Source code for transformers.models.squeezebert.configuration_squeezebert

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# Copyright 2020 The SqueezeBert authors and The HuggingFace Inc. team.
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""" SqueezeBERT model configuration """

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

logger = logging.get_logger(__name__)

    "squeezebert/squeezebert-uncased": "",
    "squeezebert/squeezebert-mnli": "",
    "squeezebert/squeezebert-mnli-headless": "",

[docs]class SqueezeBertConfig(PretrainedConfig): r""" This is the configuration class to store the configuration of a :class:`~transformers.SqueezeBertModel`. It is used to instantiate a SqueezeBERT model according to the specified arguments, defining the model 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 30522): Vocabulary size of the SqueezeBERT model. Defines the number of different tokens that can be represented by the :obj:`inputs_ids` passed when calling :class:`~transformers.SqueezeBertModel`. hidden_size (:obj:`int`, `optional`, defaults to 768): Dimensionality 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): Dimensionality of the "intermediate" (often named feed-forward) layer in the Transformer encoder. hidden_act (:obj:`str` or :obj:`Callable`, `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:`"silu"` and :obj:`"gelu_new"` are supported. hidden_dropout_prob (:obj:`float`, `optional`, defaults to 0.1): The dropout probability 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 512): 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). type_vocab_size (:obj:`int`, `optional`, defaults to 2): The vocabulary size of the :obj:`token_type_ids` passed when calling :class:`~transformers.BertModel` or :class:`~transformers.TFBertModel`. 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): pad_token_id (:obj:`int`, `optional`, defaults to 0): The ID of the token in the word embedding to use as padding. embedding_size (:obj:`int`, `optional`, defaults to 768): The dimension of the word embedding vectors. q_groups (:obj:`int`, `optional`, defaults to 4): The number of groups in Q layer. k_groups (:obj:`int`, `optional`, defaults to 4): The number of groups in K layer. v_groups (:obj:`int`, `optional`, defaults to 4): The number of groups in V layer. post_attention_groups (:obj:`int`, `optional`, defaults to 1): The number of groups in the first feed forward network layer. intermediate_groups (:obj:`int`, `optional`, defaults to 4): The number of groups in the second feed forward network layer. output_groups (:obj:`int`, `optional`, defaults to 4): The number of groups in the third feed forward network layer. Examples:: >>> from transformers import SqueezeBertModel, SqueezeBertConfig >>> # Initializing a SqueezeBERT configuration >>> configuration = SqueezeBertConfig() >>> # Initializing a model from the configuration above >>> model = SqueezeBertModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config Attributes: pretrained_config_archive_map (Dict[str, str]): A dictionary containing all the available pre-trained checkpoints. """ pretrained_config_archive_map = SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP model_type = "squeezebert" def __init__( self, vocab_size=30522, 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=512, type_vocab_size=2, initializer_range=0.02, layer_norm_eps=1e-12, pad_token_id=0, embedding_size=768, q_groups=4, k_groups=4, v_groups=4, post_attention_groups=1, intermediate_groups=4, output_groups=4, **kwargs ): super().__init__(pad_token_id=pad_token_id, **kwargs) self.vocab_size = vocab_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.embedding_size = embedding_size self.q_groups = q_groups self.k_groups = k_groups self.v_groups = v_groups self.post_attention_groups = post_attention_groups self.intermediate_groups = intermediate_groups self.output_groups = output_groups