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| # 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 | |
| # | |
| # 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. | |
| """ CANINE model configuration""" | |
| from ...configuration_utils import PretrainedConfig | |
| from ...utils import logging | |
| logger = logging.get_logger(__name__) | |
| CANINE_PRETRAINED_CONFIG_ARCHIVE_MAP = { | |
| "google/canine-s": "https://huggingface.co/google/canine-s/resolve/main/config.json", | |
| # See all CANINE models at https://huggingface.co/models?filter=canine | |
| } | |
| class CanineConfig(PretrainedConfig): | |
| r""" | |
| This is the configuration class to store the configuration of a [`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](https://huggingface.co/google/canine-s) architecture. | |
| Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the | |
| documentation from [`PretrainedConfig`] for more information. | |
| Args: | |
| 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 deep Transformer encoder. | |
| num_attention_heads (`int`, *optional*, defaults to 12): | |
| Number of attention heads for each attention layer in the Transformer encoders. | |
| intermediate_size (`int`, *optional*, defaults to 3072): | |
| Dimension of the "intermediate" (i.e., feed-forward) layer in the Transformer encoders. | |
| hidden_act (`str` or `function`, *optional*, defaults to `"gelu"`): | |
| 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 probabilitiy for all fully connected layers in the embeddings, encoders, and pooler. | |
| attention_probs_dropout_prob (`float`, *optional*, defaults to 0.1): | |
| The dropout ratio for the attention probabilities. | |
| max_position_embeddings (`int`, *optional*, defaults to 16384): | |
| The maximum sequence length that this model might ever be used with. | |
| type_vocab_size (`int`, *optional*, defaults to 16): | |
| The vocabulary size of the `token_type_ids` passed when calling [`CanineModel`]. | |
| 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. | |
| downsampling_rate (`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 (`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 `hidden_size`*2 to `hidden_size`. | |
| num_hash_functions (`int`, *optional*, defaults to 8): | |
| The number of hash functions to use. Each hash function has its own embedding matrix. | |
| num_hash_buckets (`int`, *optional*, defaults to 16384): | |
| The number of hash buckets to use. | |
| local_transformer_stride (`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: | |
| ```python | |
| >>> from transformers import CanineConfig, CanineModel | |
| >>> # Initializing a CANINE google/canine-s style configuration | |
| >>> configuration = CanineConfig() | |
| >>> # Initializing a model (with random weights) 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, | |
| 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 | |
| # 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 | |