spaCy/spacy/util.py

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import os
import importlib
import importlib.util
import re
from pathlib import Path
import random
Update spaCy for thinc 8.0.0 (#4920) * Add load_from_config function * Add train_from_config script * Merge configs and expose via spacy.config * Fix script * Suggest create_evaluation_callback * Hard-code for NER * Fix errors * Register command * Add TODO * Update train-from-config todos * Fix imports * Allow delayed setting of parser model nr_class * Get train-from-config working * Tidy up and fix scores and printing * Hide traceback if cancelled * Fix weighted score formatting * Fix score formatting * Make output_path optional * Add Tok2Vec component * Tidy up and add tok2vec_tensors * Add option to copy docs in nlp.update * Copy docs in nlp.update * Adjust nlp.update() for set_annotations * Don't shuffle pipes in nlp.update, decruft * Support set_annotations arg in component update * Support set_annotations in parser update * Add get_gradients method * Add get_gradients to parser * Update errors.py * Fix problems caused by merge * Add _link_components method in nlp * Add concept of 'listeners' and ControlledModel * Support optional attributes arg in ControlledModel * Try having tok2vec component in pipeline * Fix tok2vec component * Fix config * Fix tok2vec * Update for Example * Update for Example * Update config * Add eg2doc util * Update and add schemas/types * Update schemas * Fix nlp.update * Fix tagger * Remove hacks from train-from-config * Remove hard-coded config str * Calculate loss in tok2vec component * Tidy up and use function signatures instead of models * Support union types for registry models * Minor cleaning in Language.update * Make ControlledModel specifically Tok2VecListener * Fix train_from_config * Fix tok2vec * Tidy up * Add function for bilstm tok2vec * Fix type * Fix syntax * Fix pytorch optimizer * Add example configs * Update for thinc describe changes * Update for Thinc changes * Update for dropout/sgd changes * Update for dropout/sgd changes * Unhack gradient update * Work on refactoring _ml * Remove _ml.py module * WIP upgrade cli scripts for thinc * Move some _ml stuff to util * Import link_vectors from util * Update train_from_config * Import from util * Import from util * Temporarily add ml.component_models module * Move ml methods * Move typedefs * Update load vectors * Update gitignore * Move imports * Add PrecomputableAffine * Fix imports * Fix imports * Fix imports * Fix missing imports * Update CLI scripts * Update spacy.language * Add stubs for building the models * Update model definition * Update create_default_optimizer * Fix import * Fix comment * Update imports in tests * Update imports in spacy.cli * Fix import * fix obsolete thinc imports * update srsly pin * from thinc to ml_datasets for example data such as imdb * update ml_datasets pin * using STATE.vectors * small fix * fix Sentencizer.pipe * black formatting * rename Affine to Linear as in thinc * set validate explicitely to True * rename with_square_sequences to with_list2padded * rename with_flatten to with_list2array * chaining layernorm * small fixes * revert Optimizer import * build_nel_encoder with new thinc style * fixes using model's get and set methods * Tok2Vec in component models, various fixes * fix up legacy tok2vec code * add model initialize calls * add in build_tagger_model * small fixes * setting model dims * fixes for ParserModel * various small fixes * initialize thinc Models * fixes * consistent naming of window_size * fixes, removing set_dropout * work around Iterable issue * remove legacy tok2vec * util fix * fix forward function of tok2vec listener * more fixes * trying to fix PrecomputableAffine (not succesful yet) * alloc instead of allocate * add morphologizer * rename residual * rename fixes * Fix predict function * Update parser and parser model * fixing few more tests * Fix precomputable affine * Update component model * Update parser model * Move backprop padding to own function, for test * Update test * Fix p. affine * Update NEL * build_bow_text_classifier and extract_ngrams * Fix parser init * Fix test add label * add build_simple_cnn_text_classifier * Fix parser init * Set gpu off by default in example * Fix tok2vec listener * Fix parser model * Small fixes * small fix for PyTorchLSTM parameters * revert my_compounding hack (iterable fixed now) * fix biLSTM * Fix uniqued * PyTorchRNNWrapper fix * small fixes * use helper function to calculate cosine loss * small fixes for build_simple_cnn_text_classifier * putting dropout default at 0.0 to ensure the layer gets built * using thinc util's set_dropout_rate * moving layer normalization inside of maxout definition to optimize dropout * temp debugging in NEL * fixed NEL model by using init defaults ! * fixing after set_dropout_rate refactor * proper fix * fix test_update_doc after refactoring optimizers in thinc * Add CharacterEmbed layer * Construct tagger Model * Add missing import * Remove unused stuff * Work on textcat * fix test (again :)) after optimizer refactor * fixes to allow reading Tagger from_disk without overwriting dimensions * don't build the tok2vec prematuraly * fix CharachterEmbed init * CharacterEmbed fixes * Fix CharacterEmbed architecture * fix imports * renames from latest thinc update * one more rename * add initialize calls where appropriate * fix parser initialization * Update Thinc version * Fix errors, auto-format and tidy up imports * Fix validation * fix if bias is cupy array * revert for now * ensure it's a numpy array before running bp in ParserStepModel * no reason to call require_gpu twice * use CupyOps.to_numpy instead of cupy directly * fix initialize of ParserModel * remove unnecessary import * fixes for CosineDistance * fix device renaming * use refactored loss functions (Thinc PR 251) * overfitting test for tagger * experimental settings for the tagger: avoid zero-init and subword normalization * clean up tagger overfitting test * use previous default value for nP * remove toy config * bringing layernorm back (had a bug - fixed in thinc) * revert setting nP explicitly * remove setting default in constructor * restore values as they used to be * add overfitting test for NER * add overfitting test for dep parser * add overfitting test for textcat * fixing init for linear (previously affine) * larger eps window for textcat * ensure doc is not None * Require newer thinc * Make float check vaguer * Slop the textcat overfit test more * Fix textcat test * Fix exclusive classes for textcat * fix after renaming of alloc methods * fixing renames and mandatory arguments (staticvectors WIP) * upgrade to thinc==8.0.0.dev3 * refer to vocab.vectors directly instead of its name * rename alpha to learn_rate * adding hashembed and staticvectors dropout * upgrade to thinc 8.0.0.dev4 * add name back to avoid warning W020 * thinc dev4 * update srsly * using thinc 8.0.0a0 ! Co-authored-by: Matthew Honnibal <honnibal+gh@gmail.com> Co-authored-by: Ines Montani <ines@ines.io>
2020-01-29 19:06:46 +03:00
from typing import List
import thinc
Default settings to configurations (#4995) * fix grad_clip naming * cleaning up pretrained_vectors out of cfg * further refactoring Model init's * move Model building out of pipes * further refactor to require a model config when creating a pipe * small fixes * making cfg in nn_parser more consistent * fixing nr_class for parser * fixing nn_parser's nO * fix printing of loss * architectures in own file per type, consistent naming * convenience methods default_tagger_config and default_tok2vec_config * let create_pipe access default config if available for that component * default_parser_config * move defaults to separate folder * allow reading nlp from package or dir with argument 'name' * architecture spacy.VocabVectors.v1 to read static vectors from file * cleanup * default configs for nel, textcat, morphologizer, tensorizer * fix imports * fixing unit tests * fixes and clean up * fixing defaults, nO, fix unit tests * restore parser IO * fix IO * 'fix' serialization test * add *.cfg to manifest * fix example configs with additional arguments * replace Morpohologizer with Tagger * add IO bit when testing overfitting of tagger (currently failing) * fix IO - don't initialize when reading from disk * expand overfitting tests to also check IO goes OK * remove dropout from HashEmbed to fix Tagger performance * add defaults for sentrec * update thinc * always pass a Model instance to a Pipe * fix piped_added statement * remove obsolete W029 * remove obsolete errors * restore byte checking tests (work again) * clean up test * further test cleanup * convert from config to Model in create_pipe * bring back error when component is not initialized * cleanup * remove calls for nlp2.begin_training * use thinc.api in imports * allow setting charembed's nM and nC * fix for hardcoded nM/nC + unit test * formatting fixes * trigger build
2020-02-27 20:42:27 +03:00
from thinc.api import NumpyOps, get_current_ops, Adam, require_gpu, Config
import functools
2017-11-10 21:05:18 +03:00
import itertools
2018-02-13 14:52:48 +03:00
import numpy.random
import srsly
Generalize handling of tokenizer special cases (#4259) * Generalize handling of tokenizer special cases Handle tokenizer special cases more generally by using the Matcher internally to match special cases after the affix/token_match tokenization is complete. Instead of only matching special cases while processing balanced or nearly balanced prefixes and suffixes, this recognizes special cases in a wider range of contexts: * Allows arbitrary numbers of prefixes/affixes around special cases * Allows special cases separated by infixes Existing tests/settings that couldn't be preserved as before: * The emoticon '")' is no longer a supported special case * The emoticon ':)' in "example:)" is a false positive again When merged with #4258 (or the relevant cache bugfix), the affix and token_match properties should be modified to flush and reload all special cases to use the updated internal tokenization with the Matcher. * Remove accidentally added test case * Really remove accidentally added test * Reload special cases when necessary Reload special cases when affixes or token_match are modified. Skip reloading during initialization. * Update error code number * Fix offset and whitespace in Matcher special cases * Fix offset bugs when merging and splitting tokens * Set final whitespace on final token in inserted special case * Improve cache flushing in tokenizer * Separate cache and specials memory (temporarily) * Flush cache when adding special cases * Repeated `self._cache = PreshMap()` and `self._specials = PreshMap()` are necessary due to this bug: https://github.com/explosion/preshed/issues/21 * Remove reinitialized PreshMaps on cache flush * Update UD bin scripts * Update imports for `bin/` * Add all currently supported languages * Update subtok merger for new Matcher validation * Modify blinded check to look at tokens instead of lemmas (for corpora with tokens but not lemmas like Telugu) * Use special Matcher only for cases with affixes * Reinsert specials cache checks during normal tokenization for special cases as much as possible * Additionally include specials cache checks while splitting on infixes * Since the special Matcher needs consistent affix-only tokenization for the special cases themselves, introduce the argument `with_special_cases` in order to do tokenization with or without specials cache checks * After normal tokenization, postprocess with special cases Matcher for special cases containing affixes * Replace PhraseMatcher with Aho-Corasick Replace PhraseMatcher with the Aho-Corasick algorithm over numpy arrays of the hash values for the relevant attribute. The implementation is based on FlashText. The speed should be similar to the previous PhraseMatcher. It is now possible to easily remove match IDs and matches don't go missing with large keyword lists / vocabularies. Fixes #4308. * Restore support for pickling * Fix internal keyword add/remove for numpy arrays * Add test for #4248, clean up test * Improve efficiency of special cases handling * Use PhraseMatcher instead of Matcher * Improve efficiency of merging/splitting special cases in document * Process merge/splits in one pass without repeated token shifting * Merge in place if no splits * Update error message number * Remove UD script modifications Only used for timing/testing, should be a separate PR * Remove final traces of UD script modifications * Update UD bin scripts * Update imports for `bin/` * Add all currently supported languages * Update subtok merger for new Matcher validation * Modify blinded check to look at tokens instead of lemmas (for corpora with tokens but not lemmas like Telugu) * Add missing loop for match ID set in search loop * Remove cruft in matching loop for partial matches There was a bit of unnecessary code left over from FlashText in the matching loop to handle partial token matches, which we don't have with PhraseMatcher. * Replace dict trie with MapStruct trie * Fix how match ID hash is stored/added * Update fix for match ID vocab * Switch from map_get_unless_missing to map_get * Switch from numpy array to Token.get_struct_attr Access token attributes directly in Doc instead of making a copy of the relevant values in a numpy array. Add unsatisfactory warning for hash collision with reserved terminal hash key. (Ideally it would change the reserved terminal hash and redo the whole trie, but for now, I'm hoping there won't be collisions.) * Restructure imports to export find_matches * Implement full remove() Remove unnecessary trie paths and free unused maps. Parallel to Matcher, raise KeyError when attempting to remove a match ID that has not been added. * Switch to PhraseMatcher.find_matches * Switch to local cdef functions for span filtering * Switch special case reload threshold to variable Refer to variable instead of hard-coded threshold * Move more of special case retokenize to cdef nogil Move as much of the special case retokenization to nogil as possible. * Rewrap sort as stdsort for OS X * Rewrap stdsort with specific types * Switch to qsort * Fix merge * Improve cmp functions * Fix realloc * Fix realloc again * Initialize span struct while retokenizing * Temporarily skip retokenizing * Revert "Move more of special case retokenize to cdef nogil" This reverts commit 0b7e52c797cd8ff1548f214bd4186ebb3a7ce8b1. * Revert "Switch to qsort" This reverts commit a98d71a942fc9bca531cf5eb05cf89fa88153b60. * Fix specials check while caching * Modify URL test with emoticons The multiple suffix tests result in the emoticon `:>`, which is now retokenized into one token as a special case after the suffixes are split off. * Refactor _apply_special_cases() * Use cdef ints for span info used in multiple spots * Modify _filter_special_spans() to prefer earlier Parallel to #4414, modify _filter_special_spans() so that the earlier span is preferred for overlapping spans of the same length. * Replace MatchStruct with Entity Replace MatchStruct with Entity since the existing Entity struct is nearly identical. * Replace Entity with more general SpanC * Replace MatchStruct with SpanC * Add error in debug-data if no dev docs are available (see #4575) * Update azure-pipelines.yml * Revert "Update azure-pipelines.yml" This reverts commit ed1060cf59e5895b5fe92ad5b894fd1078ec4c49. * Use latest wasabi * Reorganise install_requires * add dframcy to universe.json (#4580) * Update universe.json [ci skip] * Fix multiprocessing for as_tuples=True (#4582) * Fix conllu script (#4579) * force extensions to avoid clash between example scripts * fix arg order and default file encoding * add example config for conllu script * newline * move extension definitions to main function * few more encodings fixes * Add load_from_docbin example [ci skip] TODO: upload the file somewhere * Update README.md * Add warnings about 3.8 (resolves #4593) [ci skip] * Fixed typo: Added space between "recognize" and "various" (#4600) * Fix DocBin.merge() example (#4599) * Replace function registries with catalogue (#4584) * Replace functions registries with catalogue * Update __init__.py * Fix test * Revert unrelated flag [ci skip] * Bugfix/dep matcher issue 4590 (#4601) * add contributor agreement for prilopes * add test for issue #4590 * fix on_match params for DependencyMacther (#4590) * Minor updates to language example sentences (#4608) * Add punctuation to Spanish example sentences * Combine multilanguage examples for lang xx * Add punctuation to nb examples * Always realloc to a larger size Avoid potential (unlikely) edge case and cymem error seen in #4604. * Add error in debug-data if no dev docs are available (see #4575) * Update debug-data for GoldCorpus / Example * Ignore None label in misaligned NER data
2019-11-13 23:24:35 +03:00
import catalogue
import sys
2020-02-28 14:20:23 +03:00
import warnings
Update spaCy for thinc 8.0.0 (#4920) * Add load_from_config function * Add train_from_config script * Merge configs and expose via spacy.config * Fix script * Suggest create_evaluation_callback * Hard-code for NER * Fix errors * Register command * Add TODO * Update train-from-config todos * Fix imports * Allow delayed setting of parser model nr_class * Get train-from-config working * Tidy up and fix scores and printing * Hide traceback if cancelled * Fix weighted score formatting * Fix score formatting * Make output_path optional * Add Tok2Vec component * Tidy up and add tok2vec_tensors * Add option to copy docs in nlp.update * Copy docs in nlp.update * Adjust nlp.update() for set_annotations * Don't shuffle pipes in nlp.update, decruft * Support set_annotations arg in component update * Support set_annotations in parser update * Add get_gradients method * Add get_gradients to parser * Update errors.py * Fix problems caused by merge * Add _link_components method in nlp * Add concept of 'listeners' and ControlledModel * Support optional attributes arg in ControlledModel * Try having tok2vec component in pipeline * Fix tok2vec component * Fix config * Fix tok2vec * Update for Example * Update for Example * Update config * Add eg2doc util * Update and add schemas/types * Update schemas * Fix nlp.update * Fix tagger * Remove hacks from train-from-config * Remove hard-coded config str * Calculate loss in tok2vec component * Tidy up and use function signatures instead of models * Support union types for registry models * Minor cleaning in Language.update * Make ControlledModel specifically Tok2VecListener * Fix train_from_config * Fix tok2vec * Tidy up * Add function for bilstm tok2vec * Fix type * Fix syntax * Fix pytorch optimizer * Add example configs * Update for thinc describe changes * Update for Thinc changes * Update for dropout/sgd changes * Update for dropout/sgd changes * Unhack gradient update * Work on refactoring _ml * Remove _ml.py module * WIP upgrade cli scripts for thinc * Move some _ml stuff to util * Import link_vectors from util * Update train_from_config * Import from util * Import from util * Temporarily add ml.component_models module * Move ml methods * Move typedefs * Update load vectors * Update gitignore * Move imports * Add PrecomputableAffine * Fix imports * Fix imports * Fix imports * Fix missing imports * Update CLI scripts * Update spacy.language * Add stubs for building the models * Update model definition * Update create_default_optimizer * Fix import * Fix comment * Update imports in tests * Update imports in spacy.cli * Fix import * fix obsolete thinc imports * update srsly pin * from thinc to ml_datasets for example data such as imdb * update ml_datasets pin * using STATE.vectors * small fix * fix Sentencizer.pipe * black formatting * rename Affine to Linear as in thinc * set validate explicitely to True * rename with_square_sequences to with_list2padded * rename with_flatten to with_list2array * chaining layernorm * small fixes * revert Optimizer import * build_nel_encoder with new thinc style * fixes using model's get and set methods * Tok2Vec in component models, various fixes * fix up legacy tok2vec code * add model initialize calls * add in build_tagger_model * small fixes * setting model dims * fixes for ParserModel * various small fixes * initialize thinc Models * fixes * consistent naming of window_size * fixes, removing set_dropout * work around Iterable issue * remove legacy tok2vec * util fix * fix forward function of tok2vec listener * more fixes * trying to fix PrecomputableAffine (not succesful yet) * alloc instead of allocate * add morphologizer * rename residual * rename fixes * Fix predict function * Update parser and parser model * fixing few more tests * Fix precomputable affine * Update component model * Update parser model * Move backprop padding to own function, for test * Update test * Fix p. affine * Update NEL * build_bow_text_classifier and extract_ngrams * Fix parser init * Fix test add label * add build_simple_cnn_text_classifier * Fix parser init * Set gpu off by default in example * Fix tok2vec listener * Fix parser model * Small fixes * small fix for PyTorchLSTM parameters * revert my_compounding hack (iterable fixed now) * fix biLSTM * Fix uniqued * PyTorchRNNWrapper fix * small fixes * use helper function to calculate cosine loss * small fixes for build_simple_cnn_text_classifier * putting dropout default at 0.0 to ensure the layer gets built * using thinc util's set_dropout_rate * moving layer normalization inside of maxout definition to optimize dropout * temp debugging in NEL * fixed NEL model by using init defaults ! * fixing after set_dropout_rate refactor * proper fix * fix test_update_doc after refactoring optimizers in thinc * Add CharacterEmbed layer * Construct tagger Model * Add missing import * Remove unused stuff * Work on textcat * fix test (again :)) after optimizer refactor * fixes to allow reading Tagger from_disk without overwriting dimensions * don't build the tok2vec prematuraly * fix CharachterEmbed init * CharacterEmbed fixes * Fix CharacterEmbed architecture * fix imports * renames from latest thinc update * one more rename * add initialize calls where appropriate * fix parser initialization * Update Thinc version * Fix errors, auto-format and tidy up imports * Fix validation * fix if bias is cupy array * revert for now * ensure it's a numpy array before running bp in ParserStepModel * no reason to call require_gpu twice * use CupyOps.to_numpy instead of cupy directly * fix initialize of ParserModel * remove unnecessary import * fixes for CosineDistance * fix device renaming * use refactored loss functions (Thinc PR 251) * overfitting test for tagger * experimental settings for the tagger: avoid zero-init and subword normalization * clean up tagger overfitting test * use previous default value for nP * remove toy config * bringing layernorm back (had a bug - fixed in thinc) * revert setting nP explicitly * remove setting default in constructor * restore values as they used to be * add overfitting test for NER * add overfitting test for dep parser * add overfitting test for textcat * fixing init for linear (previously affine) * larger eps window for textcat * ensure doc is not None * Require newer thinc * Make float check vaguer * Slop the textcat overfit test more * Fix textcat test * Fix exclusive classes for textcat * fix after renaming of alloc methods * fixing renames and mandatory arguments (staticvectors WIP) * upgrade to thinc==8.0.0.dev3 * refer to vocab.vectors directly instead of its name * rename alpha to learn_rate * adding hashembed and staticvectors dropout * upgrade to thinc 8.0.0.dev4 * add name back to avoid warning W020 * thinc dev4 * update srsly * using thinc 8.0.0a0 ! Co-authored-by: Matthew Honnibal <honnibal+gh@gmail.com> Co-authored-by: Ines Montani <ines@ines.io>
