After creating a component, the `.model` attribute is left with the value `True`, to indicate it should be created later during `from_disk()`, `from_bytes()` or `begin_training()`. This had led to confusing errors if you try to use the component without initializing the model.
To fix this, we add a method `require_model()` to the `Pipe` base class. The `require_model()` method needs to be called at the start of the `.predict()` and `.update()` methods of the components. It raises a `ValueError` if the model is not initialized. An error message has been added to `spacy.errors`.
* issue #3012: add test
* add contributor aggreement
* Make test work without models and fix typos
ten.pos_ instead of ten.orth_ and comparison against "10" instead of integer 10
I have added alpha support for the Tagalog language from the Philippines. It is the basis for the country's national language Filipino. I have heavily based the format to the EN and ES languages.
I have provided several words in the lemmatizer lookup table, added stop words from a source, translated numeric words to its Tagalog counterpart, added some tokenizer exceptions, and kept the tag map the same as the English language.
While the alpha language passed the preliminary testing that you provided, I think it needs more data to be useful for most cases.
* Added alpha support for Tagalog language
* Edited contributor template
* Included SCA; Reverted templates
* Fixed SCA template
* Fixed changes in SCA template
* Try to implement cosine loss
This one seems to be correct? Still unsure, but it performs okay
* Try to implement the von Mises-Fisher loss
This one's definitely not right yet.
The new spacy pretrain command implemented BERT/ULMFit/etc-like transfer learning, using our Language Modelling with Approximate Outputs version of BERT's cloze task. Pretraining is convenient, but in some ways it's a bit of a strange solution. All we're doing is initialising the weights. At the same time, we're putting a lot of work into our optimisation so that it's less sensitive to initial conditions, and more likely to find good optima. I discuss this a bit in the pseudo-rehearsal blog post: https://explosion.ai/blog/pseudo-rehearsal-catastrophic-forgetting
Support semi-supervised learning in spacy train
One obvious way to improve these pretraining methods is to do multi-task learning, instead of just transfer learning. This has been shown to work very well: https://arxiv.org/pdf/1809.08370.pdf . This patch makes it easy to do this sort of thing.
Add a new argument to spacy train, --raw-text. This takes a jsonl file with unlabelled data that can be used in arbitrary ways to do semi-supervised learning.
Add a new method to the Language class and to pipeline components, .rehearse(). This is like .update(), but doesn't expect GoldParse objects. It takes a batch of Doc objects, and performs an update on some semi-supervised objective.
Move the BERT-LMAO objective out from spacy/cli/pretrain.py into spacy/_ml.py, so we can create a new pipeline component, ClozeMultitask. This can be specified as a parser or NER multitask in the spacy train command. Example usage:
python -m spacy train en ./tmp ~/data/en-core-web/train/nw.json ~/data/en-core-web/dev/nw.json --pipeline parser --raw-textt ~/data/unlabelled/reddit-100k.jsonl --vectors en_vectors_web_lg --parser-multitasks cloze
Implement rehearsal methods for pipeline components
The new --raw-text argument and nlp.rehearse() method also gives us a good place to implement the the idea in the pseudo-rehearsal blog post in the parser. This works as follows:
Add a new nlp.resume_training() method. This allocates copies of pre-trained models in the pipeline, setting things up for the rehearsal updates. It also returns an optimizer object. This also greatly reduces confusion around the nlp.begin_training() method, which randomises the weights, making it not suitable for adding new labels or otherwise fine-tuning a pre-trained model.
Implement rehearsal updates on the Parser class, making it available for the dependency parser and NER. During rehearsal, the initial model is used to supervise the model being trained. The current model is asked to match the predictions of the initial model on some data. This minimises catastrophic forgetting, by keeping the model's predictions close to the original. See the blog post for details.
Implement rehearsal updates for tagger
Implement rehearsal updates for text categoriz
Currently the TextCategorizer defaults to a fairly complicated model, designed partly around the active learning requirements of Prodigy. The model's a bit slow, and not very GPU-friendly.
This patch implements a straightforward CNN model that still performs pretty well. The replacement model also makes it easy to use the LMAO pretraining, since most of the parameters are in the CNN.
The replacement model has a flag to specify whether labels are mutually exclusive, which defaults to True. This has been a common problem with the text classifier. We'll also now be able to support adding labels to pretrained models again.
Resolves#2934, #2756, #1798, #1748.
Fixes#3027.
* Allow Span.__init__ to take unicode values for the `label` argument.
* Allow `Span.label_` to be writeable.
- [x] I have submitted the spaCy Contributor Agreement.
- [x] I ran the tests, and all new and existing tests passed.
- [ ] My changes don't require a change to the documentation, or if they do, I've added all required information.
* Add todo
* Auto-format
* Update wasabi pin
* Format training results with wasabi
* Remove loading animation from model saving
Currently behaves weirdly
* Inline messages
* Remove unnecessary path2str
Already taken care of by printer
* Inline messages in CLI
* Remove unused function
* Move loading indicator into loading function
* Check for invalid whitespace entities
See #3028. The solution in this patch is pretty debateable.
What we do is give the TokenC struct a .norm field, by repurposing the previously idle .sense attribute. It's nice to repurpose a previous field because it means the TokenC doesn't change size, so even if someone's using the internals very deeply, nothing will break.
The weird thing here is that the TokenC and the LexemeC both have an attribute named NORM. This arguably assists in backwards compatibility. On the other hand, maybe it's really bad! We're changing the semantics of the attribute subtly, so maybe it's better if someone calling lex.norm gets a breakage, and instead is told to write lex.default_norm?
