Source code for transformers.models.flaubert.configuration_flaubert

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""" Flaubert configuration, based on XLM. """

from ...utils import logging
from ..xlm.configuration_xlm import XLMConfig


logger = logging.get_logger(__name__)

FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP = {
    "flaubert/flaubert_small_cased": "https://huggingface.co/flaubert/flaubert_small_cased/resolve/main/config.json",
    "flaubert/flaubert_base_uncased": "https://huggingface.co/flaubert/flaubert_base_uncased/resolve/main/config.json",
    "flaubert/flaubert_base_cased": "https://huggingface.co/flaubert/flaubert_base_cased/resolve/main/config.json",
    "flaubert/flaubert_large_cased": "https://huggingface.co/flaubert/flaubert_large_cased/resolve/main/config.json",
}


[docs]class FlaubertConfig(XLMConfig): """ This is the configuration class to store the configuration of a :class:`~transformers.FlaubertModel` or a :class:`~transformers.TFFlaubertModel`. It is used to instantiate a FlauBERT 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: pre_norm (:obj:`bool`, `optional`, defaults to :obj:`False`): Whether to apply the layer normalization before or after the feed forward layer following the attention in each layer (Vaswani et al., Tensor2Tensor for Neural Machine Translation. 2018) layerdrop (:obj:`float`, `optional`, defaults to 0.0): Probability to drop layers during training (Fan et al., Reducing Transformer Depth on Demand with Structured Dropout. ICLR 2020) vocab_size (:obj:`int`, `optional`, defaults to 30145): Vocabulary size of the FlauBERT model. Defines the number of different tokens that can be represented by the :obj:`inputs_ids` passed when calling :class:`~transformers.FlaubertModel` or :class:`~transformers.TFFlaubertModel`. emb_dim (:obj:`int`, `optional`, defaults to 2048): Dimensionality of the encoder layers and the pooler layer. n_layer (:obj:`int`, `optional`, defaults to 12): Number of hidden layers in the Transformer encoder. n_head (:obj:`int`, `optional`, defaults to 16): Number of attention heads for each attention layer in the Transformer encoder. 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 0.1): The dropout probability for the attention mechanism gelu_activation (:obj:`bool`, `optional`, defaults to :obj:`True`): Whether or not to use a `gelu` activation instead of `relu`. sinusoidal_embeddings (:obj:`bool`, `optional`, defaults to :obj:`False`): Whether or not to use sinusoidal positional embeddings instead of absolute positional embeddings. causal (:obj:`bool`, `optional`, defaults to :obj:`False`): Whether or not the model should behave in a causal manner. Causal models use a triangular attention mask in order to only attend to the left-side context instead if a bidirectional context. asm (:obj:`bool`, `optional`, defaults to :obj:`False`): Whether or not to use an adaptive log softmax projection layer instead of a linear layer for the prediction layer. n_langs (:obj:`int`, `optional`, defaults to 1): The number of languages the model handles. Set to 1 for monolingual models. use_lang_emb (:obj:`bool`, `optional`, defaults to :obj:`True`) Whether to use language embeddings. Some models use additional language embeddings, see `the multilingual models page <http://huggingface.co/transformers/multilingual.html#xlm-language-embeddings>`__ for information on how to use them. 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). embed_init_std (:obj:`float`, `optional`, defaults to 2048^-0.5): The standard deviation of the truncated_normal_initializer for initializing the embedding matrices. init_std (:obj:`int`, `optional`, defaults to 50257): The standard deviation of the truncated_normal_initializer for initializing all weight matrices except the embedding matrices. layer_norm_eps (:obj:`float`, `optional`, defaults to 1e-12): The epsilon used by the layer normalization layers. bos_index (:obj:`int`, `optional`, defaults to 0): The index of the beginning of sentence token in the vocabulary. eos_index (:obj:`int`, `optional`, defaults to 1): The index of the end of sentence token in the vocabulary. pad_index (:obj:`int`, `optional`, defaults to 2): The index of the padding token in the vocabulary. unk_index (:obj:`int`, `optional`, defaults to 3): The index of the unknown token in the vocabulary. mask_index (:obj:`int`, `optional`, defaults to 5): The index of the masking token in the vocabulary. is_encoder(:obj:`bool`, `optional`, defaults to :obj:`True`): Whether or not the initialized model should be a transformer encoder or decoder as seen in Vaswani et al. summary_type (:obj:`string`, `optional`, defaults to "first"): Argument used when doing sequence summary. Used in the sequence classification and multiple choice models. Has to be one of the following options: - :obj:`"last"`: Take the last token hidden state (like XLNet). - :obj:`"first"`: Take the first token hidden state (like BERT). - :obj:`"mean"`: Take the mean of all tokens hidden states. - :obj:`"cls_index"`: Supply a Tensor of classification token position (like GPT/GPT-2). - :obj:`"attn"`: Not implemented now, use multi-head attention. summary_use_proj (:obj:`bool`, `optional`, defaults to :obj:`True`): Argument used when doing sequence summary. Used in the sequence classification and multiple choice models. Whether or not to add a projection after the vector extraction. summary_activation (:obj:`str`, `optional`): Argument used when doing sequence summary. Used in the sequence classification and multiple choice models. Pass :obj:`"tanh"` for a tanh activation to the output, any other value will result in no activation. summary_proj_to_labels (:obj:`bool`, `optional`, defaults to :obj:`True`): Used in the sequence classification and multiple choice models. Whether the projection outputs should have :obj:`config.num_labels` or :obj:`config.hidden_size` classes. summary_first_dropout (:obj:`float`, `optional`, defaults to 0.1): Used in the sequence classification and multiple choice models. The dropout ratio to be used after the projection and activation. start_n_top (:obj:`int`, `optional`, defaults to 5): Used in the SQuAD evaluation script. end_n_top (:obj:`int`, `optional`, defaults to 5): Used in the SQuAD evaluation script. mask_token_id (:obj:`int`, `optional`, defaults to 0): Model agnostic parameter to identify masked tokens when generating text in an MLM context. lang_id (:obj:`int`, `optional`, defaults to 1): The ID of the language used by the model. This parameter is used when generating text in a given language. """ model_type = "flaubert" def __init__(self, layerdrop=0.0, pre_norm=False, pad_token_id=2, bos_token_id=0, **kwargs): """Constructs FlaubertConfig.""" super().__init__(pad_token_id=pad_token_id, bos_token_id=bos_token_id, **kwargs) self.layerdrop = layerdrop self.pre_norm = pre_norm