Source code for transformers.models.dpr.configuration_dpr

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

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


logger = logging.get_logger(__name__)

DPR_PRETRAINED_CONFIG_ARCHIVE_MAP = {
    "facebook/dpr-ctx_encoder-single-nq-base": "https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base/resolve/main/config.json",
    "facebook/dpr-question_encoder-single-nq-base": "https://huggingface.co/facebook/dpr-question_encoder-single-nq-base/resolve/main/config.json",
    "facebook/dpr-reader-single-nq-base": "https://huggingface.co/facebook/dpr-reader-single-nq-base/resolve/main/config.json",
    "facebook/dpr-ctx_encoder-multiset-base": "https://huggingface.co/facebook/dpr-ctx_encoder-multiset-base/resolve/main/config.json",
    "facebook/dpr-question_encoder-multiset-base": "https://huggingface.co/facebook/dpr-question_encoder-multiset-base/resolve/main/config.json",
    "facebook/dpr-reader-multiset-base": "https://huggingface.co/facebook/dpr-reader-multiset-base/resolve/main/config.json",
}


[docs]class DPRConfig(PretrainedConfig): r""" :class:`~transformers.DPRConfig` is the configuration class to store the configuration of a `DPRModel`. This is the configuration class to store the configuration of a :class:`~transformers.DPRContextEncoder`, :class:`~transformers.DPRQuestionEncoder`, or a :class:`~transformers.DPRReader`. It is used to instantiate the components of the DPR model. This class is a subclass of :class:`~transformers.BertConfig`. Please check the superclass for the documentation of all kwargs. Args: vocab_size (:obj:`int`, `optional`, defaults to 30522): Vocabulary size of the DPR model. Defines the different tokens that can be represented by the `inputs_ids` passed to the forward method of :class:`~transformers.BertModel`. 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" (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:`"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 `token_type_ids` passed into :class:`~transformers.BertModel`. 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.) <https://arxiv.org/abs/1803.02155>`__. 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.) <https://arxiv.org/abs/2009.13658>`__. projection_dim (:obj:`int`, `optional`, defaults to 0): Dimension of the projection for the context and question encoders. If it is set to zero (default), then no projection is done. """ model_type = "dpr" 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", projection_dim: int = 0, **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.projection_dim = projection_dim self.position_embedding_type = position_embedding_type