provided `Table`
* Reorder class definitions to be able to specify type as `Table`
* Move registry assets into test methods
* Refactor lookups tables config
Use class methods within `Lemmatizer` to provide the config for
particular modes and to load the lookups from a config.
* Add pipe and score to lemmatizer
* Simplify Tagger.score
* Add missing import
* Clean up imports and auto-format
* Remove unused kwarg
* Tidy up and auto-format
* Update docstrings for Lemmatizer
Update docstrings for Lemmatizer.
Additionally modify `is_base_form` API to take `Token` instead of
individual features.
* Update docstrings
* Remove tag map values from Tagger.add_label
* Update API docs
* Fix relative link in Lemmatizer API docs
* Update errors
* Remove beam for now (maybe)
Remove beam_utils
Update setup.py
Remove beam
* Remove GoldParse
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
* fix error numbers
* fix merge conflict
* informative error when calling to_array with wrong field
* fix error catching
* fixing language and scoring tests
* start testing get_aligned
* additional tests for new get_aligned function
* Draft create_gold_state for arc_eager oracle
* Fix import
* Fix import
* Remove TokenAnnotation code from nonproj
* fixing NER one-to-many alignment
* Fix many-to-one IOB codes
* fix test for misaligned
* attempt to fix cases with weird spaces
* fix spaces
* test_gold_biluo_different_tokenization works
* allow None as BILUO annotation
* fixed some tests + WIP roundtrip unit test
* add spaces to json output format
* minibatch utiltiy can deal with strings, docs or examples
* fix augment (needs further testing)
* various fixes in scripts - needs to be further tested
* fix test_cli
* cleanup
* correct silly typo
* add support for MORPH in to/from_array, fix morphologizer overfitting test
* fix tagger
* fix entity linker
* ensure test keeps working with non-linked entities
* pipe() takes docs, not examples
* small bug fix
* textcat bugfix
* throw informative error when running the components with the wrong type of objects
* fix parser tests to work with example (most still failing)
* fix BiluoPushDown parsing entities
* small fixes
* bugfix tok2vec
* fix renames and simple_ner labels
* various small fixes
* prevent writing dummy values like deps because that could interfer with sent_start values
* fix the fix
* implement split_sent with aligned SENT_START attribute
* test for split sentences with various alignment issues, works
* Return ArcEagerGoldParse from ArcEager
* Update parser and NER gold stuff
* Draft new GoldCorpus class
* add links to to_dict
* clean up
* fix test checking for variants
* Fix oracles
* Start updating converters
* Move converters under spacy.gold
* Move things around
* Fix naming
* Fix name
* Update converter to produce DocBin
* Update converters
* Allow DocBin to take list of Doc objects.
* Make spacy convert output docbin
* Fix import
* Fix docbin
* Fix compile in ArcEager
* Fix import
* Serialize all attrs by default
* Update converter
* Remove jsonl converter
* Add json2docs converter
* Draft Corpus class for DocBin
* Work on train script
* Update Corpus
* Update DocBin
* Allocate Doc before starting to add words
* Make doc.from_array several times faster
* Update train.py
* Fix Corpus
* Fix parser model
* Start debugging arc_eager oracle
* Update header
* Fix parser declaration
* Xfail some tests
* Skip tests that cause crashes
* Skip test causing segfault
* Remove GoldCorpus
* Update imports
* Update after removing GoldCorpus
* Fix module name of corpus
* Fix mimport
* Work on parser oracle
* Update arc_eager oracle
* Restore ArcEager.get_cost function
* Update transition system
* Update test_arc_eager_oracle
* Remove beam test
* Update test
* Unskip
* Unskip tests
* add links to to_dict
* clean up
* fix test checking for variants
* Allow DocBin to take list of Doc objects.
