U &ºcÅ&ã@sDdZddlZddlmZe e¡Zddddd œZGd d „d eƒZdS) z' Flaubert configuration, based on XLM. éNé)Ú XLMConfigz]https://s3.amazonaws.com/models.huggingface.co/bert/flaubert/flaubert_small_cased/config.jsonz^https://s3.amazonaws.com/models.huggingface.co/bert/flaubert/flaubert_base_uncased/config.jsonz\https://s3.amazonaws.com/models.huggingface.co/bert/flaubert/flaubert_base_cased/config.jsonz]https://s3.amazonaws.com/models.huggingface.co/bert/flaubert/flaubert_large_cased/config.json)zflaubert-small-casedzflaubert-base-uncasedzflaubert-base-casedzflaubert-large-casedcs*eZdZdZeZdZd ‡fdd„ Z‡ZS) ÚFlaubertConfigaÏ Configuration class to store the configuration of a `FlaubertModel`. This is the configuration class to store the configuration of a :class:`~transformers.XLMModel`. It is used to instantiate an XLM 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 `xlm-mlm-en-2048 `__ 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 different tokens that can be represented by the `inputs_ids` passed to the forward method of :class:`~transformers.FlaubertModel`. 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:`boolean`, optional, defaults to :obj:`True`): The non-linear activation function (function or string) in the encoder and pooler. If set to `True`, "gelu" will be used instead of "relu". sinusoidal_embeddings (:obj:`boolean`, optional, defaults to :obj:`False`): Whether to use sinusoidal positional embeddings instead of absolute positional embeddings. causal (:obj:`boolean`, optional, defaults to :obj:`False`): Set this to `True` for the model to 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:`boolean`, optional, defaults to :obj:`False`): Whether 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:`boolean`, optional, defaults to :obj:`True`) Whether to use language embeddings. Some models use additional language embeddings, see `the multilingual models page `__ 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:`boolean`, optional, defaults to :obj:`True`): Whether 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 for the multiple choice head in :class:`~transformers.XLMForSequenceClassification`. Is one of the following options: - 'last' => take the last token hidden state (like XLNet) - 'first' => take the first token hidden state (like Bert) - 'mean' => take the mean of all tokens hidden states - 'cls_index' => supply a Tensor of classification token position (GPT/GPT-2) - 'attn' => Not implemented now, use multi-head attention summary_use_proj (:obj:`boolean`, optional, defaults to :obj:`True`): Argument used when doing sequence summary. Used in for the multiple choice head in :class:`~transformers.XLMForSequenceClassification`. Add a projection after the vector extraction summary_activation (:obj:`string` or :obj:`None`, optional, defaults to :obj:`None`): Argument used when doing sequence summary. Used in for the multiple choice head in :class:`~transformers.XLMForSequenceClassification`. 'tanh' => add a tanh activation to the output, Other => no activation. summary_proj_to_labels (:obj:`boolean`, optional, defaults to :obj:`True`): Argument used when doing sequence summary. Used in for the multiple choice head in :class:`~transformers.XLMForSequenceClassification`. If True, the projection outputs to config.num_labels classes (otherwise to hidden_size). Default: False. summary_first_dropout (:obj:`float`, optional, defaults to 0.1): Argument used when doing sequence summary. Used in for the multiple choice head in :class:`~transformers.XLMForSequenceClassification`. Add a dropout before the projection and activation start_n_top (:obj:`int`, optional, defaults to 5): Used in the SQuAD evaluation script for XLM and XLNet. end_n_top (:obj:`int`, optional, defaults to 5): Used in the SQuAD evaluation script for XLM and XLNet. 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. ZflaubertçFérc s(tƒjf||dœ|—Ž||_||_dS)z#Constructs FlaubertConfig. )Ú pad_token_idÚ bos_token_idN)ÚsuperÚ__init__Ú layerdropÚpre_norm)Úselfr r rrÚkwargs©Ú __class__©úG/tmp/pip-unpacked-wheel-ymerj3tt/transformers/configuration_flaubert.pyr ”szFlaubertConfig.__init__)rFrr) Ú__name__Ú __module__Ú __qualname__Ú__doc__Ú&FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAPZpretrained_config_archive_mapZ model_typer Ú __classcell__rrrrr!sor) rÚloggingZconfiguration_xlmrÚ getLoggerrÚloggerrrrrrrÚs  ü