File size: 7,651 Bytes
30397d8 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 |
# coding=utf-8
# Copyright Hicham EL BOUKKOURI, Olivier FERRET, Thomas LAVERGNE, Hiroshi NOJI,
# Pierre ZWEIGENBAUM, Junichi TSUJII 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.
""" CharacterBERT model configuration"""
from transformers.configuration_utils import PretrainedConfig
from transformers.utils import logging
logger = logging.get_logger(__name__)
CHARACTER_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP = {
"helboukkouri/character-bert": "https://huggingface.co/helboukkouri/character-bert/resolve/main/config.json",
"helboukkouri/character-bert-medical": "https://huggingface.co/helboukkouri/character-bert-medical/resolve/main/config.json",
# See all CharacterBERT models at https://huggingface.co/models?filter=character_bert
}
class CharacterBertConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`CharacterBertModel`]. It is
used to instantiate an CharacterBERT 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 CharacterBERT
[helboukkouri/character-bert](https://huggingface.co/helboukkouri/character-bert) architecture.
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model
outputs. Read the documentation from [`PretrainedConfig`] for more information.
Args:
character_embeddings_dim (`int`, *optional*, defaults to `16`):
The size of the character embeddings.
cnn_activation (`str`, *optional*, defaults to `"relu"`):
The activation function to apply to the cnn representations.
cnn_filters (:
obj:*list(list(int))*, *optional*, defaults to `[[1, 32], [2, 32], [3, 64], [4, 128], [5, 256], [6, 512], [7, 1024]]`): The list of CNN filters to use in the CharacterCNN module.
num_highway_layers (`int`, *optional*, defaults to `2`):
The number of Highway layers to apply to the CNNs output.
max_word_length (`int`, *optional*, defaults to `50`):
The maximum token length in characters (actually, in bytes as any non-ascii characters will be converted to
a sequence of utf-8 bytes).
hidden_size (`int`, *optional*, defaults to 768):
Dimensionality of the encoder layers and the pooler layer.
num_hidden_layers (`int`, *optional*, defaults to 12):
Number of hidden layers in the Transformer encoder.
num_attention_heads (`int`, *optional*, defaults to 12):
Number of attention heads for each attention layer in the Transformer encoder.
intermediate_size (`int`, *optional*, defaults to 3072):
Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder.
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, encoder, 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 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 2):
The vocabulary size of the `token_type_ids` passed when calling
[`CharacterBertModel`] or [`TFCharacterBertModel`].
mlm_vocab_size (`int`, *optional*, defaults to 100000):
Size of the output vocabulary for MLM.
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_cache (`bool`, *optional*, defaults to `True`):
Whether or not the model should return the last key/values attentions (not used by all models). Only
relevant if `config.is_decoder=True`.
Example:
```python
```
>>> from transformers import CharacterBertModel, CharacterBertConfig
>>> # Initializing a CharacterBERT helboukkouri/character-bert style configuration
>>> configuration = CharacterBertConfig()
>>> # Initializing a model from the helboukkouri/character-bert style configuration
>>> model = CharacterBertModel(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
"""
model_type = "character_bert"
def __init__(
self,
character_embeddings_dim=16,
cnn_activation="relu",
cnn_filters=None,
num_highway_layers=2,
max_word_length=50,
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=512,
type_vocab_size=2,
mlm_vocab_size=100000,
initializer_range=0.02,
layer_norm_eps=1e-12,
is_encoder_decoder=False,
use_cache=True,
**kwargs
):
tie_word_embeddings = kwargs.pop("tie_word_embeddings", False)
if tie_word_embeddings:
raise ValueError(
"Cannot tie word embeddings in CharacterBERT. Please set " "`config.tie_word_embeddings=False`."
)
super().__init__(
type_vocab_size=type_vocab_size,
layer_norm_eps=layer_norm_eps,
use_cache=use_cache,
tie_word_embeddings=tie_word_embeddings,
**kwargs,
)
if cnn_filters is None:
cnn_filters = [[1, 32], [2, 32], [3, 64], [4, 128], [5, 256], [6, 512], [7, 1024]]
self.character_embeddings_dim = character_embeddings_dim
self.cnn_activation = cnn_activation
self.cnn_filters = cnn_filters
self.num_highway_layers = num_highway_layers
self.max_word_length = max_word_length
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.mlm_vocab_size = mlm_vocab_size
self.hidden_act = hidden_act
self.hidden_dropout_prob = hidden_dropout_prob
self.attention_probs_dropout_prob = attention_probs_dropout_prob
self.max_position_embeddings = max_position_embeddings
self.initializer_range = initializer_range |