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from transformers.configuration_utils import PretrainedConfig |
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class BertConfig(PretrainedConfig): |
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r""" |
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This is the configuration class to store the configuration of a :class:`~transformers.ElectraModel` or a |
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:class:`~transformers.TFElectraModel`. It is used to instantiate a ELECTRA model according to the specified |
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arguments, defining the model architecture. Instantiating a configuration with the defaults will yield a similar |
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configuration to that of the ELECTRA `google/electra-small-discriminator |
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<https://huggingface.co/google/electra-small-discriminator>`__ architecture. |
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Configuration objects inherit from :class:`~transformers.PretrainedConfig` and can be used to control the model |
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outputs. Read the documentation from :class:`~transformers.PretrainedConfig` for more information. |
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Args: |
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vocab_size (:obj:`int`, `optional`, defaults to 30522): |
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Vocabulary size of the ELECTRA model. Defines the number of different tokens that can be represented by the |
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:obj:`inputs_ids` passed when calling :class:`~transformers.ElectraModel` or |
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:class:`~transformers.TFElectraModel`. |
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embedding_size (:obj:`int`, `optional`, defaults to 128): |
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Dimensionality of the encoder layers and the pooler layer. |
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hidden_size (:obj:`int`, `optional`, defaults to 256): |
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Dimensionality of the encoder layers and the pooler layer. |
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num_hidden_layers (:obj:`int`, `optional`, defaults to 12): |
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Number of hidden layers in the Transformer encoder. |
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num_attention_heads (:obj:`int`, `optional`, defaults to 4): |
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Number of attention heads for each attention layer in the Transformer encoder. |
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intermediate_size (:obj:`int`, `optional`, defaults to 1024): |
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Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder. |
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hidden_act (:obj:`str` or :obj:`Callable`, `optional`, defaults to :obj:`"gelu"`): |
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The non-linear activation function (function or string) in the encoder and pooler. If string, |
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:obj:`"gelu"`, :obj:`"relu"`, :obj:`"silu"` and :obj:`"gelu_new"` are supported. |
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hidden_dropout_prob (:obj:`float`, `optional`, defaults to 0.1): |
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The dropout probability for all fully connected layers in the embeddings, encoder, and pooler. |
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attention_probs_dropout_prob (:obj:`float`, `optional`, defaults to 0.1): |
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The dropout ratio for the attention probabilities. |
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max_position_embeddings (:obj:`int`, `optional`, defaults to 512): |
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The maximum sequence length that this model might ever be used with. Typically set this to something large |
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just in case (e.g., 512 or 1024 or 2048). |
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type_vocab_size (:obj:`int`, `optional`, defaults to 2): |
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The vocabulary size of the :obj:`token_type_ids` passed when calling :class:`~transformers.ElectraModel` or |
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:class:`~transformers.TFElectraModel`. |
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initializer_range (:obj:`float`, `optional`, defaults to 0.02): |
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The standard deviation of the truncated_normal_initializer for initializing all weight matrices. |
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layer_norm_eps (:obj:`float`, `optional`, defaults to 1e-12): |
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The epsilon used by the layer normalization layers. |
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summary_type (:obj:`str`, `optional`, defaults to :obj:`"first"`): |
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Argument used when doing sequence summary. Used in the sequence classification and multiple choice models. |
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Has to be one of the following options: |
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- :obj:`"last"`: Take the last token hidden state (like XLNet). |
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- :obj:`"first"`: Take the first token hidden state (like BERT). |
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- :obj:`"mean"`: Take the mean of all tokens hidden states. |
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- :obj:`"cls_index"`: Supply a Tensor of classification token position (like GPT/GPT-2). |
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- :obj:`"attn"`: Not implemented now, use multi-head attention. |
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summary_use_proj (:obj:`bool`, `optional`, defaults to :obj:`True`): |
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Argument used when doing sequence summary. Used in the sequence classification and multiple choice models. |
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Whether or not to add a projection after the vector extraction. |
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summary_activation (:obj:`str`, `optional`): |
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Argument used when doing sequence summary. Used in the sequence classification and multiple choice models. |
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Pass :obj:`"gelu"` for a gelu activation to the output, any other value will result in no activation. |
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summary_last_dropout (:obj:`float`, `optional`, defaults to 0.0): |
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Argument used when doing sequence summary. Used in the sequence classification and multiple choice models. |
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The dropout ratio to be used after the projection and activation. |
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position_embedding_type (:obj:`str`, `optional`, defaults to :obj:`"absolute"`): |