2020-01-29 19:06:46 +03:00
try:
import cupy.random
except ImportError:
cupy = None
2017-10-27 15:39:09 +03:00
from .symbols import ORTH
from .compat import cupy, CudaStream
2020-02-28 14:20:23 +03:00
from .errors import Errors, Warnings
2017-10-27 15:39:09 +03:00
_PRINT_ENV = False
Update spaCy for thinc 8.0.0 (#4920) * Add load_from_config function * Add train_from_config script * Merge configs and expose via spacy.config * Fix script * Suggest create_evaluation_callback * Hard-code for NER * Fix errors * Register command * Add TODO * Update train-from-config todos * Fix imports * Allow delayed setting of parser model nr_class * Get train-from-config working * Tidy up and fix scores and printing * Hide traceback if cancelled * Fix weighted score formatting * Fix score formatting * Make output_path optional * Add Tok2Vec component * Tidy up and add tok2vec_tensors * Add option to copy docs in nlp.update * Copy docs in nlp.update * Adjust nlp.update() for set_annotations * Don't shuffle pipes in nlp.update, decruft * Support set_annotations arg in component update * Support set_annotations in parser update * Add get_gradients method * Add get_gradients to parser * Update errors.py * Fix problems caused by merge * Add _link_components method in nlp * Add concept of 'listeners' and ControlledModel * Support optional attributes arg in ControlledModel * Try having tok2vec component in pipeline * Fix tok2vec component * Fix config * Fix tok2vec * Update for Example * Update for Example * Update config * Add eg2doc util * Update and add schemas/types * Update schemas * Fix nlp.update * Fix tagger * Remove hacks from train-from-config * Remove hard-coded config str * Calculate loss in tok2vec component * Tidy up and use function signatures instead of models * Support union types for registry models * Minor cleaning in Language.update * Make ControlledModel specifically Tok2VecListener * Fix train_from_config * Fix tok2vec * Tidy up * Add function for bilstm tok2vec * Fix type * Fix syntax * Fix pytorch optimizer * Add example configs * Update for thinc describe changes * Update for Thinc changes * Update for dropout/sgd changes * Update for dropout/sgd changes * Unhack gradient update * Work on refactoring _ml * Remove _ml.py module * WIP upgrade cli scripts for thinc * Move some _ml stuff to util * Import link_vectors from util * Update train_from_config * Import from util * Import from util * Temporarily add ml.component_models module * Move ml methods * Move typedefs * Update load vectors * Update gitignore * Move imports * Add PrecomputableAffine * Fix imports * Fix imports * Fix imports * Fix missing imports * Update CLI scripts * Update spacy.language * Add stubs for building the models * Update model definition * Update create_default_optimizer * Fix import * Fix comment * Update imports in tests * Update imports in spacy.cli * Fix import * fix obsolete thinc imports * update srsly pin * from thinc to ml_datasets for example data such as imdb * update ml_datasets pin * using STATE.vectors * small fix * fix Sentencizer.pipe * black formatting * rename Affine to Linear as in thinc * set validate explicitely to True * rename with_square_sequences to with_list2padded * rename with_flatten to with_list2array * chaining layernorm * small fixes * revert Optimizer import * build_nel_encoder with new thinc style * fixes using model's get and set methods * Tok2Vec in component models, various fixes * fix up legacy tok2vec code * add model initialize calls * add in build_tagger_model * small fixes * setting model dims * fixes for ParserModel * various small fixes * initialize thinc Models * fixes * consistent naming of window_size * fixes, removing set_dropout * work around Iterable issue * remove legacy tok2vec * util fix * fix forward function of tok2vec listener * more fixes * trying to fix PrecomputableAffine (not succesful yet) * alloc instead of allocate * add morphologizer * rename residual * rename fixes * Fix predict function * Update parser and parser model * fixing few more tests * Fix precomputable affine * Update component model * Update parser model * Move backprop padding to own function, for test * Update test * Fix p. affine * Update NEL * build_bow_text_classifier and extract_ngrams * Fix parser init * Fix test add label * add build_simple_cnn_text_classifier * Fix parser init * Set gpu off by default in example * Fix tok2vec listener * Fix parser model * Small fixes * small fix for PyTorchLSTM parameters * revert my_compounding hack (iterable fixed now) * fix biLSTM * Fix uniqued * PyTorchRNNWrapper fix * small fixes * use helper function to calculate cosine loss * small fixes for build_simple_cnn_text_classifier * putting dropout default at 0.0 to ensure the layer gets built * using thinc util's set_dropout_rate * moving layer normalization inside of maxout definition to optimize dropout * temp debugging in NEL * fixed NEL model by using init defaults ! * fixing after set_dropout_rate refactor * proper fix * fix test_update_doc after refactoring optimizers in thinc * Add CharacterEmbed layer * Construct tagger Model * Add missing import * Remove unused stuff * Work on textcat * fix test (again :)) after optimizer refactor * fixes to allow reading Tagger from_disk without overwriting dimensions * don't build the tok2vec prematuraly * fix CharachterEmbed init * CharacterEmbed fixes * Fix CharacterEmbed architecture * fix imports * renames from latest thinc update * one more rename * add initialize calls where appropriate * fix parser initialization * Update Thinc version * Fix errors, auto-format and tidy up imports * Fix validation * fix if bias is cupy array * revert for now * ensure it's a numpy array before running bp in ParserStepModel * no reason to call require_gpu twice * use CupyOps.to_numpy instead of cupy directly * fix initialize of ParserModel * remove unnecessary import * fixes for CosineDistance * fix device renaming * use refactored loss functions (Thinc PR 251) * overfitting test for tagger * experimental settings for the tagger: avoid zero-init and subword normalization * clean up tagger overfitting test * use previous default value for nP * remove toy config * bringing layernorm back (had a bug - fixed in thinc) * revert setting nP explicitly * remove setting default in constructor * restore values as they used to be * add overfitting test for NER * add overfitting test for dep parser * add overfitting test for textcat * fixing init for linear (previously affine) * larger eps window for textcat * ensure doc is not None * Require newer thinc * Make float check vaguer * Slop the textcat overfit test more * Fix textcat test * Fix exclusive classes for textcat * fix after renaming of alloc methods * fixing renames and mandatory arguments (staticvectors WIP) * upgrade to thinc==8.0.0.dev3 * refer to vocab.vectors directly instead of its name * rename alpha to learn_rate * adding hashembed and staticvectors dropout * upgrade to thinc 8.0.0.dev4 * add name back to avoid warning W020 * thinc dev4 * update srsly * using thinc 8.0.0a0 ! Co-authored-by: Matthew Honnibal <honnibal+gh@gmail.com> Co-authored-by: Ines Montani <ines@ines.io>
2020-01-29 19:06:46 +03:00
class registry(thinc.registry):
Generalize handling of tokenizer special cases (#4259) * Generalize handling of tokenizer special cases Handle tokenizer special cases more generally by using the Matcher internally to match special cases after the affix/token_match tokenization is complete. Instead of only matching special cases while processing balanced or nearly balanced prefixes and suffixes, this recognizes special cases in a wider range of contexts: * Allows arbitrary numbers of prefixes/affixes around special cases * Allows special cases separated by infixes Existing tests/settings that couldn't be preserved as before: * The emoticon '")' is no longer a supported special case * The emoticon ':)' in "example:)" is a false positive again When merged with #4258 (or the relevant cache bugfix), the affix and token_match properties should be modified to flush and reload all special cases to use the updated internal tokenization with the Matcher. * Remove accidentally added test case * Really remove accidentally added test * Reload special cases when necessary Reload special cases when affixes or token_match are modified. Skip reloading during initialization. * Update error code number * Fix offset and whitespace in Matcher special cases * Fix offset bugs when merging and splitting tokens * Set final whitespace on final token in inserted special case * Improve cache flushing in tokenizer * Separate cache and specials memory (temporarily) * Flush cache when adding special cases * Repeated `self._cache = PreshMap()` and `self._specials = PreshMap()` are necessary due to this bug: https://github.com/explosion/preshed/issues/21 * Remove reinitialized PreshMaps on cache flush * Update UD bin scripts * Update imports for `bin/` * Add all currently supported languages * Update subtok merger for new Matcher validation * Modify blinded check to look at tokens instead of lemmas (for corpora with tokens but not lemmas like Telugu) * Use special Matcher only for cases with affixes * Reinsert specials cache checks during normal tokenization for special cases as much as possible * Additionally include specials cache checks while splitting on infixes * Since the special Matcher needs consistent affix-only tokenization for the special cases themselves, introduce the argument `with_special_cases` in order to do tokenization with or without specials cache checks * After normal tokenization, postprocess with special cases Matcher for special cases containing affixes * Replace PhraseMatcher with Aho-Corasick Replace PhraseMatcher with the Aho-Corasick algorithm over numpy arrays of the hash values for the relevant attribute. The implementation is based on FlashText. The speed should be similar to the previous PhraseMatcher. It is now possible to easily remove match IDs and matches don't go missing with large keyword lists / vocabularies. Fixes #4308. * Restore support for pickling * Fix internal keyword add/remove for numpy arrays * Add test for #4248, clean up test * Improve efficiency of special cases handling * Use PhraseMatcher instead of Matcher * Improve efficiency of merging/splitting special cases in document * Process merge/splits in one pass without repeated token shifting * Merge in place if no splits * Update error message number * Remove UD script modifications Only used for timing/testing, should be a separate PR * Remove final traces of UD script modifications * Update UD bin scripts * Update imports for `bin/` * Add all currently supported languages * Update subtok merger for new Matcher validation * Modify blinded check to look at tokens instead of lemmas (for corpora with tokens but not lemmas like Telugu) * Add missing loop for match ID set in search loop * Remove cruft in matching loop for partial matches There was a bit of unnecessary code left over from FlashText in the matching loop to handle partial token matches, which we don't have with PhraseMatcher. * Replace dict trie with MapStruct trie * Fix how match ID hash is stored/added * Update fix for match ID vocab * Switch from map_get_unless_missing to map_get * Switch from numpy array to Token.get_struct_attr Access token attributes directly in Doc instead of making a copy of the relevant values in a numpy array. Add unsatisfactory warning for hash collision with reserved terminal hash key. (Ideally it would change the reserved terminal hash and redo the whole trie, but for now, I'm hoping there won't be collisions.) * Restructure imports to export find_matches * Implement full remove() Remove unnecessary trie paths and free unused maps. Parallel to Matcher, raise KeyError when attempting to remove a match ID that has not been added. * Switch to PhraseMatcher.find_matches * Switch to local cdef functions for span filtering * Switch special case reload threshold to variable Refer to variable instead of hard-coded threshold * Move more of special case retokenize to cdef nogil Move as much of the special case retokenization to nogil as possible. * Rewrap sort as stdsort for OS X * Rewrap stdsort with specific types * Switch to qsort * Fix merge * Improve cmp functions * Fix realloc * Fix realloc again * Initialize span struct while retokenizing * Temporarily skip retokenizing * Revert "Move more of special case retokenize to cdef nogil" This reverts commit 0b7e52c797cd8ff1548f214bd4186ebb3a7ce8b1. * Revert "Switch to qsort" This reverts commit a98d71a942fc9bca531cf5eb05cf89fa88153b60. * Fix specials check while caching * Modify URL test with emoticons The multiple suffix tests result in the emoticon `:>`, which is now retokenized into one token as a special case after the suffixes are split off. * Refactor _apply_special_cases() * Use cdef ints for span info used in multiple spots * Modify _filter_special_spans() to prefer earlier Parallel to #4414, modify _filter_special_spans() so that the earlier span is preferred for overlapping spans of the same length. * Replace MatchStruct with Entity Replace MatchStruct with Entity since the existing Entity struct is nearly identical. * Replace Entity with more general SpanC * Replace MatchStruct with SpanC * Add error in debug-data if no dev docs are available (see #4575) * Update azure-pipelines.yml * Revert "Update azure-pipelines.yml" This reverts commit ed1060cf59e5895b5fe92ad5b894fd1078ec4c49. * Use latest wasabi * Reorganise install_requires * add dframcy to universe.json (#4580) * Update universe.json [ci skip] * Fix multiprocessing for as_tuples=True (#4582) * Fix conllu script (#4579) * force extensions to avoid clash between example scripts * fix arg order and default file encoding * add example config for conllu script * newline * move extension definitions to main function * few more encodings fixes * Add load_from_docbin example [ci skip] TODO: upload the file somewhere * Update README.md * Add warnings about 3.8 (resolves #4593) [ci skip] * Fixed typo: Added space between "recognize" and "various" (#4600) * Fix DocBin.merge() example (#4599) * Replace function registries with catalogue (#4584) * Replace functions registries with catalogue * Update __init__.py * Fix test * Revert unrelated flag [ci skip] * Bugfix/dep matcher issue 4590 (#4601) * add contributor agreement for prilopes * add test for issue #4590 * fix on_match params for DependencyMacther (#4590) * Minor updates to language example sentences (#4608) * Add punctuation to Spanish example sentences * Combine multilanguage examples for lang xx * Add punctuation to nb examples * Always realloc to a larger size Avoid potential (unlikely) edge case and cymem error seen in #4604. * Add error in debug-data if no dev docs are available (see #4575) * Update debug-data for GoldCorpus / Example * Ignore None label in misaligned NER data
2019-11-13 23:24:35 +03:00
languages = catalogue.create("spacy", "languages", entry_points=True)
architectures = catalogue.create("spacy", "architectures", entry_points=True)
lookups = catalogue.create("spacy", "lookups", entry_points=True)
factories = catalogue.create("spacy", "factories", entry_points=True)
displacy_colors = catalogue.create("spacy", "displacy_colors", entry_points=True)
2017-10-27 15:39:09 +03:00
def set_env_log(value):
global _PRINT_ENV
_PRINT_ENV = value
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def lang_class_is_loaded(lang):
"""Check whether a Language class is already loaded. Language classes are
loaded lazily, to avoid expensive setup code associated with the language
data.
lang (unicode): Two-letter language code, e.g. 'en'.
RETURNS (bool): Whether a Language class has been loaded.
"""
Generalize handling of tokenizer special cases (#4259) * Generalize handling of tokenizer special cases Handle tokenizer special cases more generally by using the Matcher internally to match special cases after the affix/token_match tokenization is complete. Instead of only matching special cases while processing balanced or nearly balanced prefixes and suffixes, this recognizes special cases in a wider range of contexts: * Allows arbitrary numbers of prefixes/affixes around special cases * Allows special cases separated by infixes Existing tests/settings that couldn't be preserved as before: * The emoticon '")' is no longer a supported special case * The emoticon ':)' in "example:)" is a false positive again When merged with #4258 (or the relevant cache bugfix), the affix and token_match properties should be modified to flush and reload all special cases to use the updated internal tokenization with the Matcher. * Remove accidentally added test case * Really remove accidentally added test * Reload special cases when necessary Reload special cases when affixes or token_match are modified. Skip reloading during initialization. * Update error code number * Fix offset and whitespace in Matcher special cases * Fix offset bugs when merging and splitting tokens * Set final whitespace on final token in inserted special case * Improve cache flushing in tokenizer * Separate cache and specials memory (temporarily) * Flush cache when adding special cases * Repeated `self._cache = PreshMap()` and `self._specials = PreshMap()` are necessary due to this bug: https://github.com/explosion/preshed/issues/21 * Remove reinitialized PreshMaps on cache flush * Update UD bin scripts * Update imports for `bin/` * Add all currently supported languages * Update subtok merger for new Matcher validation * Modify blinded check to look at tokens instead of lemmas (for corpora with tokens but not lemmas like Telugu) * Use special Matcher only for cases with affixes * Reinsert specials cache checks during normal tokenization for special cases as much as possible * Additionally include specials cache checks while splitting on infixes * Since the special Matcher needs consistent affix-only tokenization for the special cases themselves, introduce the argument `with_special_cases` in order to do tokenization with or without specials cache checks * After normal tokenization, postprocess with special cases Matcher for special cases containing affixes * Replace PhraseMatcher with Aho-Corasick Replace PhraseMatcher with the Aho-Corasick algorithm over numpy arrays of the hash values for the relevant attribute. The implementation is based on FlashText. The speed should be similar to the previous PhraseMatcher. It is now possible to easily remove match IDs and matches don't go missing with large keyword lists / vocabularies. Fixes #4308. * Restore support for pickling * Fix internal keyword add/remove for numpy arrays * Add test for #4248, clean up test * Improve efficiency of special cases handling * Use PhraseMatcher instead of Matcher * Improve efficiency of merging/splitting special cases in document * Process merge/splits in one pass without repeated token shifting * Merge in place if no splits * Update error message number * Remove UD script modifications Only used for timing/testing, should be a separate PR * Remove final traces of UD script modifications * Update UD bin scripts * Update imports for `bin/` * Add all currently supported languages * Update subtok merger for new Matcher validation * Modify blinded check to look at tokens instead of lemmas (for corpora with tokens but not lemmas like Telugu) * Add missing loop for match ID set in search loop * Remove cruft in matching loop for partial matches There was a bit of unnecessary code left over from FlashText in the matching loop to handle partial token matches, which we don't have with PhraseMatcher. * Replace dict trie with MapStruct trie * Fix how match ID hash is stored/added * Update fix for match ID vocab * Switch from map_get_unless_missing to map_get * Switch from numpy array to Token.get_struct_attr Access token attributes directly in Doc instead of making a copy of the relevant values in a numpy array. Add unsatisfactory warning for hash collision with reserved terminal hash key. (Ideally it would change the reserved terminal hash and redo the whole trie, but for now, I'm hoping there won't be collisions.) * Restructure imports to export find_matches * Implement full remove() Remove unnecessary trie paths and free unused maps. Parallel to Matcher, raise KeyError when attempting to remove a match ID that has not been added. * Switch to PhraseMatcher.find_matches * Switch to local cdef functions for span filtering * Switch special case reload threshold to variable Refer to variable instead of hard-coded threshold * Move more of special case retokenize to cdef nogil Move as much of the special case retokenization to nogil as possible. * Rewrap sort as stdsort for OS X * Rewrap stdsort with specific types * Switch to qsort * Fix merge * Improve cmp functions * Fix realloc * Fix realloc again * Initialize span struct while retokenizing * Temporarily skip retokenizing * Revert "Move more of special case retokenize to cdef nogil" This reverts commit 0b7e52c797cd8ff1548f214bd4186ebb3a7ce8b1. * Revert "Switch to qsort" This reverts commit a98d71a942fc9bca531cf5eb05cf89fa88153b60. * Fix specials check while caching * Modify URL test with emoticons The multiple suffix tests result in the emoticon `:>`, which is now retokenized into one token as a special case after the suffixes are split off. * Refactor _apply_special_cases() * Use cdef ints for span info used in multiple spots * Modify _filter_special_spans() to prefer earlier Parallel to #4414, modify _filter_special_spans() so that the earlier span is preferred for overlapping spans of the same length. * Replace MatchStruct with Entity Replace MatchStruct with Entity since the existing Entity struct is nearly identical. * Replace Entity with more general SpanC * Replace MatchStruct with SpanC * Add error in debug-data if no dev docs are available (see #4575) * Update azure-pipelines.yml * Revert "Update azure-pipelines.yml" This reverts commit ed1060cf59e5895b5fe92ad5b894fd1078ec4c49. * Use latest wasabi * Reorganise install_requires * add dframcy to universe.json (#4580) * Update universe.json [ci skip] * Fix multiprocessing for as_tuples=True (#4582) * Fix conllu script (#4579) * force extensions to avoid clash between example scripts * fix arg order and default file encoding * add example config for conllu script * newline * move extension definitions to main function * few more encodings fixes * Add load_from_docbin example [ci skip] TODO: upload the file somewhere * Update README.md * Add warnings about 3.8 (resolves #4593) [ci skip] * Fixed typo: Added space between "recognize" and "various" (#4600) * Fix DocBin.merge() example (#4599) * Replace function registries with catalogue (#4584) * Replace functions registries with catalogue * Update __init__.py * Fix test * Revert unrelated flag [ci skip] * Bugfix/dep matcher issue 4590 (#4601) * add contributor agreement for prilopes * add test for issue #4590 * fix on_match params for DependencyMacther (#4590) * Minor updates to language example sentences (#4608) * Add punctuation to Spanish example sentences * Combine multilanguage examples for lang xx * Add punctuation to nb examples * Always realloc to a larger size Avoid potential (unlikely) edge case and cymem error seen in #4604. * Add error in debug-data if no dev docs are available (see #4575) * Update debug-data for GoldCorpus / Example * Ignore None label in misaligned NER data
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return lang in registry.languages
2019-03-11 19:10:50 +03:00
def get_lang_class(lang):
"""Import and load a Language class.
