Source code for transformers.models.canine.configuration_canine

# coding=utf-8
# Copyright 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
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# See the License for the specific language governing permissions and
# limitations under the License.
""" CANINE model configuration """

from ...configuration_utils import PretrainedConfig
from ...utils import logging

logger = logging.get_logger(__name__)

    "google/canine-s": "",
    # See all CANINE models at

[docs]class CanineConfig(PretrainedConfig): r""" This is the configuration class to store the configuration of a :class:`~transformers.CanineModel`. It is used to instantiate an CANINE 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 CANINE `google/canine-s <>`__ 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: 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 deep Transformer encoder. num_attention_heads (:obj:`int`, `optional`, defaults to 12): Number of attention heads for each attention layer in the Transformer encoders. intermediate_size (:obj:`int`, `optional`, defaults to 3072): Dimension of the "intermediate" (i.e., feed-forward) layer in the Transformer encoders. hidden_act (:obj:`str` or :obj:`function`, `optional`, defaults to :obj:`"gelu"`): 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, encoders, 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 16384): The maximum sequence length that this model might ever be used with. type_vocab_size (:obj:`int`, `optional`, defaults to 16): The vocabulary size of the :obj:`token_type_ids` passed when calling :class:`~transformers.CanineModel`. 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. downsampling_rate (:obj:`int`, `optional`, defaults to 4): The rate at which to downsample the original character sequence length before applying the deep Transformer encoder. upsampling_kernel_size (:obj:`int`, `optional`, defaults to 4): The kernel size (i.e. the number of characters in each window) of the convolutional projection layer when projecting back from :obj:`hidden_size`*2 to :obj:`hidden_size`. num_hash_functions (:obj:`int`, `optional`, defaults to 8): The number of hash functions to use. Each hash function has its own embedding matrix. num_hash_buckets (:obj:`int`, `optional`, defaults to 16384): The number of hash buckets to use. local_transformer_stride (:obj:`int`, `optional`, defaults to 128): The stride of the local attention of the first shallow Transformer encoder. Defaults to 128 for good TPU/XLA memory alignment. Example:: >>> from transformers import CanineModel, CanineConfig >>> # Initializing a CANINE google/canine-s style configuration >>> configuration = CanineConfig() >>> # Initializing a model from the google/canine-s style configuration >>> model = CanineModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config """ model_type = "canine" def __init__( self, hidden_size=768, num_hidden_layers=12, num_attention_heads=12, intermediate_size=3072, hidden_act="gelu", hidden_dropout_prob=0.1, attention_probs_dropout_prob=0.1, max_position_embeddings=16384, type_vocab_size=16, initializer_range=0.02, layer_norm_eps=1e-12, use_cache=True, is_encoder_decoder=False, pad_token_id=0, bos_token_id=0xE000, eos_token_id=0xE001, downsampling_rate=4, upsampling_kernel_size=4, num_hash_functions=8, num_hash_buckets=16384, local_transformer_stride=128, # Good TPU/XLA memory alignment. **kwargs ): super().__init__(pad_token_id=pad_token_id, bos_token_id=bos_token_id, eos_token_id=eos_token_id, **kwargs) self.max_position_embeddings = max_position_embeddings self.hidden_size = hidden_size self.num_hidden_layers = num_hidden_layers self.num_attention_heads = num_attention_heads self.intermediate_size = intermediate_size self.hidden_act = hidden_act self.hidden_dropout_prob = hidden_dropout_prob self.attention_probs_dropout_prob = attention_probs_dropout_prob self.initializer_range = initializer_range self.type_vocab_size = type_vocab_size self.layer_norm_eps = layer_norm_eps self.use_cache = use_cache # Character config: self.downsampling_rate = downsampling_rate self.upsampling_kernel_size = upsampling_kernel_size self.num_hash_functions = num_hash_functions self.num_hash_buckets = num_hash_buckets self.local_transformer_stride = local_transformer_stride