mirror of https://github.com/explosion/spaCy.git
fix: Fix textcat labels to expect a Optional[Iterable[str]] instead of Optional[Dict] (#6911)
* docs: Add agreement * bug: Regression test Issue #6908 * fix: Changed from Dict to Iterable[str] Fix #6908 * Update test to use make_tempdir * fix: Fix WindowsPath error Co-authored-by: Adriane Boyd <adrianeboyd@gmail.com>
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# spaCy contributor agreement
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This spaCy Contributor Agreement (**"SCA"**) is based on the
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[Oracle Contributor Agreement](http://www.oracle.com/technetwork/oca-405177.pdf).
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The SCA applies to any contribution that you make to any product or project
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managed by us (the **"project"**), and sets out the intellectual property rights
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you grant to us in the contributed materials. The term **"us"** shall mean
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[ExplosionAI GmbH](https://explosion.ai/legal). The term
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**"you"** shall mean the person or entity identified below.
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If you agree to be bound by these terms, fill in the information requested
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below and include the filled-in version with your first pull request, under the
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folder [`.github/contributors/`](/.github/contributors/). The name of the file
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should be your GitHub username, with the extension `.md`. For example, the user
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example_user would create the file `.github/contributors/example_user.md`.
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Read this agreement carefully before signing. These terms and conditions
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constitute a binding legal agreement.
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## Contributor Agreement
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1. The term "contribution" or "contributed materials" means any source code,
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object code, patch, tool, sample, graphic, specification, manual,
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documentation, or any other material posted or submitted by you to the project.
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2. With respect to any worldwide copyrights, or copyright applications and
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registrations, in your contribution:
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* you hereby assign to us joint ownership, and to the extent that such
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assignment is or becomes invalid, ineffective or unenforceable, you hereby
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grant to us a perpetual, irrevocable, non-exclusive, worldwide, no-charge,
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royalty-free, unrestricted license to exercise all rights under those
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copyrights. This includes, at our option, the right to sublicense these same
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rights to third parties through multiple levels of sublicensees or other
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licensing arrangements;
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* you agree that each of us can do all things in relation to your
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contribution as if each of us were the sole owners, and if one of us makes
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a derivative work of your contribution, the one who makes the derivative
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work (or has it made will be the sole owner of that derivative work;
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* you agree that you will not assert any moral rights in your contribution
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against us, our licensees or transferees;
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* you agree that we may register a copyright in your contribution and
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exercise all ownership rights associated with it; and
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* you agree that neither of us has any duty to consult with, obtain the
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consent of, pay or render an accounting to the other for any use or
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distribution of your contribution.
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3. With respect to any patents you own, or that you can license without payment
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to any third party, you hereby grant to us a perpetual, irrevocable,
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non-exclusive, worldwide, no-charge, royalty-free license to:
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* make, have made, use, sell, offer to sell, import, and otherwise transfer
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your contribution in whole or in part, alone or in combination with or
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included in any product, work or materials arising out of the project to
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which your contribution was submitted, and
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* at our option, to sublicense these same rights to third parties through
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multiple levels of sublicensees or other licensing arrangements.
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4. Except as set out above, you keep all right, title, and interest in your
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contribution. The rights that you grant to us under these terms are effective
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on the date you first submitted a contribution to us, even if your submission
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took place before the date you sign these terms.
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5. You covenant, represent, warrant and agree that:
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- Each contribution that you submit is and shall be an original work of
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authorship and you can legally grant the rights set out in this SCA;
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- to the best of your knowledge, each contribution will not violate any
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third party's copyrights, trademarks, patents, or other intellectual
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property rights; and
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- each contribution shall be in compliance with U.S. export control laws and
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other applicable export and import laws. You agree to notify us if you
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become aware of any circumstance which would make any of the foregoing
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representations inaccurate in any respect. We may publicly disclose your
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participation in the project, including the fact that you have signed the SCA.
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6. This SCA is governed by the laws of the State of California and applicable
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U.S. Federal law. Any choice of law rules will not apply.
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7. Please place an “x” on one of the applicable statement below. Please do NOT
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mark both statements:
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* [x] I am signing on behalf of myself as an individual and no other person
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or entity, including my employer, has or will have rights with respect to my
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contributions.