Overall I believe this patch makes the NORM feature work the way we sort of expected it to work. Certainly it's much more like how the docs describe it, and more in line with how we've been directing people to use the norm attribute. We'll also be able to use token.norm to do stuff like spelling correction, which is pretty cool.
Fix a bug in the JSON streaming code that GoldCorpus uses. Escaped
slashes were being handled incorrectly. This bug caused low scores for
French in the early v2.1.0 alphas, because most of the data was not
being read in.
Fittingly, the document that triggered the bug was a Wikipedia article about
Perl. Parsing perl remains difficult!
* modifying FR lemmatization for nouns
* modifying FR lemmatization for nouns
* adding contributor agreement for amperinet
* adding rules for words with inclusive parentheses wrongly tokenized
* adding contributor agreement for amperinet
* adding a missing comma
* updating rules and vocabulary for French lemmatization of verbs
* updating the file with French auxiliary verb
* updating rules and vocabulary for French lemmatization of verbs
* adding contributor agreement for amperinet
* adding rules for words with inclusive parentheses wrongly tokenized
* Updated wordforms for Norwegian lemmatizer
Upload of updated lists of wordforms for the Norwegian lemmatizer (nouns, verbs, adverbs, adjectives and lookup).
* Add spaCy contributor agreement for user beatesi
* Updated wordforms for Norwegian lemmatizer
Remove hacks and wrappers, keep code in sync across our libraries and move spaCy a few steps closer to only depending on packages with binary wheels 🎉
See here: https://github.com/explosion/srsly
Serialization is hard, especially across Python versions and multiple platforms. After dealing with many subtle bugs over the years (encodings, locales, large files) our libraries like spaCy and Prodigy have steadily grown a number of utility functions to wrap the multiple serialization formats we need to support (especially json, msgpack and pickle). These wrapping functions ended up duplicated across our codebases, so we wanted to put them in one place.
At the same time, we noticed that having a lot of small dependencies was making maintainence harder, and making installation slower. To solve this, we've made srsly standalone, by including the component packages directly within it. This way we can provide all the serialization utilities we need in a single binary wheel.
srsly currently includes forks of the following packages:
ujson
msgpack
msgpack-numpy
cloudpickle
* WIP: replace json/ujson with srsly
* Replace ujson in examples
Use regular json instead of srsly to make code easier to read and follow
* Update requirements
* Fix imports
* Fix typos
* Replace msgpack with srsly
* Fix warning
## Description
Fixes#2693
Previously, the tokens `sbd` and `sentencizer` would create the same nlp pipe. Internally, both would be called `sbd`. This setup became problematic because it was hard for a user relying on the `sentencizer` pipe name to realize that their pipe's name would be `sbd` for all functions other than creating a pipe. This PR intends to change the API and API documentation to fully support `sentencizer` and drop any user-facing references to `sbd`.
### Types of change
end-user API bug
## 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.
* Support nowrap setting in util.prints
* Tidy up and fix whitespace
* Simplify script and use read_jsonl helper
* Add JSON schemas (see #2928)
* Deprecate Doc.print_tree
Will be replaced with Doc.to_json, which will produce a unified format
* Add Doc.to_json() method (see #2928)
Converts Doc objects to JSON using the same unified format as the training data. Method also supports serializing selected custom attributes in the doc._. space.
* Remove outdated test
* Add write_json and write_jsonl helpers
* WIP: Update spacy train
* Tidy up spacy train
* WIP: Use wasabi for formatting
* Add GoldParse helpers for JSON format
* WIP: add debug-data command
* Fix typo
* Add missing import
* Update wasabi pin
* Add missing import
* 💫 Refactor CLI (#2943)
To be merged into #2932.
## Description
- [x] refactor CLI To use [`wasabi`](https://github.com/ines/wasabi)
- [x] use [`black`](https://github.com/ambv/black) for auto-formatting
- [x] add `flake8` config
- [x] move all messy UD-related scripts to `cli.ud`
- [x] make converters function that take the opened file and return the converted data (instead of having them handle the IO)
### Types of change
enhancement
## 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.
* Update wasabi pin
* Delete old test
* Update errors
* Fix typo
* Tidy up and format remaining code
* Fix formatting
* Improve formatting of messages
* Auto-format remaining code
* Add tok2vec stuff to spacy.train
* Fix typo
* Update wasabi pin
* Fix path checks for when train() is called as function
* Reformat and tidy up pretrain script
* Update argument annotations
* Raise error if model language doesn't match lang
* Document new train command
<!--- Provide a general summary of your changes in the title. -->
## Description
- [x] Use [`black`](https://github.com/ambv/black) to auto-format all `.py` files.
- [x] Update flake8 config to exclude very large files (lemmatization tables etc.)
- [x] Update code to be compatible with flake8 rules
- [x] Fix various small bugs, inconsistencies and messy stuff in the language data
- [x] Update docs to explain new code style (`black`, `flake8`, when to use `# fmt: off` and `# fmt: on` and what `# noqa` means)
Once #2932 is merged, which auto-formats and tidies up the CLI, we'll be able to run `flake8 spacy` actually get meaningful results.
At the moment, the code style and linting isn't applied automatically, but I'm hoping that the new [GitHub Actions](https://github.com/features/actions) will let us auto-format pull requests and post comments with relevant linting information.