2016-03-25 20:54:45 +03:00
lang (unicode): Two-letter language code, e.g. 'en'.
RETURNS (Language): Language class.
"""
Generalize handling of tokenizer special cases (#4259) * Generalize handling of tokenizer special cases Handle tokenizer special cases more generally by using the Matcher internally to match special cases after the affix/token_match tokenization is complete. Instead of only matching special cases while processing balanced or nearly balanced prefixes and suffixes, this recognizes special cases in a wider range of contexts: * Allows arbitrary numbers of prefixes/affixes around special cases * Allows special cases separated by infixes Existing tests/settings that couldn't be preserved as before: * The emoticon '")' is no longer a supported special case * The emoticon ':)' in "example:)" is a false positive again When merged with #4258 (or the relevant cache bugfix), the affix and token_match properties should be modified to flush and reload all special cases to use the updated internal tokenization with the Matcher. * Remove accidentally added test case * Really remove accidentally added test * Reload special cases when necessary Reload special cases when affixes or token_match are modified. Skip reloading during initialization. * Update error code number * Fix offset and whitespace in Matcher special cases * Fix offset bugs when merging and splitting tokens * Set final whitespace on final token in inserted special case * Improve cache flushing in tokenizer * Separate cache and specials memory (temporarily) * Flush cache when adding special cases * Repeated `self._cache = PreshMap()` and `self._specials = PreshMap()` are necessary due to this bug: https://github.com/explosion/preshed/issues/21 * Remove reinitialized PreshMaps on cache flush * Update UD bin scripts * Update imports for `bin/` * Add all currently supported languages * Update subtok merger for new Matcher validation * Modify blinded check to look at tokens instead of lemmas (for corpora with tokens but not lemmas like Telugu) * Use special Matcher only for cases with affixes * Reinsert specials cache checks during normal tokenization for special cases as much as possible * Additionally include specials cache checks while splitting on infixes * Since the special Matcher needs consistent affix-only tokenization for the special cases themselves, introduce the argument `with_special_cases` in order to do tokenization with or without specials cache checks * After normal tokenization, postprocess with special cases Matcher for special cases containing affixes * Replace PhraseMatcher with Aho-Corasick Replace PhraseMatcher with the Aho-Corasick algorithm over numpy arrays of the hash values for the relevant attribute. The implementation is based on FlashText. The speed should be similar to the previous PhraseMatcher. It is now possible to easily remove match IDs and matches don't go missing with large keyword lists / vocabularies. Fixes #4308. * Restore support for pickling * Fix internal keyword add/remove for numpy arrays * Add test for #4248, clean up test * Improve efficiency of special cases handling * Use PhraseMatcher instead of Matcher * Improve efficiency of merging/splitting special cases in document * Process merge/splits in one pass without repeated token shifting * Merge in place if no splits * Update error message number * Remove UD script modifications Only used for timing/testing, should be a separate PR * Remove final traces of UD script modifications * Update UD bin scripts * Update imports for `bin/` * Add all currently supported languages * Update subtok merger for new Matcher validation * Modify blinded check to look at tokens instead of lemmas (for corpora with tokens but not lemmas like Telugu) * Add missing loop for match ID set in search loop * Remove cruft in matching loop for partial matches There was a bit of unnecessary code left over from FlashText in the matching loop to handle partial token matches, which we don't have with PhraseMatcher. * Replace dict trie with MapStruct trie * Fix how match ID hash is stored/added * Update fix for match ID vocab * Switch from map_get_unless_missing to map_get * Switch from numpy array to Token.get_struct_attr Access token attributes directly in Doc instead of making a copy of the relevant values in a numpy array. Add unsatisfactory warning for hash collision with reserved terminal hash key. (Ideally it would change the reserved terminal hash and redo the whole trie, but for now, I'm hoping there won't be collisions.) * Restructure imports to export find_matches * Implement full remove() Remove unnecessary trie paths and free unused maps. Parallel to Matcher, raise KeyError when attempting to remove a match ID that has not been added. * Switch to PhraseMatcher.find_matches * Switch to local cdef functions for span filtering * Switch special case reload threshold to variable Refer to variable instead of hard-coded threshold * Move more of special case retokenize to cdef nogil Move as much of the special case retokenization to nogil as possible. * Rewrap sort as stdsort for OS X * Rewrap stdsort with specific types * Switch to qsort * Fix merge * Improve cmp functions * Fix realloc * Fix realloc again * Initialize span struct while retokenizing * Temporarily skip retokenizing * Revert "Move more of special case retokenize to cdef nogil" This reverts commit 0b7e52c797cd8ff1548f214bd4186ebb3a7ce8b1. * Revert "Switch to qsort" This reverts commit a98d71a942fc9bca531cf5eb05cf89fa88153b60. * Fix specials check while caching * Modify URL test with emoticons The multiple suffix tests result in the emoticon `:>`, which is now retokenized into one token as a special case after the suffixes are split off. * Refactor _apply_special_cases() * Use cdef ints for span info used in multiple spots * Modify _filter_special_spans() to prefer earlier Parallel to #4414, modify _filter_special_spans() so that the earlier span is preferred for overlapping spans of the same length. * Replace MatchStruct with Entity Replace MatchStruct with Entity since the existing Entity struct is nearly identical. * Replace Entity with more general SpanC * Replace MatchStruct with SpanC * Add error in debug-data if no dev docs are available (see #4575) * Update azure-pipelines.yml * Revert "Update azure-pipelines.yml" This reverts commit ed1060cf59e5895b5fe92ad5b894fd1078ec4c49. * Use latest wasabi * Reorganise install_requires * add dframcy to universe.json (#4580) * Update universe.json [ci skip] * Fix multiprocessing for as_tuples=True (#4582) * Fix conllu script (#4579) * force extensions to avoid clash between example scripts * fix arg order and default file encoding * add example config for conllu script * newline * move extension definitions to main function * few more encodings fixes * Add load_from_docbin example [ci skip] TODO: upload the file somewhere * Update README.md * Add warnings about 3.8 (resolves #4593) [ci skip] * Fixed typo: Added space between "recognize" and "various" (#4600) * Fix DocBin.merge() example (#4599) * Replace function registries with catalogue (#4584) * Replace functions registries with catalogue * Update __init__.py * Fix test * Revert unrelated flag [ci skip] * Bugfix/dep matcher issue 4590 (#4601) * add contributor agreement for prilopes * add test for issue #4590 * fix on_match params for DependencyMacther (#4590) * Minor updates to language example sentences (#4608) * Add punctuation to Spanish example sentences * Combine multilanguage examples for lang xx * Add punctuation to nb examples * Always realloc to a larger size Avoid potential (unlikely) edge case and cymem error seen in #4604. * Add error in debug-data if no dev docs are available (see #4575) * Update debug-data for GoldCorpus / Example * Ignore None label in misaligned NER data
2019-11-13 23:24:35 +03:00
# Check if language is registered / entry point is available
if lang in registry.languages:
return registry.languages.get(lang)
else:
try:
2019-12-25 19:59:52 +03:00
module = importlib.import_module(f".lang.{lang}", "spacy")
except ImportError as err:
raise ImportError(Errors.E048.format(lang=lang, err=err))
Generalize handling of tokenizer special cases (#4259) * Generalize handling of tokenizer special cases Handle tokenizer special cases more generally by using the Matcher internally to match special cases after the affix/token_match tokenization is complete. Instead of only matching special cases while processing balanced or nearly balanced prefixes and suffixes, this recognizes special cases in a wider range of contexts: * Allows arbitrary numbers of prefixes/affixes around special cases * Allows special cases separated by infixes Existing tests/settings that couldn't be preserved as before: * The emoticon '")' is no longer a supported special case * The emoticon ':)' in "example:)" is a false positive again When merged with #4258 (or the relevant cache bugfix), the affix and token_match properties should be modified to flush and reload all special cases to use the updated internal tokenization with the Matcher. * Remove accidentally added test case * Really remove accidentally added test * Reload special cases when necessary Reload special cases when affixes or token_match are modified. Skip reloading during initialization. * Update error code number * Fix offset and whitespace in Matcher special cases * Fix offset bugs when merging and splitting tokens * Set final whitespace on final token in inserted special case * Improve cache flushing in tokenizer * Separate cache and specials memory (temporarily) * Flush cache when adding special cases * Repeated `self._cache = PreshMap()` and `self._specials = PreshMap()` are necessary due to this bug: https://github.com/explosion/preshed/issues/21 * Remove reinitialized PreshMaps on cache flush * Update UD bin scripts * Update imports for `bin/` * Add all currently supported languages * Update subtok merger for new Matcher validation * Modify blinded check to look at tokens instead of lemmas (for corpora with tokens but not lemmas like Telugu) * Use special Matcher only for cases with affixes * Reinsert specials cache checks during normal tokenization for special cases as much as possible * Additionally include specials cache checks while splitting on infixes * Since the special Matcher needs consistent affix-only tokenization for the special cases themselves, introduce the argument `with_special_cases` in order to do tokenization with or without specials cache checks * After normal tokenization, postprocess with special cases Matcher for special cases containing affixes * Replace PhraseMatcher with Aho-Corasick Replace PhraseMatcher with the Aho-Corasick algorithm over numpy arrays of the hash values for the relevant attribute. The implementation is based on FlashText. The speed should be similar to the previous PhraseMatcher. It is now possible to easily remove match IDs and matches don't go missing with large keyword lists / vocabularies. Fixes #4308. * Restore support for pickling * Fix internal keyword add/remove for numpy arrays * Add test for #4248, clean up test * Improve efficiency of special cases handling * Use PhraseMatcher instead of Matcher * Improve efficiency of merging/splitting special cases in document * Process merge/splits in one pass without repeated token shifting * Merge in place if no splits * Update error message number * Remove UD script modifications Only used for timing/testing, should be a separate PR * Remove final traces of UD script modifications * Update UD bin scripts * Update imports for `bin/` * Add all currently supported languages * Update subtok merger for new Matcher validation * Modify blinded check to look at tokens instead of lemmas (for corpora with tokens but not lemmas like Telugu) * Add missing loop for match ID set in search loop * Remove cruft in matching loop for partial matches There was a bit of unnecessary code left over from FlashText in the matching loop to handle partial token matches, which we don't have with PhraseMatcher. * Replace dict trie with MapStruct trie * Fix how match ID hash is stored/added * Update fix for match ID vocab * Switch from map_get_unless_missing to map_get * Switch from numpy array to Token.get_struct_attr Access token attributes directly in Doc instead of making a copy of the relevant values in a numpy array. Add unsatisfactory warning for hash collision with reserved terminal hash key. (Ideally it would change the reserved terminal hash and redo the whole trie, but for now, I'm hoping there won't be collisions.) * Restructure imports to export find_matches * Implement full remove() Remove unnecessary trie paths and free unused maps. Parallel to Matcher, raise KeyError when attempting to remove a match ID that has not been added. * Switch to PhraseMatcher.find_matches * Switch to local cdef functions for span filtering * Switch special case reload threshold to variable Refer to variable instead of hard-coded threshold * Move more of special case retokenize to cdef nogil Move as much of the special case retokenization to nogil as possible. * Rewrap sort as stdsort for OS X * Rewrap stdsort with specific types * Switch to qsort * Fix merge * Improve cmp functions * Fix realloc * Fix realloc again * Initialize span struct while retokenizing * Temporarily skip retokenizing * Revert "Move more of special case retokenize to cdef nogil" This reverts commit 0b7e52c797cd8ff1548f214bd4186ebb3a7ce8b1. * Revert "Switch to qsort" This reverts commit a98d71a942fc9bca531cf5eb05cf89fa88153b60. * Fix specials check while caching * Modify URL test with emoticons The multiple suffix tests result in the emoticon `:>`, which is now retokenized into one token as a special case after the suffixes are split off. * Refactor _apply_special_cases() * Use cdef ints for span info used in multiple spots * Modify _filter_special_spans() to prefer earlier Parallel to #4414, modify _filter_special_spans() so that the earlier span is preferred for overlapping spans of the same length. * Replace MatchStruct with Entity Replace MatchStruct with Entity since the existing Entity struct is nearly identical. * Replace Entity with more general SpanC * Replace MatchStruct with SpanC * Add error in debug-data if no dev docs are available (see #4575) * Update azure-pipelines.yml * Revert "Update azure-pipelines.yml" This reverts commit ed1060cf59e5895b5fe92ad5b894fd1078ec4c49. * Use latest wasabi * Reorganise install_requires * add dframcy to universe.json (#4580) * Update universe.json [ci skip] * Fix multiprocessing for as_tuples=True (#4582) * Fix conllu script (#4579) * force extensions to avoid clash between example scripts * fix arg order and default file encoding * add example config for conllu script * newline * move extension definitions to main function * few more encodings fixes * Add load_from_docbin example [ci skip] TODO: upload the file somewhere * Update README.md * Add warnings about 3.8 (resolves #4593) [ci skip] * Fixed typo: Added space between "recognize" and "various" (#4600) * Fix DocBin.merge() example (#4599) * Replace function registries with catalogue (#4584) * Replace functions registries with catalogue * Update __init__.py * Fix test * Revert unrelated flag [ci skip] * Bugfix/dep matcher issue 4590 (#4601) * add contributor agreement for prilopes * add test for issue #4590 * fix on_match params for DependencyMacther (#4590) * Minor updates to language example sentences (#4608) * Add punctuation to Spanish example sentences * Combine multilanguage examples for lang xx * Add punctuation to nb examples * Always realloc to a larger size Avoid potential (unlikely) edge case and cymem error seen in #4604. * Add error in debug-data if no dev docs are available (see #4575) * Update debug-data for GoldCorpus / Example * Ignore None label in misaligned NER data
2019-11-13 23:24:35 +03:00
set_lang_class(lang, getattr(module, module.__all__[0]))
return registry.languages.get(lang)
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def set_lang_class(name, cls):
"""Set a custom Language class name that can be loaded via get_lang_class.
name (unicode): Name of Language class.
cls (Language): Language class.
"""
Generalize handling of tokenizer special cases (#4259) * Generalize handling of tokenizer special cases Handle tokenizer special cases more generally by using the Matcher internally to match special cases after the affix/token_match tokenization is complete. Instead of only matching special cases while processing balanced or nearly balanced prefixes and suffixes, this recognizes special cases in a wider range of contexts: * Allows arbitrary numbers of prefixes/affixes around special cases * Allows special cases separated by infixes Existing tests/settings that couldn't be preserved as before: * The emoticon '")' is no longer a supported special case * The emoticon ':)' in "example:)" is a false positive again When merged with #4258 (or the relevant cache bugfix), the affix and token_match properties should be modified to flush and reload all special cases to use the updated internal tokenization with the Matcher. * Remove accidentally added test case * Really remove accidentally added test * Reload special cases when necessary Reload special cases when affixes or token_match are modified. Skip reloading during initialization. * Update error code number * Fix offset and whitespace in Matcher special cases * Fix offset bugs when merging and splitting tokens * Set final whitespace on final token in inserted special case * Improve cache flushing in tokenizer * Separate cache and specials memory (temporarily) * Flush cache when adding special cases * Repeated `self._cache = PreshMap()` and `self._specials = PreshMap()` are necessary due to this bug: https://github.com/explosion/preshed/issues/21 * Remove reinitialized PreshMaps on cache flush * Update UD bin scripts * Update imports for `bin/` * Add all currently supported languages * Update subtok merger for new Matcher validation * Modify blinded check to look at tokens instead of lemmas (for corpora with tokens but not lemmas like Telugu) * Use special Matcher only for cases with affixes * Reinsert specials cache checks during normal tokenization for special cases as much as possible * Additionally include specials cache checks while splitting on infixes * Since the special Matcher needs consistent affix-only tokenization for the special cases themselves, introduce the argument `with_special_cases` in order to do tokenization with or without specials cache checks * After normal tokenization, postprocess with special cases Matcher for special cases containing affixes * Replace PhraseMatcher with Aho-Corasick Replace PhraseMatcher with the Aho-Corasick algorithm over numpy arrays of the hash values for the relevant attribute. The implementation is based on FlashText. The speed should be similar to the previous PhraseMatcher. It is now possible to easily remove match IDs and matches don't go missing with large keyword lists / vocabularies. Fixes #4308. * Restore support for pickling * Fix internal keyword add/remove for numpy arrays * Add test for #4248, clean up test * Improve efficiency of special cases handling * Use PhraseMatcher instead of Matcher * Improve efficiency of merging/splitting special cases in document * Process merge/splits in one pass without repeated token shifting * Merge in place if no splits * Update error message number * Remove UD script modifications Only used for timing/testing, should be a separate PR * Remove final traces of UD script modifications * Update UD bin scripts * Update imports for `bin/` * Add all currently supported languages * Update subtok merger for new Matcher validation * Modify blinded check to look at tokens instead of lemmas (for corpora with tokens but not lemmas like Telugu) * Add missing loop for match ID set in search loop * Remove cruft in matching loop for partial matches There was a bit of unnecessary code left over from FlashText in the matching loop to handle partial token matches, which we don't have with PhraseMatcher. * Replace dict trie with MapStruct trie * Fix how match ID hash is stored/added * Update fix for match ID vocab * Switch from map_get_unless_missing to map_get * Switch from numpy array to Token.get_struct_attr Access token attributes directly in Doc instead of making a copy of the relevant values in a numpy array. Add unsatisfactory warning for hash collision with reserved terminal hash key. (Ideally it would change the reserved terminal hash and redo the whole trie, but for now, I'm hoping there won't be collisions.) * Restructure imports to export find_matches * Implement full remove() Remove unnecessary trie paths and free unused maps. Parallel to Matcher, raise KeyError when attempting to remove a match ID that has not been added. * Switch to PhraseMatcher.find_matches * Switch to local cdef functions for span filtering * Switch special case reload threshold to variable Refer to variable instead of hard-coded threshold * Move more of special case retokenize to cdef nogil Move as much of the special case retokenization to nogil as possible. * Rewrap sort as stdsort for OS X * Rewrap stdsort with specific types * Switch to qsort * Fix merge * Improve cmp functions * Fix realloc * Fix realloc again * Initialize span struct while retokenizing * Temporarily skip retokenizing * Revert "Move more of special case retokenize to cdef nogil" This reverts commit 0b7e52c797cd8ff1548f214bd4186ebb3a7ce8b1. * Revert "Switch to qsort" This reverts commit a98d71a942fc9bca531cf5eb05cf89fa88153b60. * Fix specials check while caching * Modify URL test with emoticons The multiple suffix tests result in the emoticon `:>`, which is now retokenized into one token as a special case after the suffixes are split off. * Refactor _apply_special_cases() * Use cdef ints for span info used in multiple spots * Modify _filter_special_spans() to prefer earlier Parallel to #4414, modify _filter_special_spans() so that the earlier span is preferred for overlapping spans of the same length. * Replace MatchStruct with Entity Replace MatchStruct with Entity since the existing Entity struct is nearly identical. * Replace Entity with more general SpanC * Replace MatchStruct with SpanC * Add error in debug-data if no dev docs are available (see #4575) * Update azure-pipelines.yml * Revert "Update azure-pipelines.yml" This reverts commit ed1060cf59e5895b5fe92ad5b894fd1078ec4c49. * Use latest wasabi * Reorganise install_requires * add dframcy to universe.json (#4580) * Update universe.json [ci skip] * Fix multiprocessing for as_tuples=True (#4582) * Fix conllu script (#4579) * force extensions to avoid clash between example scripts * fix arg order and default file encoding * add example config for conllu script * newline * move extension definitions to main function * few more encodings fixes * Add load_from_docbin example [ci skip] TODO: upload the file somewhere * Update README.md * Add warnings about 3.8 (resolves #4593) [ci skip] * Fixed typo: Added space between "recognize" and "various" (#4600) * Fix DocBin.merge() example (#4599) * Replace function registries with catalogue (#4584) * Replace functions registries with catalogue * Update __init__.py * Fix test * Revert unrelated flag [ci skip] * Bugfix/dep matcher issue 4590 (#4601) * add contributor agreement for prilopes * add test for issue #4590 * fix on_match params for DependencyMacther (#4590) * Minor updates to language example sentences (#4608) * Add punctuation to Spanish example sentences * Combine multilanguage examples for lang xx * Add punctuation to nb examples * Always realloc to a larger size Avoid potential (unlikely) edge case and cymem error seen in #4604. * Add error in debug-data if no dev docs are available (see #4575) * Update debug-data for GoldCorpus / Example * Ignore None label in misaligned NER data
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registry.languages.register(name, func=cls)
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def ensure_path(path):
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"""Ensure string is converted to a Path.
path: Anything. If string, it's converted to Path.
