Source code for DeBERTa.deberta.config

import json
import copy

__all__=['AbsModelConfig', 'ModelConfig']

class AbsModelConfig(object):
    def __init__(self):

    def from_dict(cls, json_object):
        """Constructs a `ModelConfig` from a Python dictionary of parameters."""
        config = cls()
        for key, value in json_object.items():
            if isinstance(value, dict):
                value = AbsModelConfig.from_dict(value)
            config.__dict__[key] = value
        return config

    def from_json_file(cls, json_file):
        """Constructs a `ModelConfig` from a json file of parameters."""
        with open(json_file, "r", encoding='utf-8') as reader:
            text =
        return cls.from_dict(json.loads(text))

    def __repr__(self):
        return str(self.to_json_string())

    def to_dict(self):
        """Serializes this instance to a Python dictionary."""
        output = copy.deepcopy(self.__dict__)
        return output

    def to_json_string(self):
        """Serializes this instance to a JSON string."""
        def _json_default(obj):
            if isinstance(obj, AbsModelConfig):
                return obj.__dict__
        return json.dumps(self.__dict__, indent=2, sort_keys=True, default=_json_default) + "\n"

[docs]class ModelConfig(AbsModelConfig): """Configuration class to store the configuration of a :class:`~DeBERTa.deberta.DeBERTa` model. Attributes: hidden_size (int): Size of the encoder layers and the pooler layer, default: `768`. num_hidden_layers (int): Number of hidden layers in the Transformer encoder, default: `12`. num_attention_heads (int): Number of attention heads for each attention layer in the Transformer encoder, default: `12`. intermediate_size (int): The size of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder, default: `3072`. hidden_act (str): The non-linear activation function (function or string) in the encoder and pooler. If string, "gelu", "relu" and "swish" are supported, default: `gelu`. hidden_dropout_prob (float): The dropout probabilitiy for all fully connected layers in the embeddings, encoder, and pooler, default: `0.1`. attention_probs_dropout_prob (float): The dropout ratio for the attention probabilities, default: `0.1`. max_position_embeddings (int): The maximum sequence length that this model might ever be used with. Typically set this to something large just in case (e.g., 512 or 1024 or 2048), default: `512`. type_vocab_size (int): The vocabulary size of the `token_type_ids` passed into `DeBERTa` model, default: `-1`. initializer_range (int): The sttdev of the _normal_initializer for initializing all weight matrices, default: `0.02`. relative_attention (:obj:`bool`): Whether use relative position encoding, default: `False`. max_relative_positions (int): The range of relative positions [`-max_position_embeddings`, `max_position_embeddings`], default: -1, use the same value as `max_position_embeddings`. padding_idx (int): The value used to pad input_ids, default: `0`. position_biased_input (:obj:`bool`): Whether add absolute position embedding to content embedding, default: `True`. pos_att_type (:obj:`str`): The type of relative position attention, it can be a combination of [`p2c`, `c2p`, `p2p`], e.g. "p2c", "p2c|c2p", "p2c|c2p|p2p"., default: "None". """ def __init__(self): """Constructs ModelConfig. """ self.hidden_size = 768 self.num_hidden_layers = 12 self.num_attention_heads = 12 self.hidden_act = "gelu" self.intermediate_size = 3072 self.hidden_dropout_prob = 0.1 self.attention_probs_dropout_prob = 0.1 self.max_position_embeddings = 512 self.type_vocab_size = 0 self.initializer_range = 0.02 self.layer_norm_eps = 1e-7 self.padding_idx = 0 self.vocab_size = -1