Source code for transformers.models.roformer.configuration_roformer

# coding=utf-8
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""" RoFormer model configuration """

from ...configuration_utils import PretrainedConfig
from ...utils import logging


logger = logging.get_logger(__name__)

ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP = {
    "junnyu/roformer_chinese_small": "https://huggingface.co/junnyu/roformer_chinese_small/resolve/main/config.json",
    "junnyu/roformer_chinese_base": "https://huggingface.co/junnyu/roformer_chinese_base/resolve/main/config.json",
    "junnyu/roformer_chinese_char_small": "https://huggingface.co/junnyu/roformer_chinese_char_small/resolve/main/config.json",
    "junnyu/roformer_chinese_char_base": "https://huggingface.co/junnyu/roformer_chinese_char_base/resolve/main/config.json",
    "junnyu/roformer_small_discriminator": "https://huggingface.co/junnyu/roformer_small_discriminator/resolve/main/config.json",
    "junnyu/roformer_small_generator": "https://huggingface.co/junnyu/roformer_small_generator/resolve/main/config.json",
    # See all RoFormer models at https://huggingface.co/models?filter=roformer
}


[docs]class RoFormerConfig(PretrainedConfig): r""" This is the configuration class to store the configuration of a :class:`~transformers.RoFormerModel`. It is used to instantiate an RoFormer model according to the specified arguments, defining the model architecture. Instantiating a configuration with the defaults will yield a similar configuration to that of the RoFormer `junnyu/roformer_chinese_base <https://huggingface.co/junnyu/roformer_chinese_base>`__ architecture. Configuration objects inherit from :class:`~transformers.PretrainedConfig` and can be used to control the model outputs. Read the documentation from :class:`~transformers.PretrainedConfig` for more information. Args: vocab_size (:obj:`int`, `optional`, defaults to 50000): Vocabulary size of the RoFormer model. Defines the number of different tokens that can be represented by the :obj:`inputs_ids` passed when calling :class:`~transformers.RoFormerModel` or :class:`~transformers.TFRoFormerModel`. embedding_size (:obj:`int`, `optional`, defaults to None): Dimensionality of the encoder layers and the pooler layer. Defaults to the :obj:`hidden_size` if not provided. hidden_size (:obj:`int`, `optional`, defaults to 768): Dimension of the encoder layers and the pooler layer. num_hidden_layers (:obj:`int`, `optional`, defaults to 12): Number of hidden layers in the Transformer encoder. num_attention_heads (:obj:`int`, `optional`, defaults to 12): Number of attention heads for each attention layer in the Transformer encoder. intermediate_size (:obj:`int`, `optional`, defaults to 3072): Dimension of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder. hidden_act (:obj:`str` or :obj:`function`, `optional`, defaults to :obj:`"gelu"`): The non-linear activation function (function or string) in the encoder and pooler. If string, :obj:`"gelu"`, :obj:`"relu"`, :obj:`"selu"` and :obj:`"gelu_new"` are supported. hidden_dropout_prob (:obj:`float`, `optional`, defaults to 0.1): The dropout probabilitiy for all fully connected layers in the embeddings, encoder, and pooler. attention_probs_dropout_prob (:obj:`float`, `optional`, defaults to 0.1): The dropout ratio for the attention probabilities. max_position_embeddings (:obj:`int`, `optional`, defaults to 1536): 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 1536). type_vocab_size (:obj:`int`, `optional`, defaults to 2): The vocabulary size of the :obj:`token_type_ids` passed when calling :class:`~transformers.RoFormerModel` or :class:`~transformers.TFRoFormerModel`. initializer_range (:obj:`float`, `optional`, defaults to 0.02): The standard deviation of the truncated_normal_initializer for initializing all weight matrices. layer_norm_eps (:obj:`float`, `optional`, defaults to 1e-12): The epsilon used by the layer normalization layers. use_cache (:obj:`bool`, `optional`, defaults to :obj:`True`): Whether or not the model should return the last key/values attentions (not used by all models). Only relevant if ``config.is_decoder=True``. rotary_value (:obj:`bool`, `optional`, defaults to :obj:`False`): Whether or not apply rotary position embeddings on value layer. Example:: >>> from transformers import RoFormerModel, RoFormerConfig >>> # Initializing a RoFormer junnyu/roformer_chinese_base style configuration >>> configuration = RoFormerConfig() >>> # Initializing a model from the junnyu/roformer_chinese_base style configuration >>> model = RoFormerModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config """ model_type = "roformer" def __init__( self, vocab_size=50000, embedding_size=None, hidden_size=768, num_hidden_layers=12, num_attention_heads=12, intermediate_size=3072, hidden_act="gelu", hidden_dropout_prob=0.1, attention_probs_dropout_prob=0.1, max_position_embeddings=1536, type_vocab_size=2, initializer_range=0.02, layer_norm_eps=1e-12, pad_token_id=0, rotary_value=False, use_cache=True, **kwargs ): super().__init__(pad_token_id=pad_token_id, **kwargs) self.vocab_size = vocab_size self.embedding_size = hidden_size if embedding_size is None else embedding_size self.hidden_size = hidden_size self.num_hidden_layers = num_hidden_layers self.num_attention_heads = num_attention_heads self.hidden_act = hidden_act self.intermediate_size = intermediate_size self.hidden_dropout_prob = hidden_dropout_prob self.attention_probs_dropout_prob = attention_probs_dropout_prob self.max_position_embeddings = max_position_embeddings self.type_vocab_size = type_vocab_size self.initializer_range = initializer_range self.layer_norm_eps = layer_norm_eps self.rotary_value = rotary_value self.use_cache = use_cache