Source code for transformers.models.big_bird.configuration_big_bird

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""" BigBird model configuration """

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

logger = logging.get_logger(__name__)

    "google/bigbird-roberta-base": "",
    "google/bigbird-roberta-large": "",
    "google/bigbird-base-trivia-itc": "",
    # See all BigBird models at

[docs]class BigBirdConfig(PretrainedConfig): r""" This is the configuration class to store the configuration of a :class:`~transformers.BigBirdModel`. It is used to instantiate an BigBird 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 BigBird `google/bigbird-roberta-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 50358): Vocabulary size of the BigBird model. Defines the number of different tokens that can be represented by the :obj:`inputs_ids` passed when calling :class:`~transformers.BigBirdModel`. 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_new"`): 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 4096): The maximum sequence length that this model might ever be used with. Typically set this to something large just in case (e.g., 1024 or 2048 or 4096). type_vocab_size (:obj:`int`, `optional`, defaults to 2): The vocabulary size of the :obj:`token_type_ids` passed when calling :class:`~transformers.BigBirdModel`. 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``. attention_type (:obj:`str`, `optional`, defaults to :obj:`"block_sparse"`) Whether to use block sparse attention (with n complexity) as introduced in paper or original attention layer (with n^2 complexity). Possible values are :obj:`"original_full"` and :obj:`"block_sparse"`. use_bias (:obj:`bool`, `optional`, defaults to :obj:`True`) Whether to use bias in query, key, value. rescale_embeddings (:obj:`bool`, `optional`, defaults to :obj:`False`) Whether to rescale embeddings with (hidden_size ** 0.5). block_size (:obj:`int`, `optional`, defaults to 64) Size of each block. Useful only when :obj:`attention_type == "block_sparse"`. num_random_blocks (:obj:`int`, `optional`, defaults to 3) Each query is going to attend these many number of random blocks. Useful only when :obj:`attention_type == "block_sparse"`. classifier_dropout (:obj:`float`, `optional`): The dropout ratio for the classification head. Example:: >>> from transformers import BigBirdModel, BigBirdConfig >>> # Initializing a BigBird google/bigbird-roberta-base style configuration >>> configuration = BigBirdConfig() >>> # Initializing a model from the google/bigbird-roberta-base style configuration >>> model = BigBirdModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config """ model_type = "big_bird" def __init__( self, vocab_size=50358, hidden_size=768, num_hidden_layers=12, num_attention_heads=12, intermediate_size=3072, hidden_act="gelu_new", hidden_dropout_prob=0.1, attention_probs_dropout_prob=0.1, max_position_embeddings=4096, type_vocab_size=2, initializer_range=0.02, layer_norm_eps=1e-12, use_cache=True, is_encoder_decoder=False, pad_token_id=0, bos_token_id=1, eos_token_id=2, sep_token_id=66, attention_type="block_sparse", use_bias=True, rescale_embeddings=False, block_size=64, num_random_blocks=3, classifier_dropout=None, **kwargs ): super().__init__( pad_token_id=pad_token_id, bos_token_id=bos_token_id, eos_token_id=eos_token_id, sep_token_id=sep_token_id, **kwargs, ) self.vocab_size = vocab_size self.max_position_embeddings = max_position_embeddings self.hidden_size = hidden_size self.num_hidden_layers = num_hidden_layers self.num_attention_heads = num_attention_heads self.intermediate_size = intermediate_size self.hidden_act = hidden_act self.hidden_dropout_prob = hidden_dropout_prob self.attention_probs_dropout_prob = attention_probs_dropout_prob self.initializer_range = initializer_range self.type_vocab_size = type_vocab_size self.layer_norm_eps = layer_norm_eps self.use_cache = use_cache self.is_encoder_decoder = is_encoder_decoder self.rescale_embeddings = rescale_embeddings self.attention_type = attention_type self.use_bias = use_bias self.block_size = block_size self.num_random_blocks = num_random_blocks self.classifier_dropout = classifier_dropout