### Types of change
enhancement, code style
## 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.
* additional unit test for new entr word not in other lists
* bugfix - unit test works
* use _latin_lower instead of alpha_lower for french
* revert back to ALPHA_LOWER (following the code for languages)
* contributor agreement
* Create aryaprabhudesai.md (#2681)
* Update _install.jade (#2688)
Typo fix: "models" -> "model"
* Add FAC to spacy.explain (resolves#2706)
* Remove docstrings for deprecated arguments (see #2703)
* When calling getoption() in conftest.py, pass a default option (#2709)
* When calling getoption() in conftest.py, pass a default option
This is necessary to allow testing an installed spacy by running:
pytest --pyargs spacy
* Add contributor agreement
* update bengali token rules for hyphen and digits (#2731)
* Less norm computations in token similarity (#2730)
* Less norm computations in token similarity
* Contributor agreement
* Remove ')' for clarity (#2737)
Sorry, don't mean to be nitpicky, I just noticed this when going through the CLI and thought it was a quick fix. That said, if this was intention than please let me know.
* added contributor agreement for mbkupfer (#2738)
* Basic support for Telugu language (#2751)
* Lex _attrs for polish language (#2750)
* Signed spaCy contributor agreement
* Added polish version of english lex_attrs
* Introduces a bulk merge function, in order to solve issue #653 (#2696)
* Fix comment
* Introduce bulk merge to increase performance on many span merges
* Sign contributor agreement
* Implement pull request suggestions
* Describe converters more explicitly (see #2643)
* Add multi-threading note to Language.pipe (resolves#2582) [ci skip]
* Fix formatting
* Fix dependency scheme docs (closes#2705) [ci skip]
* Don't set stop word in example (closes#2657) [ci skip]
* Add words to portuguese language _num_words (#2759)
* Add words to portuguese language _num_words
* Add words to portuguese language _num_words
* Update Indonesian model (#2752)
* adding e-KTP in tokenizer exceptions list
* add exception token
* removing lines with containing space as it won't matter since we use .split() method in the end, added new tokens in exception
* add tokenizer exceptions list
* combining base_norms with norm_exceptions
* adding norm_exception
* fix double key in lemmatizer
* remove unused import on punctuation.py
* reformat stop_words to reduce number of lines, improve readibility
* updating tokenizer exception
* implement is_currency for lang/id
* adding orth_first_upper in tokenizer_exceptions
* update the norm_exception list
* remove bunch of abbreviations
* adding contributors file
* Fixed spaCy+Keras example (#2763)
* bug fixes in keras example
* created contributor agreement
* Adding French hyphenated first name (#2786)
* Fix typo (closes#2784)
* Fix typo (#2795) [ci skip]
Fixed typo on line 6 "regcognizer --> recognizer"
* Adding basic support for Sinhala language. (#2788)
* adding Sinhala language package, stop words, examples and lex_attrs.
* Adding contributor agreement
* Updating contributor agreement
* Also include lowercase norm exceptions
* Fix error (#2802)
* Fix error
ValueError: cannot resize an array that references or is referenced
by another array in this way. Use the resize function
* added spaCy Contributor Agreement
* Add charlax's contributor agreement (#2805)
* agreement of contributor, may I introduce a tiny pl languge contribution (#2799)
* Contributors agreement
* Contributors agreement
* Contributors agreement
* Add jupyter=True to displacy.render in documentation (#2806)
* Revert "Also include lowercase norm exceptions"
This reverts commit 70f4e8adf3.
* Remove deprecated encoding argument to msgpack
* Set up dependency tree pattern matching skeleton (#2732)
* Fix bug when too many entity types. Fixes#2800
* Fix Python 2 test failure
* Require older msgpack-numpy
* Restore encoding arg on msgpack-numpy
* Try to fix version pin for msgpack-numpy
* Update Portuguese Language (#2790)
* Add words to portuguese language _num_words
* Add words to portuguese language _num_words
* Portuguese - Add/remove stopwords, fix tokenizer, add currency symbols
* Extended punctuation and norm_exceptions in the Portuguese language
* Correct error in spacy universe docs concerning spacy-lookup (#2814)
* Update Keras Example for (Parikh et al, 2016) implementation (#2803)
* bug fixes in keras example
* created contributor agreement
* baseline for Parikh model
* initial version of parikh 2016 implemented
* tested asymmetric models
* fixed grevious error in normalization
* use standard SNLI test file
* begin to rework parikh example
* initial version of running example
* start to document the new version
* start to document the new version
* Update Decompositional Attention.ipynb
* fixed calls to similarity
* updated the README
* import sys package duh
* simplified indexing on mapping word to IDs
* stupid python indent error
* added code from https://github.com/tensorflow/tensorflow/issues/3388 for tf bug workaround
* Fix typo (closes#2815) [ci skip]
* Update regex version dependency
* Set version to 2.0.13.dev3
* Skip seemingly problematic test
* Remove problematic test
* Try previous version of regex
* Revert "Remove problematic test"
This reverts commit bdebbef455.
* Unskip test
* Try older version of regex
* 💫 Update training examples and use minibatching (#2830)
<!--- 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.
* Visual C++ link updated (#2842) (closes#2841) [ci skip]
* New landing page
* Add contribution agreement
* Correcting lang/ru/examples.py (#2845)
* Correct some grammatical inaccuracies in lang\ru\examples.py; filled Contributor Agreement
* Correct some grammatical inaccuracies in lang\ru\examples.py
* Move contributor agreement to separate file
* Set version to 2.0.13.dev4
* Add Persian(Farsi) language support (#2797)
* Also include lowercase norm exceptions
* Remove in favour of https://github.com/explosion/spaCy/graphs/contributors
* Rule-based French Lemmatizer (#2818)
<!--- Provide a general summary of your changes in the title. -->
## Description
<!--- Use this section to describe your changes. If your changes required
testing, include information about the testing environment and the tests you
ran. If your test fixes a bug reported in an issue, don't forget to include the
issue number. If your PR is still a work in progress, that's totally fine – just
include a note to let us know. -->
Add a rule-based French Lemmatizer following the english one and the excellent PR for [greek language optimizations](https://github.com/explosion/spaCy/pull/2558) to adapt the Lemmatizer class.
