* Test on #2396: bug in Doc.get_lca_matrix()
* reimplementation of Doc.get_lca_matrix(), (closes#2396)
* reimplement Span.get_lca_matrix(), and call it from Doc.get_lca_matrix()
* tests Span.get_lca_matrix() as well as Doc.get_lca_matrix()
* implement _get_lca_matrix as a helper function in doc.pyx; call it from Doc.get_lca_matrix and Span.get_lca_matrix
* use memory view instead of np.ndarray in _get_lca_matrix (faster)
* fix bug when calling Span.get_lca_matrix; return lca matrix as np.array instead of memoryview
* cleaner conditional, add comment
* Add failing test for matcher bug #3009
* Deduplicate matches from Matcher
* Update matcher ? quantifier test
* Fix bug with ? quantifier in Matcher
The ? quantifier indicates a token may occur zero or one times. If the
token pattern fit, the matcher would fail to consider valid matches
where the token pattern did not fit. Consider a simple regex like:
.?b
If we have the string 'b', the .? part will fit --- but then the 'b' in
the pattern will not fit, leaving us with no match. The same bug left us
with too few matches in some cases. For instance, consider:
.?.?
If we have a string of length two, like 'ab', we actually have three
possible matches here: [a, b, ab]. We were only recovering 'ab'. This
should now be fixed. Note that the fix also uncovered another bug, where
we weren't deduplicating the matches. There are actually two ways we
might match 'a' and two ways we might match 'b': as the second token of the pattern,
or as the first token of the pattern. This ambiguity is spurious, so we
need to deduplicate.
Closes#2464 and #3009
* Fix Python2
* Remove check for overwritten factory
This needs to be handled differently – on first initialization, a new factory will be added and any subsequent initializations will trigger this warning, even if it's a new entry point that doesn't overwrite a built-in.
* Add helper to only load specific entry point
Useful for loading languages via entry points, so that they can be lazy-loaded. Otherwise, all entry point languages would have to be loaded upfront.
* Check entry points for custom languages
## Description
- [x] fix auto-detection of Jupyter notebooks (even if `jupyter=True` isn't set)
- [x] add `displacy.set_render_wrapper` method to define a custom function called around the HTML markup generated in all calls to `displacy.render` (can be used to allow custom integrations, callbacks and page formatting)
- [x] add option to customise host for web server
- [x] show warning if `displacy.serve` is called from within Jupyter notebooks
- [x] move error message to `spacy.errors.Errors`.
### 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.
The output weights often return negative scores for classes, especially
via the bias terms. This means that when we add a new class, we can't
rely on just zeroing the weights, or we'll end up with positive
predictions for those labels.
To solve this, we use nan values as the initial weights for new labels.
This prevents them from ever coming out on top. During backprop, we
replace the nan values with the minimum assigned score, so that we're
still able to learn these classes.
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.
