image-match/python/py/Lib/site-packages/transformers/models/mobilebert/configuration_mobilebert.py

# 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,
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"""MobileBERT model configuration"""

from huggingface_hub.dataclasses import strict

from ...configuration_utils import PreTrainedConfig
from ...utils import auto_docstring


@auto_docstring(checkpoint="google/mobilebert-uncased")
@strict
class MobileBertConfig(PreTrainedConfig):
    r"""
    embedding_size (`int`, *optional*, defaults to 128):
        The dimension of the word embedding vectors.
    trigram_input (`bool`, *optional*, defaults to `True`):
        Use a convolution of trigram as input.
    use_bottleneck (`bool`, *optional*, defaults to `True`):
        Whether to use bottleneck in BERT.
    intra_bottleneck_size (`int`, *optional*, defaults to 128):
        Size of bottleneck layer output.
    use_bottleneck_attention (`bool`, *optional*, defaults to `False`):
        Whether to use attention inputs from the bottleneck transformation.
    key_query_shared_bottleneck (`bool`, *optional*, defaults to `True`):
        Whether to use the same linear transformation for query&key in the bottleneck.
    num_feedforward_networks (`int`, *optional*, defaults to 4):
        Number of FFNs in a block.
    normalization_type (`str`, *optional*, defaults to `"no_norm"`):
        The normalization type in MobileBERT.

    Examples:

    ```python
    >>> from transformers import MobileBertConfig, MobileBertModel

    >>> # Initializing a MobileBERT configuration
    >>> configuration = MobileBertConfig()

    >>> # Initializing a model (with random weights) from the configuration above
    >>> model = MobileBertModel(configuration)

    >>> # Accessing the model configuration
    >>> configuration = model.config
    ```
    """

    model_type = "mobilebert"

    vocab_size: int = 30522
    hidden_size: int = 512
    num_hidden_layers: int = 24
    num_attention_heads: int = 4
    intermediate_size: int = 512
    hidden_act: str = "relu"
    hidden_dropout_prob: float | int = 0.0
    attention_probs_dropout_prob: float | int = 0.1
    max_position_embeddings: int = 512
    type_vocab_size: int = 2
    initializer_range: float = 0.02
    layer_norm_eps: float = 1e-12
    pad_token_id: int | None = 0
    embedding_size: int = 128
    trigram_input: bool = True
    use_bottleneck: bool = True
    intra_bottleneck_size: int = 128
    use_bottleneck_attention: bool = False
    key_query_shared_bottleneck: bool = True
    num_feedforward_networks: int = 4
    normalization_type: str = "no_norm"
    classifier_activation: bool = True
    classifier_dropout: float | int | None = None
    tie_word_embeddings: bool = True

    def __post_init__(self, **kwargs):
        if self.use_bottleneck:
            self.true_hidden_size = self.intra_bottleneck_size
        else:
            self.true_hidden_size = self.hidden_size
        super().__post_init__(**kwargs)


__all__ = ["MobileBertConfig"]