### Types of change
<!-- What type of change does your PR cover? Is it a bug fix, an enhancement
or new feature, or a change to the documentation? -->
- Lemma dictionary used can be found [here](http://infolingu.univ-mlv.fr/DonneesLinguistiques/Dictionnaires/telechargement.html), I used the XML version.
- Add several files containing exhaustive list of words for each part of speech
- Add some lemma rules
- Add POS that are not checked in the standard Lemmatizer, i.e PRON, DET, ADV and AUX
- Modify the Lemmatizer class to check in lookup table as a last resort if POS not mentionned
- Modify the lemmatize function to check in lookup table as a last resort
- Init files are updated so the model can support all the functionalities mentioned above
- Add words to tokenizer_exceptions_list.py in respect to regex used in tokenizer_exceptions.py
## 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.
* Set version to 2.0.13
* Fix formatting and consistency
* Update docs for new version [ci skip]
* Increment version [ci skip]
* Add info on wheels [ci skip]
* Adding "This is a sentence" example to Sinhala (#2846)
* Add wheels badge
* Update badge [ci skip]
* Update README.rst [ci skip]
* Update murmurhash pin
* Increment version to 2.0.14.dev0
* Update GPU docs for v2.0.14
* Add wheel to setup_requires
* Import prefer_gpu and require_gpu functions from Thinc
* Add tests for prefer_gpu() and require_gpu()
* Update requirements and setup.py
* Workaround bug in thinc require_gpu
* Set version to v2.0.14
* Update push-tag script
* Unhack prefer_gpu
* Require thinc 6.10.6
* Update prefer_gpu and require_gpu docs [ci skip]
* Fix specifiers for GPU
* Set version to 2.0.14.dev1
* Set version to 2.0.14
* Update Thinc version pin
* Increment version
* Fix msgpack-numpy version pin
* Increment version
* Update version to 2.0.16
* Update version [ci skip]
* Redundant ')' in the Stop words' example (#2856)
<!--- Provide a general summary of your changes in the title. -->
## Description
<!--- Use this section to describe your changes. If your changes required
testing, include information about the testing environment and the tests you
ran. If your test fixes a bug reported in an issue, don't forget to include the
issue number. If your PR is still a work in progress, that's totally fine – just
include a note to let us know. -->
### Types of change
<!-- What type of change does your PR cover? Is it a bug fix, an enhancement
or new feature, or a change to the documentation? -->
## Checklist
<!--- Before you submit the PR, go over this checklist and make sure you can
tick off all the boxes. [] -> [x] -->
- [ ] I have submitted the spaCy Contributor Agreement.
- [ ] I ran the tests, and all new and existing tests passed.
- [ ] My changes don't require a change to the documentation, or if they do, I've added all required information.
* Documentation improvement regarding joblib and SO (#2867)
Some documentation improvements
## Description
1. Fixed the dead URL to joblib
2. Fixed Stack Overflow brand name (with space)
### Types of change
Documentation
## 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.
* raise error when setting overlapping entities as doc.ents (#2880)
* Fix out-of-bounds access in NER training
The helper method state.B(1) gets the index of the first token of the
buffer, or -1 if no such token exists. Normally this is safe because we
pass this to functions like state.safe_get(), which returns an empty
token. Here we used it directly as an array index, which is not okay!
This error may have been the cause of out-of-bounds access errors during
training. Similar errors may still be around, so much be hunted down.
Hunting this one down took a long time...I printed out values across
training runs and diffed, looking for points of divergence between
runs, when no randomness should be allowed.
* Change PyThaiNLP Url (#2876)
* Fix missing comma
* Add example showing a fix-up rule for space entities
* Set version to 2.0.17.dev0
* Update regex version
* Revert "Update regex version"
This reverts commit 62358dd867.
* Try setting older regex version, to align with conda
* Set version to 2.0.17
* Add spacy-js to universe [ci-skip]
* Add spacy-raspberry to universe (closes#2889)
* Add script to validate universe json [ci skip]
* Removed space in docs + added contributor indo (#2909)
* - removed unneeded space in documentation
* - added contributor info
* Allow input text of length up to max_length, inclusive (#2922)
* Include universe spec for spacy-wordnet component (#2919)
* feat: include universe spec for spacy-wordnet component
* chore: include spaCy contributor agreement
* Minor formatting changes [ci skip]
* Fix image [ci skip]
Twitter URL doesn't work on live site
* Check if the word is in one of the regular lists specific to each POS (#2886)
* 💫 Create random IDs for SVGs to prevent ID clashes (#2927)
Resolves#2924.
## Description
Fixes problem where multiple visualizations in Jupyter notebooks would have clashing arc IDs, resulting in weirdly positioned arc labels. Generating a random ID prefix so even identical parses won't receive the same IDs for consistency (even if effect of ID clash isn't noticable here.)
