* added tag_map for indonesian
* changed tag map from .py to .txt to see if tests pass
* added symbols import
* added utf8 encoding flag
* added missing SCONJ symbol
* Auto-format
* Remove unused imports
* Make tag map available in Indonesian defaults
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## Description
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testing, include information about the testing environment and the tests you
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Fix a bug in the test of JapaneseTokenizer.
This PR may require @polm's review.
### 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? -->
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(util): fix decaying function output
* fix(util): better test and adhere to code standards
* fix(util): correct variable name, pytestify test, update website text
* Fix code for bag-of-words feature extraction
The _ml.py module had a redundant copy of a function to extract unigram
bag-of-words features, except one had a bug that set values to 0.
Another function allowed extraction of bigram features. Replace all three
with a new function that supports arbitrary ngram sizes and also allows
control of which attribute is used (e.g. ORTH, LOWER, etc).
* Support 'bow' architecture for TextCategorizer
This allows efficient ngram bag-of-words models, which are better when
the classifier needs to run quickly, especially when the texts are long.
Pass architecture="bow" to use it. The extra arguments ngram_size and
attr are also available, e.g. ngram_size=2 means unigram and bigram
features will be extracted.
* Fix size limits in train_textcat example
* Explain architectures better in docs
v2.1 introduced a regression when deserializing the parser after
parser.add_label() had been called. The code around the class mapping is
pretty confusing currently, as it was written to accommodate backwards
model compatibility. It needs to be revised when the models are next
retrained.
Closes#3433
spaCy v2.1 switched to the built-in re module, where v2.0 had been using
the third-party regex library. When the tokenizer was deserialized on
Python2.7, the `re.compile()` function was called with expressions that
featured escaped unicode codepoints that were not in Python2.7's unicode
database.
Problems occurred when we had a range between two of these unknown
codepoints, like this:
```
'[\\uAA77-\\uAA79]'
```
On Python2.7, the unknown codepoints are not unescaped correctly,
resulting in arbitrary out-of-range characters being matched by the
expression.
This problem does not occur if we instead have a range between two
unicode literals, rather than the escape sequences. To fix the bug, we
therefore add a new compat function that unescapes unicode sequences
using the `ast.literal_eval()` function. Care is taken to ensure we
do not also escape non-unicode sequences.
Closes#3356.
- [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.
I wrote a small script to read the UD English training data and check
that our tag map and morph rules were resulting in the best POS map.
This hadn't been done for some time, and there have been various changes
to the UD schema since it has been done. After these changes we should
see much better agreement between our POS assignments and the UD POS
tags.
