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	fix merge conflict
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					@ -13,11 +13,7 @@ from thinc.api import Model, use_pytorch_for_gpu_memory
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import random
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					import random
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from ..gold import GoldCorpus
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					from ..gold import GoldCorpus
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<<<<<<< HEAD
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from ..gold import Example
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=======
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from ..lookups import Lookups
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					from ..lookups import Lookups
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>>>>>>> origin/develop
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from .. import util
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					from .. import util
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from ..errors import Errors
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					from ..errors import Errors
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from ..ml import models  # don't remove - required to load the built-in architectures
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					from ..ml import models  # don't remove - required to load the built-in architectures
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					@ -374,27 +370,17 @@ def train(
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def create_train_batches(nlp, corpus, cfg):
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					def create_train_batches(nlp, corpus, cfg):
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    epochs_todo = cfg.get("max_epochs", 0)
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					    epochs_todo = cfg.get("max_epochs", 0)
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    while True:
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					    while True:
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<<<<<<< HEAD
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        train_examples = list(corpus.train_dataset(
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            nlp,
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            noise_level=0.0,
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            orth_variant_level=cfg["orth_variant_level"],
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            gold_preproc=cfg["gold_preproc"],
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            max_length=cfg["max_length"],
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            ignore_misaligned=True
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        ))
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=======
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        train_examples = list(
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					        train_examples = list(
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            corpus.train_dataset(
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					            corpus.train_dataset(
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                nlp,
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					                nlp,
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                noise_level=0.0, # I think this is deprecated?
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					                noise_level=cfg["noise_level"], # I think this is deprecated?
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                orth_variant_level=cfg["orth_variant_level"],
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					                orth_variant_level=cfg["orth_variant_level"],
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                gold_preproc=cfg["gold_preproc"],
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					                gold_preproc=cfg["gold_preproc"],
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                max_length=cfg["max_length"],
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					                max_length=cfg["max_length"],
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                ignore_misaligned=True,
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					                ignore_misaligned=True,
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            )
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					            )
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        )
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					        )
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>>>>>>> origin/develop
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        if len(train_examples) == 0:
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					        if len(train_examples) == 0:
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            raise ValueError(Errors.E988)
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					            raise ValueError(Errors.E988)
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        random.shuffle(train_examples)
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					        random.shuffle(train_examples)
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