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# 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 ...configuration_utils import PretrainedConfig
from ...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 |