RETURNS: Path or original argument.
"""
if isinstance(path, str):
return Path(path)
else:
return path
2016-09-24 21:26:17 +03:00
Reduce size of language data (#4141) * Move Turkish lemmas to a json file Rather than a large dict in Python source, the data is now a big json file. This includes a method for loading the json file, falling back to a compressed file, and an update to MANIFEST.in that excludes json in the spacy/lang directory. This focuses on Turkish specifically because it has the most language data in core. * Transition all lemmatizer.py files to json This covers all lemmatizer.py files of a significant size (>500k or so). Small files were left alone. None of the affected files have logic, so this was pretty straightforward. One unusual thing is that the lemma data for Urdu doesn't seem to be used anywhere. That may require further investigation. * Move large lang data to json for fr/nb/nl/sv These are the languages that use a lemmatizer directory (rather than a single file) and are larger than English. For most of these languages there were many language data files, in which case only the large ones (>500k or so) were converted to json. It may or may not be a good idea to migrate the remaining Python files to json in the future. * Fix id lemmas.json The contents of this file were originally just copied from the Python source, but that used single quotes, so it had to be properly converted to json first. * Add .json.gz to gitignore This covers the json.gz files built as part of distribution. * Add language data gzip to build process Currently this gzip data on every build; it works, but it should be changed to only gzip when the source file has been updated. * Remove Danish lemmatizer.py Missed this when I added the json. * Update to match latest explosion/srsly#9 The way gzipped json is loaded/saved in srsly changed a bit. * Only compress language data if necessary If a .json.gz file exists and is newer than the corresponding json file, it's not recompressed. * Move en/el language data to json This only affected files >500kb, which was nouns for both languages and the generic lookup table for English. * Remove empty files in Norwegian tokenizer It's unclear why, but the Norwegian (nb) tokenizer had empty files for adj/adv/noun/verb lemmas. This may have been a result of copying the structure of the English lemmatizer. This removed the files, but still creates the empty sets in the lemmatizer. That may not actually be necessary. * Remove dubious entries in English lookup.json " furthest" and " skilled" - both prefixed with a space - were in the English lookup table. That seems obviously wrong so I have removed them. * Fix small issues with en/fr lemmatizers The en tokenizer was including the removed _nouns.py file, so that's removed. The fr tokenizer is unusual in that it has a lemmatizer directory with both __init__.py and lemmatizer.py. lemmatizer.py had not been converted to load the json language data, so that was fixed. * Auto-format * Auto-format * Update srsly pin * Consistently use pathlib paths
2019-08-20 15:54:11 +03:00
def load_language_data(path):
"""Load JSON language data using the given path as a base. If the provided
path isn't present, will attempt to load a gzipped version before giving up.
Reduce size of language data (#4141) * Move Turkish lemmas to a json file Rather than a large dict in Python source, the data is now a big json file. This includes a method for loading the json file, falling back to a compressed file, and an update to MANIFEST.in that excludes json in the spacy/lang directory. This focuses on Turkish specifically because it has the most language data in core. * Transition all lemmatizer.py files to json This covers all lemmatizer.py files of a significant size (>500k or so). Small files were left alone. None of the affected files have logic, so this was pretty straightforward. One unusual thing is that the lemma data for Urdu doesn't seem to be used anywhere. That may require further investigation. * Move large lang data to json for fr/nb/nl/sv These are the languages that use a lemmatizer directory (rather than a single file) and are larger than English. For most of these languages there were many language data files, in which case only the large ones (>500k or so) were converted to json. It may or may not be a good idea to migrate the remaining Python files to json in the future. * Fix id lemmas.json The contents of this file were originally just copied from the Python source, but that used single quotes, so it had to be properly converted to json first. * Add .json.gz to gitignore This covers the json.gz files built as part of distribution. * Add language data gzip to build process Currently this gzip data on every build; it works, but it should be changed to only gzip when the source file has been updated. * Remove Danish lemmatizer.py Missed this when I added the json. * Update to match latest explosion/srsly#9 The way gzipped json is loaded/saved in srsly changed a bit. * Only compress language data if necessary If a .json.gz file exists and is newer than the corresponding json file, it's not recompressed. * Move en/el language data to json This only affected files >500kb, which was nouns for both languages and the generic lookup table for English. * Remove empty files in Norwegian tokenizer It's unclear why, but the Norwegian (nb) tokenizer had empty files for adj/adv/noun/verb lemmas. This may have been a result of copying the structure of the English lemmatizer. This removed the files, but still creates the empty sets in the lemmatizer. That may not actually be necessary. * Remove dubious entries in English lookup.json " furthest" and " skilled" - both prefixed with a space - were in the English lookup table. That seems obviously wrong so I have removed them. * Fix small issues with en/fr lemmatizers The en tokenizer was including the removed _nouns.py file, so that's removed. The fr tokenizer is unusual in that it has a lemmatizer directory with both __init__.py and lemmatizer.py. lemmatizer.py had not been converted to load the json language data, so that was fixed. * Auto-format * Auto-format * Update srsly pin * Consistently use pathlib paths
2019-08-20 15:54:11 +03:00
path (unicode / Path): The data to load.
RETURNS: The loaded data.
Reduce size of language data (#4141) * Move Turkish lemmas to a json file Rather than a large dict in Python source, the data is now a big json file. This includes a method for loading the json file, falling back to a compressed file, and an update to MANIFEST.in that excludes json in the spacy/lang directory. This focuses on Turkish specifically because it has the most language data in core. * Transition all lemmatizer.py files to json This covers all lemmatizer.py files of a significant size (>500k or so). Small files were left alone. None of the affected files have logic, so this was pretty straightforward. One unusual thing is that the lemma data for Urdu doesn't seem to be used anywhere. That may require further investigation. * Move large lang data to json for fr/nb/nl/sv These are the languages that use a lemmatizer directory (rather than a single file) and are larger than English. For most of these languages there were many language data files, in which case only the large ones (>500k or so) were converted to json. It may or may not be a good idea to migrate the remaining Python files to json in the future. * Fix id lemmas.json The contents of this file were originally just copied from the Python source, but that used single quotes, so it had to be properly converted to json first. * Add .json.gz to gitignore This covers the json.gz files built as part of distribution. * Add language data gzip to build process Currently this gzip data on every build; it works, but it should be changed to only gzip when the source file has been updated. * Remove Danish lemmatizer.py Missed this when I added the json. * Update to match latest explosion/srsly#9 The way gzipped json is loaded/saved in srsly changed a bit. * Only compress language data if necessary If a .json.gz file exists and is newer than the corresponding json file, it's not recompressed. * Move en/el language data to json This only affected files >500kb, which was nouns for both languages and the generic lookup table for English. * Remove empty files in Norwegian tokenizer It's unclear why, but the Norwegian (nb) tokenizer had empty files for adj/adv/noun/verb lemmas. This may have been a result of copying the structure of the English lemmatizer. This removed the files, but still creates the empty sets in the lemmatizer. That may not actually be necessary. * Remove dubious entries in English lookup.json " furthest" and " skilled" - both prefixed with a space - were in the English lookup table. That seems obviously wrong so I have removed them. * Fix small issues with en/fr lemmatizers The en tokenizer was including the removed _nouns.py file, so that's removed. The fr tokenizer is unusual in that it has a lemmatizer directory with both __init__.py and lemmatizer.py. lemmatizer.py had not been converted to load the json language data, so that was fixed. * Auto-format * Auto-format * Update srsly pin * Consistently use pathlib paths
2019-08-20 15:54:11 +03:00
"""
path = ensure_path(path)
if path.exists():
Reduce size of language data (#4141) * Move Turkish lemmas to a json file Rather than a large dict in Python source, the data is now a big json file. This includes a method for loading the json file, falling back to a compressed file, and an update to MANIFEST.in that excludes json in the spacy/lang directory. This focuses on Turkish specifically because it has the most language data in core. * Transition all lemmatizer.py files to json This covers all lemmatizer.py files of a significant size (>500k or so). Small files were left alone. None of the affected files have logic, so this was pretty straightforward. One unusual thing is that the lemma data for Urdu doesn't seem to be used anywhere. That may require further investigation. * Move large lang data to json for fr/nb/nl/sv These are the languages that use a lemmatizer directory (rather than a single file) and are larger than English. For most of these languages there were many language data files, in which case only the large ones (>500k or so) were converted to json. It may or may not be a good idea to migrate the remaining Python files to json in the future. * Fix id lemmas.json The contents of this file were originally just copied from the Python source, but that used single quotes, so it had to be properly converted to json first. * Add .json.gz to gitignore This covers the json.gz files built as part of distribution. * Add language data gzip to build process Currently this gzip data on every build; it works, but it should be changed to only gzip when the source file has been updated. * Remove Danish lemmatizer.py Missed this when I added the json. * Update to match latest explosion/srsly#9 The way gzipped json is loaded/saved in srsly changed a bit. * Only compress language data if necessary If a .json.gz file exists and is newer than the corresponding json file, it's not recompressed. * Move en/el language data to json This only affected files >500kb, which was nouns for both languages and the generic lookup table for English. * Remove empty files in Norwegian tokenizer It's unclear why, but the Norwegian (nb) tokenizer had empty files for adj/adv/noun/verb lemmas. This may have been a result of copying the structure of the English lemmatizer. This removed the files, but still creates the empty sets in the lemmatizer. That may not actually be necessary. * Remove dubious entries in English lookup.json " furthest" and " skilled" - both prefixed with a space - were in the English lookup table. That seems obviously wrong so I have removed them. * Fix small issues with en/fr lemmatizers The en tokenizer was including the removed _nouns.py file, so that's removed. The fr tokenizer is unusual in that it has a lemmatizer directory with both __init__.py and lemmatizer.py. lemmatizer.py had not been converted to load the json language data, so that was fixed. * Auto-format * Auto-format * Update srsly pin * Consistently use pathlib paths
2019-08-20 15:54:11 +03:00
return srsly.read_json(path)
path = path.with_suffix(path.suffix + ".gz")
if path.exists():
return srsly.read_gzip_json(path)
raise ValueError(Errors.E160.format(path=path))
def get_module_path(module):
if not hasattr(module, "__module__"):
raise ValueError(Errors.E169.format(module=repr(module)))
return Path(sys.modules[module.__module__].__file__).parent
Reduce size of language data (#4141) * Move Turkish lemmas to a json file Rather than a large dict in Python source, the data is now a big json file. This includes a method for loading the json file, falling back to a compressed file, and an update to MANIFEST.in that excludes json in the spacy/lang directory. This focuses on Turkish specifically because it has the most language data in core. * Transition all lemmatizer.py files to json This covers all lemmatizer.py files of a significant size (>500k or so). Small files were left alone. None of the affected files have logic, so this was pretty straightforward. One unusual thing is that the lemma data for Urdu doesn't seem to be used anywhere. That may require further investigation. * Move large lang data to json for fr/nb/nl/sv These are the languages that use a lemmatizer directory (rather than a single file) and are larger than English. For most of these languages there were many language data files, in which case only the large ones (>500k or so) were converted to json. It may or may not be a good idea to migrate the remaining Python files to json in the future. * Fix id lemmas.json The contents of this file were originally just copied from the Python source, but that used single quotes, so it had to be properly converted to json first. * Add .json.gz to gitignore This covers the json.gz files built as part of distribution. * Add language data gzip to build process Currently this gzip data on every build; it works, but it should be changed to only gzip when the source file has been updated. * Remove Danish lemmatizer.py Missed this when I added the json. * Update to match latest explosion/srsly#9 The way gzipped json is loaded/saved in srsly changed a bit. * Only compress language data if necessary If a .json.gz file exists and is newer than the corresponding json file, it's not recompressed. * Move en/el language data to json This only affected files >500kb, which was nouns for both languages and the generic lookup table for English. * Remove empty files in Norwegian tokenizer It's unclear why, but the Norwegian (nb) tokenizer had empty files for adj/adv/noun/verb lemmas. This may have been a result of copying the structure of the English lemmatizer. This removed the files, but still creates the empty sets in the lemmatizer. That may not actually be necessary. * Remove dubious entries in English lookup.json " furthest" and " skilled" - both prefixed with a space - were in the English lookup table. That seems obviously wrong so I have removed them. * Fix small issues with en/fr lemmatizers The en tokenizer was including the removed _nouns.py file, so that's removed. The fr tokenizer is unusual in that it has a lemmatizer directory with both __init__.py and lemmatizer.py. lemmatizer.py had not been converted to load the json language data, so that was fixed. * Auto-format * Auto-format * Update srsly pin * Consistently use pathlib paths
2019-08-20 15:54:11 +03:00
def load_model(name, **overrides):
"""Load a model from a package or data path.
name (unicode): Package name or model path.
**overrides: Specific overrides, like pipeline components to disable.
RETURNS (Language): `Language` class with the loaded model.
"""
if isinstance(name, str): # name or string path
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if is_package(name): # installed as package
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return load_model_from_package(name, **overrides)
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if Path(name).exists(): # path to model data directory
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return load_model_from_path(Path(name), **overrides)
elif hasattr(name, "exists"): # Path or Path-like to model data
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return load_model_from_path(name, **overrides)
raise IOError(Errors.E050.format(name=name))
2017-05-09 00:51:15 +03:00
2017-06-05 14:02:31 +03:00
def load_model_from_package(name, **overrides):
"""Load a model from an installed package."""
cls = importlib.import_module(name)
return cls.load(**overrides)
def load_model_from_path(model_path, meta=False, **overrides):
"""Load a model from a data directory path. Creates Language class with
pipeline from meta.json and then calls from_disk() with path."""
if not meta:
meta = get_model_meta(model_path)
Default settings to configurations (#4995) * fix grad_clip naming * cleaning up pretrained_vectors out of cfg * further refactoring Model init's * move Model building out of pipes * further refactor to require a model config when creating a pipe * small fixes * making cfg in nn_parser more consistent * fixing nr_class for parser * fixing nn_parser's nO * fix printing of loss * architectures in own file per type, consistent naming * convenience methods default_tagger_config and default_tok2vec_config * let create_pipe access default config if available for that component * default_parser_config * move defaults to separate folder * allow reading nlp from package or dir with argument 'name' * architecture spacy.VocabVectors.v1 to read static vectors from file * cleanup * default configs for nel, textcat, morphologizer, tensorizer * fix imports * fixing unit tests * fixes and clean up * fixing defaults, nO, fix unit tests * restore parser IO * fix IO * 'fix' serialization test * add *.cfg to manifest * fix example configs with additional arguments * replace Morpohologizer with Tagger * add IO bit when testing overfitting of tagger (currently failing) * fix IO - don't initialize when reading from disk * expand overfitting tests to also check IO goes OK * remove dropout from HashEmbed to fix Tagger performance * add defaults for sentrec * update thinc * always pass a Model instance to a Pipe * fix piped_added statement * remove obsolete W029 * remove obsolete errors * restore byte checking tests (work again) * clean up test * further test cleanup * convert from config to Model in create_pipe * bring back error when component is not initialized * cleanup * remove calls for nlp2.begin_training * use thinc.api in imports * allow setting charembed's nM and nC * fix for hardcoded nM/nC + unit test * formatting fixes * trigger build
2020-02-27 20:42:27 +03:00
nlp_config = get_model_config(model_path)
if nlp_config.get("nlp", None):
return load_model_from_config(nlp_config["nlp"])
# Support language factories registered via entry points (e.g. custom
# language subclass) while keeping top-level language identifier "lang"
lang = meta.get("lang_factory", meta["lang"])
cls = get_lang_class(lang)
nlp = cls(meta=meta, **overrides)
pipeline = meta.get("pipeline", [])
factories = meta.get("factories", {})
disable = overrides.get("disable", [])
if pipeline is True:
pipeline = nlp.Defaults.pipe_names
elif pipeline in (False, None):
pipeline = []
for name in pipeline:
if name not in disable:
config = meta.get("pipeline_args", {}).get(name, {})
factory = factories.get(name, name)
Default settings to configurations (#4995) * fix grad_clip naming * cleaning up pretrained_vectors out of cfg * further refactoring Model init's * move Model building out of pipes * further refactor to require a model config when creating a pipe * small fixes * making cfg in nn_parser more consistent * fixing nr_class for parser * fixing nn_parser's nO * fix printing of loss * architectures in own file per type, consistent naming * convenience methods default_tagger_config and default_tok2vec_config * let create_pipe access default config if available for that component * default_parser_config * move defaults to separate folder * allow reading nlp from package or dir with argument 'name' * architecture spacy.VocabVectors.v1 to read static vectors from file * cleanup * default configs for nel, textcat, morphologizer, tensorizer * fix imports * fixing unit tests * fixes and clean up * fixing defaults, nO, fix unit tests * restore parser IO * fix IO * 'fix' serialization test * add *.cfg to manifest * fix example configs with additional arguments * replace Morpohologizer with Tagger * add IO bit when testing overfitting of tagger (currently failing) * fix IO - don't initialize when reading from disk * expand overfitting tests to also check IO goes OK * remove dropout from HashEmbed to fix Tagger performance * add defaults for sentrec * update thinc * always pass a Model instance to a Pipe * fix piped_added statement * remove obsolete W029 * remove obsolete errors * restore byte checking tests (work again) * clean up test * further test cleanup * convert from config to Model in create_pipe * bring back error when component is not initialized * cleanup * remove calls for nlp2.begin_training * use thinc.api in imports * allow setting charembed's nM and nC * fix for hardcoded nM/nC + unit test * formatting fixes * trigger build
2020-02-27 20:42:27 +03:00
if nlp_config.get(name, None):
model_config = nlp_config[name]["model"]
config["model"] = model_config
component = nlp.create_pipe(factory, config=config)
nlp.add_pipe(component, name=name)
return nlp.from_disk(model_path, exclude=disable)
2017-06-05 14:02:31 +03:00
Default settings to configurations (#4995) * fix grad_clip naming * cleaning up pretrained_vectors out of cfg * further refactoring Model init's * move Model building out of pipes * further refactor to require a model config when creating a pipe * small fixes * making cfg in nn_parser more consistent * fixing nr_class for parser * fixing nn_parser's nO * fix printing of loss * architectures in own file per type, consistent naming * convenience methods default_tagger_config and default_tok2vec_config * let create_pipe access default config if available for that component * default_parser_config * move defaults to separate folder * allow reading nlp from package or dir with argument 'name' * architecture spacy.VocabVectors.v1 to read static vectors from file * cleanup * default configs for nel, textcat, morphologizer, tensorizer * fix imports * fixing unit tests * fixes and clean up * fixing defaults, nO, fix unit tests * restore parser IO * fix IO * 'fix' serialization test * add *.cfg to manifest * fix example configs with additional arguments * replace Morpohologizer with Tagger * add IO bit when testing overfitting of tagger (currently failing) * fix IO - don't initialize when reading from disk * expand overfitting tests to also check IO goes OK * remove dropout from HashEmbed to fix Tagger performance * add defaults for sentrec * update thinc * always pass a Model instance to a Pipe * fix piped_added statement * remove obsolete W029 * remove obsolete errors * restore byte checking tests (work again) * clean up test * further test cleanup * convert from config to Model in create_pipe * bring back error when component is not initialized * cleanup * remove calls for nlp2.begin_training * use thinc.api in imports * allow setting charembed's nM and nC * fix for hardcoded nM/nC + unit test * formatting fixes * trigger build
2020-02-27 20:42:27 +03:00
def load_model_from_config(nlp_config):
if "name" in nlp_config:
nlp = load_model(**nlp_config)
elif "lang" in nlp_config:
lang_class = get_lang_class(nlp_config["lang"])
nlp = lang_class()
else:
raise ValueError(Errors.E993)
if "pipeline" in nlp_config:
for name, component_cfg in nlp_config["pipeline"].items():
factory = component_cfg.pop("factory")
component = nlp.create_pipe(factory, config=component_cfg)
nlp.add_pipe(component, name=name)
return nlp
def load_model_from_init_py(init_file, **overrides):
"""Helper function to use in the `load()` method of a model package's
__init__.py.
init_file (unicode): Path to model's __init__.py, i.e. `__file__`.
