Source code for transformers.models.blenderbot.configuration_blenderbot

#!/usr/bin/env python3
# coding=utf-8
# Copyright (c) Facebook, Inc. and Huggingface, 2020
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"""
BlenderbotConfig has the same signature as BartConfig. We only rewrite the signature in order to document
blenderbot-90M defaults.
"""
from ..bart.configuration_bart import BartConfig


BLENDERBOT_PRETRAINED_CONFIG_ARCHIVE_MAP = {
    "facebook/blenderbot-3B": "https://cdn.huggingface.co/facebook/blenderbot-3B/config.json",
    "facebook/blenderbot-90M": "https://cdn.huggingface.co/facebook/blenderbot-90M/config.json",
}


[docs]class BlenderbotConfig(BartConfig): r""" This is the configuration class to store the configuration of a :class:`~transformers.BlenderbotForConditionalGeneration`. It inherits from :class:`~transformers.BartConfig` and has the same signature with different defaults. 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 54944): Vocabulary size of the BERT model. Defines the number of different tokens that can be represented by the :obj:`inputs_ids` passed when calling :class:`~transformers.BlenderbotForConditionalGeneration`. d_model (:obj:`int`, `optional`, defaults to 512): Dimensionality of the layers and the pooler layer. encoder_layers (:obj:`int`, `optional`, defaults to 8): Number of encoder layers, 6 are used for the `blenderbot-90M` model. decoder_layers (:obj:`int`, `optional`, defaults to 8): Number of decoder layers, 6 are used for the `blenderbot-90M` model. 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 2048): Dimensionality of the "intermediate" (often named feed-forward) layer in decoder. encoder_ffn_dim (:obj:`int`, `optional`, defaults to 2048): Dimensionality of the "intermediate" (often named 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 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_bias_logits (:obj:`bool`, `optional`, defaults to :obj:`False`): This should be completed, specific to marian. normalize_before (:obj:`bool`, `optional`, defaults to :obj:`False`): Call layernorm before attention ops. normalize_embedding (:obj:`bool`, `optional`, defaults to :obj:`True`): Call layernorm after embeddings. 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:`False`): Why not add another layernorm? do_blenderbot_90_layernorm (:obj:`bool`, `optional`, defaults to :obj:`True`): Blenderbot-90m checkpoint uses `layernorm_embedding` one line earlier in the decoder. 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 set 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 = "blenderbot" def __init__( self, activation_dropout=0.0, extra_pos_embeddings=0, activation_function="gelu", vocab_size=54944, d_model=512, encoder_ffn_dim=2048, encoder_layers=8, encoder_attention_heads=16, decoder_ffn_dim=2048, decoder_layers=8, decoder_attention_heads=16, encoder_layerdrop=0.0, decoder_layerdrop=0.0, attention_dropout=0.0, dropout=0.1, max_position_embeddings=512, classifier_dropout=0.0, is_encoder_decoder=True, pad_token_id=1, bos_token_id=0, eos_token_id=2, normalize_before=False, add_final_layer_norm=False, do_blenderbot_90_layernorm=True, scale_embedding=False, normalize_embedding=True, static_position_embeddings=False, add_bias_logits=False, force_bos_token_to_be_generated=False, **common_kwargs ): r""" Examples:: >>> from transformers import BlenderbotConfig >>> config = BlenderbotConfig.from_pretrained('facebook/blenderbot-90M') """ if "hidden_size" in common_kwargs: raise ValueError("hidden size is called d_model") 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, vocab_size=vocab_size, d_model=d_model, encoder_ffn_dim=encoder_ffn_dim, encoder_layers=encoder_layers, encoder_layerdrop=encoder_layerdrop, encoder_attention_heads=encoder_attention_heads, decoder_layerdrop=decoder_layerdrop, decoder_ffn_dim=decoder_ffn_dim, decoder_layers=decoder_layers, normalize_before=normalize_before, normalize_embedding=normalize_embedding, static_position_embeddings=static_position_embeddings, add_bias_logits=add_bias_logits, force_bos_token_to_be_generated=force_bos_token_to_be_generated, do_blenderbot_90_layernorm=do_blenderbot_90_layernorm, add_final_layer_norm=add_final_layer_norm, scale_embedding=scale_embedding, attention_dropout=attention_dropout, dropout=dropout, classifier_dropout=classifier_dropout, activation_dropout=activation_dropout, max_position_embeddings=max_position_embeddings, extra_pos_embeddings=extra_pos_embeddings, activation_function=activation_function, decoder_attention_heads=decoder_attention_heads, **common_kwargs, )