tensorflow/models · error · ValueError

`max_predictions_per_seq` must be set.

Error message

`max_predictions_per_seq` must be set.

What it means

Error "`max_predictions_per_seq` must be set." thrown in tensorflow/models.

Source

Thrown at official/nlp/data/pretrain_dataloader.py:505

      - 'single_token': Samples individual tokens as prediction targets.
      - 'token_span': Samples spans of tokens as prediction targets.
      - 'whole_word': Samples individual words as prediction targets.
      - 'word_span': Samples spans of words as prediction targets.

    Args:
      inputs: The input tokens.
      boundary: The `int` Tensor of indices indicating whole word boundaries.
        This is used in 'whole_word' and 'word_span'

    Returns:
      The sampled `bool` input mask.

    Raises:
      `ValueError`: if `max_predictions_per_seq` is not set or if boundary is
        not provided for 'whole_word' and 'word_span' sample strategies.
    """
    if self._max_predictions_per_seq is None:
      raise ValueError('`max_predictions_per_seq` must be set.')

    if boundary is None and 'word' in self._sample_strategy:
      raise ValueError('`boundary` must be provided for {} strategy'.format(
          self._sample_strategy))

    if self._sample_strategy == 'single_token':
      return self._single_token_mask(inputs)
    elif self._sample_strategy == 'token_span':
      return self._token_span_mask(inputs)
    elif self._sample_strategy == 'whole_word':
      return self._whole_word_mask(inputs, boundary)
    elif self._sample_strategy == 'word_span':
      return self._word_span_mask(inputs, boundary)
    else:
      raise NotImplementedError('Invalid sample strategy.')

  def _get_factorization(self, inputs: tf.Tensor, input_mask: tf.Tensor):
    """Samples a permutation of the factorization order.

View on GitHub (pinned to e006f5f0d5)

When it happens

Trigger: Thrown at official/nlp/data/pretrain_dataloader.py:505 when the library encounters an invalid state.

Common situations: See trigger scenarios.

AI-assisted analysis of tensorflow/models@e006f5f0d5 (2026-08-24). Data as JSON: /api/errors/5e87a62eb357bc9f. Report an issue: GitHub.