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seq2seq

mindnlp.engine.train_args.seq2seq.Seq2SeqTrainingArguments dataclass

Bases: TrainingArguments

PARAMETER DESCRIPTION
sortish_sampler

Whether to use a sortish sampler or not. Only possible if the underlying datasets are Seq2SeqDataset for now but will become generally available in the near future.

It sorts the inputs according to lengths in order to minimize the padding size, with a bit of randomness for the training set.

TYPE: `bool`, *optional*, defaults to `False` DEFAULT: False

predict_with_generate

Whether to use generate to calculate generative metrics (ROUGE, BLEU).

TYPE: `bool`, *optional*, defaults to `False` DEFAULT: False

generation_max_length

The max_length to use on each evaluation loop when predict_with_generate=True. Will default to the max_length value of the model configuration.

TYPE: `int`, *optional* DEFAULT: None

generation_num_beams

The num_beams to use on each evaluation loop when predict_with_generate=True. Will default to the num_beams value of the model configuration.

TYPE: `int`, *optional* DEFAULT: None

generation_config

Allows to load a [~generation.GenerationConfig] from the from_pretrained method. This can be either:

  • a string, the model id of a pretrained model configuration hosted inside a model repo on huggingface.co.
  • a path to a directory containing a configuration file saved using the [~GenerationConfig.save_pretrained] method, e.g., ./my_model_directory/.
  • a [~generation.GenerationConfig] object.

TYPE: `str` or `Path` or [`~generation.GenerationConfig`], *optional* DEFAULT: None

Source code in mindnlp/engine/train_args/seq2seq.py 
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@dataclass
class Seq2SeqTrainingArguments(TrainingArguments):
    """
    Args:
        sortish_sampler (`bool`, *optional*, defaults to `False`):
            Whether to use a *sortish sampler* or not. Only possible if the underlying datasets are *Seq2SeqDataset*
            for now but will become generally available in the near future.

            It sorts the inputs according to lengths in order to minimize the padding size, with a bit of randomness
            for the training set.
        predict_with_generate (`bool`, *optional*, defaults to `False`):
            Whether to use generate to calculate generative metrics (ROUGE, BLEU).
        generation_max_length (`int`, *optional*):
            The `max_length` to use on each evaluation loop when `predict_with_generate=True`. Will default to the
            `max_length` value of the model configuration.
        generation_num_beams (`int`, *optional*):
            The `num_beams` to use on each evaluation loop when `predict_with_generate=True`. Will default to the
            `num_beams` value of the model configuration.
        generation_config (`str` or `Path` or [`~generation.GenerationConfig`], *optional*):
            Allows to load a [`~generation.GenerationConfig`] from the `from_pretrained` method. This can be either:

            - a string, the *model id* of a pretrained model configuration hosted inside a model repo on
              huggingface.co.
            - a path to a *directory* containing a configuration file saved using the
              [`~GenerationConfig.save_pretrained`] method, e.g., `./my_model_directory/`.
            - a [`~generation.GenerationConfig`] object.
    """
    sortish_sampler: bool = field(default=False, metadata={"help": "Whether to use SortishSampler or not."})
    predict_with_generate: bool = field(
        default=False, metadata={"help": "Whether to use generate to calculate generative metrics (ROUGE, BLEU)."}
    )
    generation_max_length: Optional[int] = field(
        default=None,
        metadata={
            "help": (
                "The `max_length` to use on each evaluation loop when `predict_with_generate=True`. Will default "
                "to the `max_length` value of the model configuration."
            )
        },
    )
    generation_num_beams: Optional[int] = field(
        default=None,
        metadata={
            "help": (
                "The `num_beams` to use on each evaluation loop when `predict_with_generate=True`. Will default "
                "to the `num_beams` value of the model configuration."
            )
        },
    )
    generation_config: Optional[Union[str, Path, GenerationConfig]] = field(
        default=None,
        metadata={
            "help": "Model id, file path or url pointing to a GenerationConfig json file, to use during prediction."
        },
    )

    def to_dict(self):
        """
        Serializes this instance while replace `Enum` by their values and `GenerationConfig` by dictionaries (for JSON
        serialization support). It obfuscates the token values by removing their value.
        """
        # filter out fields that are defined as field(init=False)
        d = super().to_dict()
        for k, v in d.items():
            if isinstance(v, GenerationConfig):
                d[k] = v.to_dict()
        return d

mindnlp.engine.train_args.seq2seq.Seq2SeqTrainingArguments.to_dict()

Serializes this instance while replace Enum by their values and GenerationConfig by dictionaries (for JSON serialization support). It obfuscates the token values by removing their value.

Source code in mindnlp/engine/train_args/seq2seq.py 
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def to_dict(self):
    """
    Serializes this instance while replace `Enum` by their values and `GenerationConfig` by dictionaries (for JSON
    serialization support). It obfuscates the token values by removing their value.
    """
    # filter out fields that are defined as field(init=False)
    d = super().to_dict()
    for k, v in d.items():
        if isinstance(v, GenerationConfig):
            d[k] = v.to_dict()
    return d