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Address random results in slow readers tests (#9544)
* Set random seed for dataset shuffling * Use more dev examples for non-zero scores
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@ -1,6 +1,6 @@
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from typing import Dict, Iterable, Callable
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import pytest
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from thinc.api import Config
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from thinc.api import Config, fix_random_seed
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from spacy import Language
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from spacy.util import load_model_from_config, registry, resolve_dot_names
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from spacy.schemas import ConfigSchemaTraining
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@ -64,8 +64,8 @@ def test_readers():
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@pytest.mark.parametrize(
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"reader,additional_config",
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[
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("ml_datasets.imdb_sentiment.v1", {"train_limit": 10, "dev_limit": 2}),
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("ml_datasets.dbpedia.v1", {"train_limit": 10, "dev_limit": 2}),
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("ml_datasets.imdb_sentiment.v1", {"train_limit": 10, "dev_limit": 10}),
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("ml_datasets.dbpedia.v1", {"train_limit": 10, "dev_limit": 10}),
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("ml_datasets.cmu_movies.v1", {"limit": 10, "freq_cutoff": 200, "split": 0.8}),
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],
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)
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@ -93,6 +93,7 @@ def test_cat_readers(reader, additional_config):
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factory = "textcat_multilabel"
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"""
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config = Config().from_str(nlp_config_string)
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fix_random_seed(config["training"]["seed"])
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config["corpora"]["@readers"] = reader
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config["corpora"].update(additional_config)
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nlp = load_model_from_config(config, auto_fill=True)
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