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Tests for CLI app - init config generates train-able config (#12173)
* remove migration support form * initial test commit * add fixture * add combo test * pull out parameter example data * fix formatting on examples * remove unused import * remove unncessary fmt:off instructions * only set logger level if verbose flag is explicitly set --------- Co-authored-by: svlandeg <svlandeg@github.com>
This commit is contained in:
Peter Baumgartner
GitHub
parent
186889ec9c
commit
a0a195688f
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@ -40,7 +40,8 @@ def assemble_cli(
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DOCS: https://spacy.io/api/cli#assemble
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"""
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util.logger.setLevel(logging.DEBUG if verbose else logging.INFO)
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if verbose:
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util.logger.setLevel(logging.DEBUG)
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# Make sure all files and paths exists if they are needed
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if not config_path or (str(config_path) != "-" and not config_path.exists()):
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msg.fail("Config file not found", config_path, exits=1)
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@ -52,8 +52,8 @@ def find_threshold_cli(
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DOCS: https://spacy.io/api/cli#find-threshold
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"""
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util.logger.setLevel(logging.DEBUG if verbose else logging.INFO)
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if verbose:
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util.logger.setLevel(logging.DEBUG)
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import_code(code_path)
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find_threshold(
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model=model,
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@ -39,7 +39,8 @@ def init_vectors_cli(
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you can use in the [initialize] block of your config to initialize
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a model with vectors.
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"""
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util.logger.setLevel(logging.DEBUG if verbose else logging.INFO)
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if verbose:
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util.logger.setLevel(logging.DEBUG)
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msg.info(f"Creating blank nlp object for language '{lang}'")
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nlp = util.get_lang_class(lang)()
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if jsonl_loc is not None:
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@ -87,7 +88,8 @@ def init_pipeline_cli(
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use_gpu: int = Opt(-1, "--gpu-id", "-g", help="GPU ID or -1 for CPU")
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# fmt: on
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):
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util.logger.setLevel(logging.DEBUG if verbose else logging.INFO)
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if verbose:
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util.logger.setLevel(logging.DEBUG)
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overrides = parse_config_overrides(ctx.args)
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import_code(code_path)
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setup_gpu(use_gpu)
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@ -116,7 +118,8 @@ def init_labels_cli(
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"""Generate JSON files for the labels in the data. This helps speed up the
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training process, since spaCy won't have to preprocess the data to
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extract the labels."""
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util.logger.setLevel(logging.DEBUG if verbose else logging.INFO)
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if verbose:
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util.logger.setLevel(logging.DEBUG)
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if not output_path.exists():
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output_path.mkdir(parents=True)
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overrides = parse_config_overrides(ctx.args)
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@ -47,7 +47,8 @@ def train_cli(
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DOCS: https://spacy.io/api/cli#train
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"""
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util.logger.setLevel(logging.DEBUG if verbose else logging.INFO)
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if verbose:
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util.logger.setLevel(logging.DEBUG)
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overrides = parse_config_overrides(ctx.args)
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import_code(code_path)
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train(config_path, output_path, use_gpu=use_gpu, overrides=overrides)
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@ -6,7 +6,7 @@ import srsly
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from typer.testing import CliRunner
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from spacy.cli._util import app, get_git_version
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from spacy.tokens import Doc, DocBin
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from spacy.tokens import Doc, DocBin, Span
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from .util import make_tempdir, normalize_whitespace
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@ -267,3 +267,162 @@ def test_find_function_invalid():
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function = "spacy.TextCatBOW.v666"
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result = CliRunner().invoke(app, ["find-function", function])
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assert f"Couldn't find registered function: '{function}'" in result.stdout
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example_words_1 = ["I", "like", "cats"]
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example_words_2 = ["I", "like", "dogs"]
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example_lemmas_1 = ["I", "like", "cat"]
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example_lemmas_2 = ["I", "like", "dog"]
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example_tags = ["PRP", "VBP", "NNS"]
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example_morphs = [
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"Case=Nom|Number=Sing|Person=1|PronType=Prs",
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"Tense=Pres|VerbForm=Fin",
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"Number=Plur",
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]
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example_deps = ["nsubj", "ROOT", "dobj"]
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example_pos = ["PRON", "VERB", "NOUN"]
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example_ents = ["O", "O", "I-ANIMAL"]
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example_spans = [(2, 3, "ANIMAL")]
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TRAIN_EXAMPLE_1 = dict(
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words=example_words_1,
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lemmas=example_lemmas_1,
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tags=example_tags,
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morphs=example_morphs,
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deps=example_deps,
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heads=[1, 1, 1],
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pos=example_pos,
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ents=example_ents,
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spans=example_spans,
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cats={"CAT": 1.0, "DOG": 0.0},
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)
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TRAIN_EXAMPLE_2 = dict(
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words=example_words_2,
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lemmas=example_lemmas_2,
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tags=example_tags,
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morphs=example_morphs,
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deps=example_deps,