**overrides: Specific overrides, like pipeline components to disable.
RETURNS (Language): `Language` class with loaded model.
"""
model_path = Path(init_file).parent
meta = get_model_meta(model_path)
2019-12-25 19:59:52 +03:00
data_dir = f"{meta['lang']}_{meta['name']}-{meta['version']}"
data_path = model_path / data_dir
if not model_path.exists():
raise IOError(Errors.E052.format(path=data_path))
2017-06-05 14:02:31 +03:00
return load_model_from_path(data_path, meta, **overrides)
Default settings to configurations (#4995) * fix grad_clip naming * cleaning up pretrained_vectors out of cfg * further refactoring Model init's * move Model building out of pipes * further refactor to require a model config when creating a pipe * small fixes * making cfg in nn_parser more consistent * fixing nr_class for parser * fixing nn_parser's nO * fix printing of loss * architectures in own file per type, consistent naming * convenience methods default_tagger_config and default_tok2vec_config * let create_pipe access default config if available for that component * default_parser_config * move defaults to separate folder * allow reading nlp from package or dir with argument 'name' * architecture spacy.VocabVectors.v1 to read static vectors from file * cleanup * default configs for nel, textcat, morphologizer, tensorizer * fix imports * fixing unit tests * fixes and clean up * fixing defaults, nO, fix unit tests * restore parser IO * fix IO * 'fix' serialization test * add *.cfg to manifest * fix example configs with additional arguments * replace Morpohologizer with Tagger * add IO bit when testing overfitting of tagger (currently failing) * fix IO - don't initialize when reading from disk * expand overfitting tests to also check IO goes OK * remove dropout from HashEmbed to fix Tagger performance * add defaults for sentrec * update thinc * always pass a Model instance to a Pipe * fix piped_added statement * remove obsolete W029 * remove obsolete errors * restore byte checking tests (work again) * clean up test * further test cleanup * convert from config to Model in create_pipe * bring back error when component is not initialized * cleanup * remove calls for nlp2.begin_training * use thinc.api in imports * allow setting charembed's nM and nC * fix for hardcoded nM/nC + unit test * formatting fixes * trigger build
2020-02-27 20:42:27 +03:00
def load_config(path, create_objects=False):
Update spaCy for thinc 8.0.0 (#4920) * Add load_from_config function * Add train_from_config script * Merge configs and expose via spacy.config * Fix script * Suggest create_evaluation_callback * Hard-code for NER * Fix errors * Register command * Add TODO * Update train-from-config todos * Fix imports * Allow delayed setting of parser model nr_class * Get train-from-config working * Tidy up and fix scores and printing * Hide traceback if cancelled * Fix weighted score formatting * Fix score formatting * Make output_path optional * Add Tok2Vec component * Tidy up and add tok2vec_tensors * Add option to copy docs in nlp.update * Copy docs in nlp.update * Adjust nlp.update() for set_annotations * Don't shuffle pipes in nlp.update, decruft * Support set_annotations arg in component update * Support set_annotations in parser update * Add get_gradients method * Add get_gradients to parser * Update errors.py * Fix problems caused by merge * Add _link_components method in nlp * Add concept of 'listeners' and ControlledModel * Support optional attributes arg in ControlledModel * Try having tok2vec component in pipeline * Fix tok2vec component * Fix config * Fix tok2vec * Update for Example * Update for Example * Update config * Add eg2doc util * Update and add schemas/types * Update schemas * Fix nlp.update * Fix tagger * Remove hacks from train-from-config * Remove hard-coded config str * Calculate loss in tok2vec component * Tidy up and use function signatures instead of models * Support union types for registry models * Minor cleaning in Language.update * Make ControlledModel specifically Tok2VecListener * Fix train_from_config * Fix tok2vec * Tidy up * Add function for bilstm tok2vec * Fix type * Fix syntax * Fix pytorch optimizer * Add example configs * Update for thinc describe changes * Update for Thinc changes * Update for dropout/sgd changes * Update for dropout/sgd changes * Unhack gradient update * Work on refactoring _ml * Remove _ml.py module * WIP upgrade cli scripts for thinc * Move some _ml stuff to util * Import link_vectors from util * Update train_from_config * Import from util * Import from util * Temporarily add ml.component_models module * Move ml methods * Move typedefs * Update load vectors * Update gitignore * Move imports * Add PrecomputableAffine * Fix imports * Fix imports * Fix imports * Fix missing imports * Update CLI scripts * Update spacy.language * Add stubs for building the models * Update model definition * Update create_default_optimizer * Fix import * Fix comment * Update imports in tests * Update imports in spacy.cli * Fix import * fix obsolete thinc imports * update srsly pin * from thinc to ml_datasets for example data such as imdb * update ml_datasets pin * using STATE.vectors * small fix * fix Sentencizer.pipe * black formatting * rename Affine to Linear as in thinc * set validate explicitely to True * rename with_square_sequences to with_list2padded * rename with_flatten to with_list2array * chaining layernorm * small fixes * revert Optimizer import * build_nel_encoder with new thinc style * fixes using model's get and set methods * Tok2Vec in component models, various fixes * fix up legacy tok2vec code * add model initialize calls * add in build_tagger_model * small fixes * setting model dims * fixes for ParserModel * various small fixes * initialize thinc Models * fixes * consistent naming of window_size * fixes, removing set_dropout * work around Iterable issue * remove legacy tok2vec * util fix * fix forward function of tok2vec listener * more fixes * trying to fix PrecomputableAffine (not succesful yet) * alloc instead of allocate * add morphologizer * rename residual * rename fixes * Fix predict function * Update parser and parser model * fixing few more tests * Fix precomputable affine * Update component model * Update parser model * Move backprop padding to own function, for test * Update test * Fix p. affine * Update NEL * build_bow_text_classifier and extract_ngrams * Fix parser init * Fix test add label * add build_simple_cnn_text_classifier * Fix parser init * Set gpu off by default in example * Fix tok2vec listener * Fix parser model * Small fixes * small fix for PyTorchLSTM parameters * revert my_compounding hack (iterable fixed now) * fix biLSTM * Fix uniqued * PyTorchRNNWrapper fix * small fixes * use helper function to calculate cosine loss * small fixes for build_simple_cnn_text_classifier * putting dropout default at 0.0 to ensure the layer gets built * using thinc util's set_dropout_rate * moving layer normalization inside of maxout definition to optimize dropout * temp debugging in NEL * fixed NEL model by using init defaults ! * fixing after set_dropout_rate refactor * proper fix * fix test_update_doc after refactoring optimizers in thinc * Add CharacterEmbed layer * Construct tagger Model * Add missing import * Remove unused stuff * Work on textcat * fix test (again :)) after optimizer refactor * fixes to allow reading Tagger from_disk without overwriting dimensions * don't build the tok2vec prematuraly * fix CharachterEmbed init * CharacterEmbed fixes * Fix CharacterEmbed architecture * fix imports * renames from latest thinc update * one more rename * add initialize calls where appropriate * fix parser initialization * Update Thinc version * Fix errors, auto-format and tidy up imports * Fix validation * fix if bias is cupy array * revert for now * ensure it's a numpy array before running bp in ParserStepModel * no reason to call require_gpu twice * use CupyOps.to_numpy instead of cupy directly * fix initialize of ParserModel * remove unnecessary import * fixes for CosineDistance * fix device renaming * use refactored loss functions (Thinc PR 251) * overfitting test for tagger * experimental settings for the tagger: avoid zero-init and subword normalization * clean up tagger overfitting test * use previous default value for nP * remove toy config * bringing layernorm back (had a bug - fixed in thinc) * revert setting nP explicitly * remove setting default in constructor * restore values as they used to be * add overfitting test for NER * add overfitting test for dep parser * add overfitting test for textcat * fixing init for linear (previously affine) * larger eps window for textcat * ensure doc is not None * Require newer thinc * Make float check vaguer * Slop the textcat overfit test more * Fix textcat test * Fix exclusive classes for textcat * fix after renaming of alloc methods * fixing renames and mandatory arguments (staticvectors WIP) * upgrade to thinc==8.0.0.dev3 * refer to vocab.vectors directly instead of its name * rename alpha to learn_rate * adding hashembed and staticvectors dropout * upgrade to thinc 8.0.0.dev4 * add name back to avoid warning W020 * thinc dev4 * update srsly * using thinc 8.0.0a0 ! Co-authored-by: Matthew Honnibal <honnibal+gh@gmail.com> Co-authored-by: Ines Montani <ines@ines.io>
2020-01-29 19:06:46 +03:00
"""Load a Thinc-formatted config file, optionally filling in objects where
the config references registry entries. See "Thinc config files" for details.
path (unicode or Path): Path to the config file
create_objects (bool): Whether to automatically create objects when the config
references registry entries. Defaults to False.
RETURNS (dict): The objects from the config file.
"""
config = thinc.config.Config().from_disk(path)
if create_objects:
return registry.make_from_config(config, validate=True)
else:
return config
2020-05-02 15:09:21 +03:00
def load_config_from_str(string, create_objects=False):
"""Load a Thinc-formatted config, optionally filling in objects where
the config references registry entries. See "Thinc config files" for details.
string (unicode or Path): Text contents of the config file.
create_objects (bool): Whether to automatically create objects when the config
references registry entries. Defaults to False.
RETURNS (dict): The objects from the config file.
"""
config = thinc.config.Config().from_str(string)
if create_objects:
return registry.make_from_config(config, validate=True)
else:
return config
def get_model_meta(path):
"""Get model meta.json from a directory path and validate its contents.
path (unicode or Path): Path to model directory.
RETURNS (dict): The model's meta data.
"""
model_path = ensure_path(path)
if not model_path.exists():
raise IOError(Errors.E052.format(path=model_path))
meta_path = model_path / "meta.json"
if not meta_path.is_file():
Default settings to configurations (#4995) * fix grad_clip naming * cleaning up pretrained_vectors out of cfg * further refactoring Model init's * move Model building out of pipes * further refactor to require a model config when creating a pipe * small fixes * making cfg in nn_parser more consistent * fixing nr_class for parser * fixing nn_parser's nO * fix printing of loss * architectures in own file per type, consistent naming * convenience methods default_tagger_config and default_tok2vec_config * let create_pipe access default config if available for that component * default_parser_config * move defaults to separate folder * allow reading nlp from package or dir with argument 'name' * architecture spacy.VocabVectors.v1 to read static vectors from file * cleanup * default configs for nel, textcat, morphologizer, tensorizer * fix imports * fixing unit tests * fixes and clean up * fixing defaults, nO, fix unit tests * restore parser IO * fix IO * 'fix' serialization test * add *.cfg to manifest * fix example configs with additional arguments * replace Morpohologizer with Tagger * add IO bit when testing overfitting of tagger (currently failing) * fix IO - don't initialize when reading from disk * expand overfitting tests to also check IO goes OK * remove dropout from HashEmbed to fix Tagger performance * add defaults for sentrec * update thinc * always pass a Model instance to a Pipe * fix piped_added statement * remove obsolete W029 * remove obsolete errors * restore byte checking tests (work again) * clean up test * further test cleanup * convert from config to Model in create_pipe * bring back error when component is not initialized * cleanup * remove calls for nlp2.begin_training * use thinc.api in imports * allow setting charembed's nM and nC * fix for hardcoded nM/nC + unit test * formatting fixes * trigger build
2020-02-27 20:42:27 +03:00
raise IOError(Errors.E053.format(path=meta_path, name="meta.json"))
meta = srsly.read_json(meta_path)
for setting in ["lang", "name", "version"]:
2017-08-29 12:21:44 +03:00
if setting not in meta or not meta[setting]:
raise ValueError(Errors.E054.format(setting=setting))
return meta
Default settings to configurations (#4995) * fix grad_clip naming * cleaning up pretrained_vectors out of cfg * further refactoring Model init's * move Model building out of pipes * further refactor to require a model config when creating a pipe * small fixes * making cfg in nn_parser more consistent * fixing nr_class for parser * fixing nn_parser's nO * fix printing of loss * architectures in own file per type, consistent naming * convenience methods default_tagger_config and default_tok2vec_config * let create_pipe access default config if available for that component * default_parser_config * move defaults to separate folder * allow reading nlp from package or dir with argument 'name' * architecture spacy.VocabVectors.v1 to read static vectors from file * cleanup * default configs for nel, textcat, morphologizer, tensorizer * fix imports * fixing unit tests * fixes and clean up * fixing defaults, nO, fix unit tests * restore parser IO * fix IO * 'fix' serialization test * add *.cfg to manifest * fix example configs with additional arguments * replace Morpohologizer with Tagger * add IO bit when testing overfitting of tagger (currently failing) * fix IO - don't initialize when reading from disk * expand overfitting tests to also check IO goes OK * remove dropout from HashEmbed to fix Tagger performance * add defaults for sentrec * update thinc * always pass a Model instance to a Pipe * fix piped_added statement * remove obsolete W029 * remove obsolete errors * restore byte checking tests (work again) * clean up test * further test cleanup * convert from config to Model in create_pipe * bring back error when component is not initialized * cleanup * remove calls for nlp2.begin_training * use thinc.api in imports * allow setting charembed's nM and nC * fix for hardcoded nM/nC + unit test * formatting fixes * trigger build
2020-02-27 20:42:27 +03:00
def get_model_config(path):
"""Get the model's config from a directory path.
path (unicode or Path): Path to model directory.
RETURNS (Config): The model's config data.
"""
model_path = ensure_path(path)
if not model_path.exists():
raise IOError(Errors.E052.format(path=model_path))
config_path = model_path / "config.cfg"
# model directories are allowed not to have config files ?
if not config_path.is_file():
return Config({})
# raise IOError(Errors.E053.format(path=config_path, name="config.cfg"))
return Config().from_disk(config_path)
def is_package(name):
"""Check if string maps to a package installed via pip.
2017-05-14 02:30:29 +03:00
name (unicode): Name of package.
RETURNS (bool): True if installed package, False if not.
2017-05-09 00:51:15 +03:00
"""
import pkg_resources
name = name.lower() # compare package name against lowercase name
packages = pkg_resources.working_set.by_key.keys()
2017-05-09 00:51:15 +03:00
for package in packages:
if package.lower().replace("-", "_") == name:
2017-05-09 00:51:15 +03:00
return True
return False
def get_package_path(name):
"""Get the path to an installed package.
name (unicode): Package name.
RETURNS (Path): Path to installed package.
"""
name = name.lower() # use lowercase version to be safe
2017-05-09 00:51:15 +03:00
# Here we're importing the module just to find it. This is worryingly
# indirect, but it's otherwise very difficult to find the package.
pkg = importlib.import_module(name)
return Path(pkg.__file__).parent
2017-05-09 00:51:15 +03:00
def is_in_jupyter():
2017-05-21 02:12:09 +03:00
"""Check if user is running spaCy from a Jupyter notebook by detecting the
IPython kernel. Mainly used for the displaCy visualizer.
RETURNS (bool): True if in Jupyter, False if not.
