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141 lines
6.3 KiB
141 lines
6.3 KiB
# Copyright 2010, DPR authors, The Hugging Face Team.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""DPR model configuration"""
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from ...configuration_utils import PreTrainedConfig
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from ...utils import logging
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logger = logging.get_logger(__name__)
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class DPRConfig(PreTrainedConfig):
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r"""
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[`DPRConfig`] is the configuration class to store the configuration of a *DPRModel*.
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This is the configuration class to store the configuration of a [`DPRContextEncoder`], [`DPRQuestionEncoder`], or a
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[`DPRReader`]. It is used to instantiate the components of the DPR model according to the specified arguments,
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defining the model component architectures. Instantiating a configuration with the defaults will yield a similar
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configuration to that of the DPRContextEncoder
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[facebook/dpr-ctx_encoder-single-nq-base](https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base)
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architecture.
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This class is a subclass of [`BertConfig`]. Please check the superclass for the documentation of all kwargs.
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Args:
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vocab_size (`int`, *optional*, defaults to 30522):
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Vocabulary size of the DPR model. Defines the different tokens that can be represented by the *inputs_ids*
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passed to the forward method of [`BertModel`].
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hidden_size (`int`, *optional*, defaults to 768):
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Dimensionality of the encoder layers and the pooler layer.
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num_hidden_layers (`int`, *optional*, defaults to 12):
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Number of hidden layers in the Transformer encoder.
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num_attention_heads (`int`, *optional*, defaults to 12):
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Number of attention heads for each attention layer in the Transformer encoder.
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intermediate_size (`int`, *optional*, defaults to 3072):
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Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder.
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hidden_act (`str` or `function`, *optional*, defaults to `"gelu"`):
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The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
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`"relu"`, `"silu"` and `"gelu_new"` are supported.
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hidden_dropout_prob (`float`, *optional*, defaults to 0.1):
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The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
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attention_probs_dropout_prob (`float`, *optional*, defaults to 0.1):
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The dropout ratio for the attention probabilities.
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max_position_embeddings (`int`, *optional*, defaults to 512):
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The maximum sequence length that this model might ever be used with. Typically set this to something large
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just in case (e.g., 512 or 1024 or 2048).
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type_vocab_size (`int`, *optional*, defaults to 2):
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The vocabulary size of the *token_type_ids* passed into [`BertModel`].
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initializer_range (`float`, *optional*, defaults to 0.02):
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The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
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layer_norm_eps (`float`, *optional*, defaults to 1e-12):
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The epsilon used by the layer normalization layers.
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pad_token_id (`int`, *optional*, defaults to 0):
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Padding token id.
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bos_token_id (`int`, *optional*):
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Beginning of stream token id.
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eos_token_id (`int`, *optional*):
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End of stream token id.
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projection_dim (`int`, *optional*, defaults to 0):
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Dimension of the projection for the context and question encoders. If it is set to zero (default), then no
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projection is done.
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is_decoder (`bool`, *optional*, defaults to `False`):
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Whether to only use the decoder in an encoder-decoder architecture, otherwise it has no effect on
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decoder-only or encoder-only architectures.
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add_cross_attention (`bool`, *optional*, defaults to `False`):
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Whether cross-attention layers should be added to the model.
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Example:
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```python
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>>> from transformers import DPRConfig, DPRContextEncoder
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>>> # Initializing a DPR facebook/dpr-ctx_encoder-single-nq-base style configuration
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>>> configuration = DPRConfig()
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>>> # Initializing a model (with random weights) from the facebook/dpr-ctx_encoder-single-nq-base style configuration
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>>> model = DPRContextEncoder(configuration)
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>>> # Accessing the model configuration
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>>> configuration = model.config
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```"""
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model_type = "dpr"
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def __init__(
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self,
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vocab_size=30522,
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hidden_size=768,
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num_hidden_layers=12,
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num_attention_heads=12,
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intermediate_size=3072,
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hidden_act="gelu",
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hidden_dropout_prob=0.1,
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attention_probs_dropout_prob=0.1,
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max_position_embeddings=512,
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type_vocab_size=2,
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initializer_range=0.02,
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layer_norm_eps=1e-12,
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pad_token_id=0,
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bos_token_id=None,
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eos_token_id=None,
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projection_dim: int = 0,
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is_decoder=False,
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add_cross_attention=False,
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**kwargs,
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):
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super().__init__(**kwargs)
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self.pad_token_id = pad_token_id
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self.bos_token_id = bos_token_id
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self.eos_token_id = eos_token_id
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self.is_decoder = is_decoder
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self.add_cross_attention = add_cross_attention
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self.vocab_size = vocab_size
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self.hidden_size = hidden_size
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self.num_hidden_layers = num_hidden_layers
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self.num_attention_heads = num_attention_heads
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self.hidden_act = hidden_act
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self.intermediate_size = intermediate_size
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self.hidden_dropout_prob = hidden_dropout_prob
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self.attention_probs_dropout_prob = attention_probs_dropout_prob
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self.max_position_embeddings = max_position_embeddings
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self.type_vocab_size = type_vocab_size
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self.initializer_range = initializer_range
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self.layer_norm_eps = layer_norm_eps
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self.projection_dim = projection_dim
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__all__ = ["DPRConfig"]
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