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Unhack eval
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@ -197,9 +197,9 @@ def train_while_improving(
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if not (step % eval_frequency):
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if not (step % eval_frequency):
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if optimizer.averages:
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if optimizer.averages:
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with nlp.use_params(optimizer.averages):
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with nlp.use_params(optimizer.averages):
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score, other_scores = evaluate(step)
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score, other_scores = evaluate()
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else:
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else:
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score, other_scores = evaluate(step)
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score, other_scores = evaluate()
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results.append((score, step))
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results.append((score, step))
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is_best_checkpoint = score == max(results)[0]
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is_best_checkpoint = score == max(results)[0]
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else:
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else:
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@ -248,10 +248,8 @@ def create_evaluation_callback(
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) -> Callable[[], Tuple[float, Dict[str, float]]]:
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) -> Callable[[], Tuple[float, Dict[str, float]]]:
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weights = {key: value for key, value in weights.items() if value is not None}
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weights = {key: value for key, value in weights.items() if value is not None}
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def evaluate(step) -> Tuple[float, Dict[str, float]]:
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def evaluate() -> Tuple[float, Dict[str, float]]:
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# Limit dev_examples by steps, so we don't spend longer on
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dev_examples = list(dev_corpus(nlp))
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# the estimation than we have training.
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dev_examples = list(itertools.islice(dev_corpus(nlp), step))
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try:
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try:
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scores = nlp.evaluate(dev_examples)
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scores = nlp.evaluate(dev_examples)
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except KeyError as e:
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except KeyError as e:
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