* Use cosine loss in Cloze multitask
* Fix char_embed for gpu
* Call resume_training for base model in train CLI
* Fix bilstm_depth default in pretrain command
* Implement character-based pretraining objective
* Use chars loss in ClozeMultitask
* Add method to decode predicted characters
* Fix number characters
* Rescale gradients for mlm
* Fix char embed+vectors in ml
* Fix pipes
* Fix pretrain args
* Move get_characters_loss
* Fix import
* Fix import
* Mention characters loss option in pretrain
* Remove broken 'self attention' option in pretrain
* Revert "Remove broken 'self attention' option in pretrain"
This reverts commit 56b820f6af.
* Document 'characters' objective of pretrain
* Convert custom user_data to token extension format
Convert the user_data values so that they can be loaded as custom token
extensions for `inflection`, `reading_form`, `sub_tokens`, and `lemma`.
* Reset Underscore state in ja tokenizer tests
Move `Lemmatizer.is_base_form` to the language settings so that each
language can provide a language-specific method as
`LanguageDefaults.is_base_form`.
The existing English-specific `Lemmatizer.is_base_form` is moved to
`EnglishDefaults`.
* Skip special tag _SP in check for new tag map
In `Tagger.begin_training()` check for new tags aside from `_SP` in the
new tag map initialized from the provided gold tuples when determining
whether to reinitialize the morphology with the new tag map.
* Simplify _SP check
* user_dict fields: adding inflections, reading_forms, sub_tokens
deleting: unidic_tags
improve code readability around the token alignment procedure
* add test cases, replace fugashi with sudachipy in conftest
* move bunsetu.py to spaCy Universe as a pipeline component BunsetuRecognizer
* tag is space -> both surface and tag are spaces
* consider len(text)==0
* Fix warning message for lemmatization tables
* Add a warning when the `lexeme_norm` table is empty. (Given the
relatively lang-specific loading for `Lookups`, it seemed like too much
overhead to dynamically extract the list of languages, so for now it's
hard-coded.)
* Added Examples for Tamil Sentences
#### Description
This PR add example sentences for the Tamil language which were missing as per issue #1107
#### Type of Change
This is an enhancement.
* Accepting spaCy Contributor Agreement
* Signed on my behalf as an individual
* Fix warning message for lemmatization tables
* Add a warning when the `lexeme_norm` table is empty. (Given the
relatively lang-specific loading for `Lookups`, it seemed like too much
overhead to dynamically extract the list of languages, so for now it's
hard-coded.)
* Added Examples for Tamil Sentences
#### Description
This PR add example sentences for the Tamil language which were missing as per issue #1107
#### Type of Change
This is an enhancement.
* Accepting spaCy Contributor Agreement
* Signed on my behalf as an individual
* added setting for neighbour sentence in NEL
* added spaCy contributor agreement
* added multi sentence also for training
* made the try-except block smaller
* added setting for neighbour sentence in NEL
* added spaCy contributor agreement
* added multi sentence also for training
* made the try-except block smaller
* Use `config` dict for tokenizer settings
* Add serialization of split mode setting
* Add tests for tokenizer split modes and serialization of split mode
setting
Based on #5561
* Add more rules to deal with Japanese UD mappings
Japanese UD rules sometimes give different UD tags to tokens with the
same underlying POS tag. The UD spec indicates these cases should be
disambiguated using the output of a tool called "comainu", but rules are
enough to get the right result.
These rules are taken from Ginza at time of writing, see #3756.
* Add new tags from GSD
This is a few rare tags that aren't in Unidic but are in the GSD data.
* Add basic Japanese sentencization
This code is taken from Ginza again.
* Add sentenceizer quote handling
Could probably add more paired characters but this will do for now. Also
includes some tests.
* Replace fugashi with SudachiPy
* Modify tag format to match GSD annotations
Some of the tests still need to be updated, but I want to get this up
for testing training.
* Deal with case with closing punct without opening
* refactor resolve_pos()
* change tag field separator from "," to "-"
* add TAG_ORTH_MAP
* add TAG_BIGRAM_MAP
* revise rules for 連体詞
* revise rules for 連体詞
* improve POS about 2%
* add syntax_iterator.py (not mature yet)
* improve syntax_iterators.py
* improve syntax_iterators.py
* add phrases including nouns and drop NPs consist of STOP_WORDS
* First take at noun chunks
This works in many situations but still has issues in others.
If the start of a subtree has no noun, then nested phrases can be
generated.
また行きたい、そんな気持ちにさせてくれるお店です。
[そんな気持ち, また行きたい、そんな気持ちにさせてくれるお店]
For some reason て gets included sometimes. Not sure why.
ゲンに連れ添って円盤生物を調査するパートナーとなる。
[て円盤生物, ...]
Some phrases that look like they should be split are grouped together;
not entirely sure that's wrong. This whole thing becomes one chunk:
道の駅遠山郷北側からかぐら大橋南詰現道交点までの1.060kmのみ開通済み
* Use new generic get_words_and_spaces
The new get_words_and_spaces function is simpler than what was used in
Japanese, so it's good to be able to switch to it. However, there was an
issue. The new function works just on text, so POS info could get out of
sync. Fixing this required a small change to the way dtokens (tokens
with POS and lemma info) were generated.
Specifically, multiple extraneous spaces now become a single token, so
when generating dtokens multiple space tokens should be created in a
row.
* Fix noun_chunks, should be working now
* Fix some tests, add naughty strings tests
Some of the existing tests changed because the tokenization mode of
Sudachi changed to the more fine-grained A mode.
Sudachi also has issues with some strings, so this adds a test against
the naughty strings.
* Remove empty Sudachi tokens
Not doing this creates zero-length tokens and causes errors in the
internal spaCy processing.
* Add yield_bunsetu back in as a separate piece of code
Co-authored-by: Hiroshi Matsuda <40782025+hiroshi-matsuda-rit@users.noreply.github.com>
Co-authored-by: hiroshi <hiroshi_matsuda@megagon.ai>
This reverts commit 9393253b66.
The model shouldn't need to see all examples, and actually in v3 there's
no equivalent step. All examples are provided to the component, for the
component to do stuff like figuring out the labels. The model just needs
to do stuff like shape inference.
If `_SP` is already in the tag map, use the mapping from `_SP` instead
of `SP` so that `SP` can be a valid non-space tag. (Chinese has a
non-space tag `SP` which was overriding the mapping of `_SP` to
`SPACE`.)
Restructure Polish lemmatizer not to depend on lookups data in
`__init__` since the lemmatizer is initialized before the lookups data
is loaded from a saved model. The lookups tables are accessed first in
`__call__` instead once the data is available.
* Fix most_similar for vectors with unused rows
Address issues related to the unused rows in the vector table and
`most_similar`:
* Update `most_similar()` to search only through rows that are in use
according to `key2row`.
* Raise an error when `most_similar(n=n)` is larger than the number of
vectors in the table.
* Set and restore `_unset` correctly when vectors are added or
deserialized so that new vectors are added in the correct row.
* Set data and keys to the same length in `Vocab.prune_vectors()` to
avoid spurious entries in `key2row`.
* Fix regression test using `most_similar`
Co-authored-by: Matthew Honnibal <honnibal+gh@gmail.com>
* Add warning for misaligned character offset spans
* Resolve conflict
* Filter warnings in example scripts
Filter warnings in example scripts to show warnings once, in particular
warnings about misaligned entities.
Co-authored-by: Ines Montani <ines@ines.io>