"""
# https://stackoverflow.com/a/39662359/6400719
try:
shell = get_ipython().__class__.__name__
if shell == "ZMQInteractiveShell":
return True # Jupyter notebook or qtconsole
except NameError:
return False # Probably standard Python interpreter
return False
def get_component_name(component):
if hasattr(component, "name"):
return component.name
if hasattr(component, "__name__"):
return component.__name__
if hasattr(component, "__class__") and hasattr(component.__class__, "__name__"):
return component.__class__.__name__
return repr(component)
def get_cuda_stream(require=False, non_blocking=True):
Update spaCy for thinc 8.0.0 (#4920) * Add load_from_config function * Add train_from_config script * Merge configs and expose via spacy.config * Fix script * Suggest create_evaluation_callback * Hard-code for NER * Fix errors * Register command * Add TODO * Update train-from-config todos * Fix imports * Allow delayed setting of parser model nr_class * Get train-from-config working * Tidy up and fix scores and printing * Hide traceback if cancelled * Fix weighted score formatting * Fix score formatting * Make output_path optional * Add Tok2Vec component * Tidy up and add tok2vec_tensors * Add option to copy docs in nlp.update * Copy docs in nlp.update * Adjust nlp.update() for set_annotations * Don't shuffle pipes in nlp.update, decruft * Support set_annotations arg in component update * Support set_annotations in parser update * Add get_gradients method * Add get_gradients to parser * Update errors.py * Fix problems caused by merge * Add _link_components method in nlp * Add concept of 'listeners' and ControlledModel * Support optional attributes arg in ControlledModel * Try having tok2vec component in pipeline * Fix tok2vec component * Fix config * Fix tok2vec * Update for Example * Update for Example * Update config * Add eg2doc util * Update and add schemas/types * Update schemas * Fix nlp.update * Fix tagger * Remove hacks from train-from-config * Remove hard-coded config str * Calculate loss in tok2vec component * Tidy up and use function signatures instead of models * Support union types for registry models * Minor cleaning in Language.update * Make ControlledModel specifically Tok2VecListener * Fix train_from_config * Fix tok2vec * Tidy up * Add function for bilstm tok2vec * Fix type * Fix syntax * Fix pytorch optimizer * Add example configs * Update for thinc describe changes * Update for Thinc changes * Update for dropout/sgd changes * Update for dropout/sgd changes * Unhack gradient update * Work on refactoring _ml * Remove _ml.py module * WIP upgrade cli scripts for thinc * Move some _ml stuff to util * Import link_vectors from util * Update train_from_config * Import from util * Import from util * Temporarily add ml.component_models module * Move ml methods * Move typedefs * Update load vectors * Update gitignore * Move imports * Add PrecomputableAffine * Fix imports * Fix imports * Fix imports * Fix missing imports * Update CLI scripts * Update spacy.language * Add stubs for building the models * Update model definition * Update create_default_optimizer * Fix import * Fix comment * Update imports in tests * Update imports in spacy.cli * Fix import * fix obsolete thinc imports * update srsly pin * from thinc to ml_datasets for example data such as imdb * update ml_datasets pin * using STATE.vectors * small fix * fix Sentencizer.pipe * black formatting * rename Affine to Linear as in thinc * set validate explicitely to True * rename with_square_sequences to with_list2padded * rename with_flatten to with_list2array * chaining layernorm * small fixes * revert Optimizer import * build_nel_encoder with new thinc style * fixes using model's get and set methods * Tok2Vec in component models, various fixes * fix up legacy tok2vec code * add model initialize calls * add in build_tagger_model * small fixes * setting model dims * fixes for ParserModel * various small fixes * initialize thinc Models * fixes * consistent naming of window_size * fixes, removing set_dropout * work around Iterable issue * remove legacy tok2vec * util fix * fix forward function of tok2vec listener * more fixes * trying to fix PrecomputableAffine (not succesful yet) * alloc instead of allocate * add morphologizer * rename residual * rename fixes * Fix predict function * Update parser and parser model * fixing few more tests * Fix precomputable affine * Update component model * Update parser model * Move backprop padding to own function, for test * Update test * Fix p. affine * Update NEL * build_bow_text_classifier and extract_ngrams * Fix parser init * Fix test add label * add build_simple_cnn_text_classifier * Fix parser init * Set gpu off by default in example * Fix tok2vec listener * Fix parser model * Small fixes * small fix for PyTorchLSTM parameters * revert my_compounding hack (iterable fixed now) * fix biLSTM * Fix uniqued * PyTorchRNNWrapper fix * small fixes * use helper function to calculate cosine loss * small fixes for build_simple_cnn_text_classifier * putting dropout default at 0.0 to ensure the layer gets built * using thinc util's set_dropout_rate * moving layer normalization inside of maxout definition to optimize dropout * temp debugging in NEL * fixed NEL model by using init defaults ! * fixing after set_dropout_rate refactor * proper fix * fix test_update_doc after refactoring optimizers in thinc * Add CharacterEmbed layer * Construct tagger Model * Add missing import * Remove unused stuff * Work on textcat * fix test (again :)) after optimizer refactor * fixes to allow reading Tagger from_disk without overwriting dimensions * don't build the tok2vec prematuraly * fix CharachterEmbed init * CharacterEmbed fixes * Fix CharacterEmbed architecture * fix imports * renames from latest thinc update * one more rename * add initialize calls where appropriate * fix parser initialization * Update Thinc version * Fix errors, auto-format and tidy up imports * Fix validation * fix if bias is cupy array * revert for now * ensure it's a numpy array before running bp in ParserStepModel * no reason to call require_gpu twice * use CupyOps.to_numpy instead of cupy directly * fix initialize of ParserModel * remove unnecessary import * fixes for CosineDistance * fix device renaming * use refactored loss functions (Thinc PR 251) * overfitting test for tagger * experimental settings for the tagger: avoid zero-init and subword normalization * clean up tagger overfitting test * use previous default value for nP * remove toy config * bringing layernorm back (had a bug - fixed in thinc) * revert setting nP explicitly * remove setting default in constructor * restore values as they used to be * add overfitting test for NER * add overfitting test for dep parser * add overfitting test for textcat * fixing init for linear (previously affine) * larger eps window for textcat * ensure doc is not None * Require newer thinc * Make float check vaguer * Slop the textcat overfit test more * Fix textcat test * Fix exclusive classes for textcat * fix after renaming of alloc methods * fixing renames and mandatory arguments (staticvectors WIP) * upgrade to thinc==8.0.0.dev3 * refer to vocab.vectors directly instead of its name * rename alpha to learn_rate * adding hashembed and staticvectors dropout * upgrade to thinc 8.0.0.dev4 * add name back to avoid warning W020 * thinc dev4 * update srsly * using thinc 8.0.0a0 ! Co-authored-by: Matthew Honnibal <honnibal+gh@gmail.com> Co-authored-by: Ines Montani <ines@ines.io>
2020-01-29 19:06:46 +03:00
ops = get_current_ops()
if CudaStream is None:
return None
Update spaCy for thinc 8.0.0 (#4920) * Add load_from_config function * Add train_from_config script * Merge configs and expose via spacy.config * Fix script * Suggest create_evaluation_callback * Hard-code for NER * Fix errors * Register command * Add TODO * Update train-from-config todos * Fix imports * Allow delayed setting of parser model nr_class * Get train-from-config working * Tidy up and fix scores and printing * Hide traceback if cancelled * Fix weighted score formatting * Fix score formatting * Make output_path optional * Add Tok2Vec component * Tidy up and add tok2vec_tensors * Add option to copy docs in nlp.update * Copy docs in nlp.update * Adjust nlp.update() for set_annotations * Don't shuffle pipes in nlp.update, decruft * Support set_annotations arg in component update * Support set_annotations in parser update * Add get_gradients method * Add get_gradients to parser * Update errors.py * Fix problems caused by merge * Add _link_components method in nlp * Add concept of 'listeners' and ControlledModel * Support optional attributes arg in ControlledModel * Try having tok2vec component in pipeline * Fix tok2vec component * Fix config * Fix tok2vec * Update for Example * Update for Example * Update config * Add eg2doc util * Update and add schemas/types * Update schemas * Fix nlp.update * Fix tagger * Remove hacks from train-from-config * Remove hard-coded config str * Calculate loss in tok2vec component * Tidy up and use function signatures instead of models * Support union types for registry models * Minor cleaning in Language.update * Make ControlledModel specifically Tok2VecListener * Fix train_from_config * Fix tok2vec * Tidy up * Add function for bilstm tok2vec * Fix type * Fix syntax * Fix pytorch optimizer * Add example configs * Update for thinc describe changes * Update for Thinc changes * Update for dropout/sgd changes * Update for dropout/sgd changes * Unhack gradient update * Work on refactoring _ml * Remove _ml.py module * WIP upgrade cli scripts for thinc * Move some _ml stuff to util * Import link_vectors from util * Update train_from_config * Import from util * Import from util * Temporarily add ml.component_models module * Move ml methods * Move typedefs * Update load vectors * Update gitignore * Move imports * Add PrecomputableAffine * Fix imports * Fix imports * Fix imports * Fix missing imports * Update CLI scripts * Update spacy.language * Add stubs for building the models * Update model definition * Update create_default_optimizer * Fix import * Fix comment * Update imports in tests * Update imports in spacy.cli * Fix import * fix obsolete thinc imports * update srsly pin * from thinc to ml_datasets for example data such as imdb * update ml_datasets pin * using STATE.vectors * small fix * fix Sentencizer.pipe * black formatting * rename Affine to Linear as in thinc * set validate explicitely to True * rename with_square_sequences to with_list2padded * rename with_flatten to with_list2array * chaining layernorm * small fixes * revert Optimizer import * build_nel_encoder with new thinc style * fixes using model's get and set methods * Tok2Vec in component models, various fixes * fix up legacy tok2vec code * add model initialize calls * add in build_tagger_model * small fixes * setting model dims * fixes for ParserModel * various small fixes * initialize thinc Models * fixes * consistent naming of window_size * fixes, removing set_dropout * work around Iterable issue * remove legacy tok2vec * util fix * fix forward function of tok2vec listener * more fixes * trying to fix PrecomputableAffine (not succesful yet) * alloc instead of allocate * add morphologizer * rename residual * rename fixes * Fix predict function * Update parser and parser model * fixing few more tests * Fix precomputable affine * Update component model * Update parser model * Move backprop padding to own function, for test * Update test * Fix p. affine * Update NEL * build_bow_text_classifier and extract_ngrams * Fix parser init * Fix test add label * add build_simple_cnn_text_classifier * Fix parser init * Set gpu off by default in example * Fix tok2vec listener * Fix parser model * Small fixes * small fix for PyTorchLSTM parameters * revert my_compounding hack (iterable fixed now) * fix biLSTM * Fix uniqued * PyTorchRNNWrapper fix * small fixes * use helper function to calculate cosine loss * small fixes for build_simple_cnn_text_classifier * putting dropout default at 0.0 to ensure the layer gets built * using thinc util's set_dropout_rate * moving layer normalization inside of maxout definition to optimize dropout * temp debugging in NEL * fixed NEL model by using init defaults ! * fixing after set_dropout_rate refactor * proper fix * fix test_update_doc after refactoring optimizers in thinc * Add CharacterEmbed layer * Construct tagger Model * Add missing import * Remove unused stuff * Work on textcat * fix test (again :)) after optimizer refactor * fixes to allow reading Tagger from_disk without overwriting dimensions * don't build the tok2vec prematuraly * fix CharachterEmbed init * CharacterEmbed fixes * Fix CharacterEmbed architecture * fix imports * renames from latest thinc update * one more rename * add initialize calls where appropriate * fix parser initialization * Update Thinc version * Fix errors, auto-format and tidy up imports * Fix validation * fix if bias is cupy array * revert for now * ensure it's a numpy array before running bp in ParserStepModel * no reason to call require_gpu twice * use CupyOps.to_numpy instead of cupy directly * fix initialize of ParserModel * remove unnecessary import * fixes for CosineDistance * fix device renaming * use refactored loss functions (Thinc PR 251) * overfitting test for tagger * experimental settings for the tagger: avoid zero-init and subword normalization * clean up tagger overfitting test * use previous default value for nP * remove toy config * bringing layernorm back (had a bug - fixed in thinc) * revert setting nP explicitly * remove setting default in constructor * restore values as they used to be * add overfitting test for NER * add overfitting test for dep parser * add overfitting test for textcat * fixing init for linear (previously affine) * larger eps window for textcat * ensure doc is not None * Require newer thinc * Make float check vaguer * Slop the textcat overfit test more * Fix textcat test * Fix exclusive classes for textcat * fix after renaming of alloc methods * fixing renames and mandatory arguments (staticvectors WIP) * upgrade to thinc==8.0.0.dev3 * refer to vocab.vectors directly instead of its name * rename alpha to learn_rate * adding hashembed and staticvectors dropout * upgrade to thinc 8.0.0.dev4 * add name back to avoid warning W020 * thinc dev4 * update srsly * using thinc 8.0.0a0 ! Co-authored-by: Matthew Honnibal <honnibal+gh@gmail.com> Co-authored-by: Ines Montani <ines@ines.io>
2020-01-29 19:06:46 +03:00
elif isinstance(ops, NumpyOps):
return None
else:
return CudaStream(non_blocking=non_blocking)
def get_async(stream, numpy_array):
if cupy is None:
return numpy_array
else:
array = cupy.ndarray(numpy_array.shape, order="C", dtype=numpy_array.dtype)
array.set(numpy_array, stream=stream)
return array
2017-05-26 13:37:45 +03:00
Update spaCy for thinc 8.0.0 (#4920) * Add load_from_config function * Add train_from_config script * Merge configs and expose via spacy.config * Fix script * Suggest create_evaluation_callback * Hard-code for NER * Fix errors * Register command * Add TODO * Update train-from-config todos * Fix imports * Allow delayed setting of parser model nr_class * Get train-from-config working * Tidy up and fix scores and printing * Hide traceback if cancelled * Fix weighted score formatting * Fix score formatting * Make output_path optional * Add Tok2Vec component * Tidy up and add tok2vec_tensors * Add option to copy docs in nlp.update * Copy docs in nlp.update * Adjust nlp.update() for set_annotations * Don't shuffle pipes in nlp.update, decruft * Support set_annotations arg in component update * Support set_annotations in parser update * Add get_gradients method * Add get_gradients to parser * Update errors.py * Fix problems caused by merge * Add _link_components method in nlp * Add concept of 'listeners' and ControlledModel * Support optional attributes arg in ControlledModel * Try having tok2vec component in pipeline * Fix tok2vec component * Fix config * Fix tok2vec * Update for Example * Update for Example * Update config * Add eg2doc util * Update and add schemas/types * Update schemas * Fix nlp.update * Fix tagger * Remove hacks from train-from-config * Remove hard-coded config str * Calculate loss in tok2vec component * Tidy up and use function signatures instead of models * Support union types for registry models * Minor cleaning in Language.update * Make ControlledModel specifically Tok2VecListener * Fix train_from_config * Fix tok2vec * Tidy up * Add function for bilstm tok2vec * Fix type * Fix syntax * Fix pytorch optimizer * Add example configs * Update for thinc describe changes * Update for Thinc changes * Update for dropout/sgd changes * Update for dropout/sgd changes * Unhack gradient update * Work on refactoring _ml * Remove _ml.py module * WIP upgrade cli scripts for thinc * Move some _ml stuff to util * Import link_vectors from util * Update train_from_config * Import from util * Import from util * Temporarily add ml.component_models module * Move ml methods * Move typedefs * Update load vectors * Update gitignore * Move imports * Add PrecomputableAffine * Fix imports * Fix imports * Fix imports * Fix missing imports * Update CLI scripts * Update spacy.language * Add stubs for building the models * Update model definition * Update create_default_optimizer * Fix import * Fix comment * Update imports in tests * Update imports in spacy.cli * Fix import * fix obsolete thinc imports * update srsly pin * from thinc to ml_datasets for example data such as imdb * update ml_datasets pin * using STATE.vectors * small fix * fix Sentencizer.pipe * black formatting * rename Affine to Linear as in thinc * set validate explicitely to True * rename with_square_sequences to with_list2padded * rename with_flatten to with_list2array * chaining layernorm * small fixes * revert Optimizer import * build_nel_encoder with new thinc style * fixes using model's get and set methods * Tok2Vec in component models, various fixes * fix up legacy tok2vec code * add model initialize calls * add in build_tagger_model * small fixes * setting model dims * fixes for ParserModel * various small fixes * initialize thinc Models * fixes * consistent naming of window_size * fixes, removing set_dropout * work around Iterable issue * remove legacy tok2vec * util fix * fix forward function of tok2vec listener * more fixes * trying to fix PrecomputableAffine (not succesful yet) * alloc instead of allocate * add morphologizer * rename residual * rename fixes * Fix predict function * Update parser and parser model * fixing few more tests * Fix precomputable affine * Update component model * Update parser model * Move backprop padding to own function, for test * Update test * Fix p. affine * Update NEL * build_bow_text_classifier and extract_ngrams * Fix parser init * Fix test add label * add build_simple_cnn_text_classifier * Fix parser init * Set gpu off by default in example * Fix tok2vec listener * Fix parser model * Small fixes * small fix for PyTorchLSTM parameters * revert my_compounding hack (iterable fixed now) * fix biLSTM * Fix uniqued * PyTorchRNNWrapper fix * small fixes * use helper function to calculate cosine loss * small fixes for build_simple_cnn_text_classifier * putting dropout default at 0.0 to ensure the layer gets built * using thinc util's set_dropout_rate * moving layer normalization inside of maxout definition to optimize dropout * temp debugging in NEL * fixed NEL model by using init defaults ! * fixing after set_dropout_rate refactor * proper fix * fix test_update_doc after refactoring optimizers in thinc * Add CharacterEmbed layer * Construct tagger Model * Add missing import * Remove unused stuff * Work on textcat * fix test (again :)) after optimizer refactor * fixes to allow reading Tagger from_disk without overwriting dimensions * don't build the tok2vec prematuraly * fix CharachterEmbed init * CharacterEmbed fixes * Fix CharacterEmbed architecture * fix imports * renames from latest thinc update * one more rename * add initialize calls where appropriate * fix parser initialization * Update Thinc version * Fix errors, auto-format and tidy up imports * Fix validation * fix if bias is cupy array * revert for now * ensure it's a numpy array before running bp in ParserStepModel * no reason to call require_gpu twice * use CupyOps.to_numpy instead of cupy directly * fix initialize of ParserModel * remove unnecessary import * fixes for CosineDistance * fix device renaming * use refactored loss functions (Thinc PR 251) * overfitting test for tagger * experimental settings for the tagger: avoid zero-init and subword normalization * clean up tagger overfitting test * use previous default value for nP * remove toy config * bringing layernorm back (had a bug - fixed in thinc) * revert setting nP explicitly * remove setting default in constructor * restore values as they used to be * add overfitting test for NER * add overfitting test for dep parser * add overfitting test for textcat * fixing init for linear (previously affine) * larger eps window for textcat * ensure doc is not None * Require newer thinc * Make float check vaguer * Slop the textcat overfit test more * Fix textcat test * Fix exclusive classes for textcat * fix after renaming of alloc methods * fixing renames and mandatory arguments (staticvectors WIP) * upgrade to thinc==8.0.0.dev3 * refer to vocab.vectors directly instead of its name * rename alpha to learn_rate * adding hashembed and staticvectors dropout * upgrade to thinc 8.0.0.dev4 * add name back to avoid warning W020 * thinc dev4 * update srsly * using thinc 8.0.0a0 ! Co-authored-by: Matthew Honnibal <honnibal+gh@gmail.com> Co-authored-by: Ines Montani <ines@ines.io>
2020-01-29 19:06:46 +03:00
def eg2doc(example):
"""Get a Doc object from an Example (or if it's a Doc, use it directly)"""
# Put the import here to avoid circular import problems
from .tokens.doc import Doc
return example if isinstance(example, Doc) else example.doc
def env_opt(name, default=None):
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if type(default) is float:
type_convert = float
else:
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type_convert = int
if "SPACY_" + name.upper() in os.environ:
value = type_convert(os.environ["SPACY_" + name.upper()])
if _PRINT_ENV:
print(name, "=", repr(value), "via", "$SPACY_" + name.upper())
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return value
elif name in os.environ:
value = type_convert(os.environ[name])
if _PRINT_ENV:
print(name, "=", repr(value), "via", "$" + name)
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return value
else:
if _PRINT_ENV:
print(name, "=", repr(default), "by default")
return default
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def read_regex(path):
path = ensure_path(path)
with path.open(encoding="utf8") as file_:
entries = file_.read().split("\n")
expression = "|".join(
["^" + re.escape(piece) for piece in entries if piece.strip()]
)
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return re.compile(expression)
def compile_prefix_regex(entries):
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"""Compile a sequence of prefix rules into a regex object.
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entries (tuple): The prefix rules, e.g. spacy.lang.punctuation.TOKENIZER_PREFIXES.
RETURNS (regex object): The regex object. to be used for Tokenizer.prefix_search.
"""
if "(" in entries:
# Handle deprecated data
expression = "|".join(
["^" + re.escape(piece) for piece in entries if piece.strip()]
)
return re.compile(expression)
else:
expression = "|".join(["^" + piece for piece in entries if piece.strip()])
return re.compile(expression)
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def compile_suffix_regex(entries):
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"""Compile a sequence of suffix rules into a regex object.
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entries (tuple): The suffix rules, e.g. spacy.lang.punctuation.TOKENIZER_SUFFIXES.
RETURNS (regex object): The regex object. to be used for Tokenizer.suffix_search.
"""
expression = "|".join([piece + "$" for piece in entries if piece.strip()])
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return re.compile(expression)
def compile_infix_regex(entries):
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"""Compile a sequence of infix rules into a regex object.
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entries (tuple): The infix rules, e.g. spacy.lang.punctuation.TOKENIZER_INFIXES.
RETURNS (regex object): The regex object. to be used for Tokenizer.infix_finditer.
"""
expression = "|".join([piece for piece in entries if piece.strip()])
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return re.compile(expression)
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def add_lookups(default_func, *lookups):
"""Extend an attribute function with special cases. If a word is in the
lookups, the value is returned. Otherwise the previous function is used.
default_func (callable): The default function to execute.
*lookups (dict): Lookup dictionary mapping string to attribute value.
RETURNS (callable): Lexical attribute getter.
"""
# This is implemented as functools.partial instead of a closure, to allow
# pickle to work.
return functools.partial(_get_attr_unless_lookup, default_func, lookups)
def _get_attr_unless_lookup(default_func, lookups, string):
for lookup in lookups:
if string in lookup:
return lookup[string]
return default_func(string)
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def update_exc(base_exceptions, *addition_dicts):
"""Update and validate tokenizer exceptions. Will overwrite exceptions.
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base_exceptions (dict): Base exceptions.
*addition_dicts (dict): Exceptions to add to the base dict, in order.
RETURNS (dict): Combined tokenizer exceptions.
"""
exc = dict(base_exceptions)
for additions in addition_dicts:
for orth, token_attrs in additions.items():
if not all(isinstance(attr[ORTH], str) for attr in token_attrs):
raise ValueError(Errors.E055.format(key=orth, orths=token_attrs))
described_orth = "".join(attr[ORTH] for attr in token_attrs)
if orth != described_orth:
raise ValueError(Errors.E056.format(key=orth, orths=described_orth))
exc.update(additions)
exc = expand_exc(exc, "'", "")
return exc
def expand_exc(excs, search, replace):
"""Find string in tokenizer exceptions, duplicate entry and replace string.
For example, to add additional versions with typographic apostrophes.
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excs (dict): Tokenizer exceptions.
search (unicode): String to find and replace.
replace (unicode): Replacement.
RETURNS (dict): Combined tokenizer exceptions.
"""
def _fix_token(token, search, replace):
fixed = dict(token)
fixed[ORTH] = fixed[ORTH].replace(search, replace)
return fixed
new_excs = dict(excs)
for token_string, tokens in excs.items():
if search in token_string:
new_key = token_string.replace(search, replace)
new_value = [_fix_token(t, search, replace) for t in tokens]
new_excs[new_key] = new_value
return new_excs
def normalize_slice(length, start, stop, step=None):
if not (step is None or step == 1):
raise ValueError(Errors.E057)
if start is None:
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start = 0
elif start < 0:
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start += length
start = min(length, max(0, start))
if stop is None:
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stop = length
elif stop < 0:
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stop += length
stop = min(length, max(start, stop))
return start, stop
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def minibatch(items, size=8):
"""Iterate over batches of items. `size` may be an iterator,
so that batch-size can vary on each step.
