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class BidirectionalLanguageModelTokenEmbedder(LanguageModelTokenEmbedder):
 | def __init__(
 |     self,
 |     archive_file: str,
 |     dropout: float = None,
 |     bos_eos_tokens: Tuple[str, str] = ("<S>", "</S>"),
 |     remove_bos_eos: bool = True,
 |     requires_grad: bool = False
 | ) -> None

Compute a single layer of representations from a bidirectional language model. This is done by computing a learned scalar average of the layers from the LM. Typically the LM's weights will be fixed, but they can be fine tuned by setting requires_grad.


  • archive_file : str
    An archive file, typically model.tar.gz, from a BidirectionalLanguageModel. The contextualizer used by the LM must satisfy two requirements:

    1. It must have a num_layers field.
    2. It must take a boolean return_all_layers parameter in its constructor.

    See BidirectionalLanguageModelTransformer for their definitions.

  • dropout : float, optional
    The dropout value to be applied to the representations.

  • bos_eos_tokens : Tuple[str, str], optional (default = ("<S>", "</S>"))
    These will be indexed and placed around the indexed tokens. Necessary if the language model was trained with them, but they were injected external to an indexer.
  • remove_bos_eos : bool, optional (default = True)
    Typically the provided token indexes will be augmented with begin-sentence and end-sentence tokens. (Alternatively, you can pass bos_eos_tokens.) If this flag is True the corresponding embeddings will be removed from the return values.

    Warning: This only removes a single start and single end token! - requires_grad : bool, optional (default = False)
    If True, compute gradient of bidirectional language model parameters for fine tuning.