* Fix compile in ArcEager
* Serialize all attrs by default
Move converters under spacy.gold
Move things around
Fix naming
Fix name
Update converter to produce DocBin
Update converters
Make spacy convert output docbin
Fix import
Fix docbin
Fix import
Update converter
Remove jsonl converter
Add json2docs converter
* Allocate Doc before starting to add words
* Make doc.from_array several times faster
* Start updating converters
* Work on train script
* Draft Corpus class for DocBin
Update Corpus
Fix Corpus
* Update DocBin
Add missing strings when serializing
* Update train.py
* Fix parser model
* Start debugging arc_eager oracle
* Update header
* Fix parser declaration
* Xfail some tests
Skip tests that cause crashes
Skip test causing segfault
* Remove GoldCorpus
Update imports
Update after removing GoldCorpus
Fix module name of corpus
Fix mimport
* Work on parser oracle
Update arc_eager oracle
Restore ArcEager.get_cost function
Update transition system
* Update tests
Remove beam test
Update test
Unskip
Unskip tests
* Add get_aligned_parse method in Example
Fix Example.get_aligned_parse
* Add kwargs to Corpus.dev_dataset to match train_dataset
* Update nonproj
* Use get_aligned_parse in ArcEager
* Add another arc-eager oracle test
* Remove Example.doc property
Remove Example.doc
Remove Example.doc
Remove Example.doc
Remove Example.doc
* Update ArcEager oracle
Fix Break oracle
* Debugging
* Fix Corpus
* Fix eg.doc
* Format
* small fixes
* limit arg for Corpus
* fix test_roundtrip_docs_to_docbin
* fix test_make_orth_variants
* fix add_label test
* Update tests
* avoid writing temp dir in json2docs, fixing 4402 test
* Update test
* Add missing costs to NER oracle
* Update test
* Work on Example.get_aligned_ner method
* Clean up debugging
* Xfail tests
* Remove prints
* Remove print
* Xfail some tests
* Replace unseen labels for parser
* Update test
* Update test
* Xfail test
* Fix Corpus
* fix imports
* fix docs_to_json
* various small fixes
* cleanup
* Support gold_preproc in Corpus
* Support gold_preproc
* Pass gold_preproc setting into corpus
* Remove debugging
* Fix gold_preproc
* Fix json2docs converter
* Fix convert command
* Fix flake8
* Fix import
* fix output_dir (converted to Path by typer)
* fix var
* bugfix: update states after creating golds to avoid out of bounds indexing
* Improve efficiency of ArEager oracle
* pull merge_sent into iob2docs to avoid Doc creation for each line
* fix asserts
* bugfix excl Span.end in iob2docs
* Support max_length in Corpus
* Fix arc_eager oracle
* Filter out uannotated sentences in NER
* Remove debugging in parser
* Simplify NER alignment
* Fix conversion of NER data
* Fix NER init_gold_batch
* Tweak efficiency of precomputable affine
* Update onto-json default
* Update gold test for NER
* Fix parser test
* Update test
* Add NER data test
* Fix convert for single file
* Fix test
* Hack scorer to avoid evaluating non-nered data
* Fix handling of NER data in Example
* Output unlabelled spans from O biluo tags in iob_utils
* Fix unset variable
* Return kept examples from init_gold_batch
* Return examples from init_gold_batch
* Dont return Example from init_gold_batch
* Set spaces on gold doc after conversion
* Add test
* Fix spaces reading
* Improve NER alignment
* Improve handling of missing values in NER
* Restore the 'cutting' in parser training
* Add assertion
* Print epochs