"""
if isinstance(size, int):
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size_ = itertools.repeat(size)
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else:
size_ = size
items = iter(items)
while True:
batch_size = next(size_)
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batch = list(itertools.islice(items, int(batch_size)))
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if len(batch) == 0:
break
yield list(batch)
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def compounding(start, stop, compound):
"""Yield an infinite series of compounding values. Each time the
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generator is called, a value is produced by multiplying the previous
value by the compound rate.
EXAMPLE:
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>>> sizes = compounding(1., 10., 1.5)
>>> assert next(sizes) == 1.
>>> assert next(sizes) == 1 * 1.5
>>> assert next(sizes) == 1.5 * 1.5
"""
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def clip(value):
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return max(value, stop) if (start > stop) else min(value, stop)
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curr = float(start)
while True:
yield clip(curr)
curr *= compound
def stepping(start, stop, steps):
"""Yield an infinite series of values that step from a start value to a
final value over some number of steps. Each step is (stop-start)/steps.
After the final value is reached, the generator continues yielding that
value.
EXAMPLE:
>>> sizes = stepping(1., 200., 100)
>>> assert next(sizes) == 1.
>>> assert next(sizes) == 1 * (200.-1.) / 100
>>> assert next(sizes) == 1 + (200.-1.) / 100 + (200.-1.) / 100
"""
def clip(value):
return max(value, stop) if (start > stop) else min(value, stop)
curr = float(start)
while True:
yield clip(curr)
curr += (stop - start) / steps
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def decaying(start, stop, decay):
"""Yield an infinite series of linearly decaying values."""
curr = float(start)
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while True:
yield max(curr, stop)
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curr -= decay
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def minibatch_by_words(examples, size, tuples=True, count_words=len):
"""Create minibatches of a given number of words."""
if isinstance(size, int):
size_ = itertools.repeat(size)
elif isinstance(size, List):
Update spaCy for thinc 8.0.0 (#4920) * Add load_from_config function * Add train_from_config script * Merge configs and expose via spacy.config * Fix script * Suggest create_evaluation_callback * Hard-code for NER * Fix errors * Register command * Add TODO * Update train-from-config todos * Fix imports * Allow delayed setting of parser model nr_class * Get train-from-config working * Tidy up and fix scores and printing * Hide traceback if cancelled * Fix weighted score formatting * Fix score formatting * Make output_path optional * Add Tok2Vec component * Tidy up and add tok2vec_tensors * Add option to copy docs in nlp.update * Copy docs in nlp.update * Adjust nlp.update() for set_annotations * Don't shuffle pipes in nlp.update, decruft * Support set_annotations arg in component update * Support set_annotations in parser update * Add get_gradients method * Add get_gradients to parser * Update errors.py * Fix problems caused by merge * Add _link_components method in nlp * Add concept of 'listeners' and ControlledModel * Support optional attributes arg in ControlledModel * Try having tok2vec component in pipeline * Fix tok2vec component * Fix config * Fix tok2vec * Update for Example * Update for Example * Update config * Add eg2doc util * Update and add schemas/types * Update schemas * Fix nlp.update * Fix tagger * Remove hacks from train-from-config * Remove hard-coded config str * Calculate loss in tok2vec component * Tidy up and use function signatures instead of models * Support union types for registry models * Minor cleaning in Language.update * Make ControlledModel specifically Tok2VecListener * Fix train_from_config * Fix tok2vec * Tidy up * Add function for bilstm tok2vec * Fix type * Fix syntax * Fix pytorch optimizer * Add example configs * Update for thinc describe changes * Update for Thinc changes * Update for dropout/sgd changes * Update for dropout/sgd changes * Unhack gradient update * Work on refactoring _ml * Remove _ml.py module * WIP upgrade cli scripts for thinc * Move some _ml stuff to util * Import link_vectors from util * Update train_from_config * Import from util * Import from util * Temporarily add ml.component_models module * Move ml methods * Move typedefs * Update load vectors * Update gitignore * Move imports * Add PrecomputableAffine * Fix imports * Fix imports * Fix imports * Fix missing imports * Update CLI scripts * Update spacy.language * Add stubs for building the models * Update model definition * Update create_default_optimizer * Fix import * Fix comment * Update imports in tests * Update imports in spacy.cli * Fix import * fix obsolete thinc imports * update srsly pin * from thinc to ml_datasets for example data such as imdb * update ml_datasets pin * using STATE.vectors * small fix * fix Sentencizer.pipe * black formatting * rename Affine to Linear as in thinc * set validate explicitely to True * rename with_square_sequences to with_list2padded * rename with_flatten to with_list2array * chaining layernorm * small fixes * revert Optimizer import * build_nel_encoder with new thinc style * fixes using model's get and set methods * Tok2Vec in component models, various fixes * fix up legacy tok2vec code * add model initialize calls * add in build_tagger_model * small fixes * setting model dims * fixes for ParserModel * various small fixes * initialize thinc Models * fixes * consistent naming of window_size * fixes, removing set_dropout * work around Iterable issue * remove legacy tok2vec * util fix * fix forward function of tok2vec listener * more fixes * trying to fix PrecomputableAffine (not succesful yet) * alloc instead of allocate * add morphologizer * rename residual * rename fixes * Fix predict function * Update parser and parser model * fixing few more tests * Fix precomputable affine * Update component model * Update parser model * Move backprop padding to own function, for test * Update test * Fix p. affine * Update NEL * build_bow_text_classifier and extract_ngrams * Fix parser init * Fix test add label * add build_simple_cnn_text_classifier * Fix parser init * Set gpu off by default in example * Fix tok2vec listener * Fix parser model * Small fixes * small fix for PyTorchLSTM parameters * revert my_compounding hack (iterable fixed now) * fix biLSTM * Fix uniqued * PyTorchRNNWrapper fix * small fixes * use helper function to calculate cosine loss * small fixes for build_simple_cnn_text_classifier * putting dropout default at 0.0 to ensure the layer gets built * using thinc util's set_dropout_rate * moving layer normalization inside of maxout definition to optimize dropout * temp debugging in NEL * fixed NEL model by using init defaults ! * fixing after set_dropout_rate refactor * proper fix * fix test_update_doc after refactoring optimizers in thinc * Add CharacterEmbed layer * Construct tagger Model * Add missing import * Remove unused stuff * Work on textcat * fix test (again :)) after optimizer refactor * fixes to allow reading Tagger from_disk without overwriting dimensions * don't build the tok2vec prematuraly * fix CharachterEmbed init * CharacterEmbed fixes * Fix CharacterEmbed architecture * fix imports * renames from latest thinc update * one more rename * add initialize calls where appropriate * fix parser initialization * Update Thinc version * Fix errors, auto-format and tidy up imports * Fix validation * fix if bias is cupy array * revert for now * ensure it's a numpy array before running bp in ParserStepModel * no reason to call require_gpu twice * use CupyOps.to_numpy instead of cupy directly * fix initialize of ParserModel * remove unnecessary import * fixes for CosineDistance * fix device renaming * use refactored loss functions (Thinc PR 251) * overfitting test for tagger * experimental settings for the tagger: avoid zero-init and subword normalization * clean up tagger overfitting test * use previous default value for nP * remove toy config * bringing layernorm back (had a bug - fixed in thinc) * revert setting nP explicitly * remove setting default in constructor * restore values as they used to be * add overfitting test for NER * add overfitting test for dep parser * add overfitting test for textcat * fixing init for linear (previously affine) * larger eps window for textcat * ensure doc is not None * Require newer thinc * Make float check vaguer * Slop the textcat overfit test more * Fix textcat test * Fix exclusive classes for textcat * fix after renaming of alloc methods * fixing renames and mandatory arguments (staticvectors WIP) * upgrade to thinc==8.0.0.dev3 * refer to vocab.vectors directly instead of its name * rename alpha to learn_rate * adding hashembed and staticvectors dropout * upgrade to thinc 8.0.0.dev4 * add name back to avoid warning W020 * thinc dev4 * update srsly * using thinc 8.0.0a0 ! Co-authored-by: Matthew Honnibal <honnibal+gh@gmail.com> Co-authored-by: Ines Montani <ines@ines.io>
2020-01-29 19:06:46 +03:00
size_ = iter(size)
else:
size_ = size
examples = iter(examples)
while True:
batch_size = next(size_)
batch = []
while batch_size >= 0:
try:
example = next(examples)
except StopIteration:
if batch:
yield batch
return
batch_size -= count_words(example.doc)
batch.append(example)
if batch:
yield batch
2017-11-07 01:45:36 +03:00
def itershuffle(iterable, bufsize=1000):
"""Shuffle an iterator. This works by holding `bufsize` items back
and yielding them sometime later. Obviously, this is not unbiased
but should be good enough for batching. Larger bufsize means less bias.
From https://gist.github.com/andres-erbsen/1307752
iterable (iterable): Iterator to shuffle.
bufsize (int): Items to hold back.
YIELDS (iterable): The shuffled iterator.
"""
iterable = iter(iterable)
buf = []
try:
while True:
for i in range(random.randint(1, bufsize - len(buf))):
buf.append(next(iterable))
2017-11-07 01:45:36 +03:00
random.shuffle(buf)
for i in range(random.randint(1, bufsize)):
if buf:
yield buf.pop()
else:
break
except StopIteration:
random.shuffle(buf)
while buf:
yield buf.pop()
raise StopIteration
2019-05-08 03:33:40 +03:00
def filter_spans(spans):
"""Filter a sequence of spans and remove duplicates or overlaps. Useful for
creating named entities (where one token can only be part of one entity) or
when merging spans with `Retokenizer.merge`. When spans overlap, the (first)
longest span is preferred over shorter spans.
spans (iterable): The spans to filter.
RETURNS (list): The filtered spans.
"""
get_sort_key = lambda span: (span.end - span.start, -span.start)
2019-05-08 03:33:40 +03:00
sorted_spans = sorted(spans, key=get_sort_key, reverse=True)
result = []
seen_tokens = set()
for span in sorted_spans:
# Check for end - 1 here because boundaries are inclusive
if span.start not in seen_tokens and span.end - 1 not in seen_tokens:
result.append(span)
seen_tokens.update(range(span.start, span.end))
result = sorted(result, key=lambda span: span.start)
return result
2017-05-29 11:13:42 +03:00
def to_bytes(getters, exclude):
serialized = {}
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for key, getter in getters.items():
# Split to support file names like meta.json
if key.split(".")[0] not in exclude:
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serialized[key] = getter()
return srsly.msgpack_dumps(serialized)
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def from_bytes(bytes_data, setters, exclude):
msg = srsly.msgpack_loads(bytes_data)
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for key, setter in setters.items():
# Split to support file names like meta.json
if key.split(".")[0] not in exclude and key in msg:
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setter(msg[key])
return msg
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def to_disk(path, writers, exclude):
path = ensure_path(path)
if not path.exists():
path.mkdir()
for key, writer in writers.items():
# Split to support file names like meta.json
if key.split(".")[0] not in exclude:
2017-05-31 14:42:39 +03:00
writer(path / key)
return path
def from_disk(path, readers, exclude):
path = ensure_path(path)
for key, reader in readers.items():
# Split to support file names like meta.json
if key.split(".")[0] not in exclude:
2017-10-16 21:55:00 +03:00
reader(path / key)
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return path
def import_file(name, loc):
"""Import module from a file. Used to load models from a directory.
name (unicode): Name of module to load.
loc (unicode / Path): Path to the file.
RETURNS: The loaded module.
"""
loc = str(loc)
spec = importlib.util.spec_from_file_location(name, str(loc))
module = importlib.util.module_from_spec(spec)
spec.loader.exec_module(module)
return module
2017-05-14 18:50:23 +03:00
def minify_html(html):
"""Perform a template-specific, rudimentary HTML minification for displaCy.
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Disclaimer: NOT a general-purpose solution, only removes indentation and
newlines.
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html (unicode): Markup to minify.
RETURNS (unicode): "Minified" HTML.
"""
return html.strip().replace(" ", "").replace("\n", "")
2017-09-21 03:16:35 +03:00
def escape_html(text):
"""Replace <, >, &, " with their HTML encoded representation. Intended to
prevent HTML errors in rendered displaCy markup.
text (unicode): The original text.
RETURNS (unicode): Equivalent text to be safely used within HTML.
"""
text = text.replace("&", "&amp;")
text = text.replace("<", "&lt;")
text = text.replace(">", "&gt;")
text = text.replace('"', "&quot;")
return text
2017-09-21 03:16:35 +03:00
def use_gpu(gpu_id):
Update spaCy for thinc 8.0.0 (#4920) * Add load_from_config function * Add train_from_config script * Merge configs and expose via spacy.config * Fix script * Suggest create_evaluation_callback * Hard-code for NER * Fix errors * Register command * Add TODO * Update train-from-config todos * Fix imports * Allow delayed setting of parser model nr_class * Get train-from-config working * Tidy up and fix scores and printing * Hide traceback if cancelled * Fix weighted score formatting * Fix score formatting * Make output_path optional * Add Tok2Vec component * Tidy up and add tok2vec_tensors * Add option to copy docs in nlp.update * Copy docs in nlp.update * Adjust nlp.update() for set_annotations * Don't shuffle pipes in nlp.update, decruft * Support set_annotations arg in component update * Support set_annotations in parser update * Add get_gradients method * Add get_gradients to parser * Update errors.py * Fix problems caused by merge * Add _link_components method in nlp * Add concept of 'listeners' and ControlledModel * Support optional attributes arg in ControlledModel * Try having tok2vec component in pipeline * Fix tok2vec component * Fix config * Fix tok2vec * Update for Example * Update for Example * Update config * Add eg2doc util * Update and add schemas/types * Update schemas * Fix nlp.update * Fix tagger * Remove hacks from train-from-config * Remove hard-coded config str * Calculate loss in tok2vec component * Tidy up and use function signatures instead of models * Support union types for registry models * Minor cleaning in Language.update * Make ControlledModel specifically Tok2VecListener * Fix train_from_config * Fix tok2vec * Tidy up * Add function for bilstm tok2vec * Fix type * Fix syntax * Fix pytorch optimizer * Add example configs * Update for thinc describe changes * Update for Thinc changes * Update for dropout/sgd changes * Update for dropout/sgd changes * Unhack gradient update * Work on refactoring _ml * Remove _ml.py module * WIP upgrade cli scripts for thinc * Move some _ml stuff to util * Import link_vectors from util * Update train_from_config * Import from util * Import from util * Temporarily add ml.component_models module * Move ml methods * Move typedefs * Update load vectors * Update gitignore * Move imports * Add PrecomputableAffine * Fix imports * Fix imports * Fix imports * Fix missing imports * Update CLI scripts * Update spacy.language * Add stubs for building the models * Update model definition * Update create_default_optimizer * Fix import * Fix comment * Update imports in tests * Update imports in spacy.cli * Fix import * fix obsolete thinc imports * update srsly pin * from thinc to ml_datasets for example data such as imdb * update ml_datasets pin * using STATE.vectors * small fix * fix Sentencizer.pipe * black formatting * rename Affine to Linear as in thinc * set validate explicitely to True * rename with_square_sequences to with_list2padded * rename with_flatten to with_list2array * chaining layernorm * small fixes * revert Optimizer import * build_nel_encoder with new thinc style * fixes using model's get and set methods * Tok2Vec in component models, various fixes * fix up legacy tok2vec code * add model initialize calls * add in build_tagger_model * small fixes * setting model dims * fixes for ParserModel * various small fixes * initialize thinc Models * fixes * consistent naming of window_size * fixes, removing set_dropout * work around Iterable issue * remove legacy tok2vec * util fix * fix forward function of tok2vec listener * more fixes * trying to fix PrecomputableAffine (not succesful yet) * alloc instead of allocate * add morphologizer * rename residual * rename fixes * Fix predict function * Update parser and parser model * fixing few more tests * Fix precomputable affine * Update component model * Update parser model * Move backprop padding to own function, for test * Update test * Fix p. affine * Update NEL * build_bow_text_classifier and extract_ngrams * Fix parser init * Fix test add label * add build_simple_cnn_text_classifier * Fix parser init * Set gpu off by default in example * Fix tok2vec listener * Fix parser model * Small fixes * small fix for PyTorchLSTM parameters * revert my_compounding hack (iterable fixed now) * fix biLSTM * Fix uniqued * PyTorchRNNWrapper fix * small fixes * use helper function to calculate cosine loss * small fixes for build_simple_cnn_text_classifier * putting dropout default at 0.0 to ensure the layer gets built * using thinc util's set_dropout_rate * moving layer normalization inside of maxout definition to optimize dropout * temp debugging in NEL * fixed NEL model by using init defaults ! * fixing after set_dropout_rate refactor * proper fix * fix test_update_doc after refactoring optimizers in thinc * Add CharacterEmbed layer * Construct tagger Model * Add missing import * Remove unused stuff * Work on textcat * fix test (again :)) after optimizer refactor * fixes to allow reading Tagger from_disk without overwriting dimensions * don't build the tok2vec prematuraly * fix CharachterEmbed init * CharacterEmbed fixes * Fix CharacterEmbed architecture * fix imports * renames from latest thinc update * one more rename * add initialize calls where appropriate * fix parser initialization * Update Thinc version * Fix errors, auto-format and tidy up imports * Fix validation * fix if bias is cupy array * revert for now * ensure it's a numpy array before running bp in ParserStepModel * no reason to call require_gpu twice * use CupyOps.to_numpy instead of cupy directly * fix initialize of ParserModel * remove unnecessary import * fixes for CosineDistance * fix device renaming * use refactored loss functions (Thinc PR 251) * overfitting test for tagger * experimental settings for the tagger: avoid zero-init and subword normalization * clean up tagger overfitting test * use previous default value for nP * remove toy config * bringing layernorm back (had a bug - fixed in thinc) * revert setting nP explicitly * remove setting default in constructor * restore values as they used to be * add overfitting test for NER * add overfitting test for dep parser * add overfitting test for textcat * fixing init for linear (previously affine) * larger eps window for textcat * ensure doc is not None * Require newer thinc * Make float check vaguer * Slop the textcat overfit test more * Fix textcat test * Fix exclusive classes for textcat * fix after renaming of alloc methods * fixing renames and mandatory arguments (staticvectors WIP) * upgrade to thinc==8.0.0.dev3 * refer to vocab.vectors directly instead of its name * rename alpha to learn_rate * adding hashembed and staticvectors dropout * upgrade to thinc 8.0.0.dev4 * add name back to avoid warning W020 * thinc dev4 * update srsly * using thinc 8.0.0a0 ! Co-authored-by: Matthew Honnibal <honnibal+gh@gmail.com> Co-authored-by: Ines Montani <ines@ines.io>
2020-01-29 19:06:46 +03:00
return require_gpu(gpu_id)
2018-02-13 14:42:23 +03:00
def fix_random_seed(seed=0):
2018-02-13 14:52:48 +03:00
random.seed(seed)
numpy.random.seed(seed)
if cupy is not None:
cupy.random.seed(seed)
def get_serialization_exclude(serializers, exclude, kwargs):
"""Helper function to validate serialization args and manage transition from
keyword arguments (pre v2.1) to exclude argument.
"""
exclude = list(exclude)
# Split to support file names like meta.json
options = [name.split(".")[0] for name in serializers]
for key, value in kwargs.items():
if key in ("vocab",) and value is False:
2020-02-28 14:20:23 +03:00
warnings.warn(Warnings.W015.format(arg=key), DeprecationWarning)
exclude.append(key)
elif key.split(".")[0] in options:
raise ValueError(Errors.E128.format(arg=key))
# TODO: user warning?
return exclude
class SimpleFrozenDict(dict):
"""Simplified implementation of a frozen dict, mainly used as default
function or method argument (for arguments that should default to empty
dictionary). Will raise an error if user or spaCy attempts to add to dict.
