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configuration_canine.py
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configuration_canine.py
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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