142 lines
6.5 KiB
Python
142 lines
6.5 KiB
Python
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# coding=utf-8
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# Copyright 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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""" CANINE 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 CANINE_PRETRAINED_CONFIG_ARCHIVE_MAP # noqa: F401, E402
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class CanineConfig(PretrainedConfig):
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r"""
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This is the configuration class to store the configuration of a [`CanineModel`]. It is used to instantiate an
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CANINE model according to the specified arguments, defining the model architecture. Instantiating a configuration
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with the defaults will yield a similar configuration to that of the CANINE
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[google/canine-s](https://huggingface.co/google/canine-s) 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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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 deep Transformer encoder.
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num_attention_heads (`int`, *optional*, defaults to 12):
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Number of attention heads for each attention layer in the Transformer encoders.
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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 encoders.
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hidden_act (`str` or `function`, *optional*, defaults to `"gelu"`):
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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, encoders, and pooler.
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attention_probs_dropout_prob (`float`, *optional*, defaults to 0.1):
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The dropout ratio for the attention probabilities.
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max_position_embeddings (`int`, *optional*, defaults to 16384):
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The maximum sequence length that this model might ever be used with.
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type_vocab_size (`int`, *optional*, defaults to 16):
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The vocabulary size of the `token_type_ids` passed when calling [`CanineModel`].
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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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pad_token_id (`int`, *optional*, defaults to 0):
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Padding token id.
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bos_token_id (`int`, *optional*, defaults to 57344):
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Beginning of stream token id.
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eos_token_id (`int`, *optional*, defaults to 57345):
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End of stream token id.
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downsampling_rate (`int`, *optional*, defaults to 4):
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The rate at which to downsample the original character sequence length before applying the deep Transformer
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encoder.
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upsampling_kernel_size (`int`, *optional*, defaults to 4):
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The kernel size (i.e. the number of characters in each window) of the convolutional projection layer when
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projecting back from `hidden_size`*2 to `hidden_size`.
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num_hash_functions (`int`, *optional*, defaults to 8):
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The number of hash functions to use. Each hash function has its own embedding matrix.
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num_hash_buckets (`int`, *optional*, defaults to 16384):
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The number of hash buckets to use.
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local_transformer_stride (`int`, *optional*, defaults to 128):
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The stride of the local attention of the first shallow Transformer encoder. Defaults to 128 for good
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TPU/XLA memory alignment.
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Example:
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```python
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>>> from transformers import CanineConfig, CanineModel
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>>> # Initializing a CANINE google/canine-s style configuration
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>>> configuration = CanineConfig()
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>>> # Initializing a model (with random weights) from the google/canine-s style configuration
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>>> model = CanineModel(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 = "canine"
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def __init__(
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self,
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hidden_size=768,
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num_hidden_layers=12,
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num_attention_heads=12,
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intermediate_size=3072,
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hidden_act="gelu",
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hidden_dropout_prob=0.1,
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attention_probs_dropout_prob=0.1,
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max_position_embeddings=16384,
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type_vocab_size=16,
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initializer_range=0.02,
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layer_norm_eps=1e-12,
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pad_token_id=0,
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bos_token_id=0xE000,
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eos_token_id=0xE001,
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downsampling_rate=4,
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upsampling_kernel_size=4,
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num_hash_functions=8,
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num_hash_buckets=16384,
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local_transformer_stride=128, # Good TPU/XLA memory alignment.
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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.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.num_attention_heads = num_attention_heads
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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.attention_probs_dropout_prob = attention_probs_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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# Character config:
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self.downsampling_rate = downsampling_rate
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self.upsampling_kernel_size = upsampling_kernel_size
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self.num_hash_functions = num_hash_functions
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self.num_hash_buckets = num_hash_buckets
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self.local_transformer_stride = local_transformer_stride
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