Source code for tf_transformers.models.bart.configuration_bart

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""" Bart model configuration """
from tf_transformers.core import TransformerConfig


[docs]class BartConfig(TransformerConfig): r""" This is the configuration class to store the configuration of a :class:`~tf_transformers.models.BartModel`. It is used to instantiate an ALBERT 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 ALBERT `base <https://huggingface.co/bert-base-uncased>`__ architecture. Configuration objects inherit from :class:`~tf_transformers.models.TransformerConfig` and can be used to control the model outputs. Read the documentation from :class:`~tf_transformers.models.TransformerConfig` for more information. Args: vocab_size (:obj:`int`, `optional`, defaults to 30522): Vocabulary size of the ALBERT model. Defines the number of different tokens that can be represented by the :obj:`inputs_ids` passed when calling :class:`~tf_transformers.model.BartModel` or :class:`~tf_transformers.models.BartEncoder`. embedding_size (:obj:`int`, `optional`, defaults to 128): Dimensionality of vocabulary embeddings. embedding_projection_size (:obj:`int`): Dimensionality of the encoder layers and the pooler layer. Useful for Bart. num_hidden_layers (:obj:`int`, `optional`, defaults to 12): Number of hidden layers in the Transformer encoder. num_attention_heads (:obj:`int`, `optional`, defaults to 12): Number of attention heads for each attention layer in the Transformer encoder. attention_head_size (:obj:`int`): Size of attention heads in each layer. Normally (embedding_size//num_attention_heads). intermediate_size (:obj:`int`, `optional`, defaults to 3072): The dimensionality of the "intermediate" (often named feed-forward) layer in the Transformer encoder. hidden_act (:obj:`str` or :obj:`Callable`, `optional`, defaults to :obj:`"gelu"`): The non-linear activation function (function or string) in the encoder and pooler. If string, :obj:`"gelu"`, :obj:`"relu"`, :obj:`"silu"` and many are supported. hidden_dropout_prob (:obj:`float`, `optional`, defaults to 0): The dropout probability for all fully connected layers in the embeddings, encoder, and pooler. max_position_embeddings (:obj:`int`, `optional`, defaults to 512): The maximum sequence length that this model might ever be used with. Typically set this to something large (e.g., 512 or 1024 or 2048). type_vocab_size (:obj:`int`, `optional`, defaults to 2): The vocabulary size of the :obj:`token_type_ids` passed when calling :class:`~transformers.BartModel` or :class:`~transformers.TFBartModel`. initializer_range (:obj:`float`, `optional`, defaults to 0.02): The standard deviation of the truncated_normal_initializer for initializing all weight matrices. layer_norm_epsilon (:obj:`float`, `optional`, defaults to 1e-12): The epsilon used by the layer normalization layers. classifier_dropout_prob (:obj:`float`, `optional`, defaults to 0.1): The dropout ratio for attached classifiers. position_embedding_type (:obj:`str`, `optional`, defaults to :obj:`"absolute"`): Type of position embedding. Choose one of :obj:`"absolute"`, :obj:`"relative_key"`, :obj:`"relative_key_query"`. For positional embeddings use :obj:`"absolute"`. For more information on :obj:`"relative_key"`, please refer to `Self-Attention with Relative Position Representations (Shaw et al.) <https://arxiv.org/abs/1803.02155>`__. For more information on :obj:`"relative_key_query"`, please refer to `Method 4` in `Improve Transformer Models with Better Relative Position Embeddings (Huang et al.) <https://arxiv.org/abs/2009.13658>`__. num_hidden_groups (:obj:`int`, `optional`, defaults to 1): Number of groups for the hidden layers, parameters in the same group are shared. Examples:: >>> from tf_transformers.models import BartConfig, BartModel >>> # Initializing an bert-base-uncased style configuration >>> configuration = BartConfig() >>> # Initializing an Bart different style configuration >>> configuration_new = BartConfig( ... embedding_size=768, ... num_attention_heads=12, ... intermediate_size=3072, ... ) >>> # Initializing a model from the original configuration >>> model = BartModel.from_config(configuration) >>> # Accessing the model configuration >>> configuration = model._config_dict # This has more details than original configuration """ def __init__( self, vocab_size=50265, embedding_size=768, num_hidden_layers=12, num_attention_heads=64, attention_head_size=64, intermediate_size=3072, hidden_act="gelu", intermediate_act="gelu", hidden_dropout_prob=0, attention_probs_dropout_prob=0, max_position_embeddings=1024, type_vocab_size=-1, initializer_range=0.02, layer_norm_epsilon=1e-5, position_embedding_type="absolute", decoder_start_token_id=2, ): super().__init__( vocab_size=vocab_size, embedding_size=embedding_size, num_hidden_layers=num_hidden_layers, num_attention_heads=num_attention_heads, attention_head_size=attention_head_size, hidden_act=hidden_act, intermediate_act=intermediate_act, intermediate_size=intermediate_size, hidden_dropout_prob=hidden_dropout_prob, attention_probs_dropout_prob=attention_probs_dropout_prob, max_position_embeddings=max_position_embeddings, type_vocab_size=type_vocab_size, initializer_range=initializer_range, layer_norm_epsilon=layer_norm_epsilon, position_embedding_type=position_embedding_type, decoder_start_token_id=decoder_start_token_id, )