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* [ ] I am signing on behalf of my employer or a legal entity and I have the
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actual authority to contractually bind that entity.
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## Contributor Details
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| Field | Entry |
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| ----------------------------- | -------------------- |
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| Name | Rene Octavio Q. Dias |
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| Company name (if applicable) | |
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| Title or role (if applicable) | |
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| Date | 2020-02-03 |
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| GitHub username | reneoctavio |
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| Website (optional) | |
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@ -308,7 +308,7 @@ class TextCategorizer(TrainablePipe):
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get_examples: Callable[[], Iterable[Example]],
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get_examples: Callable[[], Iterable[Example]],
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*,
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*,
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nlp: Optional[Language] = None,
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nlp: Optional[Language] = None,
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labels: Optional[Dict] = None,
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labels: Optional[Iterable[str]] = None,
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positive_label: Optional[str] = None,
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positive_label: Optional[str] = None,
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) -> None:
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) -> None:
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"""Initialize the pipe for training, using a representative set
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"""Initialize the pipe for training, using a representative set
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@ -137,7 +137,7 @@ class MultiLabel_TextCategorizer(TextCategorizer):
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get_examples: Callable[[], Iterable[Example]],
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get_examples: Callable[[], Iterable[Example]],
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*,
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*,
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nlp: Optional[Language] = None,
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nlp: Optional[Language] = None,
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labels: Optional[Dict] = None,
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labels: Optional[Iterable[str]] = None,
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):
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):
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"""Initialize the pipe for training, using a representative set
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"""Initialize the pipe for training, using a representative set
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of data examples.
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of data examples.
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@ -0,0 +1,102 @@
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import pytest
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import spacy
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from spacy.language import Language
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from spacy.tokens import DocBin
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from spacy import util
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from spacy.schemas import ConfigSchemaInit
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from spacy.training.initialize import init_nlp
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from ..util import make_tempdir
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TEXTCAT_WITH_LABELS_ARRAY_CONFIG = """
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[paths]
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train = "TRAIN_PLACEHOLDER"
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raw = null
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init_tok2vec = null
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vectors = null
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[system]
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seed = 0
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gpu_allocator = null
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[nlp]
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lang = "en"
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pipeline = ["textcat"]
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tokenizer = {"@tokenizers":"spacy.Tokenizer.v1"}
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disabled = []
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before_creation = null
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after_creation = null
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after_pipeline_creation = null
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batch_size = 1000
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[components]
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[components.textcat]
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factory = "TEXTCAT_PLACEHOLDER"
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[corpora]
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[corpora.train]
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@readers = "spacy.Corpus.v1"
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path = ${paths:train}
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[corpora.dev]
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@readers = "spacy.Corpus.v1"
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path = ${paths:train}
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[training]
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train_corpus = "corpora.train"
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dev_corpus = "corpora.dev"
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seed = ${system.seed}
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gpu_allocator = ${system.gpu_allocator}
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frozen_components = []
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before_to_disk = null
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[pretraining]
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[initialize]
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vectors = ${paths.vectors}
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init_tok2vec = ${paths.init_tok2vec}
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vocab_data = null
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lookups = null
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before_init = null
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after_init = null
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[initialize.components]
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[initialize.components.textcat]
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labels = ['label1', 'label2']
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[initialize.tokenizer]
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"""
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@pytest.mark.parametrize(
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"component_name",
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["textcat", "textcat_multilabel"],
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)
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def test_textcat_initialize_labels_validation(component_name):
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"""Test intializing textcat with labels in a list"""
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def create_data(out_file):
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nlp = spacy.blank("en")
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doc = nlp.make_doc("Some text")
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doc.cats = {"label1": 0, "label2": 1}
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out_data = DocBin(docs=[doc]).to_bytes()
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with out_file.open("wb") as file_:
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file_.write(out_data)
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with make_tempdir() as tmp_path:
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train_path = tmp_path / "train.spacy"
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create_data(train_path)
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config_str = TEXTCAT_WITH_LABELS_ARRAY_CONFIG.replace(
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"TEXTCAT_PLACEHOLDER", component_name
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)
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config_str = config_str.replace("TRAIN_PLACEHOLDER", train_path.as_posix())
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config = util.load_config_from_str(config_str)
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init_nlp(config)
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