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| # coding=utf-8 | |
| # Copyright 2020 The HuggingFace Team. 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. | |
| """ MobileBERT model configuration """ | |
| from ...configuration_utils import PretrainedConfig | |
| from ...utils import logging | |
| logger = logging.get_logger(__name__) | |
| MOBILEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP = { | |
| "mobilebert-uncased": "https://huggingface.co/google/mobilebert-uncased/resolve/main/config.json" | |
| } | |
| class MobileBertConfig(PretrainedConfig): | |
| r""" | |
| This is the configuration class to store the configuration of a :class:`~transformers.MobileBertModel` or a | |
| :class:`~transformers.TFMobileBertModel`. It is used to instantiate a MobileBERT model according to the specified | |
| arguments, defining the model 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 MobileBERT model. Defines the number of different tokens that can be represented by | |
| the :obj:`inputs_ids` passed when calling :class:`~transformers.MobileBertModel` or | |
| :class:`~transformers.TFMobileBertModel`. | |
| hidden_size (:obj:`int`, `optional`, defaults to 512): | |
| Dimensionality of the encoder layers and the pooler layer. | |
| num_hidden_layers (:obj:`int`, `optional`, defaults to 24): | |
| 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 512): | |
| Dimensionality of the "intermediate" (often named feed-forward) layer in the Transformer encoder. | |
| hidden_act (:obj:`str` or :obj:`function`, `optional`, defaults to :obj:`"relu"`): | |
| 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 0.0): | |
| 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.MobileBertModel` | |
| or :class:`~transformers.TFMobileBertModel`. | |
| 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. | |
| pad_token_id (:obj:`int`, `optional`, defaults to 0): | |
| The ID of the token in the word embedding to use as padding. | |
| embedding_size (:obj:`int`, `optional`, defaults to 128): | |
| The dimension of the word embedding vectors. | |
| trigram_input (:obj:`bool`, `optional`, defaults to :obj:`True`): | |
| Use a convolution of trigram as input. | |
| use_bottleneck (:obj:`bool`, `optional`, defaults to :obj:`True`): | |
| Whether to use bottleneck in BERT. | |
| intra_bottleneck_size (:obj:`int`, `optional`, defaults to 128): | |
| Size of bottleneck layer output. | |
| use_bottleneck_attention (:obj:`bool`, `optional`, defaults to :obj:`False`): | |
| Whether to use attention inputs from the bottleneck transformation. | |
| key_query_shared_bottleneck (:obj:`bool`, `optional`, defaults to :obj:`True`): | |
| Whether to use the same linear transformation for query&key in the bottleneck. | |
| num_feedforward_networks (:obj:`int`, `optional`, defaults to 4): | |
| Number of FFNs in a block. | |
| normalization_type (:obj:`str`, `optional`, defaults to :obj:`"no_norm"`): | |
| The normalization type in MobileBERT. | |
| Examples:: | |
| >>> from transformers import MobileBertModel, MobileBertConfig | |
| >>> # Initializing a MobileBERT configuration | |
| >>> configuration = MobileBertConfig() | |
| >>> # Initializing a model from the configuration above | |
| >>> model = MobileBertModel(configuration) | |
| >>> # Accessing the model configuration | |
| >>> configuration = model.config | |
| Attributes: pretrained_config_archive_map (Dict[str, str]): A dictionary containing all the available pre-trained | |
| checkpoints. | |
| """ | |
| pretrained_config_archive_map = MOBILEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP | |
| model_type = "mobilebert" | |
| def __init__( | |
| self, | |
| vocab_size=30522, | |
| hidden_size=512, | |
| num_hidden_layers=24, | |
| num_attention_heads=4, | |
| intermediate_size=512, | |
| hidden_act="relu", | |
| hidden_dropout_prob=0.0, | |
| 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, | |
| embedding_size=128, | |
| trigram_input=True, | |
| use_bottleneck=True, | |
| intra_bottleneck_size=128, | |
| use_bottleneck_attention=False, | |
| key_query_shared_bottleneck=True, | |
| num_feedforward_networks=4, | |
| normalization_type="no_norm", | |
| classifier_activation=True, | |
| **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.embedding_size = embedding_size | |
| self.trigram_input = trigram_input | |
| self.use_bottleneck = use_bottleneck | |
| self.intra_bottleneck_size = intra_bottleneck_size | |
| self.use_bottleneck_attention = use_bottleneck_attention | |
| self.key_query_shared_bottleneck = key_query_shared_bottleneck | |
| self.num_feedforward_networks = num_feedforward_networks | |
| self.normalization_type = normalization_type | |
| self.classifier_activation = classifier_activation | |
| if self.use_bottleneck: | |
| self.true_hidden_size = intra_bottleneck_size | |
| else: | |
| self.true_hidden_size = hidden_size | |