* Correct code example for Span.lemma_ in API Docs (#13405)
* Correct documented return type of Vocab.to_bytes in API docs
* Correct wording for Vectors.__init__ in API docs
This PR removes the dependency on langcodes introduced in #9342.
While the introduction of langcodes allows a significantly wider range of language codes, there are some unexpected side effects:
zh-Hant (Traditional Chinese) should be mapped to zh intead of None, as spaCy's Chinese model is based on pkuseg which supports tokenization of both Simplified and Traditional Chinese.
Since it is possible that spaCy may have a model for Norwegian Nynorsk in the future, mapping no (macrolanguage Norwegian) to nb (Norwegian Bokmål) might be misleading. In that case, the user should be asked to specify nb or nn (Norwegian Nynorsk) specifically or consult the doc.
Same as above for regional variants of languages such as en_gb and en_us.
Overall, IMHO, introducing an extra dependency just for the conversion of language codes is an overkill. It is possible that most user just need the conversion between 2/3-letter ISO codes and a simple dictionary lookup should suffice.
With this PR, ISO 639-1 and ISO 639-3 codes are supported. ISO 639-2/B (bibliographic codes which are not favored and used in ISO 639-3) and deprecated ISO 639-1/2 codes are also supported to maximize backward compatibility.
* fix type annotation in docs
* only restore entities after loss calculation
* restore entities of sample in initialization
* rename overfitting function
* fix EL scorer
* Relax test
* fix formatting
* Update spacy/pipeline/entity_linker.py
Co-authored-by: Raphael Mitsch <r.mitsch@outlook.com>
* rename to _ensure_ents
* further rename
* allow for scorer to be None
---------
Co-authored-by: Raphael Mitsch <r.mitsch@outlook.com>
* Add spacy.TextCatParametricAttention.v1
This layer provides is a simplification of the ensemble classifier that
only uses paramteric attention. We have found empirically that with a
sufficient amount of training data, using the ensemble classifier with
BoW does not provide significant improvement in classifier accuracy.
However, plugging in a BoW classifier does reduce GPU training and
inference performance substantially, since it uses a GPU-only kernel.
* Fix merge fallout
* Add TextCatReduce.v1
This is a textcat classifier that pools the vectors generated by a
tok2vec implementation and then applies a classifier to the pooled
representation. Three reductions are supported for pooling: first, max,
and mean. When multiple reductions are enabled, the reductions are
concatenated before providing them to the classification layer.
This model is a generalization of the TextCatCNN model, which only
supports mean reductions and is a bit of a misnomer, because it can also
be used with transformers. This change also reimplements TextCatCNN.v2
using the new TextCatReduce.v1 layer.
* Doc fixes
Co-authored-by: Sofie Van Landeghem <svlandeg@users.noreply.github.com>
* Fully specify `TextCatCNN` <-> `TextCatReduce` equivalence
* Move TextCatCNN docs to legacy, in prep for moving to spacy-legacy
* Add back a test for TextCatCNN.v2
* Replace TextCatCNN in pipe configurations and templates
* Add an infobox to the `TextCatReduce` section with an `TextCatCNN` anchor
* Add last reduction (`use_reduce_last`)
* Remove non-working TextCatCNN Netlify redirect
* Revert layer changes for the quickstart
* Revert one more quickstart change
* Remove unused import
* Fix docstring
* Fix setting name in error message
---------
Co-authored-by: Sofie Van Landeghem <svlandeg@users.noreply.github.com>
Co-authored-by: Adriane Boyd <adrianeboyd@gmail.com>
Martin Schorfmann