* ensure Language passes on valid examples for initialization
* fix tagger model initialization
* check for valid get_examples across components
* assume labels were added before begin_training
* fix senter initialization
* fix morphologizer initialization
* use methods to check arguments
* test textcat init, requires thinc>=8.0.0a31
* fix tok2vec init
* fix entity linker init
* use islice
* fix simple NER
* cleanup debug model
* fix assert statements
* fix tests
* throw error when adding a label if the output layer can't be resized anymore
* fix test
* add failing test for simple_ner
* UX improvements
* morphologizer UX
* assume begin_training gets a representative set and processes the labels
* remove assumptions for output of untrained NER model
* restore test for original purpose
This was the only registry that expected the registered objects to be dictionaries instead of functions that return something. We can still support plain dicts but we should also support functions for consistency
The Language.use_params method was failing if you passed in None, which
meant we had to use awkward conditionals for the parameter averaging.
This solves the problem.
Add official support for the `DependencyMatcher`. Redesign the pattern
specification. Fix and extend operator implementations. Update API docs
and add usage docs.
Patterns
--------
Refactor pattern structure to:
```
{
"LEFT_ID": str,
"REL_OP": str,
"RIGHT_ID": str,
"RIGHT_ATTRS": dict,
}
```
The first node contains only `RIGHT_ID` and `RIGHT_ATTRS` and all
subsequent nodes contain all four keys.
New operators
-------------
Because of the way patterns are constructed from left to right, it's
helpful to have `follows` operators along with `precedes` operators. Add
operators for simple precedes / follows alongside immediate precedes /
follows.
* `.*`: precedes
* `;`: immediately follows
* `;*`: follows
Operator fixes
--------------
* `<` and `<<` do not include the node itself
* Fix reversed order for all operators involving linear precedence (`.`,
all sibling operators)
* Linear precedence operators do not match nodes outside the same parse
Additional fixes
----------------
* Use v3 Matcher API
* Support `get` and `remove`
* Support pickling
Follow-ups to the parser efficiency fix.
* Avoid introducing new counter for number of pushes
* Base cut on number of transitions, keeping it more even
* Reintroduce the randomization we had in v2.
The parser training makes use of a trick for long documents, where we
use the oracle to cut up the document into sections, so that we can have
batch items in the middle of a document. For instance, if we have one
document of 600 words, we might make 6 states, starting at words 0, 100,
200, 300, 400 and 500.
The problem is for v3, I screwed this up and didn't stop parsing! So
instead of a batch of [100, 100, 100, 100, 100, 100], we'd have a batch
of [600, 500, 400, 300, 200, 100]. Oops.
The implementation here could probably be improved, it's annoying to
have this extra variable in the state. But this'll do.
This makes the v3 parser training 5-10 times faster, depending on document
lengths. This problem wasn't in v2.
A long time ago we went to some trouble to try to clean up "unused"
strings, to avoid the `StringStore` growing in long-running processes.
This never really worked reliably, and I think it was a really wrong
approach. It's much better to let the user reload the `nlp` object as
necessary, now that the string encoding is stable (in v1, the string IDs
were sequential integers, making reloading the NLP object really
annoying.)
The extra book-keeping does make some performance difference, and the
feature is unsed, so it's past time we killed it.
* Prevent Tagger model init with 0 labels
Raise an error before trying to initialize a tagger model with 0 labels.
* Add dummy tagger label for test
* Remove tagless tagger model initializiation
* Fix error number after merge
* Add dummy tagger label to test
* Fix formatting
Co-authored-by: Matthew Honnibal <honnibal+gh@gmail.com>
* rename to spacy-transformers.TransformerListener
* add some more tok2vec tests
* use select_pipes
* fix docs - annotation setter was not changed in the end
Sort the returned matches by rule order (the `match_id`) so that the
rules are applied in the order they were added. This is necessary, for
instance, if the `AttributeRuler` is used for the tag map and later
rules require POS tags.
Serialize `AttributeRuler.patterns` instead of the individual lists to
simplify the serialized and so that patterns are reloaded exactly as
they were originally provided (preserving `_attrs_unnormed`).
* Add AttributeRuler.score
Add scoring for TAG / POS / MORPH / LEMMA if these are present in the
assigned token attributes.
Add default score weights (that don't really make a lot of sense) so
that the scores are in the default config in some form.
* Update docs
* Fix up/download of http and local paths
* Support git_sparse_checkout for assets
* Fix scorer
* Handle already-present directories for git assets
* Improve convert command
* Fix support for existant files in git assets
* Support branches in git sparse checkout
* Format
* Fix git assets
* Document git block in assets
* Fix test
* Fix test
* Revert "Fix test"
This reverts commit cf3097260f.
* Revert "Fix test"
This reverts commit 964d636e27.
* Dont multiply p/r/f by 100
* Display scores * 100 during training
* Update stop_words.py
Hebrew STOP WORDS
* Update stop_words.py
* contributor
* contributor
* add some common domain extentions
support human number 1K/1M....
* support human number 1K/1M....
* hebrew number tokenize
1K/1M implement in EN
* test human tokenize fix
* test
* heb like num
revert human number change
* heb like num
* Create lex_attrs.py
Hello,
I am missing a CZECH language in SpaCy. So I would like to help to push it a little. This file is base on others lex_attrs.py files just with translation to Czech.
* Update __init__.py
Updated for use with new Czech Lex_attrs file
* Update stop_words.py
* Create test_text.py
* add like_num testing for czech
Co-authored-by: holubvl3 <47881982+holubvl3@users.noreply.github.com>
Co-authored-by: holubvl3 <vilemrousi@gmail.com>
Co-authored-by: Vladimír Holubec <vholubec@arcdata.cz>
* Create lex_attrs.py
Hello,
I am missing a CZECH language in SpaCy. So I would like to help to push it a little. This file is base on others lex_attrs.py files just with translation to Czech.
* Update __init__.py
Updated for use with new Czech Lex_attrs file
* Update stop_words.py
* Create test_text.py
Co-authored-by: Vladimír Holubec <vholubec@arcdata.cz>
- Accept any case for label names in ents and colors option, even if actual predicted label uses different casing
- Don't text-transform: uppercase visually, if it's important to users that the label is represented as-is in the UI
- As much as I dislike YAML, it seemed like a better format here because it allows us to add comments if we want to explain the different recommendations
- Don't include the generated JS in the repo by default and build it on the fly when running or deploying the site. This ensures it's always up to date.
- Simplify jinja_to_js script and use fewer dependencies
* candidate generator as separate part of EL config
* update comment
* ent instead of str as input for candidate generation
* Span instead of str: correct type indication
* fix types
* unit test to create new candidate generator
* fix replace_pipe argument passing
* move error message, general cleanup
* add vocab back to KB constructor
* provide KB as callable from Vocab arg
* rename to kb_loader, fix KB serialization as part of the EL pipe
* fix typo
* reformatting
* cleanup
* fix comment
* fix wrongly duplicated code from merge conflict
* rename dump to to_disk
* from_disk instead of load_bulk
* update test after recent removal of set_morphology in tagger
* remove old doc
