Llama-3.1-8B-DALv0.1
/
venv
/lib
/python3.12
/site-packages
/transformers
/models
/fnet
/configuration_fnet.py
# coding=utf-8 | |
# Copyright 2021 Google AI and The HuggingFace Inc. 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. | |
"""FNet model configuration""" | |
from ...configuration_utils import PretrainedConfig | |
from ...utils import logging | |
logger = logging.get_logger(__name__) | |
class FNetConfig(PretrainedConfig): | |
r""" | |
This is the configuration class to store the configuration of a [`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 | |
[google/fnet-base](https://huggingface.co/google/fnet-base) architecture. | |
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the | |
documentation from [`PretrainedConfig`] for more information. | |
Args: | |
vocab_size (`int`, *optional*, defaults to 32000): | |
Vocabulary size of the FNet model. Defines the number of different tokens that can be represented by the | |
`inputs_ids` passed when calling [`FNetModel`] or [`TFFNetModel`]. | |
hidden_size (`int`, *optional*, defaults to 768): | |
Dimension of the encoder layers and the pooler layer. | |
num_hidden_layers (`int`, *optional*, defaults to 12): | |
Number of hidden layers in the Transformer encoder. | |
intermediate_size (`int`, *optional*, defaults to 3072): | |
Dimension of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder. | |
hidden_act (`str` or `function`, *optional*, defaults to `"gelu_new"`): | |
The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`, | |
`"relu"`, `"selu"` and `"gelu_new"` are supported. | |
hidden_dropout_prob (`float`, *optional*, defaults to 0.1): | |
The dropout probability for all fully connected layers in the embeddings, encoder, and pooler. | |
max_position_embeddings (`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 (`int`, *optional*, defaults to 4): | |
The vocabulary size of the `token_type_ids` passed when calling [`FNetModel`] or [`TFFNetModel`]. | |
initializer_range (`float`, *optional*, defaults to 0.02): | |
The standard deviation of the truncated_normal_initializer for initializing all weight matrices. | |
layer_norm_eps (`float`, *optional*, defaults to 1e-12): | |
The epsilon used by the layer normalization layers. | |
use_tpu_fourier_optimizations (`bool`, *optional*, defaults to `False`): | |
Determines whether to use TPU optimized FFTs. If `True`, the model will favor axis-wise FFTs transforms. | |
Set to `False` for GPU/CPU hardware, in which case n-dimensional FFTs are used. | |
tpu_short_seq_length (`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 `True` and the input sequence is shorter than or | |
equal to 4096 tokens. | |
Example: | |
```python | |
>>> from transformers import FNetConfig, FNetModel | |
>>> # Initializing a FNet fnet-base style configuration | |
>>> configuration = FNetConfig() | |
>>> # Initializing a model (with random weights) 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 | |