### Types of change
bug fix
## 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.
* Fix typo [ci skip]
* fixes symbolic link on py3 and windows (#2949)
* fixes symbolic link on py3 and windows
during setup of spacy using command
python -m spacy link en_core_web_sm en
closes#2948
* Update spacy/compat.py
Co-Authored-By: cicorias <cicorias@users.noreply.github.com>
* Fix formatting
* Update universe [ci skip]
* Catalan Language Support (#2940)
* Catalan language Support
* Ddding Catalan to documentation
* Sort languages alphabetically [ci skip]
* Update tests for pytest 4.x (#2965)
<!--- Provide a general summary of your changes in the title. -->
## Description
- [x] Replace marks in params for pytest 4.0 compat ([see here](https://docs.pytest.org/en/latest/deprecations.html#marks-in-pytest-mark-parametrize))
- [x] Un-xfail passing tests (some fixes in a recent update resolved a bunch of issues, but tests were apparently never updated here)
### Types of change
<!-- What type of change does your PR cover? Is it a bug fix, an enhancement
or new feature, or a change to the documentation? -->
## 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.
* Fix regex pin to harmonize with conda (#2964)
* Update README.rst
* Fix bug where Vocab.prune_vector did not use 'batch_size' (#2977)
Fixes#2976
* Fix typo
* Fix typo
* Remove duplicate file
* Require thinc 7.0.0.dev2
Fixes bug in gpu_ops that would use cupy instead of numpy on CPU
* Add missing import
* Fix error IDs
* Fix tests
These experiments were completed a few weeks ago, but I didn't make the PR, pending model release.
Token vector width: 128->96
Hidden width: 128->64
Embed size: 5000->2000
Dropout: 0.2->0.1
Updated optimizer defaults (unclear how important?)
This should improve speed, model size and load time, while keeping
similar or slightly better accuracy.
The tl;dr is we prefer to prevent over-fitting by reducing model size,
rather than using more dropout.
* Auto-format tests with black
* Add flake8 config
* Tidy up and remove unused imports
* Fix redefinitions of test functions
* Replace orths_and_spaces with words and spaces
* Fix compatibility with pytest 4.0
* xfail test for now
Test was previously overwritten by following test due to naming conflict, so failure wasn't reported
* Unfail passing test
* Only use fixture via arguments
Fixes pytest 4.0 compatibility
Our epic matrix multiplication odyssey is drawing to a close...
I've now finally got the Blis linear algebra routines in a self-contained Python package, with wheels for Windows, Linux and OSX. The only missing platform at the moment is Windows Python 2.7. The result is at https://github.com/explosion/cython-blis
Thinc v7.0.0 will make the change to Blis. I've put a Thinc v7.0.0.dev0 up on PyPi so that we can test these changes with the CI, and even get them out to spacy-nightly, before Thinc v7.0.0 is released. This PR also updates the other dependencies to be in line with the current versions master is using. I've also resolved the msgpack deprecation problems, and gotten spaCy and Thinc up to date with the latest Cython.
The point of switching to Blis is to have control of how our matrix multiplications are executed across platforms. When we were using numpy for this, a different library would be used on pip and conda, OSX would use Accelerate, etc. This would open up different bugs and performance problems, especially when multi-threading was introduced.
With the change to Blis, we now strictly single-thread the matrix multiplications. This will make it much easier to use multiprocessing to parallelise the runtime, since we won't have nested parallelism problems to deal with.
* Use blis
* Use -2 arg to Cython
* Update dependencies
* Fix requirements
* Update setup dependencies
* Fix requirement typo
* Fix msgpack errors
* Remove Python27 test from Appveyor, until Blis works there
* Auto-format setup.py
* Fix murmurhash version
<!--- Provide a general summary of your changes in the title. -->
## Description
- [x] Replace marks in params for pytest 4.0 compat ([see here](https://docs.pytest.org/en/latest/deprecations.html#marks-in-pytest-mark-parametrize))
- [x] Un-xfail passing tests (some fixes in a recent update resolved a bunch of issues, but tests were apparently never updated here)
### Types of change
<!-- What type of change does your PR cover? Is it a bug fix, an enhancement
or new feature, or a change to the documentation? -->
## 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.
* fixes symbolic link on py3 and windows
during setup of spacy using command
python -m spacy link en_core_web_sm en
closes#2948
* Update spacy/compat.py
Co-Authored-By: cicorias <cicorias@users.noreply.github.com>
Resolves#2924.
## Description
Fixes problem where multiple visualizations in Jupyter notebooks would have clashing arc IDs, resulting in weirdly positioned arc labels. Generating a random ID prefix so even identical parses won't receive the same IDs for consistency (even if effect of ID clash isn't noticable here.)
### Types of change
bug fix
## 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.
* Allow matching non-orth attributes in PhraseMatcher (see #1971)
Usage: PhraseMatcher(nlp.vocab, attr='POS')
* Allow attr argument to be int
* Fix formatting
* Fix typo
The helper method state.B(1) gets the index of the first token of the
buffer, or -1 if no such token exists. Normally this is safe because we
pass this to functions like state.safe_get(), which returns an empty
token. Here we used it directly as an array index, which is not okay!
This error may have been the cause of out-of-bounds access errors during
training. Similar errors may still be around, so much be hunted down.
Hunting this one down took a long time...I printed out values across
training runs and diffed, looking for points of divergence between
runs, when no randomness should be allowed.
The helper method state.B(1) gets the index of the first token of the
buffer, or -1 if no such token exists. Normally this is safe because we
pass this to functions like state.safe_get(), which returns an empty
token. Here we used it directly as an array index, which is not okay!
This error may have been the cause of out-of-bounds access errors during
training. Similar errors may still be around, so much be hunted down.
