Source code for transformers.models.bert.configuration_bert

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# Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team.
# Copyright (c) 2018, NVIDIA CORPORATION.  All rights reserved.
# Licensed under the Apache License, Version 2.0 (the "License");
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""" BERT model configuration """

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

logger = logging.get_logger(__name__)

    "bert-base-uncased": "",
    "bert-large-uncased": "",
    "bert-base-cased": "",
    "bert-large-cased": "",
    "bert-base-multilingual-uncased": "",
    "bert-base-multilingual-cased": "",
    "bert-base-chinese": "",
    "bert-base-german-cased": "",
    "bert-large-uncased-whole-word-masking": "",
    "bert-large-cased-whole-word-masking": "",
    "bert-large-uncased-whole-word-masking-finetuned-squad": "",
    "bert-large-cased-whole-word-masking-finetuned-squad": "",
    "bert-base-cased-finetuned-mrpc": "",
    "bert-base-german-dbmdz-cased": "",
    "bert-base-german-dbmdz-uncased": "",
    "cl-tohoku/bert-base-japanese": "",
    "cl-tohoku/bert-base-japanese-whole-word-masking": "",
    "cl-tohoku/bert-base-japanese-char": "",
    "cl-tohoku/bert-base-japanese-char-whole-word-masking": "",
    "TurkuNLP/bert-base-finnish-cased-v1": "",
    "TurkuNLP/bert-base-finnish-uncased-v1": "",
    "wietsedv/bert-base-dutch-cased": "",
    # See all BERT models at

[docs]class BertConfig(PretrainedConfig): r""" This is the configuration class to store the configuration of a :class:`~transformers.BertModel` or a :class:`~transformers.TFBertModel`. It is used to instantiate a BERT 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 BERT `bert-base-uncased <>`__ 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 BERT model. Defines the number of different tokens that can be represented by the :obj:`inputs_ids` passed when calling :class:`~transformers.BertModel` or :class:`~transformers.TFBertModel`. 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): The epsilon used by the layer normalization layers. gradient_checkpointing (:obj:`bool`, `optional`, defaults to :obj:`False`): If True, use gradient checkpointing to save memory at the expense of slower backward pass. position_embedding_type (:obj:`str`, `optional`, defaults to :obj:`"absolute"`): Type of position embedding. Choose one of :obj:`"absolute"`, :obj:`"relative_key"`, :obj:`"relative_key_query"`. For positional embeddings use :obj:`"absolute"`. For more information on :obj:`"relative_key"`, please refer to `Self-Attention with Relative Position Representations (Shaw et al.) <>`__. For more information on :obj:`"relative_key_query"`, please refer to `Method 4` in `Improve Transformer Models with Better Relative Position Embeddings (Huang et al.) <>`__. Examples:: >>> from transformers import BertModel, BertConfig >>> # Initializing a BERT bert-base-uncased style configuration >>> configuration = BertConfig() >>> # Initializing a model from the bert-base-uncased style configuration >>> model = BertModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config """ model_type = "bert" 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, gradient_checkpointing=False, position_embedding_type="absolute", **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.gradient_checkpointing = gradient_checkpointing self.position_embedding_type = position_embedding_type