# coding=utf-8
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""" FNet model configuration """
from ...configuration_utils import PretrainedConfig
from ...utils import logging
logger = logging.get_logger(__name__)
FNET_PRETRAINED_CONFIG_ARCHIVE_MAP = {
"google/fnet-base": "https://huggingface.co/google/fnet-base/resolve/main/config.json",
"google/fnet-large": "https://huggingface.co/google/fnet-large/resolve/main/config.json"
# See all FNet models at https://huggingface.co/models?filter=fnet
}
[docs]class FNetConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a :class:`~transformers.FNetModel`. It is used to
instantiate an FNet 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 FNet `fnet-base
<https://huggingface.co/google/fnet-base>`__ 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 32000):
Vocabulary size of the FNet model. Defines the number of different tokens that can be represented by the
:obj:`inputs_ids` passed when calling :class:`~transformers.FNetModel` or
:class:`~transformers.TFFNetModel`.
hidden_size (:obj:`int`, `optional`, defaults to 768):
Dimension 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.
intermediate_size (:obj:`int`, `optional`, defaults to 3072):
Dimension of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder.
hidden_act (:obj:`str` or :obj:`function`, `optional`, defaults to :obj:`"gelu_new"`):
The non-linear activation function (function or string) in the encoder and pooler. If string,
:obj:`"gelu"`, :obj:`"relu"`, :obj:`"selu"` and :obj:`"gelu_new"` are supported.
hidden_dropout_prob (:obj:`float`, `optional`, defaults to 0.1):
The dropout probabilitiy for all fully connected layers in the embeddings, encoder, and pooler.
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 4):
The vocabulary size of the :obj:`token_type_ids` passed when calling :class:`~transformers.FNetModel` or
:class:`~transformers.TFFNetModel`.
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.
use_tpu_fourier_optimizations (:obj:`bool`, `optional`, defaults to :obj:`False`):
Determines whether to use TPU optimized FFTs. If :obj:`True`, the model will favor axis-wise FFTs
transforms. Set to :obj:`False` for GPU/CPU hardware, in which case n-dimensional FFTs are used.
tpu_short_seq_length (:obj:`int`, `optional`, defaults to 512):
The sequence length that is expected by the model when using TPUs. This will be used to initialize the DFT
matrix only when `use_tpu_fourier_optimizations` is set to :obj:`True` and the input sequence is shorter
than or equal to 4096 tokens.
Example::
>>> from transformers import FNetModel, FNetConfig
>>> # Initializing a FNet fnet-base style configuration
>>> configuration = FNetConfig()
>>> # Initializing a model from the fnet-base style configuration
>>> model = FNetModel(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
"""
model_type = "fnet"
def __init__(
self,
vocab_size=32000,
hidden_size=768,
num_hidden_layers=12,
intermediate_size=3072,
hidden_act="gelu_new",
hidden_dropout_prob=0.1,
max_position_embeddings=512,
type_vocab_size=4,
initializer_range=0.02,
layer_norm_eps=1e-12,
use_tpu_fourier_optimizations=False,
tpu_short_seq_length=512,
pad_token_id=3,
bos_token_id=1,
eos_token_id=2,
**kwargs
):
super().__init__(pad_token_id=pad_token_id, bos_token_id=bos_token_id, eos_token_id=eos_token_id, **kwargs)
self.vocab_size = vocab_size
self.max_position_embeddings = max_position_embeddings
self.hidden_size = hidden_size
self.num_hidden_layers = num_hidden_layers
self.intermediate_size = intermediate_size
self.hidden_act = hidden_act
self.hidden_dropout_prob = hidden_dropout_prob
self.initializer_range = initializer_range
self.type_vocab_size = type_vocab_size
self.layer_norm_eps = layer_norm_eps
self.use_tpu_fourier_optimizations = use_tpu_fourier_optimizations
self.tpu_short_seq_length = tpu_short_seq_length