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| # coding=utf-8 | |
| # 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"); | |
| # you may not use this file except in compliance with the License. | |
| # You may obtain a copy of the License at | |
| # | |
| # http://www.apache.org/licenses/LICENSE-2.0 | |
| # | |
| # Unless required by applicable law or agreed to in writing, software | |
| # distributed under the License is distributed on an "AS IS" BASIS, | |
| # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | |
| # See the License for the specific language governing permissions and | |
| # limitations under the License. | |
| """ ELECTRA model configuration """ | |
| from ...configuration_utils import PretrainedConfig | |
| from ...utils import logging | |
| logger = logging.get_logger(__name__) | |
| ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP = { | |
| "google/electra-small-generator": "https://huggingface.co/google/electra-small-generator/resolve/main/config.json", | |
| "google/electra-base-generator": "https://huggingface.co/google/electra-base-generator/resolve/main/config.json", | |
| "google/electra-large-generator": "https://huggingface.co/google/electra-large-generator/resolve/main/config.json", | |
| "google/electra-small-discriminator": "https://huggingface.co/google/electra-small-discriminator/resolve/main/config.json", | |
| "google/electra-base-discriminator": "https://huggingface.co/google/electra-base-discriminator/resolve/main/config.json", | |
| "google/electra-large-discriminator": "https://huggingface.co/google/electra-large-discriminator/resolve/main/config.json", | |
| } | |
| class ElectraConfig(PretrainedConfig): | |
| r""" | |
| This is the configuration class to store the configuration of a :class:`~transformers.ElectraModel` or a | |
| :class:`~transformers.TFElectraModel`. It is used to instantiate a ELECTRA 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 ELECTRA `google/electra-small-discriminator | |
| <https://huggingface.co/google/electra-small-discriminator>`__ 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 ELECTRA model. Defines the number of different tokens that can be represented by the | |
| :obj:`inputs_ids` passed when calling :class:`~transformers.ElectraModel` or | |
| :class:`~transformers.TFElectraModel`. | |
| embedding_size (:obj:`int`, `optional`, defaults to 128): | |
| Dimensionality of the encoder layers and the pooler layer. | |
| hidden_size (:obj:`int`, `optional`, defaults to 256): | |
| 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 4): | |
| Number of attention heads for each attention layer in the Transformer encoder. | |
| intermediate_size (:obj:`int`, `optional`, defaults to 1024): | |
| Dimensionality of the "intermediate" (i.e., 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.ElectraModel` or | |
| :class:`~transformers.TFElectraModel`. | |
| 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. | |
| summary_type (:obj:`str`, `optional`, defaults to :obj:`"first"`): | |
| Argument used when doing sequence summary. Used in the sequence classification and multiple choice models. | |
| Has to be one of the following options: | |
| - :obj:`"last"`: Take the last token hidden state (like XLNet). | |
| - :obj:`"first"`: Take the first token hidden state (like BERT). | |
| - :obj:`"mean"`: Take the mean of all tokens hidden states. | |
| - :obj:`"cls_index"`: Supply a Tensor of classification token position (like GPT/GPT-2). | |
| - :obj:`"attn"`: Not implemented now, use multi-head attention. | |
| summary_use_proj (:obj:`bool`, `optional`, defaults to :obj:`True`): | |
| Argument used when doing sequence summary. Used in the sequence classification and multiple choice models. | |
| Whether or not to add a projection after the vector extraction. | |
| summary_activation (:obj:`str`, `optional`): | |
| Argument used when doing sequence summary. Used in the sequence classification and multiple choice models. | |
| Pass :obj:`"gelu"` for a gelu activation to the output, any other value will result in no activation. | |
| summary_last_dropout (:obj:`float`, `optional`, defaults to 0.0): | |
| Argument used when doing sequence summary. Used in the sequence classification and multiple choice models. | |
| The dropout ratio to be used after the projection and activation. | |
| 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>`__. | |
| Examples:: | |
| >>> from transformers import ElectraModel, ElectraConfig | |
| >>> # Initializing a ELECTRA electra-base-uncased style configuration | |
| >>> configuration = ElectraConfig() | |
| >>> # Initializing a model from the electra-base-uncased style configuration | |
| >>> model = ElectraModel(configuration) | |
| >>> # Accessing the model configuration | |
| >>> configuration = model.config | |
| """ | |
| model_type = "electra" | |
| def __init__( | |
| self, | |
| vocab_size=30522, | |
| embedding_size=128, | |
| hidden_size=256, | |
| num_hidden_layers=12, | |
| num_attention_heads=4, | |
| intermediate_size=1024, | |
| 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, | |
| summary_type="first", | |
| summary_use_proj=True, | |
| summary_activation="gelu", | |
| summary_last_dropout=0.1, | |
| pad_token_id=0, | |
| position_embedding_type="absolute", | |
| **kwargs | |
| ): | |
| super().__init__(pad_token_id=pad_token_id, **kwargs) | |
| self.vocab_size = vocab_size | |
| self.embedding_size = embedding_size | |
| self.hidden_size = hidden_size | |
| self.num_hidden_layers = num_hidden_layers | |
| self.num_attention_heads = num_attention_heads | |
| self.intermediate_size = intermediate_size | |
| self.hidden_act = hidden_act | |
| 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.summary_type = summary_type | |
| self.summary_use_proj = summary_use_proj | |
| self.summary_activation = summary_activation | |
| self.summary_last_dropout = summary_last_dropout | |
| self.position_embedding_type = position_embedding_type | |