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masked_language_model

allennlp_models.lm.models.masked_language_model

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MaskedLanguageModel#

@Model.register("masked_language_model")
class MaskedLanguageModel(Model):
 | def __init__(
 |     self,
 |     vocab: Vocabulary,
 |     text_field_embedder: TextFieldEmbedder,
 |     language_model_head: LanguageModelHead,
 |     contextualizer: Seq2SeqEncoder = None,
 |     target_namespace: str = "bert",
 |     dropout: float = 0.0,
 |     initializer: InitializerApplicator = None,
 |     **kwargs
 | ) -> None

The MaskedLanguageModel embeds some input tokens (including some which are masked), contextualizes them, then predicts targets for the masked tokens, computing a loss against known targets.

NOTE: This was developed for use in a demo, not for training. It's possible that it will still work for training a masked LM, but it is very likely that some other code would be much more efficient for that. This does compute correct gradients of the loss, because we use that in our demo, so in principle it should be able to train a model, we just don't necessarily endorse that use.

Parameters

  • vocab : Vocabulary
  • text_field_embedder : TextFieldEmbedder
    Used to embed the indexed tokens we get in forward.
  • language_model_head : LanguageModelHead
    The torch.nn.Module that goes from the hidden states output by the contextualizer to logits over some output vocabulary.
  • contextualizer : Seq2SeqEncoder, optional (default = None)
    Used to "contextualize" the embeddings. This is optional because the contextualization might actually be done in the text field embedder.
  • target_namespace : str, optional (default = 'bert')
    Namespace to use to convert predicted token ids to strings in Model.make_output_human_readable.
  • dropout : float, optional (default = 0.0)
    If specified, dropout is applied to the contextualized embeddings before computation of the softmax. The contextualized embeddings themselves are returned without dropout.

forward#

class MaskedLanguageModel(Model):
 | ...
 | def forward(
 |     self,
 |     tokens: TextFieldTensors,
 |     mask_positions: torch.BoolTensor,
 |     target_ids: TextFieldTensors = None
 | ) -> Dict[str, torch.Tensor]

Parameters

  • tokens : TextFieldTensors
    The output of TextField.as_tensor() for a batch of sentences.
  • mask_positions : torch.LongTensor
    The positions in tokens that correspond to [MASK] tokens that we should try to fill in. Shape should be (batch_size, num_masks).
  • target_ids : TextFieldTensors
    This is a list of token ids that correspond to the mask positions we're trying to fill. It is the output of a TextField, purely for convenience, so we can handle wordpiece tokenizers and such without having to do crazy things in the dataset reader. We assume that there is exactly one entry in the dictionary, and that it has a shape identical to mask_positions - one target token per mask position.

get_metrics#

class MaskedLanguageModel(Model):
 | ...
 | def get_metrics(self, reset: bool = False)

make_output_human_readable#

class MaskedLanguageModel(Model):
 | ...
 | @overrides
 | def make_output_human_readable(
 |     self,
 |     output_dict: Dict[str, torch.Tensor]
 | ) -> Dict[str, torch.Tensor]

default_predictor#

class MaskedLanguageModel(Model):
 | ...
 | default_predictor = "masked_language_model"