* Restore random cuts in parser/ner training
* Implement Doc.copy
* Implement Example.copy
* Copy examples at the start of Language.update
* Don't unset example docs
* Tweak parser model slightly
* attempt to fix _guess_spaces
* _add_entities_to_doc first, so that links don't get overwritten
* fixing get_aligned_ner for one-to-many
* fix indexing into x_text
* small fix biluo_tags_from_offsets
* Add onto-ner config
* Simplify NER alignment
* Fix NER scoring for partially annotated documents
* fix indexing into x_text
* fix test_cli failing tests by ignoring spans in doc.ents with empty label
* Fix limit
* Improve NER alignment
* Fix count_train
* Remove print statement
* fix tests, we're not having nothing but None
* fix clumsy fingers
* Fix tests
* Fix doc.ents
* Remove empty docs in Corpus and improve limit
* Update config
Co-authored-by: svlandeg <sofie.vanlandeghem@gmail.com>
* Reduce stored lexemes data, move feats to lookups
* Move non-derivable lexemes features (`norm / cluster / prob`) to
`spacy-lookups-data` as lookups
* Get/set `norm` in both lookups and `LexemeC`, serialize in lookups
* Remove `cluster` and `prob` from `LexemesC`, get/set/serialize in
lookups only
* Remove serialization of lexemes data as `vocab/lexemes.bin`
* Remove `SerializedLexemeC`
* Remove `Lexeme.to_bytes/from_bytes`
* Modify normalization exception loading:
* Always create `Vocab.lookups` table `lexeme_norm` for
normalization exceptions
* Load base exceptions from `lang.norm_exceptions`, but load
language-specific exceptions from lookups
* Set `lex_attr_getter[NORM]` including new lookups table in
`BaseDefaults.create_vocab()` and when deserializing `Vocab`
* Remove all cached lexemes when deserializing vocab to override
existing normalizations with the new normalizations (as a replacement
for the previous step that replaced all lexemes data with the
deserialized data)
* Skip English normalization test
Skip English normalization test because the data is now in
`spacy-lookups-data`.
* Remove norm exceptions
Moved to spacy-lookups-data.
* Move norm exceptions test to spacy-lookups-data
* Load extra lookups from spacy-lookups-data lazily
Load extra lookups (currently for cluster and prob) lazily from the
entry point `lg_extra` as `Vocab.lookups_extra`.
* Skip creating lexeme cache on load
To improve model loading times, do not create the full lexeme cache when
loading. The lexemes will be created on demand when processing.
* Identify numeric values in Lexeme.set_attrs()
With the removal of a special case for `PROB`, also identify `float` to
avoid trying to convert it with the `StringStore`.
* Skip lexeme cache init in from_bytes
* Unskip and update lookups tests for python3.6+
* Update vocab pickle to include lookups_extra
* Update vocab serialization tests
Check strings rather than lexemes since lexemes aren't initialized
automatically, account for addition of "_SP".
* Re-skip lookups test because of python3.5
* Skip PROB/float values in Lexeme.set_attrs
* Convert is_oov from lexeme flag to lex in vectors
Instead of storing `is_oov` as a lexeme flag, `is_oov` reports whether
the lexeme has a vector.
Co-authored-by: Matthew Honnibal <honnibal+gh@gmail.com>
Reconstruction of the original PR #4697 by @MiniLau.
Removes unused `SENT_END` symbol and `IS_SENT_END` from `Matcher` schema
because the Matcher is only going to be able to support `IS_SENT_START`.