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heads=[1, 1, 1],
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pos=example_pos,
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ents=example_ents,
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spans=example_spans,
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cats={"CAT": 0.0, "DOG": 1.0},
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)
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@pytest.mark.slow
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@pytest.mark.parametrize(
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"component,examples",
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[
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("tagger", [TRAIN_EXAMPLE_1, TRAIN_EXAMPLE_2]),
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("morphologizer", [TRAIN_EXAMPLE_1, TRAIN_EXAMPLE_2]),
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("trainable_lemmatizer", [TRAIN_EXAMPLE_1, TRAIN_EXAMPLE_2]),
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("parser", [TRAIN_EXAMPLE_1] * 30),
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("ner", [TRAIN_EXAMPLE_1, TRAIN_EXAMPLE_2]),
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("spancat", [TRAIN_EXAMPLE_1, TRAIN_EXAMPLE_2]),
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("textcat", [TRAIN_EXAMPLE_1, TRAIN_EXAMPLE_2]),
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],
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)
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def test_init_config_trainable(component, examples, en_vocab):
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if component == "textcat":
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train_docs = []
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for example in examples:
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doc = Doc(en_vocab, words=example["words"])
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doc.cats = example["cats"]
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train_docs.append(doc)
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elif component == "spancat":
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train_docs = []
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for example in examples:
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doc = Doc(en_vocab, words=example["words"])
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doc.spans["sc"] = [
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Span(doc, start, end, label) for start, end, label in example["spans"]
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]
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train_docs.append(doc)
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else:
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train_docs = []
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for example in examples:
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# cats, spans are not valid kwargs for instantiating a Doc
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example = {k: v for k, v in example.items() if k not in ("cats", "spans")}
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doc = Doc(en_vocab, **example)
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train_docs.append(doc)
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with make_tempdir() as d_in:
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train_bin = DocBin(docs=train_docs)
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train_bin.to_disk(d_in / "train.spacy")
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dev_bin = DocBin(docs=train_docs)
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dev_bin.to_disk(d_in / "dev.spacy")
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init_config_result = CliRunner().invoke(
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app,
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[
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"init",
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"config",
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f"{d_in}/config.cfg",
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"--lang",
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"en",
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"--pipeline",
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component,
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],
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)
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assert init_config_result.exit_code == 0
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train_result = CliRunner().invoke(
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app,
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[
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"train",
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f"{d_in}/config.cfg",
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"--paths.train",
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f"{d_in}/train.spacy",
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"--paths.dev",
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f"{d_in}/dev.spacy",
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"--output",
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f"{d_in}/model",
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],
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)
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assert train_result.exit_code == 0
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assert Path(d_in / "model" / "model-last").exists()
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@pytest.mark.slow
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@pytest.mark.parametrize(
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"component,examples",
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[("tagger,parser,morphologizer", [TRAIN_EXAMPLE_1, TRAIN_EXAMPLE_2] * 15)],
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)
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def test_init_config_trainable_multiple(component, examples, en_vocab):
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train_docs = []
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for example in examples:
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example = {k: v for k, v in example.items() if k not in ("cats", "spans")}
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doc = Doc(en_vocab, **example)
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train_docs.append(doc)
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with make_tempdir() as d_in:
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train_bin = DocBin(docs=train_docs)
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train_bin.to_disk(d_in / "train.spacy")
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dev_bin = DocBin(docs=train_docs)
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dev_bin.to_disk(d_in / "dev.spacy")
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init_config_result = CliRunner().invoke(
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app,
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[
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"init",
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"config",
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f"{d_in}/config.cfg",
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"--lang",
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"en",
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"--pipeline",
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component,
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],
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)
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assert init_config_result.exit_code == 0
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train_result = CliRunner().invoke(
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app,
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[
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"train",
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f"{d_in}/config.cfg",
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"--paths.train",
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f"{d_in}/train.spacy",
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"--paths.dev",
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f"{d_in}/dev.spacy",
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"--output",
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f"{d_in}/model",
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],
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)
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assert train_result.exit_code == 0
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assert Path(d_in / "model" / "model-last").exists()
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