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""" CharacterBERT model configuration""" |
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from transformers.configuration_utils import PretrainedConfig |
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from transformers.utils import logging |
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logger = logging.get_logger(__name__) |
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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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} |
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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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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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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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Example: |
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```python |
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``` |
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>>> from transformers import CharacterBertModel, CharacterBertConfig |
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>>> # Initializing a CharacterBERT helboukkouri/character-bert style configuration |
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>>> configuration = CharacterBertConfig() |
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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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>>> # 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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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 |
