While developing v2.1, I ran a bunch of hyper-parameter search
experiments to find settings that performed well for spaCy's NER and
parser. I ended up changing the default Adam settings from beta1=0.9,
beta2=0.999, eps=1e-8 to beta1=0.8, beta2=0.8, eps=1e-5. This was giving
a small improvement in accuracy (like, 0.4%).
Months later, I run the models with Prodigy, which uses beam-search
decoding even when the model has been trained with a greedy objective.
The new models performed terribly...So, wtf? After a couple of days
debugging, I figured out that the new optimizer settings was causing the
model to converge to solutions where the top-scoring class often had
a score of like, -80. The variance on the weights had gone up
enormously. I guess I needed to update the L2 regularisation as well?
Anyway. Let's just revert the change --- if the optimizer is finding
such extreme solutions, that seems bad, and not nearly worth the small
improvement in accuracy.
Currently training a slate of models, to verify the accuracy change is minimal.
Once the training is complete, we can merge this.
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## Description
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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
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### Types of change
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or new feature, or a change to the documentation? -->
## Checklist
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Add and document CLI options for batch size, max doc length, min doc length for `spacy pretrain`.
Also improve CLI output.
Closes#3216
## Checklist
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* merging conllu/conll and conllubio scripts
* tabs to spaces
* removing conllubio2json from converters/__init__.py
* Move not-really-CLI tests to misc
* Add converter test using no-ud data
* Fix test I broke
* removing include_biluo parameter
* fixing read_conllx
* remove include_biluo from convert.py
* label in span not writable anymore
* more explicit unit test and error message for readonly label
* bit more explanation (view)
* error msg tailored to specific case
* fix None case
Closes#2091.
## Description
With the new `vocab.writing_system` property introduced in #3390 (exposed via the language defaults), I was able to finally fix this (I think!). Based on the `Doc`, dispaCy now detects whether it's a RTL or LTR language and adjusts the visualization accordingly. Wherever possible, I've also added `direction` and `lang` attributes.
Entity visualization now looks like this:
<img width="318" alt="Screenshot 2019-03-11 at 16 06 51" src="https://user-images.githubusercontent.com/13643239/54136866-d97afd80-441c-11e9-8c27-3d46994cc833.png">
And dependencies like this (ignore the most likely incorrect tags and dependencies):
<img width="621" alt="Screenshot 2019-03-11 at 16 51 59" src="https://user-images.githubusercontent.com/13643239/54137771-8b66f980-441e-11e9-8460-0682b95eef2a.png">
### Types of change
enhancement, bug fix
## Checklist
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tick off all the boxes. [] -> [x] -->
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* Add xfail test for vocab.writing_system
* Add vocab.writing_system property
* Set Language.Defaults.writing_system
* Set default writing system
* Remove xfail on test_vocab_writing_system
Closes#2203. Closes#3268.
Lemmas set from outside the `Morphology` class were being overwritten. The result was especially confusing when deserialising, as it meant some lemmas could change when storing and retrieving a `Doc` object.
This PR applies two fixes:
1) When we go to set the lemma in the `Morphology` class, first check whether a lemma is already set. If so, don't overwrite.
2) When we load with `doc.from_array()`, take care to apply the `TAG` field first. This allows other fields to overwrite the `TAG` implied properties, if they're provided explicitly (e.g. the `LEMMA`).
## Checklist
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tick off all the boxes. [] -> [x] -->
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* Add component_cfg kwarg to begin_training
* Document component_cfg arg to begin_training
* Update docs and auto-format
* Support component_cfg across Language
* Format
* Update docs and docstrings [ci skip]
* Fix begin_training
* Make serialization methods consistent
exclude keyword argument instead of random named keyword arguments and deprecation handling
* Update docs and add section on serialization fields
* Use default return instead of else
* Add Doc.is_nered to indicate if entities have been set
* Add properties in Doc.to_json if they were set, not if they're available
This way, if a processed Doc exports "pos": None, it means that the tag was explicitly unset. If it exports "ents": [], it means that entity annotations are available but that this document doesn't contain any entities. Before, this would have been unclear and problematic for training.
<!--- 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
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tick off all the boxes. [] -> [x] -->
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- [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.