Hunting this one down took a long time...I printed out values across
training runs and diffed, looking for points of divergence between
runs, when no randomness should be allowed.
<!--- Provide a general summary of your changes in the title. -->
## Description
<!--- Use this section to describe your changes. If your changes required
testing, include information about the testing environment and the tests you
ran. If your test fixes a bug reported in an issue, don't forget to include the
issue number. If your PR is still a work in progress, that's totally fine – just
include a note to let us know. -->
Add a rule-based French Lemmatizer following the english one and the excellent PR for [greek language optimizations](https://github.com/explosion/spaCy/pull/2558) to adapt the Lemmatizer class.
### Types of change
<!-- What type of change does your PR cover? Is it a bug fix, an enhancement
or new feature, or a change to the documentation? -->
- Lemma dictionary used can be found [here](http://infolingu.univ-mlv.fr/DonneesLinguistiques/Dictionnaires/telechargement.html), I used the XML version.
- Add several files containing exhaustive list of words for each part of speech
- Add some lemma rules
- Add POS that are not checked in the standard Lemmatizer, i.e PRON, DET, ADV and AUX
- Modify the Lemmatizer class to check in lookup table as a last resort if POS not mentionned
- Modify the lemmatize function to check in lookup table as a last resort
- Init files are updated so the model can support all the functionalities mentioned above
- Add words to tokenizer_exceptions_list.py in respect to regex used in tokenizer_exceptions.py
## 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.
* Correct some grammatical inaccuracies in lang\ru\examples.py; filled Contributor Agreement
* Correct some grammatical inaccuracies in lang\ru\examples.py
* Move contributor agreement to separate file
* Add words to portuguese language _num_words
* Add words to portuguese language _num_words
* Portuguese - Add/remove stopwords, fix tokenizer, add currency symbols
* Extended punctuation and norm_exceptions in the Portuguese language
* Fix error
ValueError: cannot resize an array that references or is referenced
by another array in this way. Use the resize function
* added spaCy Contributor Agreement
The set_children_from_heads function assumed parse trees were
projective. However, non-projective parses may be passed in during
deserialization, or after deprojectivising. This caused incorrect
sentence boundaries to be set for non-projective parses. Close#2772.
* adding e-KTP in tokenizer exceptions list
* add exception token
* removing lines with containing space as it won't matter since we use .split() method in the end, added new tokens in exception
* add tokenizer exceptions list
* combining base_norms with norm_exceptions
* adding norm_exception
* fix double key in lemmatizer
* remove unused import on punctuation.py
* reformat stop_words to reduce number of lines, improve readibility
* updating tokenizer exception
* implement is_currency for lang/id
* adding orth_first_upper in tokenizer_exceptions
* update the norm_exception list
* remove bunch of abbreviations
* adding contributors file
Sorry, don't mean to be nitpicky, I just noticed this when going through the CLI and thought it was a quick fix. That said, if this was intention than please let me know.
* When calling getoption() in conftest.py, pass a default option
This is necessary to allow testing an installed spacy by running:
pytest --pyargs spacy
* Add contributor agreement
* subword_features: Controls whether subword features are used in the
word embeddings. True by default (specifically, prefix, suffix and word
shape). Should be set to False for languages like Chinese and Japanese.
* conv_depth: Depth of the convolutional layers. Defaults to 4.
The parser.begin_training() method was rewritten in v2.1. The rewrite
introduced a regression, where if you added labels prior to
begin_training(), these labels were discarded. This patch fixes that.
Our JSON training format is annoying to work with, and we've wanted to
retire it for some time. In the meantime, we can at least add some
missing functions to make it easier to live with.
This patch adds a function that generates the JSON format from a list
of Doc objects, one per paragraph. This should be a convenient way to handle
a lot of data conversions: whatever format you have the source
information in, you can use it to setup a Doc object. This approach
should offer better future-proofing as well. Hopefully, we can steadily
rewrite code that is sensitive to the current data-format, so that it
instead goes through this function. Then when we change the data format,
we won't have such a problem.
I have added numbers in hindi lex_attrs.py file according to Indian numbering system(https://en.wikipedia.org/wiki/Indian_numbering_system) and here are there english translations:
'शून्य' => zero
'एक' => one
'दो' => two
'तीन' => three
'चार' => four
'पांच' => five
'छह' => six
'सात'=>seven
'आठ' => eight
'नौ' => nine
'दस' => ten
'ग्यारह' => eleven
'बारह' => twelve
'तेरह' => thirteen
'चौदह' => fourteen
'पंद्रह' => fifteen
'सोलह'=> sixteen
'सत्रह' => seventeen
'अठारह' => eighteen
'उन्नीस' => nineteen
'बीस' => twenty
'तीस' => thirty
'चालीस' => forty
'पचास' => fifty
'साठ' => sixty
'सत्तर' => seventy
'अस्सी' => eighty
'नब्बे' => ninety
'सौ' => hundred
'हज़ार' => thousand
'लाख' => hundred thousand
'करोड़' => ten million
'अरब' => billion
'खरब' => hundred billion
<!--- Provide a general summary of your changes in the title. -->
## Description
<!--- Use this section to describe your changes. If your changes required
testing, include information about the testing environment and the tests you
ran. If your test fixes a bug reported in an issue, don't forget to include the
issue number. If your PR is still a work in progress, that's totally fine – just
include a note to let us know. -->
### Types of change
<!-- What type of change does your PR cover? Is it a bug fix, an enhancement
or new feature, or a change to the documentation? -->
## 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.
* Exceptions for single letter words ending sentence
Sentences ending in "i." (as in "... peka i."), "m." (as in "...än 2000 m."), should be tokenized as two separate tokens.
