Source code for transformers.models.prophetnet.configuration_prophetnet

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""" ProphetNet model configuration """

from ...configuration_utils import PretrainedConfig
from ...utils import logging

logger = logging.get_logger(__name__)

    "microsoft/prophetnet-large-uncased": "",

[docs]class ProphetNetConfig(PretrainedConfig): r""" This is the configuration class to store the configuration of a :class:`~transformers.ProphetNetModel`. It is used to instantiate a ProphetNet 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: activation_dropout (:obj:`float`, `optional`, defaults to 0.1): The dropout ratio for activations inside the fully connected layer. 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. vocab_size (:obj:`int`, `optional`, defaults to 30522): Vocabulary size of the ProphetNET model. Defines the number of different tokens that can be represented by the :obj:`inputs_ids` passed when calling :class:`~transformers.ProphetNetModel`. hidden_size (:obj:`int`, `optional`, defaults to 1024): Dimensionality of the layers and the pooler layer. encoder_ffn_dim (:obj:`int`, `optional`, defaults to 4096): Dimensionality of the "intermediate" (often named feed-forward) layer in decoder. num_encoder_layers (:obj:`int`, `optional`, defaults to 12): Number of encoder layers. num_encoder_attention_heads (:obj:`int`, `optional`, defaults to 16): Number of attention heads for each attention layer in the Transformer encoder. decoder_ffn_dim (:obj:`int`, `optional`, defaults to 4096): Dimensionality of the ``intermediate`` (often named feed-forward) layer in decoder. num_decoder_layers (:obj:`int`, `optional`, defaults to 12): Number of decoder layers. num_decoder_attention_heads (:obj:`int`, `optional`, defaults to 16): Number of attention heads for each attention layer in the Transformer decoder. attention_dropout (:obj:`float`, `optional`, defaults to 0.1): The dropout ratio for the attention probabilities. dropout (:obj:`float`, `optional`, defaults to 0.1): 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 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_cross_attention (:obj:`bool`, `optional`, defaults to :obj:`True`): Whether cross-attention layers should be added to the model. is_encoder_decoder (:obj:`bool`, `optional`, defaults to :obj:`True`): Whether this is an encoder/decoder model. 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. eos_token_id (:obj:`int`, `optional`, defaults to 2) End of stream token id. ngram (:obj:`int`, `optional`, defaults to 2) Number of future tokens to predict. Set to 1 to be same as traditional Language model to predict next first token. num_buckets (:obj:`int`, `optional`, defaults to 32) The number of buckets to use for each attention layer. This is for relative position calculation. See the `T5 paper <see>`__ for more details. relative_max_distance (:obj:`int`, `optional`, defaults to 128) Relative distances greater than this number will be put into the last same bucket. This is for relative position calculation. See the `T5 paper <see>`__ for more details. disable_ngram_loss (:obj:`bool`, `optional`, defaults to :obj:`False`): Whether be trained predicting only the next first token. eps (:obj:`float`, `optional`, defaults to 0.0): Controls the ``epsilon`` parameter value for label smoothing in the loss calculation. If set to 0, no label smoothing is performed. use_cache (:obj:`bool`, `optional`, defaults to :obj:`True`): Whether or not the model should return the last key/values attentions (not used by all models). gradient_checkpointing (:obj:`bool`, `optional`, defaults to :obj:`False`): If True, use gradient checkpointing to save memory at the expense of slower backward pass. """ model_type = "prophetnet" keys_to_ignore_at_inference = ["past_key_values"] def __init__( self, activation_dropout=0.1, activation_function="gelu", vocab_size=30522, hidden_size=1024, encoder_ffn_dim=4096, num_encoder_layers=12, num_encoder_attention_heads=16, decoder_ffn_dim=4096, num_decoder_layers=12, num_decoder_attention_heads=16, attention_dropout=0.1, dropout=0.1, max_position_embeddings=512, init_std=0.02, is_encoder_decoder=True, add_cross_attention=True, decoder_start_token_id=0, ngram=2, num_buckets=32, relative_max_distance=128, disable_ngram_loss=False, gradient_checkpointing=False, eps=0.0, use_cache=True, pad_token_id=0, bos_token_id=1, eos_token_id=2, **kwargs ): super().__init__( pad_token_id=pad_token_id, bos_token_id=bos_token_id, eos_token_id=eos_token_id, is_encoder_decoder=is_encoder_decoder, add_cross_attention=add_cross_attention, decoder_start_token_id=decoder_start_token_id, **kwargs, ) self.vocab_size = vocab_size self.hidden_size = hidden_size self.encoder_ffn_dim = encoder_ffn_dim self.num_encoder_layers = num_encoder_layers self.num_encoder_attention_heads = num_encoder_attention_heads self.decoder_ffn_dim = decoder_ffn_dim self.num_decoder_layers = num_decoder_layers self.num_decoder_attention_heads = num_decoder_attention_heads self.max_position_embeddings = max_position_embeddings self.init_std = init_std # Normal(0, this parameter) self.activation_function = activation_function # parameters for prophetnet self.ngram = ngram self.num_buckets = num_buckets self.relative_max_distance = relative_max_distance self.disable_ngram_loss = disable_ngram_loss self.eps = eps # 3 Types of Dropout self.attention_dropout = attention_dropout self.activation_dropout = activation_dropout self.dropout = dropout self.use_cache = use_cache # 4 Training Args (should be removed soon) self.gradient_checkpointing = gradient_checkpointing @property def num_attention_heads(self) -> int: return self.num_encoder_attention_heads @property def num_hidden_layers(self) -> int: return self.num_encoder_layers + self.num_decoder_layers