* Improve token head verification
Improve the verification for valid token heads when heads are set:
* in `Token.head`: heads come from the same document
* in `Doc.from_array()`: head indices are within the bounds of the
document
* Improve error message
* Add load_from_config function
* Add train_from_config script
* Merge configs and expose via spacy.config
* Fix script
* Suggest create_evaluation_callback
* Hard-code for NER
* Fix errors
* Register command
* Add TODO
* Update train-from-config todos
* Fix imports
* Allow delayed setting of parser model nr_class
* Get train-from-config working
* Tidy up and fix scores and printing
* Hide traceback if cancelled
* Fix weighted score formatting
* Fix score formatting
* Make output_path optional
* Add Tok2Vec component
* Tidy up and add tok2vec_tensors
* Add option to copy docs in nlp.update
* Copy docs in nlp.update
* Adjust nlp.update() for set_annotations
* Don't shuffle pipes in nlp.update, decruft
* Support set_annotations arg in component update
* Support set_annotations in parser update
* Add get_gradients method
* Add get_gradients to parser
* Update errors.py
* Fix problems caused by merge
* Add _link_components method in nlp
* Add concept of 'listeners' and ControlledModel
* Support optional attributes arg in ControlledModel
* Try having tok2vec component in pipeline
* Fix tok2vec component
* Fix config
* Fix tok2vec
* Update for Example
* Update for Example
* Update config
* Add eg2doc util
* Update and add schemas/types
* Update schemas
* Fix nlp.update
* Fix tagger
* Remove hacks from train-from-config
* Remove hard-coded config str
* Calculate loss in tok2vec component
* Tidy up and use function signatures instead of models
* Support union types for registry models
* Minor cleaning in Language.update
* Make ControlledModel specifically Tok2VecListener
* Fix train_from_config
* Fix tok2vec
* Tidy up
* Add function for bilstm tok2vec
* Fix type
* Fix syntax
* Fix pytorch optimizer
* Add example configs
* Update for thinc describe changes
* Update for Thinc changes
* Update for dropout/sgd changes
* Update for dropout/sgd changes
* Unhack gradient update
* Work on refactoring _ml
* Remove _ml.py module
* WIP upgrade cli scripts for thinc
* Move some _ml stuff to util
* Import link_vectors from util
* Update train_from_config
* Import from util
* Import from util
* Temporarily add ml.component_models module
* Move ml methods
* Move typedefs
* Update load vectors
* Update gitignore
* Move imports
* Add PrecomputableAffine
* Fix imports
* Fix imports
* Fix imports
* Fix missing imports
* Update CLI scripts
* Update spacy.language
* Add stubs for building the models
* Update model definition
* Update create_default_optimizer
* Fix import
* Fix comment
* Update imports in tests
* Update imports in spacy.cli
* Fix import
* fix obsolete thinc imports
* update srsly pin
* from thinc to ml_datasets for example data such as imdb
* update ml_datasets pin
* using STATE.vectors
* small fix
* fix Sentencizer.pipe
* black formatting
* rename Affine to Linear as in thinc
* set validate explicitely to True
* rename with_square_sequences to with_list2padded
* rename with_flatten to with_list2array
* chaining layernorm
* small fixes
* revert Optimizer import
* build_nel_encoder with new thinc style
* fixes using model's get and set methods
* Tok2Vec in component models, various fixes
* fix up legacy tok2vec code
* add model initialize calls
* add in build_tagger_model
* small fixes
* setting model dims
* fixes for ParserModel
* various small fixes
* initialize thinc Models
* fixes
* consistent naming of window_size
* fixes, removing set_dropout
* work around Iterable issue
* remove legacy tok2vec
* util fix
* fix forward function of tok2vec listener
* more fixes
* trying to fix PrecomputableAffine (not succesful yet)
* alloc instead of allocate
* add morphologizer
* rename residual
* rename fixes
* Fix predict function
* Update parser and parser model
* fixing few more tests
* Fix precomputable affine
* Update component model
* Update parser model
* Move backprop padding to own function, for test
* Update test
* Fix p. affine
* Update NEL
* build_bow_text_classifier and extract_ngrams
* Fix parser init
* Fix test add label
* add build_simple_cnn_text_classifier
* Fix parser init
* Set gpu off by default in example
* Fix tok2vec listener
* Fix parser model
* Small fixes
* small fix for PyTorchLSTM parameters
* revert my_compounding hack (iterable fixed now)
* fix biLSTM
* Fix uniqued
* PyTorchRNNWrapper fix
* small fixes
* use helper function to calculate cosine loss
* small fixes for build_simple_cnn_text_classifier
* putting dropout default at 0.0 to ensure the layer gets built
* using thinc util's set_dropout_rate
* moving layer normalization inside of maxout definition to optimize dropout
* temp debugging in NEL
* fixed NEL model by using init defaults !