* Add test
## Description
Related issues: #2379 (should be fixed by separating model tests)
* **total execution time down from > 300 seconds to under 60 seconds** 🎉
* removed all model-specific tests that could only really be run manually anyway – those will now live in a separate test suite in the [`spacy-models`](https://github.com/explosion/spacy-models) repository and are already integrated into our new model training infrastructure
* changed all relative imports to absolute imports to prepare for moving the test suite from `/spacy/tests` to `/tests` (it'll now always test against the installed version)
* merged old regression tests into collections, e.g. `test_issue1001-1500.py` (about 90% of the regression tests are very short anyways)
* tidied up and rewrote existing tests wherever possible
### Todo
- [ ] move tests to `/tests` and adjust CI commands accordingly
- [x] move model test suite from internal repo to `spacy-models`
- [x] ~~investigate why `pipeline/test_textcat.py` is flakey~~
- [x] review old regression tests (leftover files) and see if they can be merged, simplified or deleted
- [ ] update documentation on how to run tests
### Types of change
enhancement, tests
## 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.
- [ ] My changes don't require a change to the documentation, or if they do, I've added all required information.
This patch improves tokenizer speed by about 10%, and reduces memory usage in the `Vocab` by removing a redundant index. The `vocab._by_orth` and `vocab._by_hash` indexed on different data in v1, but in v2 the orth and the hash are identical.
The patch also fixes an uninitialized variable in the tokenizer, the `has_special` flag. This checks whether a chunk we're tokenizing triggers a special-case rule. If it does, then we avoid caching within the chunk. This check led to incorrectly rejecting some chunks from the cache.
With the `en_core_web_md` model, we now tokenize the IMDB train data at 503,104k words per second. Prior to this patch, we had 465,764k words per second.
Before switching to the regex library and supporting more languages, we had 1.3m words per second for the tokenizer. In order to recover the missing speed, we need to:
* Fix the variable-length lookarounds in the suffix, infix and `token_match` rules
* Improve the performance of the `token_match` regex
* Switch back from the `regex` library to the `re` library.
## 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.
This jargon is not offencive but emotionally colored as funny due to its deviation from the norm for various reasons: immitating a dialect, deliberately wrong spelling emphasizing its low colloquial nature, obsolete form, foreign borrowing with native flections, etc.
Dmitry Briukhanov, Linguist & Pythonist
* Add helper function for reading in JSONL
* Add rule-based NER component
* Fix whitespace
* Add component to factories
* Add tests
* Add option to disable indent on json_dumps compat
Otherwise, reading JSONL back in line by line won't work
* Fix error code
List created by taking the 2000 top words from a Wikipedia dump and
removing everything that wasn't hiragana.
Tried going through kanji words and deciding what to keep but there were
too many obvious non-stopwords (東京 was in the top 500) and many other
words where it wasn't clear if they should be included or not.
<!--- Provide a general summary of your changes in the title. -->
## Description
This PR corrects the German lemma form for the word "Rang". Initially, the lemma form was "ringen", which is not correct, because it refers to the verb ("ringen") and not to the noun ("Rang").
### Types of change
The lemma form for "Rang" is corrected to "Rang", see also the [Duden](https://www.duden.de/rechtschreibung/Rang) entry.
## 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.
<!--- Provide a general summary of your changes in the title. -->
Referring #2452, fixing displacy arrow directions to match the input.
## Description
The fix is simply replacing `direction is 'left'` with `direction == 'left'` to include the case `direction` is a `str` and not a `unicode`.
### Types of change
bug fix
## Checklist
<!--- Before you submit the PR, go over this checklist and make sure you can
tick off all the boxes. [] -> [x] -->
- [ ] I have submitted the spaCy Contributor Agreement.
- [ ] 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.
* Pass through "silent" kwarg to the wrapper in the spacy module init.
reference issue #2196
* Pass through "silent" kwarg to the wrapper in the spacy module init.
reference issue #2196
* contributor agreement
* issue_2385 add tests for iob_to_biluo converter function
* issue_2385 fix and modify iob_to_biluo function to accept either iob or biluo tags in cli.converter
* issue_2385 add test to fix b char bug
* add contributor agreement
* fill contributor agreement
## Description
Fix for issue #2361 :
replace &, <, >, " with &amp; , &lt; , &gt; , &quot; in before rendering svg
## 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.
- [ ] I ran the tests, and all new and existing tests passed.
(As discussed in the comments to #2361)
- [x] My changes don't require a change to the documentation, or if they do, I've added all required information.
* Simplify is_config() and normalize_string_keys()
* Use __in__ to avoid the nested _ands_ and _ors_.
* Dict comprehension directly tracks with the doc string
* Keep more basic loop in normalize_string_keys
* Whitespace
* Add logic to filter out warning IDs via environment variable
Usage: SPACY_WARNING_EXCLUDE=W001,W007
* Add warnings for empty vectors
* Add warning if no word vectors are used in .similarity methods
For example, if only tensors are available in small models – should hopefully clear up some confusion around this
* Capture warnings in tests
* Rename SPACY_WARNING_EXCLUDE to SPACY_WARNING_IGNORE
* Go back to using requests instead of urllib (closes#2320)
Fewer dependencies are good, but this one was simply causing too many other problems around SSL verification and Python 2/3 compatibility. requests is a popular enough package that it's okay for spaCy to depend on it – and this will hopefully make model downloads less flakey.
