mirror of https://github.com/explosion/spaCy.git
8 Commits
| Author | SHA1 | Message | Date |
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Sofie Van Landeghem
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569cc98982
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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> |
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Sofie Van Landeghem
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e48a09df4e |
Example class for training data (#4543)
* OrigAnnot class instead of gold.orig_annot list of zipped tuples * from_orig to replace from_annot_tuples * rename to RawAnnot * some unit tests for GoldParse creation and internal format * removing orig_annot and switching to lists instead of tuple * rewriting tuples to use RawAnnot (+ debug statements, WIP) * fix pop() changing the data * small fixes * pop-append fixes * return RawAnnot for existing GoldParse to have uniform interface * clean up imports * fix merge_sents * add unit test for 4402 with new structure (not working yet) * introduce DocAnnot * typo fixes * add unit test for merge_sents * rename from_orig to from_raw * fixing unit tests * fix nn parser * read_annots to produce text, doc_annot pairs * _make_golds fix * rename golds_to_gold_annots * small fixes * fix encoding * have golds_to_gold_annots use DocAnnot * missed a spot * merge_sents as function in DocAnnot * allow specifying only part of the token-level annotations * refactor with Example class + underlying dicts * pipeline components to work with Example objects (wip) * input checking * fix yielding * fix calls to update * small fixes * fix scorer unit test with new format * fix kwargs order * fixes for ud and conllu scripts * fix reading data for conllu script * add in proper errors (not fixed numbering yet to avoid merge conflicts) * fixing few more small bugs * fix EL script |
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Zhuoru Lin
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10d88b09bb |
Bugfix/fix wikidata train entity linker (#4509)
* Fix labels_discard Nonetype iteration error * Contributor agreement for Zhuoru Lin * Enhance EntityLinker.predict() to handle labels_discard is None case. |
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Sofie Van Landeghem
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2d249a9502 |
KB extensions and better parsing of WikiData (#4375)
* fix overflow error on windows * more documentation & logging fixes * md fix * 3 different limit parameters to play with execution time * bug fixes directory locations * small fixes * exclude dev test articles from prior probabilities stats * small fixes * filtering wikidata entities, removing numeric and meta items * adding aliases from wikidata also to the KB * fix adding WD aliases * adding also new aliases to previously added entities * fixing comma's * small doc fixes * adding subclassof filtering * append alias functionality in KB * prevent appending the same entity-alias pair * fix for appending WD aliases * remove date filter * remove unnecessary import * small corrections and reformatting * remove WD aliases for now (too slow) * removing numeric entities from training and evaluation * small fixes * shortcut during prediction if there is only one candidate * add counts and fscore logging, remove FP NER from evaluation * fix entity_linker.predict to take docs instead of single sentences * remove enumeration sentences from the WP dataset * entity_linker.update to process full doc instead of single sentence * spelling corrections and dump locations in readme * NLP IO fix * reading KB is unnecessary at the end of the pipeline * small logging fix * remove empty files |
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Ines Montani
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b6670bf0c2 | Use consistent spelling | |
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Euan Dowers
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a6830d60e8 |
Changes to wiki_entity_linker (#4235)
* Changes to wiki_entity_linker * No more f-strings * Make some requested changes * Add back option to get descriptions from wd not wp * Fix logs * Address comments and clean evaluation * Remove type hints * Refactor evaluation, add back metrics by label * Address comments * Log training performance as well as dev |
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adrianeboyd
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8fe7bdd0fa |
Improve token pattern checking without validation (#4105)
* Fix typo in rule-based matching docs * Improve token pattern checking without validation Add more detailed token pattern checks without full JSON pattern validation and provide more detailed error messages. Addresses #4070 (also related: #4063, #4100). * Check whether top-level attributes in patterns and attr for PhraseMatcher are in token pattern schema * Check whether attribute value types are supported in general (as opposed to per attribute with full validation) * Report various internal error types (OverflowError, AttributeError, KeyError) as ValueError with standard error messages * Check for tagger/parser in PhraseMatcher pipeline for attributes TAG, POS, LEMMA, and DEP * Add error messages with relevant details on how to use validate=True or nlp() instead of nlp.make_doc() * Support attr=TEXT for PhraseMatcher * Add NORM to schema * Expand tests for pattern validation, Matcher, PhraseMatcher, and EntityRuler * Remove unnecessary .keys() * Rephrase error messages * Add another type check to Matcher Add another type check to Matcher for more understandable error messages in some rare cases. * Support phrase_matcher_attr=TEXT for EntityRuler * Don't use spacy.errors in examples and bin scripts * Fix error code * Auto-format Also try get Azure pipelines to finally start a build :( * Update errors.py Co-authored-by: Ines Montani <ines@ines.io> Co-authored-by: Matthew Honnibal <honnibal+gh@gmail.com> |
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Sofie Van Landeghem
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0ba1b5eebc |
CLI scripts for entity linking (wikipedia & generic) (#4091)
* document token ent_kb_id * document span kb_id * update pipeline documentation * prior and context weights as bool's instead * entitylinker api documentation * drop for both models * finish entitylinker documentation * small fixes * documentation for KB * candidate documentation * links to api pages in code * small fix * frequency examples as counts for consistency * consistent documentation about tensors returned by predict * add entity linking to usage 101 * add entity linking infobox and KB section to 101 * entity-linking in linguistic features * small typo corrections * training example and docs for entity_linker * predefined nlp and kb * revert back to similarity encodings for simplicity (for now) * set prior probabilities to 0 when excluded * code clean up * bugfix: deleting kb ID from tokens when entities were removed * refactor train el example to use either model or vocab * pretrain_kb example for example kb generation * add to training docs for KB + EL example scripts * small fixes * error numbering * ensure the language of vocab and nlp stay consistent across serialization * equality with = * avoid conflict in errors file * add error 151 * final adjustements to the train scripts - consistency * update of goldparse documentation * small corrections * push commit * turn kb_creator into CLI script (wip) * proper parameters for training entity vectors * wikidata pipeline split up into two executable scripts * remove context_width * move wikidata scripts in bin directory, remove old dummy script * refine KB script with logs and preprocessing options * small edits * small improvements to logging of EL CLI script |