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Type of position embedding. Choose one of :obj:`"absolute"`, :obj:`"relative_key"`, |
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:obj:`"relative_key_query"`. For positional embeddings use :obj:`"absolute"`. For more information on |
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:obj:`"relative_key"`, please refer to `Self-Attention with Relative Position Representations (Shaw et al.) |
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<https://arxiv.org/abs/1803.02155>`__. For more information on :obj:`"relative_key_query"`, please refer to |
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`Method 4` in `Improve Transformer Models with Better Relative Position Embeddings (Huang et al.) |
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<https://arxiv.org/abs/2009.13658>`__. |
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classifier_dropout (:obj:`float`, `optional`): |
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The dropout ratio for the classification head. |
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Examples:: |
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>>> from transformers import ElectraModel, ElectraConfig |
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>>> # Initializing a ELECTRA electra-base-uncased style configuration |
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>>> configuration = ElectraConfig() |
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>>> # Initializing a model from the electra-base-uncased style configuration |
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>>> model = ElectraModel(configuration) |
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>>> # Accessing the model configuration |
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>>> configuration = model.config |
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""" |
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model_type = "bert" |
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def __init__( |
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self, |
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vocab_size=30522, |
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embedding_size=128, |
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hidden_size=256, |
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num_hidden_layers=12, |
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num_attention_heads=4, |
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intermediate_size=1024, |
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hidden_act="gelu", |
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hidden_dropout_prob=0.1, |
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attention_probs_dropout_prob=0.1, |
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max_position_embeddings=512, |
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type_vocab_size=2, |
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initializer_range=0.02, |
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layer_norm_eps=1e-12, |
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summary_type="first", |
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summary_use_proj=True, |
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summary_activation="gelu", |
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summary_last_dropout=0.1, |
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pad_token_id=0, |
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position_embedding_type="absolute", |
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classifier_dropout=None, |
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prenorm=False, |
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mup=False, |
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embedding_norm_layer_type="layer_norm", |
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embedding_num_groups=1, |
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attn_norm_layer_type="layer_norm", |
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attn_num_groups=1, |
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output_mult=1, |
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readout_zero_init=False, |
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query_zero_init=False, |
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**kwargs, |
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): |
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super().__init__(pad_token_id=pad_token_id, **kwargs) |
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self.vocab_size = vocab_size |
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self.embedding_size = embedding_size |
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self.hidden_size = hidden_size |
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self.num_hidden_layers = num_hidden_layers |
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self.num_attention_heads = num_attention_heads |
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self.intermediate_size = intermediate_size |
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self.hidden_act = hidden_act |
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self.hidden_dropout_prob = hidden_dropout_prob |
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self.attention_probs_dropout_prob = attention_probs_dropout_prob |
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self.max_position_embeddings = max_position_embeddings |
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self.type_vocab_size = type_vocab_size |
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self.initializer_range = initializer_range |
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self.layer_norm_eps = layer_norm_eps |
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if isinstance(self.layer_norm_eps, str): |
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self.layer_norm_eps = float(self.layer_norm_eps) |
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self.summary_type = summary_type |
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self.summary_use_proj = summary_use_proj |
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self.summary_activation = summary_activation |
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self.summary_last_dropout = summary_last_dropout |
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self.position_embedding_type = position_embedding_type |
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self.classifier_dropout = classifier_dropout |
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self.prenorm = prenorm |
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self.mup = mup |
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self.embedding_norm_layer_type = embedding_norm_layer_type |
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self.embedding_num_groups = embedding_num_groups |
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self.attn_norm_layer_type = attn_norm_layer_type |
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self.attn_num_groups = attn_num_groups |
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self.output_mult = output_mult |
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self.readout_zero_init = readout_zero_init |
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self.query_zero_init = query_zero_init |
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