"""
def __setitem__(self, key, value):
raise NotImplementedError(Errors.E095)
def pop(self, key, default=None):
raise NotImplementedError(Errors.E095)
def update(self, other):
raise NotImplementedError(Errors.E095)
class DummyTokenizer(object):
# add dummy methods for to_bytes, from_bytes, to_disk and from_disk to
# allow serialization (see #1557)
def to_bytes(self, **kwargs):
2019-02-07 23:00:04 +03:00
return b""
def from_bytes(self, _bytes_data, **kwargs):
return self
def to_disk(self, _path, **kwargs):
return None
def from_disk(self, _path, **kwargs):
return self
Update spaCy for thinc 8.0.0 (#4920) * Add load_from_config function * Add train_from_config script * Merge configs and expose via spacy.config * Fix script * Suggest create_evaluation_callback * Hard-code for NER * Fix errors * Register command * Add TODO * Update train-from-config todos * Fix imports * Allow delayed setting of parser model nr_class * Get train-from-config working * Tidy up and fix scores and printing * Hide traceback if cancelled * Fix weighted score formatting * Fix score formatting * Make output_path optional * Add Tok2Vec component * Tidy up and add tok2vec_tensors * Add option to copy docs in nlp.update * Copy docs in nlp.update * Adjust nlp.update() for set_annotations * Don't shuffle pipes in nlp.update, decruft * Support set_annotations arg in component update * Support set_annotations in parser update * Add get_gradients method * Add get_gradients to parser * Update errors.py * Fix problems caused by merge * Add _link_components method in nlp * Add concept of 'listeners' and ControlledModel * Support optional attributes arg in ControlledModel * Try having tok2vec component in pipeline * Fix tok2vec component * Fix config * Fix tok2vec * Update for Example * Update for Example * Update config * Add eg2doc util * Update and add schemas/types * Update schemas * Fix nlp.update * Fix tagger * Remove hacks from train-from-config * Remove hard-coded config str * Calculate loss in tok2vec component * Tidy up and use function signatures instead of models * Support union types for registry models * Minor cleaning in Language.update * Make ControlledModel specifically Tok2VecListener * Fix train_from_config * Fix tok2vec * Tidy up * Add function for bilstm tok2vec * Fix type * Fix syntax * Fix pytorch optimizer * Add example configs * Update for thinc describe changes * Update for Thinc changes * Update for dropout/sgd changes * Update for dropout/sgd changes * Unhack gradient update * Work on refactoring _ml * Remove _ml.py module * WIP upgrade cli scripts for thinc * Move some _ml stuff to util * Import link_vectors from util * Update train_from_config * Import from util * Import from util * Temporarily add ml.component_models module * Move ml methods * Move typedefs * Update load vectors * Update gitignore * Move imports * Add PrecomputableAffine * Fix imports * Fix imports * Fix imports * Fix missing imports * Update CLI scripts * Update spacy.language * Add stubs for building the models * Update model definition * Update create_default_optimizer * Fix import * Fix comment * Update imports in tests * Update imports in spacy.cli * Fix import * fix obsolete thinc imports * update srsly pin * from thinc to ml_datasets for example data such as imdb * update ml_datasets pin * using STATE.vectors * small fix * fix Sentencizer.pipe * black formatting * rename Affine to Linear as in thinc * set validate explicitely to True * rename with_square_sequences to with_list2padded * rename with_flatten to with_list2array * chaining layernorm * small fixes * revert Optimizer import * build_nel_encoder with new thinc style * fixes using model's get and set methods * Tok2Vec in component models, various fixes * fix up legacy tok2vec code * add model initialize calls * add in build_tagger_model * small fixes * setting model dims * fixes for ParserModel * various small fixes * initialize thinc Models * fixes * consistent naming of window_size * fixes, removing set_dropout * work around Iterable issue * remove legacy tok2vec * util fix * fix forward function of tok2vec listener * more fixes * trying to fix PrecomputableAffine (not succesful yet) * alloc instead of allocate * add morphologizer * rename residual * rename fixes * Fix predict function * Update parser and parser model * fixing few more tests * Fix precomputable affine * Update component model * Update parser model * Move backprop padding to own function, for test * Update test * Fix p. affine * Update NEL * build_bow_text_classifier and extract_ngrams * Fix parser init * Fix test add label * add build_simple_cnn_text_classifier * Fix parser init * Set gpu off by default in example * Fix tok2vec listener * Fix parser model * Small fixes * small fix for PyTorchLSTM parameters * revert my_compounding hack (iterable fixed now) * fix biLSTM * Fix uniqued * PyTorchRNNWrapper fix * small fixes * use helper function to calculate cosine loss * small fixes for build_simple_cnn_text_classifier * putting dropout default at 0.0 to ensure the layer gets built * using thinc util's set_dropout_rate * moving layer normalization inside of maxout definition to optimize dropout * temp debugging in NEL * fixed NEL model by using init defaults ! * fixing after set_dropout_rate refactor * proper fix * fix test_update_doc after refactoring optimizers in thinc * Add CharacterEmbed layer * Construct tagger Model * Add missing import * Remove unused stuff * Work on textcat * fix test (again :)) after optimizer refactor * fixes to allow reading Tagger from_disk without overwriting dimensions * don't build the tok2vec prematuraly * fix CharachterEmbed init * CharacterEmbed fixes * Fix CharacterEmbed architecture * fix imports * renames from latest thinc update * one more rename * add initialize calls where appropriate * fix parser initialization * Update Thinc version * Fix errors, auto-format and tidy up imports * Fix validation * fix if bias is cupy array * revert for now * ensure it's a numpy array before running bp in ParserStepModel * no reason to call require_gpu twice * use CupyOps.to_numpy instead of cupy directly * fix initialize of ParserModel * remove unnecessary import * fixes for CosineDistance * fix device renaming * use refactored loss functions (Thinc PR 251) * overfitting test for tagger * experimental settings for the tagger: avoid zero-init and subword normalization * clean up tagger overfitting test * use previous default value for nP * remove toy config * bringing layernorm back (had a bug - fixed in thinc) * revert setting nP explicitly * remove setting default in constructor * restore values as they used to be * add overfitting test for NER * add overfitting test for dep parser * add overfitting test for textcat * fixing init for linear (previously affine) * larger eps window for textcat * ensure doc is not None * Require newer thinc * Make float check vaguer * Slop the textcat overfit test more * Fix textcat test * Fix exclusive classes for textcat * fix after renaming of alloc methods * fixing renames and mandatory arguments (staticvectors WIP) * upgrade to thinc==8.0.0.dev3 * refer to vocab.vectors directly instead of its name * rename alpha to learn_rate * adding hashembed and staticvectors dropout * upgrade to thinc 8.0.0.dev4 * add name back to avoid warning W020 * thinc dev4 * update srsly * using thinc 8.0.0a0 ! Co-authored-by: Matthew Honnibal <honnibal+gh@gmail.com> Co-authored-by: Ines Montani <ines@ines.io>
2020-01-29 19:06:46 +03:00
def link_vectors_to_models(vocab):
vectors = vocab.vectors
if vectors.name is None:
vectors.name = VECTORS_KEY
if vectors.data.size != 0:
2020-02-28 14:20:23 +03:00
warnings.warn(Warnings.W020.format(shape=vectors.data.shape))
Update spaCy for thinc 8.0.0 (#4920) * Add load_from_config function * Add train_from_config script * Merge configs and expose via spacy.config * Fix script * Suggest create_evaluation_callback * Hard-code for NER * Fix errors * Register command * Add TODO * Update train-from-config todos * Fix imports * Allow delayed setting of parser model nr_class * Get train-from-config working * Tidy up and fix scores and printing * Hide traceback if cancelled * Fix weighted score formatting * Fix score formatting * Make output_path optional * Add Tok2Vec component * Tidy up and add tok2vec_tensors * Add option to copy docs in nlp.update * Copy docs in nlp.update * Adjust nlp.update() for set_annotations * Don't shuffle pipes in nlp.update, decruft * Support set_annotations arg in component update * Support set_annotations in parser update * Add get_gradients method * Add get_gradients to parser * Update errors.py * Fix problems caused by merge * Add _link_components method in nlp * Add concept of 'listeners' and ControlledModel * Support optional attributes arg in ControlledModel * Try having tok2vec component in pipeline * Fix tok2vec component * Fix config * Fix tok2vec * Update for Example * Update for Example * Update config * Add eg2doc util * Update and add schemas/types * Update schemas * Fix nlp.update * Fix tagger * Remove hacks from train-from-config * Remove hard-coded config str * Calculate loss in tok2vec component * Tidy up and use function signatures instead of models * Support union types for registry models * Minor cleaning in Language.update * Make ControlledModel specifically Tok2VecListener * Fix train_from_config * Fix tok2vec * Tidy up * Add function for bilstm tok2vec * Fix type * Fix syntax * Fix pytorch optimizer * Add example configs * Update for thinc describe changes * Update for Thinc changes * Update for dropout/sgd changes * Update for dropout/sgd changes * Unhack gradient update * Work on refactoring _ml * Remove _ml.py module * WIP upgrade cli scripts for thinc * Move some _ml stuff to util * Import link_vectors from util * Update train_from_config * Import from util * Import from util * Temporarily add ml.component_models module * Move ml methods * Move typedefs * Update load vectors * Update gitignore * Move imports * Add PrecomputableAffine * Fix imports * Fix imports * Fix imports * Fix missing imports * Update CLI scripts * Update spacy.language * Add stubs for building the models * Update model definition * Update create_default_optimizer * Fix import * Fix comment * Update imports in tests * Update imports in spacy.cli * Fix import * fix obsolete thinc imports * update srsly pin * from thinc to ml_datasets for example data such as imdb * update ml_datasets pin * using STATE.vectors * small fix * fix Sentencizer.pipe * black formatting * rename Affine to Linear as in thinc * set validate explicitely to True * rename with_square_sequences to with_list2padded * rename with_flatten to with_list2array * chaining layernorm * small fixes * revert Optimizer import * build_nel_encoder with new thinc style * fixes using model's get and set methods * Tok2Vec in component models, various fixes * fix up legacy tok2vec code * add model initialize calls * add in build_tagger_model * small fixes * setting model dims * fixes for ParserModel * various small fixes * initialize thinc Models * fixes * consistent naming of window_size * fixes, removing set_dropout * work around Iterable issue * remove legacy tok2vec * util fix * fix forward function of tok2vec listener * more fixes * trying to fix PrecomputableAffine (not succesful yet) * alloc instead of allocate * add morphologizer * rename residual * rename fixes * Fix predict function * Update parser and parser model * fixing few more tests * Fix precomputable affine * Update component model * Update parser model * Move backprop padding to own function, for test * Update test * Fix p. affine * Update NEL * build_bow_text_classifier and extract_ngrams * Fix parser init * Fix test add label * add build_simple_cnn_text_classifier * Fix parser init * Set gpu off by default in example * Fix tok2vec listener * Fix parser model * Small fixes * small fix for PyTorchLSTM parameters * revert my_compounding hack (iterable fixed now) * fix biLSTM * Fix uniqued * PyTorchRNNWrapper fix * small fixes * use helper function to calculate cosine loss * small fixes for build_simple_cnn_text_classifier * putting dropout default at 0.0 to ensure the layer gets built * using thinc util's set_dropout_rate * moving layer normalization inside of maxout definition to optimize dropout * temp debugging in NEL * fixed NEL model by using init defaults ! * fixing after set_dropout_rate refactor * proper fix * fix test_update_doc after refactoring optimizers in thinc * Add CharacterEmbed layer * Construct tagger Model * Add missing import * Remove unused stuff * Work on textcat * fix test (again :)) after optimizer refactor * fixes to allow reading Tagger from_disk without overwriting dimensions * don't build the tok2vec prematuraly * fix CharachterEmbed init * CharacterEmbed fixes * Fix CharacterEmbed architecture * fix imports * renames from latest thinc update * one more rename * add initialize calls where appropriate * fix parser initialization * Update Thinc version * Fix errors, auto-format and tidy up imports * Fix validation * fix if bias is cupy array * revert for now * ensure it's a numpy array before running bp in ParserStepModel * no reason to call require_gpu twice * use CupyOps.to_numpy instead of cupy directly * fix initialize of ParserModel * remove unnecessary import * fixes for CosineDistance * fix device renaming * use refactored loss functions (Thinc PR 251) * overfitting test for tagger * experimental settings for the tagger: avoid zero-init and subword normalization * clean up tagger overfitting test * use previous default value for nP * remove toy config * bringing layernorm back (had a bug - fixed in thinc) * revert setting nP explicitly * remove setting default in constructor * restore values as they used to be * add overfitting test for NER * add overfitting test for dep parser * add overfitting test for textcat * fixing init for linear (previously affine) * larger eps window for textcat * ensure doc is not None * Require newer thinc * Make float check vaguer * Slop the textcat overfit test more * Fix textcat test * Fix exclusive classes for textcat * fix after renaming of alloc methods * fixing renames and mandatory arguments (staticvectors WIP) * upgrade to thinc==8.0.0.dev3 * refer to vocab.vectors directly instead of its name * rename alpha to learn_rate * adding hashembed and staticvectors dropout * upgrade to thinc 8.0.0.dev4 * add name back to avoid warning W020 * thinc dev4 * update srsly * using thinc 8.0.0a0 ! Co-authored-by: Matthew Honnibal <honnibal+gh@gmail.com> Co-authored-by: Ines Montani <ines@ines.io>
2020-01-29 19:06:46 +03:00
for word in vocab:
if word.orth in vectors.key2row:
word.rank = vectors.key2row[word.orth]
else:
word.rank = 0
VECTORS_KEY = "spacy_pretrained_vectors"
def create_default_optimizer():
learn_rate = env_opt("learn_rate", 0.001)
beta1 = env_opt("optimizer_B1", 0.9)
beta2 = env_opt("optimizer_B2", 0.999)
eps = env_opt("optimizer_eps", 1e-8)
L2 = env_opt("L2_penalty", 1e-6)
grad_clip = env_opt("grad_norm_clip", 10.0)
L2_is_weight_decay = env_opt("L2_is_weight_decay", False)
optimizer = Adam(
learn_rate,
L2=L2,
beta1=beta1,
beta2=beta2,
eps=eps,
grad_clip=grad_clip,
L2_is_weight_decay=L2_is_weight_decay,
)
Update spaCy for thinc 8.0.0 (#4920) * Add load_from_config function * Add train_from_config script * Merge configs and expose via spacy.config * Fix script * Suggest create_evaluation_callback * Hard-code for NER * Fix errors * Register command * Add TODO * Update train-from-config todos * Fix imports * Allow delayed setting of parser model nr_class * Get train-from-config working * Tidy up and fix scores and printing * Hide traceback if cancelled * Fix weighted score formatting * Fix score formatting * Make output_path optional * Add Tok2Vec component * Tidy up and add tok2vec_tensors * Add option to copy docs in nlp.update * Copy docs in nlp.update * Adjust nlp.update() for set_annotations * Don't shuffle pipes in nlp.update, decruft * Support set_annotations arg in component update * Support set_annotations in parser update * Add get_gradients method * Add get_gradients to parser * Update errors.py * Fix problems caused by merge * Add _link_components method in nlp * Add concept of 'listeners' and ControlledModel * Support optional attributes arg in ControlledModel * Try having tok2vec component in pipeline * Fix tok2vec component * Fix config * Fix tok2vec * Update for Example * Update for Example * Update config * Add eg2doc util * Update and add schemas/types * Update schemas * Fix nlp.update * Fix tagger * Remove hacks from train-from-config * Remove hard-coded config str * Calculate loss in tok2vec component * Tidy up and use function signatures instead of models * Support union types for registry models * Minor cleaning in Language.update * Make ControlledModel specifically Tok2VecListener * Fix train_from_config * Fix tok2vec * Tidy up * Add function for bilstm tok2vec * Fix type * Fix syntax * Fix pytorch optimizer * Add example configs * Update for thinc describe changes * Update for Thinc changes * Update for dropout/sgd changes * Update for dropout/sgd changes * Unhack gradient update * Work on refactoring _ml * Remove _ml.py module * WIP upgrade cli scripts for thinc * Move some _ml stuff to util * Import link_vectors from util * Update train_from_config * Import from util * Import from util * Temporarily add ml.component_models module * Move ml methods * Move typedefs * Update load vectors * Update gitignore * Move imports * Add PrecomputableAffine * Fix imports * Fix imports * Fix imports * Fix missing imports * Update CLI scripts * Update spacy.language * Add stubs for building the models * Update model definition * Update create_default_optimizer * Fix import * Fix comment * Update imports in tests * Update imports in spacy.cli * Fix import * fix obsolete thinc imports * update srsly pin * from thinc to ml_datasets for example data such as imdb * update ml_datasets pin * using STATE.vectors * small fix * fix Sentencizer.pipe * black formatting * rename Affine to Linear as in thinc * set validate explicitely to True * rename with_square_sequences to with_list2padded * rename with_flatten to with_list2array * chaining layernorm * small fixes * revert Optimizer import * build_nel_encoder with new thinc style * fixes using model's get and set methods * Tok2Vec in component models, various fixes * fix up legacy tok2vec code * add model initialize calls * add in build_tagger_model * small fixes * setting model dims * fixes for ParserModel * various small fixes * initialize thinc Models * fixes * consistent naming of window_size * fixes, removing set_dropout * work around Iterable issue * remove legacy tok2vec * util fix * fix forward function of tok2vec listener * more fixes * trying to fix PrecomputableAffine (not succesful yet) * alloc instead of allocate * add morphologizer * rename residual * rename fixes * Fix predict function * Update parser and parser model * fixing few more tests * Fix precomputable affine * Update component model * Update parser model * Move backprop padding to own function, for test * Update test * Fix p. affine * Update NEL * build_bow_text_classifier and extract_ngrams * Fix parser init * Fix test add label * add build_simple_cnn_text_classifier * Fix parser init * Set gpu off by default in example * Fix tok2vec listener * Fix parser model * Small fixes * small fix for PyTorchLSTM parameters * revert my_compounding hack (iterable fixed now) * fix biLSTM * Fix uniqued * PyTorchRNNWrapper fix * small fixes * use helper function to calculate cosine loss * small fixes for build_simple_cnn_text_classifier * putting dropout default at 0.0 to ensure the layer gets built * using thinc util's set_dropout_rate * moving layer normalization inside of maxout definition to optimize dropout * temp debugging in NEL * fixed NEL model by using init defaults ! * fixing after set_dropout_rate refactor * proper fix * fix test_update_doc after refactoring optimizers in thinc * Add CharacterEmbed layer * Construct tagger Model * Add missing import * Remove unused stuff * Work on textcat * fix test (again :)) after optimizer refactor * fixes to allow reading Tagger from_disk without overwriting dimensions * don't build the tok2vec prematuraly * fix CharachterEmbed init * CharacterEmbed fixes * Fix CharacterEmbed architecture * fix imports * renames from latest thinc update * one more rename * add initialize calls where appropriate * fix parser initialization * Update Thinc version * Fix errors, auto-format and tidy up imports * Fix validation * fix if bias is cupy array * revert for now * ensure it's a numpy array before running bp in ParserStepModel * no reason to call require_gpu twice * use CupyOps.to_numpy instead of cupy directly * fix initialize of ParserModel * remove unnecessary import * fixes for CosineDistance * fix device renaming * use refactored loss functions (Thinc PR 251) * overfitting test for tagger * experimental settings for the tagger: avoid zero-init and subword normalization * clean up tagger overfitting test * use previous default value for nP * remove toy config * bringing layernorm back (had a bug - fixed in thinc) * revert setting nP explicitly * remove setting default in constructor * restore values as they used to be * add overfitting test for NER * add overfitting test for dep parser * add overfitting test for textcat * fixing init for linear (previously affine) * larger eps window for textcat * ensure doc is not None * Require newer thinc * Make float check vaguer * Slop the textcat overfit test more * Fix textcat test * Fix exclusive classes for textcat * fix after renaming of alloc methods * fixing renames and mandatory arguments (staticvectors WIP) * upgrade to thinc==8.0.0.dev3 * refer to vocab.vectors directly instead of its name * rename alpha to learn_rate * adding hashembed and staticvectors dropout * upgrade to thinc 8.0.0.dev4 * add name back to avoid warning W020 * thinc dev4 * update srsly * using thinc 8.0.0a0 ! Co-authored-by: Matthew Honnibal <honnibal+gh@gmail.com> Co-authored-by: Ines Montani <ines@ines.io>
2020-01-29 19:06:46 +03:00
return optimizer