# 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,
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""" ALBERT model configuration """
from .configuration_utils import PretrainedConfig
ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP = {
'albert-base-v1': "https://s3.amazonaws.com/models.huggingface.co/bert/albert-base-config.json",
'albert-large-v1': "https://s3.amazonaws.com/models.huggingface.co/bert/albert-large-config.json",
'albert-xlarge-v1': "https://s3.amazonaws.com/models.huggingface.co/bert/albert-xlarge-config.json",
'albert-xxlarge-v1': "https://s3.amazonaws.com/models.huggingface.co/bert/albert-xxlarge-config.json",
'albert-base-v2': "https://s3.amazonaws.com/models.huggingface.co/bert/albert-base-v2-config.json",
'albert-large-v2': "https://s3.amazonaws.com/models.huggingface.co/bert/albert-large-v2-config.json",
'albert-xlarge-v2': "https://s3.amazonaws.com/models.huggingface.co/bert/albert-xlarge-v2-config.json",
'albert-xxlarge-v2': "https://s3.amazonaws.com/models.huggingface.co/bert/albert-xxlarge-v2-config.json",
}
[docs]class AlbertConfig(PretrainedConfig):
"""Configuration for `AlbertModel`.
The default settings match the configuration of model `albert_xxlarge`.
"""
pretrained_config_archive_map = ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP
def __init__(self,
vocab_size_or_config_json_file=30000,
embedding_size=128,
hidden_size=4096,
num_hidden_layers=12,
num_hidden_groups=1,
num_attention_heads=64,
intermediate_size=16384,
inner_group_num=1,
hidden_act="gelu_new",
hidden_dropout_prob=0,
attention_probs_dropout_prob=0,
max_position_embeddings=512,
type_vocab_size=2,
initializer_range=0.02,
layer_norm_eps=1e-12, **kwargs):
"""Constructs AlbertConfig.
Args:
vocab_size: Vocabulary size of `inputs_ids` in `AlbertModel`.
embedding_size: size of voc embeddings.
hidden_size: Size of the encoder layers and the pooler layer.
num_hidden_layers: Number of hidden layers in the Transformer encoder.
num_hidden_groups: Number of group for the hidden layers, parameters in
the same group are shared.
num_attention_heads: Number of attention heads for each attention layer in
the Transformer encoder.
intermediate_size: The size of the "intermediate" (i.e., feed-forward)
layer in the Transformer encoder.
inner_group_num: int, number of inner repetition of attention and ffn.
down_scale_factor: float, the scale to apply
hidden_act: The non-linear activation function (function or string) in the
encoder and pooler.
hidden_dropout_prob: The dropout probability for all fully connected
layers in the embeddings, encoder, and pooler.
attention_probs_dropout_prob: The dropout ratio for the attention
probabilities.
max_position_embeddings: 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: The vocabulary size of the `token_type_ids` passed into
`AlbertModel`.
initializer_range: The stdev of the truncated_normal_initializer for
initializing all weight matrices.
"""
super(AlbertConfig, self).__init__(**kwargs)
self.vocab_size = vocab_size_or_config_json_file
self.embedding_size = embedding_size
self.hidden_size = hidden_size
self.num_hidden_layers = num_hidden_layers
self.num_hidden_groups = num_hidden_groups
self.num_attention_heads = num_attention_heads
self.inner_group_num = inner_group_num
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