* fixing after set_dropout_rate refactor
* proper fix
* fix test_update_doc after refactoring optimizers in thinc
* Add CharacterEmbed layer
* Construct tagger Model
* Add missing import
* Remove unused stuff
* Work on textcat
* fix test (again :)) after optimizer refactor
* fixes to allow reading Tagger from_disk without overwriting dimensions
* don't build the tok2vec prematuraly
* fix CharachterEmbed init
* CharacterEmbed fixes
* Fix CharacterEmbed architecture
* fix imports
* renames from latest thinc update
* one more rename
* add initialize calls where appropriate
* fix parser initialization
* Update Thinc version
* Fix errors, auto-format and tidy up imports
* Fix validation
* fix if bias is cupy array
* revert for now
* ensure it's a numpy array before running bp in ParserStepModel
* no reason to call require_gpu twice
* use CupyOps.to_numpy instead of cupy directly
* fix initialize of ParserModel
* remove unnecessary import
* fixes for CosineDistance
* fix device renaming
* use refactored loss functions (Thinc PR 251)
* overfitting test for tagger
* experimental settings for the tagger: avoid zero-init and subword normalization
* clean up tagger overfitting test
* use previous default value for nP
* remove toy config
* bringing layernorm back (had a bug - fixed in thinc)
* revert setting nP explicitly
* remove setting default in constructor
* restore values as they used to be
* add overfitting test for NER
* add overfitting test for dep parser
* add overfitting test for textcat
* fixing init for linear (previously affine)
* larger eps window for textcat
* ensure doc is not None
* Require newer thinc
* Make float check vaguer
* Slop the textcat overfit test more
* Fix textcat test
* Fix exclusive classes for textcat
* fix after renaming of alloc methods
* fixing renames and mandatory arguments (staticvectors WIP)
* upgrade to thinc==8.0.0.dev3
* refer to vocab.vectors directly instead of its name
* rename alpha to learn_rate
* adding hashembed and staticvectors dropout
* upgrade to thinc 8.0.0.dev4
* add name back to avoid warning W020
* thinc dev4
* update srsly
* using thinc 8.0.0a0 !
Co-authored-by: Matthew Honnibal <honnibal+gh@gmail.com>
Co-authored-by: Ines Montani <ines@ines.io>
* Restructure tag maps for MorphAnalysis changes
Prepare tag maps for upcoming MorphAnalysis changes that allow
arbritrary features.
* Use default tag map rather than duplicating for ca / uk / vi
* Import tag map into defaults for ga
* Modify tag maps so all morphological fields and features are strings
* Move features from `"Other"` to the top level
* Rewrite tuples as strings separated by `","`
* Rewrite morph symbols for fr lemmatizer as strings
* Export MorphAnalysis under spacy.tokens
* Modify morphology to support arbitrary features
Modify `Morphology` and `MorphAnalysis` so that arbitrary features are
supported.
* Modify `MorphAnalysisC` so that it can support arbitrary features and
multiple values per field. `MorphAnalysisC` is redesigned to contain:
* key: hash of UD FEATS string of morphological features
* array of `MorphFeatureC` structs that each contain a hash of `Field`
and `Field=Value` for a given morphological feature, which makes it
possible to:
* find features by field
* represent multiple values for a given field
* `get_field()` is renamed to `get_by_field()` and is no longer `nogil`.
Instead a new helper function `get_n_by_field()` is `nogil` and returns
`n` features by field.
* `MorphAnalysis.get()` returns all possible values for a field as a
list of individual features such as `["Tense=Pres", "Tense=Past"]`.
* `MorphAnalysis`'s `str()` and `repr()` are the UD FEATS string.
* `Morphology.feats_to_dict()` converts a UD FEATS string to a dict
where:
* Each field has one entry in the dict
* Multiple values remain separated by a separator in the value string
* `Token.morph_` returns the UD FEATS string and you can set
`Token.morph_` with a UD FEATS string or with a tag map dict.
* Modify get_by_field to use np.ndarray
Modify `get_by_field()` to use np.ndarray. Remove `max_results` from
`get_n_by_field()` and always iterate over all the fields.
* Rewrite without MorphFeatureC
* Add shortcut for existing feats strings as keys
Add shortcut for existing feats strings as keys in `Morphology.add()`.
