120 lines
5.5 KiB
Python
120 lines
5.5 KiB
Python
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# coding=utf-8
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# Copyright 2021 Google AI and The HuggingFace Inc. team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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""" FNet model configuration"""
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from ...configuration_utils import PretrainedConfig
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from ...utils import logging
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logger = logging.get_logger(__name__)
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from ..deprecated._archive_maps import FNET_PRETRAINED_CONFIG_ARCHIVE_MAP # noqa: F401, E402
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class FNetConfig(PretrainedConfig):
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r"""
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This is the configuration class to store the configuration of a [`FNetModel`]. It is used to instantiate an FNet
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model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
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defaults will yield a similar configuration to that of the FNet
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[google/fnet-base](https://huggingface.co/google/fnet-base) architecture.
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Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
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documentation from [`PretrainedConfig`] for more information.
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Args:
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vocab_size (`int`, *optional*, defaults to 32000):
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Vocabulary size of the FNet model. Defines the number of different tokens that can be represented by the
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`inputs_ids` passed when calling [`FNetModel`] or [`TFFNetModel`].
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hidden_size (`int`, *optional*, defaults to 768):
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Dimension of the encoder layers and the pooler layer.
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num_hidden_layers (`int`, *optional*, defaults to 12):
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Number of hidden layers in the Transformer encoder.
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intermediate_size (`int`, *optional*, defaults to 3072):
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Dimension of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder.
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hidden_act (`str` or `function`, *optional*, defaults to `"gelu_new"`):
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The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
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`"relu"`, `"selu"` and `"gelu_new"` are supported.
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hidden_dropout_prob (`float`, *optional*, defaults to 0.1):
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The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
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max_position_embeddings (`int`, *optional*, defaults to 512):
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The maximum sequence length that this model might ever be used with. Typically set this to something large
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just in case (e.g., 512 or 1024 or 2048).
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type_vocab_size (`int`, *optional*, defaults to 4):
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The vocabulary size of the `token_type_ids` passed when calling [`FNetModel`] or [`TFFNetModel`].
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initializer_range (`float`, *optional*, defaults to 0.02):
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The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
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layer_norm_eps (`float`, *optional*, defaults to 1e-12):
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The epsilon used by the layer normalization layers.
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use_tpu_fourier_optimizations (`bool`, *optional*, defaults to `False`):
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Determines whether to use TPU optimized FFTs. If `True`, the model will favor axis-wise FFTs transforms.
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Set to `False` for GPU/CPU hardware, in which case n-dimensional FFTs are used.
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tpu_short_seq_length (`int`, *optional*, defaults to 512):
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The sequence length that is expected by the model when using TPUs. This will be used to initialize the DFT
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matrix only when *use_tpu_fourier_optimizations* is set to `True` and the input sequence is shorter than or
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equal to 4096 tokens.
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Example:
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```python
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>>> from transformers import FNetConfig, FNetModel
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>>> # Initializing a FNet fnet-base style configuration
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>>> configuration = FNetConfig()
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>>> # Initializing a model (with random weights) from the fnet-base style configuration
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>>> model = FNetModel(configuration)
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>>> # Accessing the model configuration
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>>> configuration = model.config
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```"""
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model_type = "fnet"
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def __init__(
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self,
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vocab_size=32000,
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hidden_size=768,
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num_hidden_layers=12,
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intermediate_size=3072,
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hidden_act="gelu_new",
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hidden_dropout_prob=0.1,
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max_position_embeddings=512,
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type_vocab_size=4,
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initializer_range=0.02,
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layer_norm_eps=1e-12,
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use_tpu_fourier_optimizations=False,
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tpu_short_seq_length=512,
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pad_token_id=3,
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bos_token_id=1,
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eos_token_id=2,
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**kwargs,
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):
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super().__init__(pad_token_id=pad_token_id, bos_token_id=bos_token_id, eos_token_id=eos_token_id, **kwargs)
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self.vocab_size = vocab_size
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self.max_position_embeddings = max_position_embeddings
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self.hidden_size = hidden_size
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self.num_hidden_layers = num_hidden_layers
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self.intermediate_size = intermediate_size
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self.hidden_act = hidden_act
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self.hidden_dropout_prob = hidden_dropout_prob
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self.initializer_range = initializer_range
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self.type_vocab_size = type_vocab_size
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self.layer_norm_eps = layer_norm_eps
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self.use_tpu_fourier_optimizations = use_tpu_fourier_optimizations
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self.tpu_short_seq_length = tpu_short_seq_length
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