Source code for transformers.models.mbart.configuration_mbart

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""" MBART configuration """

from ...utils import logging
from ..bart.configuration_bart import BartConfig


logger = logging.get_logger(__name__)

MBART_PRETRAINED_CONFIG_ARCHIVE_MAP = {
    "facebook/mbart-large-en-ro": "https://huggingface.co/facebook/mbart-large-en-ro/resolve/main/config.json",
    "facebook/mbart-large-cc25": "https://huggingface.co/facebook/mbart-large-cc25/resolve/main/config.json",
}


[docs]class MBartConfig(BartConfig): """ This is the configuration class to store the configuration of a :class:`~transformers.MBartForConditionalGeneration`. It is used to instantiate a BART model according to the specified arguments, defining the model architecture. Configuration objects inherit from :class:`~transformers.PretrainedConfig` and can be used to control the model outputs. Read the documentation from :class:`~transformers.PretrainedConfig` for more information. Args: vocab_size (:obj:`int`, `optional`, defaults to 250027): Vocabulary size of the MBART model. Defines the number of different tokens that can be represented by the :obj:`inputs_ids` passed when calling :class:`~transformers.MBartForConditionalGeneration`. d_model (:obj:`int`, `optional`, defaults to 1024): Dimensionality of the layers and the pooler layer. encoder_layers (:obj:`int`, `optional`, defaults to 12): Number of encoder layers. decoder_layers (:obj:`int`, `optional`, defaults to 12): Number of decoder layers. encoder_attention_heads (:obj:`int`, `optional`, defaults to 16): Number of attention heads for each attention layer in the Transformer encoder. decoder_attention_heads (:obj:`int`, `optional`, defaults to 16): Number of attention heads for each attention layer in the Transformer decoder. decoder_ffn_dim (:obj:`int`, `optional`, defaults to 4096): Dimensionality of the "intermediate" (i.e., feed-forward) layer in decoder. encoder_ffn_dim (:obj:`int`, `optional`, defaults to 4096): Dimensionality of the "intermediate" (i.e., feed-forward) layer in decoder. activation_function (:obj:`str` or :obj:`function`, `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 :obj:`"gelu_new"` are supported. dropout (:obj:`float`, `optional`, defaults to 0.1): The dropout probability for all fully connected layers in the embeddings, encoder, and pooler. attention_dropout (:obj:`float`, `optional`, defaults to 0.0): The dropout ratio for the attention probabilities. activation_dropout (:obj:`float`, `optional`, defaults to 0.0): The dropout ratio for activations inside the fully connected layer. classifier_dropout (:obj:`float`, `optional`, defaults to 0.0): The dropout ratio for classifier. max_position_embeddings (:obj:`int`, `optional`, defaults to 1024): 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). init_std (:obj:`float`, `optional`, defaults to 0.02): The standard deviation of the truncated_normal_initializer for initializing all weight matrices. add_bias_logits (:obj:`bool`, `optional`, defaults to :obj:`False`): This should be completed, specific to marian. normalize_before (:obj:`bool`, `optional`, defaults to :obj:`True`): Call layernorm before attention ops. normalize_embedding (:obj:`bool`, `optional`, defaults to :obj:`True`): Call layernorm after embeddings. Only True for Bart. static_position_embeddings (:obj:`bool`, `optional`, defaults to :obj:`False`): Don't learn positional embeddings, use sinusoidal. add_final_layer_norm (:obj:`bool`, `optional`, defaults to :obj:`True`): Why not add another layernorm? scale_embedding (:obj:`bool`, `optional`, defaults to :obj:`False`): Scale embeddings by diving by sqrt(d_model). eos_token_id (:obj:`int`, `optional`, defaults to 2) End of stream token id. pad_token_id (:obj:`int`, `optional`, defaults to 1) Padding token id. bos_token_id (:obj:`int`, `optional`, defaults to 0) Beginning of stream token id. encoder_layerdrop: (:obj:`float`, `optional`, defaults to 0.0): The LayerDrop probability for the encoder. See the `LayerDrop paper <see https://arxiv.org/abs/1909.11556>`__ for more details. decoder_layerdrop: (:obj:`float`, `optional`, defaults to 0.0): The LayerDrop probability for the decoder. See the `LayerDrop paper <see https://arxiv.org/abs/1909.11556>`__ for more details. extra_pos_embeddings: (:obj:`int`, `optional`, defaults to 2): How many extra learned positional embeddings to use. Should be equal to :obj:`pad_token_id+1`. is_encoder_decoder (:obj:`bool`, `optional`, defaults to :obj:`True`): Whether this is an encoder/decoder model force_bos_token_to_be_generated (:obj:`bool`, `optional`, defaults to :obj:`False`): Whether or not to force BOS token to be generated at step 1 (after ``decoder_start_token_id``). """ model_type = "mbart" keys_to_ignore_at_inference = ["past_key_values"]