* Check for '_' as empty analysis when adding morphs
* Extend helper converters in Morphology
Add and extend helper converters that convert and normalize between:
* UD FEATS strings (`"Case=dat,gen|Number=sing"`)
* per-field dict of feats (`{"Case": "dat,gen", "Number": "sing"}`)
* list of individual features (`["Case=dat", "Case=gen",
"Number=sing"]`)
All converters sort fields and values where applicable.
* remove duplicate unit test
* unit test (currently failing) for issue 4267
* bugfix: ensure doc.ents preserves kb_id annotations
* fix in setting doc.ents with empty label
* rename
* test for presetting an entity to a certain type
* allow overwriting Outside + blocking presets
* fix actions when previous label needs to be kept
* fix default ent_iob in set entities
* cleaner solution with U- action
* remove debugging print statements
* unit tests with explicit transitions and is_valid testing
* remove U- from move_names explicitly
* remove unit tests with pre-trained models that don't work
* remove (working) unit tests with pre-trained models
* clean up unit tests
* move unit tests
* small fixes
* remove two TODO's from doc.ents comments
* Adjust Table API and add docs
* Add attributes and update description [ci skip]
* Use strings.get_string_id instead of hash_string
* Fix table method calls
* Make orth arg in Lemmatizer.lookup optional
Fall back to string, which is now handled by Table.__contains__ out-of-the-box
* Fix method name
* Auto-format
* Improve load_language_data helper
* WIP: Add Lookups implementation
* Start moving lemma data over to JSON
* WIP: move data over for more languages
* Convert more languages
* Fix lemmatizer fixtures in tests
* Finish conversion
* Auto-format JSON files
* Fix test for now
* Make sure tables are stored on instance
* Update docstrings
* Update docstrings and errors
* Update test
* Add Lookups.__len__
* Add serialization methods
* Add Lookups.remove_table
* Use msgpack for serialization to disk
* Fix file exists check
* Try using OrderedDict for everything
* Update .flake8 [ci skip]
* Try fixing serialization
* Update test_lookups.py
* Update test_serialize_vocab_strings.py
* Lookups / Tables now work
This implements the stubs in the Lookups/Table classes. Currently this
is in Cython but with no type declarations, so that could be improved.
* Add lookups to setup.py
* Actually add lookups pyx
The previous commit added the old py file...
* Lookups work-in-progress
* Move from pyx back to py
* Add string based lookups, fix serialization
* Update tests, language/lemmatizer to work with string lookups
There are some outstanding issues here:
- a pickling-related test fails due to the bloom filter
- some custom lemmatizers (fr/nl at least) have issues
More generally, there's a question of how to deal with the case where
you have a string but want to use the lookup table. Currently the table
allows access by string or id, but that's getting pretty awkward.
* Change lemmatizer lookup method to pass (orth, string)
* Fix token lookup
* Fix French lookup
* Fix lt lemmatizer test
* Fix Dutch lemmatizer
* Fix lemmatizer lookup test
This was using a normal dict instead of a Table, so checks for the
string instead of an integer key failed.
* Make uk/nl/ru lemmatizer lookup methods consistent
The mentioned tokenizers all have their own implementation of the
`lookup` method, which accesses a `Lookups` table. The way that was
called in `token.pyx` was changed so this should be updated to have the
same arguments as `lookup` in `lemmatizer.py` (specificially (orth/id,
string)).
Prior to this change tests weren't failing, but there would probably be
issues with normal use of a model. More tests should proably be added.
Additionally, the language-specific `lookup` implementations seem like
they might not be needed, since they handle things like lower-casing
that aren't actually language specific.
* Make recently added Greek method compatible
* Remove redundant class/method
Leftovers from a merge not cleaned up adequately.
<!--- Provide a general summary of your changes in the title. -->
## Description
* tidy up and adjust Cython code to code style
* improve docstrings and make calling `help()` nicer
* add URLs to new docs pages to docstrings wherever possible, mostly to user-facing objects
* fix various typos and inconsistencies in docs
### Types of change
enhancement, docs
## 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.