* Only download model if not installed (see #1456)
Use #egg=model==version to allow pip to check for existing installations. The download is only started if no installation matching the package/version is found. Fixes a long-standing inconvenience.
* Pass additional options to pip when installing model (resolves#1456)
Treat all additional arguments passed to the download command as pip options to allow user to customise the command. For example:
python -m spacy download en --user
* Add CLI option to enable installing model package dependencies
* Revert "Add CLI option to enable installing model package dependencies"
This reverts commit 9336ffe695.
* Update documentation
* Add Romanian lemmatizer lookup table.
Adapted from http://www.lexiconista.com/datasets/lemmatization/
by replacing cedillas with commas (ș and ț).
The original dataset is licensed under the Open Database License.
* Fix one blatant issue in the Romanian lemmatizer
* Romanian examples file
* Add ro_tokenizer in conftest
* Add Romanian lemmatizer test
* Update lex_attrs.py
Fixed spelling mistakes of some numbers (according to Brazilian Portuguese).
* Update lex_attrs.py
As requested, I've included the correct spelling for both Brazilian Portuguese and Portuguese Portuguese.
I will advise however, that the two are separated in the future. Brazilian Portuguese is a very different language from the original one, although most of the writing is unified, the way people talk in both countries is radically different. Keeping both languages as one may lead to bigger issues in the future, especially when it comes to spell checking.
* Add contraction forms of some common stopwords
All the stopwords added contain the apostrophe" ' "or " ’ ".
* Adds contributor agreement mauryaland
* Update mauryaland.md
* Port Japanese mecab tokenizer from v1
This brings the Mecab-based Japanese tokenization introduced in #1246 to
spaCy v2. There isn't a JapaneseTagger implementation yet, but POS tag
information from Mecab is stored in a token extension. A tag map is also
included.
As a reminder, Mecab is required because Universal Dependencies are
based on Unidic tags, and Janome doesn't support Unidic.
Things to check:
1. Is this the right way to use a token extension?
2. What's the right way to implement a JapaneseTagger? The approach in
#1246 relied on `tag_from_strings` which is just gone now. I guess the
best thing is to just try training spaCy's default Tagger?
-POLM
* Add tagging/make_doc and tests
* Remove erroneous lemma lookup år > åra in Swedish
* Add contributors agreement
* Add contrib agreement to correct directory
* Revert change to CONTRIBUTOR_AGREEMENT
* Remove incorrect lemma lookup gäng->gänga
In modern Swedish, "gäng" is mostly associated with "gang" or "group of people". The removed lemma lookup lemmatized it to the verb "thread".
* Add contrib agreement to correct directory
* Revert change to CONTRIBUTOR_AGREEMENT
* Add spacy.errors module
* Update deprecation and user warnings
* Replace errors and asserts with new error message system
* Remove redundant asserts
* Fix whitespace
* Add messages for print/util.prints statements
* Fix typo
* Fix typos
* Move CLI messages to spacy.cli._messages
* Add decorator to display error code with message
An implementation like this is nice because it only modifies the string when it's retrieved from the containing class – so we don't have to worry about manipulating tracebacks etc.
* Remove unused link in spacy.about
* Update errors for invalid pipeline components
* Improve error for unknown factories
* Add displaCy warnings
* Update formatting consistency
* Move error message to spacy.errors
* Update errors and check if doc returned by component is None
This patch takes a step towards #1487 by introducing the
doc.retokenize() context manager, to handle merging spans, and soon
splitting tokens.
The idea is to do merging and splitting like this:
with doc.retokenize() as retokenizer:
for start, end, label in matches:
retokenizer.merge(doc[start : end], attrs={'ent_type': label})
The retokenizer accumulates the merge requests, and applies them
together at the end of the block. This will allow retokenization to be
more efficient, and much less error prone.
A retokenizer.split() function will then be added, to handle splitting a
single token into multiple tokens. These methods take `Span` and `Token`
objects; if the user wants to go directly from offsets, they can append
to the .merges and .splits lists on the retokenizer.
The doc.merge() method's behaviour remains unchanged, so this patch
should be 100% backwards incompatible (modulo bugs). Internally,
doc.merge() fixes up the arguments (to handle the various deprecated styles),
opens the retokenizer, and makes the single merge.
We can later start making deprecation warnings on direct calls to doc.merge(),
to migrate people to use of the retokenize context manager.
Changed python set to cpp stl set #2032
## Description
Changed python set to cpp stl set. CPP stl set works better due to the logarithmic run time of its methods. Finding minimum in the cpp set is done in constant time as opposed to the worst case linear runtime of python set. Operations such as find,count,insert,delete are also done in either constant and logarithmic time thus making cpp set a better option to manage vectors.
Reference : http://www.cplusplus.com/reference/set/set/
### Types of change
Enhancement for `Vectors` for faster initialising of word vectors(fasttext)
This patch addresses #1660, which was caused by keying all pre-trained
vectors with the same ID when telling Thinc how to refer to them. This
meant that if multiple models were loaded that had pre-trained vectors,
errors or incorrect behaviour resulted.
The vectors class now includes a .name attribute, which defaults to:
{nlp.meta['lang']_nlp.meta['name']}.vectors
The vectors name is set in the cfg of the pipeline components under the
key pretrained_vectors. This replaces the previous cfg key
pretrained_dims.
In order to make existing models compatible with this change, we check
for the pretrained_dims key when loading models in from_disk and
from_bytes, and add the cfg key pretrained_vectors if we find it.
