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Create configuration_character_bert.py

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  1. configuration_character_bert.py +156 -0
configuration_character_bert.py ADDED
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+ # coding=utf-8
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+ # Copyright Hicham EL BOUKKOURI, Olivier FERRET, Thomas LAVERGNE, Hiroshi NOJI,
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+ # Pierre ZWEIGENBAUM, Junichi TSUJII and The HuggingFace Inc. team.
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+ # 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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+ """ CharacterBERT model configuration"""
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+
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+ from transformers.configuration_utils import PretrainedConfig
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+ from transformers.utils import logging
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+
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+
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+ logger = logging.get_logger(__name__)
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+
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+ CHARACTER_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP = {
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+ "helboukkouri/character-bert": "https://huggingface.co/helboukkouri/character-bert/resolve/main/config.json",
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+ "helboukkouri/character-bert-medical": "https://huggingface.co/helboukkouri/character-bert-medical/resolve/main/config.json",
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+ # See all CharacterBERT models at https://huggingface.co/models?filter=character_bert
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+ }
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+
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+
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+ class CharacterBertConfig(PretrainedConfig):
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+ r"""
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+ This is the configuration class to store the configuration of a [`CharacterBertModel`]. It is
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+ used to instantiate an CharacterBERT model according to the specified arguments, defining the model architecture.
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+ Instantiating a configuration with the defaults will yield a similar configuration to that of the CharacterBERT
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+ [helboukkouri/character-bert](https://huggingface.co/helboukkouri/character-bert) architecture.
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+
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+ Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model
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+ outputs. Read the documentation from [`PretrainedConfig`] for more information.
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+
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+
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+ Args:
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+ character_embeddings_dim (`int`, *optional*, defaults to `16`):
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+ The size of the character embeddings.
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+ cnn_activation (`str`, *optional*, defaults to `"relu"`):
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+ The activation function to apply to the cnn representations.
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+ cnn_filters (:
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+ 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.
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+ num_highway_layers (`int`, *optional*, defaults to `2`):
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+ The number of Highway layers to apply to the CNNs output.
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+ max_word_length (`int`, *optional*, defaults to `50`):
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+ The maximum token length in characters (actually, in bytes as any non-ascii characters will be converted to
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+ a sequence of utf-8 bytes).
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+ hidden_size (`int`, *optional*, defaults to 768):
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+ Dimensionality 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 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 encoder.
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+ intermediate_size (`int`, *optional*, defaults to 3072):
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+ Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder.
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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,
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+ `"gelu"`, `"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 probabilitiy for all fully connected layers in the embeddings, encoder, 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 512):
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+ The maximum sequence length that this model might ever be used with. Typically set this to something large
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+ just in case (e.g., 512 or 1024 or 2048).
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+ type_vocab_size (`int`, *optional*, defaults to 2):
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+ The vocabulary size of the `token_type_ids` passed when calling
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+ [`CharacterBertModel`] or [`TFCharacterBertModel`].
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+ mlm_vocab_size (`int`, *optional*, defaults to 100000):
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+ Size of the output vocabulary for MLM.
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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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+ use_cache (`bool`, *optional*, defaults to `True`):
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+ Whether or not the model should return the last key/values attentions (not used by all models). Only
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+ relevant if `config.is_decoder=True`.
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+
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+ Example:
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+
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+ ```python
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+
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+ ```
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+
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+ >>> from transformers import CharacterBertModel, CharacterBertConfig
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+
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+ >>> # Initializing a CharacterBERT helboukkouri/character-bert style configuration
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+ >>> configuration = CharacterBertConfig()
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+
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+ >>> # Initializing a model from the helboukkouri/character-bert style configuration
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+ >>> model = CharacterBertModel(configuration)
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+
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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 = "character_bert"
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+
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+ def __init__(
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+ self,
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+ character_embeddings_dim=16,
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+ cnn_activation="relu",
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+ cnn_filters=None,
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+ num_highway_layers=2,
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+ max_word_length=50,
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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=512,
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+ type_vocab_size=2,
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+ mlm_vocab_size=100000,
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+ initializer_range=0.02,
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+ layer_norm_eps=1e-12,
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+ is_encoder_decoder=False,
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+ use_cache=True,
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+ **kwargs
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+ ):
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+ tie_word_embeddings = kwargs.pop("tie_word_embeddings", False)
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+ if tie_word_embeddings:
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+ raise ValueError(
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+ "Cannot tie word embeddings in CharacterBERT. Please set " "`config.tie_word_embeddings=False`."
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+ )
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+ super().__init__(
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+ type_vocab_size=type_vocab_size,
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+ layer_norm_eps=layer_norm_eps,
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+ use_cache=use_cache,
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+ tie_word_embeddings=tie_word_embeddings,
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+ **kwargs,
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+ )
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+ if cnn_filters is None:
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+ cnn_filters = [[1, 32], [2, 32], [3, 64], [4, 128], [5, 256], [6, 512], [7, 1024]]
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+ self.character_embeddings_dim = character_embeddings_dim
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+ self.cnn_activation = cnn_activation
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+ self.cnn_filters = cnn_filters
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+ self.num_highway_layers = num_highway_layers
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+ self.max_word_length = max_word_length
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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.mlm_vocab_size = mlm_vocab_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.max_position_embeddings = max_position_embeddings
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+ self.initializer_range = initializer_range