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* Update with WIP * Update with WIP * Update with pipeline serialization * Update types and pipe factories * Add deep merge, tidy up and add tests * Fix pipe creation from config * Don't validate default configs on load * Update spacy/language.py Co-authored-by: Ines Montani <ines@ines.io> * Adjust factory/component meta error * Clean up factory args and remove defaults * Add test for failing empty dict defaults * Update pipeline handling and methods * provide KB as registry function instead of as object * small change in test to make functionality more clear * update example script for EL configuration * Fix typo * Simplify test * Simplify test * splitting pipes.pyx into separate files * moving default configs to each component file * fix batch_size type * removing default values from component constructors where possible (TODO: test 4725) * skip instead of xfail * Add test for config -> nlp with multiple instances * pipeline.pipes -> pipeline.pipe * Tidy up, document, remove kwargs * small cleanup/generalization for Tok2VecListener * use DEFAULT_UPSTREAM field * revert to avoid circular imports * Fix tests * Replace deprecated arg * Make model dirs require config * fix pickling of keyword-only arguments in constructor * WIP: clean up and integrate full config * Add helper to handle function args more reliably Now also includes keyword-only args * Fix config composition and serialization * Improve config debugging and add visual diff * Remove unused defaults and fix type * Remove pipeline and factories from meta * Update spacy/default_config.cfg Co-authored-by: Sofie Van Landeghem <svlandeg@users.noreply.github.com> * Update spacy/default_config.cfg * small UX edits * avoid printing stack trace for debug CLI commands * Add support for language-specific factories * specify the section of the config which holds the model to debug * WIP: add Language.from_config * Update with language data refactor WIP * Auto-format * Add backwards-compat handling for Language.factories * Update morphologizer.pyx * Fix morphologizer * Update and simplify lemmatizers * Fix Japanese tests * Port over tagger changes * Fix Chinese and tests * Update to latest Thinc * WIP: xfail first Russian lemmatizer test * Fix component-specific overrides * fix nO for output layers in debug_model * Fix default value * Fix tests and don't pass objects in config * Fix deep merging * Fix lemma lookup data registry Only load the lookups if an entry is available in the registry (and if spacy-lookups-data is installed) * Add types * Add Vocab.from_config * Fix typo * Fix tests * Make config copying more elegant * Fix pipe analysis * Fix lemmatizers and is_base_form * WIP: move language defaults to config * Fix morphology type * Fix vocab * Remove comment * Update to latest Thinc * Add morph rules to config * Tidy up * Remove set_morphology option from tagger factory * Hack use_gpu * Move [pipeline] to top-level block and make [nlp.pipeline] list Allows separating component blocks from component order – otherwise, ordering the config would mean a changed component order, which is bad. Also allows initial config to define more components and not use all of them * Fix use_gpu and resume in CLI * Auto-format * Remove resume from config * Fix formatting and error * [pipeline] -> [components] * Fix types * Fix tagger test: requires set_morphology? Co-authored-by: Sofie Van Landeghem <svlandeg@users.noreply.github.com> Co-authored-by: svlandeg <sofie.vanlandeghem@gmail.com> Co-authored-by: Matthew Honnibal <honnibal+gh@gmail.com>
38 lines
1.5 KiB
Python
38 lines
1.5 KiB
Python
import numpy as np
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from spacy.lang.en import English
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def test_issue5082():
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# Ensure the 'merge_entities' pipeline does something sensible for the vectors of the merged tokens
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nlp = English()
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vocab = nlp.vocab
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array1 = np.asarray([0.1, 0.5, 0.8], dtype=np.float32)
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array2 = np.asarray([-0.2, -0.6, -0.9], dtype=np.float32)
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array3 = np.asarray([0.3, -0.1, 0.7], dtype=np.float32)
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array4 = np.asarray([0.5, 0, 0.3], dtype=np.float32)
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array34 = np.asarray([0.4, -0.05, 0.5], dtype=np.float32)
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vocab.set_vector("I", array1)
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vocab.set_vector("like", array2)
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vocab.set_vector("David", array3)
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vocab.set_vector("Bowie", array4)
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text = "I like David Bowie"
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patterns = [
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{"label": "PERSON", "pattern": [{"LOWER": "david"}, {"LOWER": "bowie"}]}
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]
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ruler = nlp.add_pipe("entity_ruler")
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ruler.add_patterns(patterns)
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parsed_vectors_1 = [t.vector for t in nlp(text)]
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assert len(parsed_vectors_1) == 4
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np.testing.assert_array_equal(parsed_vectors_1[0], array1)
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np.testing.assert_array_equal(parsed_vectors_1[1], array2)
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np.testing.assert_array_equal(parsed_vectors_1[2], array3)
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np.testing.assert_array_equal(parsed_vectors_1[3], array4)
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nlp.add_pipe("merge_entities")
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parsed_vectors_2 = [t.vector for t in nlp(text)]
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assert len(parsed_vectors_2) == 3
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np.testing.assert_array_equal(parsed_vectors_2[0], array1)
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np.testing.assert_array_equal(parsed_vectors_2[1], array2)
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np.testing.assert_array_equal(parsed_vectors_2[2], array34)
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