Source code for transformers.configuration_xlm

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""" XLM configuration """


import logging

from .configuration_utils import PretrainedConfig


logger = logging.getLogger(__name__)

XLM_PRETRAINED_CONFIG_ARCHIVE_MAP = {
    "xlm-mlm-en-2048": "https://s3.amazonaws.com/models.huggingface.co/bert/xlm-mlm-en-2048-config.json",
    "xlm-mlm-ende-1024": "https://s3.amazonaws.com/models.huggingface.co/bert/xlm-mlm-ende-1024-config.json",
    "xlm-mlm-enfr-1024": "https://s3.amazonaws.com/models.huggingface.co/bert/xlm-mlm-enfr-1024-config.json",
    "xlm-mlm-enro-1024": "https://s3.amazonaws.com/models.huggingface.co/bert/xlm-mlm-enro-1024-config.json",
    "xlm-mlm-tlm-xnli15-1024": "https://s3.amazonaws.com/models.huggingface.co/bert/xlm-mlm-tlm-xnli15-1024-config.json",
    "xlm-mlm-xnli15-1024": "https://s3.amazonaws.com/models.huggingface.co/bert/xlm-mlm-xnli15-1024-config.json",
    "xlm-clm-enfr-1024": "https://s3.amazonaws.com/models.huggingface.co/bert/xlm-clm-enfr-1024-config.json",
    "xlm-clm-ende-1024": "https://s3.amazonaws.com/models.huggingface.co/bert/xlm-clm-ende-1024-config.json",
    "xlm-mlm-17-1280": "https://s3.amazonaws.com/models.huggingface.co/bert/xlm-mlm-17-1280-config.json",
    "xlm-mlm-100-1280": "https://s3.amazonaws.com/models.huggingface.co/bert/xlm-mlm-100-1280-config.json",
}


[docs]class XLMConfig(PretrainedConfig): """ 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 <https://huggingface.co/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: vocab_size (:obj:`int`, optional, defaults to 30145): Vocabulary size of the XLM model. Defines the different tokens that can be represented by the `inputs_ids` passed to the forward method of :class:`~transformers.XLMModel`. 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 <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:`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. Example:: from transformers import XLMConfig, XLMModel # Initializing a XLM configuration configuration = XLMConfig() # Initializing a model from the configuration model = XLMModel(configuration) # Accessing the model configuration configuration = model.config Attributes: pretrained_config_archive_map (Dict[str, str]): A dictionary containing all the available pre-trained checkpoints. """ pretrained_config_archive_map = XLM_PRETRAINED_CONFIG_ARCHIVE_MAP model_type = "xlm" def __init__( self, vocab_size=30145, emb_dim=2048, n_layers=12, n_heads=16, dropout=0.1, attention_dropout=0.1, gelu_activation=True, sinusoidal_embeddings=False, causal=False, asm=False, n_langs=1, use_lang_emb=True, max_position_embeddings=512, embed_init_std=2048 ** -0.5, layer_norm_eps=1e-12, init_std=0.02, bos_index=0, eos_index=1, pad_index=2, unk_index=3, mask_index=5, is_encoder=True, summary_type="first", summary_use_proj=True, summary_activation=None, summary_proj_to_labels=True, summary_first_dropout=0.1, start_n_top=5, end_n_top=5, mask_token_id=0, lang_id=0, **kwargs ): """Constructs XLMConfig. """ super().__init__(**kwargs) self.vocab_size = vocab_size self.emb_dim = emb_dim self.n_layers = n_layers self.n_heads = n_heads self.dropout = dropout self.attention_dropout = attention_dropout self.gelu_activation = gelu_activation self.sinusoidal_embeddings = sinusoidal_embeddings self.causal = causal self.asm = asm self.n_langs = n_langs self.use_lang_emb = use_lang_emb self.layer_norm_eps = layer_norm_eps self.bos_index = bos_index self.eos_index = eos_index self.pad_index = pad_index self.unk_index = unk_index self.mask_index = mask_index self.is_encoder = is_encoder self.max_position_embeddings = max_position_embeddings self.embed_init_std = embed_init_std self.init_std = init_std self.summary_type = summary_type self.summary_use_proj = summary_use_proj self.summary_activation = summary_activation self.summary_proj_to_labels = summary_proj_to_labels self.summary_first_dropout = summary_first_dropout self.start_n_top = start_n_top self.end_n_top = end_n_top self.mask_token_id = mask_token_id self.lang_id = lang_id if "n_words" in kwargs: self.n_words = kwargs["n_words"] @property def n_words(self): # For backward compatibility return self.vocab_size @n_words.setter def n_words(self, value): # For backward compatibility self.vocab_size = value @property def hidden_size(self): return self.emb_dim @property def num_attention_heads(self): return self.n_heads @property def num_hidden_layers(self): return self.n_layers