132 lines
6.7 KiB
Python
132 lines
6.7 KiB
Python
# coding=utf-8
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# Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team.
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# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
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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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""" ELECTRA model configuration """
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import logging
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from configuration_utils import PretrainedConfig
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logger = logging.getLogger(__name__)
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ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP = {
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"google/electra-small-generator": "https://s3.amazonaws.com/models.huggingface.co/bert/google/electra-small-generator/config.json",
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"google/electra-base-generator": "https://s3.amazonaws.com/models.huggingface.co/bert/google/electra-base-generator/config.json",
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"google/electra-large-generator": "https://s3.amazonaws.com/models.huggingface.co/bert/google/electra-large-generator/config.json",
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"google/electra-small-discriminator": "https://s3.amazonaws.com/models.huggingface.co/bert/google/electra-small-discriminator/config.json",
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"google/electra-base-discriminator": "https://s3.amazonaws.com/models.huggingface.co/bert/google/electra-base-discriminator/config.json",
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"google/electra-large-discriminator": "https://s3.amazonaws.com/models.huggingface.co/bert/google/electra-large-discriminator/config.json",
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}
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class ElectraConfig(PretrainedConfig):
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r"""
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This is the configuration class to store the configuration of a :class:`~transformers.ElectraModel`.
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It is used to instantiate an ELECTRA model according to the specified arguments, defining the model
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architecture. Instantiating a configuration with the defaults will yield a similar configuration to that of
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the ELECTRA `google/electra-small-discriminator <https://huggingface.co/google/electra-small-discriminator>`__
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architecture.
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Configuration objects inherit from :class:`~transformers.PretrainedConfig` and can be used
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to control the model outputs. Read the documentation from :class:`~transformers.PretrainedConfig`
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for more information.
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Args:
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vocab_size (:obj:`int`, optional, defaults to 30522):
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Vocabulary size of the ELECTRA model. Defines the different tokens that
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can be represented by the `inputs_ids` passed to the forward method of :class:`~transformers.ElectraModel`.
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embedding_size (:obj:`int`, optional, defaults to 128):
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Dimensionality of the encoder layers and the pooler layer.
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hidden_size (:obj:`int`, optional, defaults to 256):
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Dimensionality of the encoder layers and the pooler layer.
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num_hidden_layers (:obj:`int`, optional, defaults to 12):
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Number of hidden layers in the Transformer encoder.
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num_attention_heads (:obj:`int`, optional, defaults to 4):
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Number of attention heads for each attention layer in the Transformer encoder.
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intermediate_size (:obj:`int`, optional, defaults to 1024):
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Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder.
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hidden_act (:obj:`str` or :obj:`function`, optional, defaults to "gelu"):
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The non-linear activation function (function or string) in the encoder and pooler.
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If string, "gelu", "relu", "swish" and "gelu_new" are supported.
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hidden_dropout_prob (:obj:`float`, optional, defaults to 0.1):
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The dropout probabilitiy for all fully connected layers in the embeddings, encoder, and pooler.
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attention_probs_dropout_prob (:obj:`float`, optional, defaults to 0.1):
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The dropout ratio for the attention probabilities.
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max_position_embeddings (:obj:`int`, optional, defaults to 512):
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The maximum sequence length that this model might ever be used with.
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Typically set this to something large just in case (e.g., 512 or 1024 or 2048).
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type_vocab_size (:obj:`int`, optional, defaults to 2):
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The vocabulary size of the `token_type_ids` passed into :class:`~transformers.ElectraModel`.
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initializer_range (:obj:`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 (:obj:`float`, optional, defaults to 1e-12):
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The epsilon used by the layer normalization layers.
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Example::
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from transformers import ElectraModel, ElectraConfig
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# Initializing a ELECTRA electra-base-uncased style configuration
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configuration = ElectraConfig()
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# Initializing a model from the electra-base-uncased style configuration
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model = ElectraModel(configuration)
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# Accessing the model configuration
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configuration = model.config
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Attributes:
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pretrained_config_archive_map (Dict[str, str]):
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A dictionary containing all the available pre-trained checkpoints.
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"""
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pretrained_config_archive_map = ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP
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model_type = "electra"
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def __init__(
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self,
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vocab_size=30522,
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embedding_size=128,
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hidden_size=256,
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num_hidden_layers=12,
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num_attention_heads=4,
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intermediate_size=1024,
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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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**kwargs
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):
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super().__init__(pad_token_id=pad_token_id, **kwargs)
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self.vocab_size = vocab_size
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self.embedding_size = embedding_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.intermediate_size = intermediate_size
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self.hidden_act = hidden_act
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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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