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""" LTG-BERT configutation """ |
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from transformers.configuration_utils import PretrainedConfig |
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class LtgBertConfig(PretrainedConfig): |
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r""" |
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This is the configuration class to store the configuration of a [`LtgBertModel`]. It is used to |
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instantiate an LTG-BERT model according to the specified arguments, defining the model architecture. |
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Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the |
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documentation from [`PretrainedConfig`] for more information. |
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Args: |
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vocab_size (`int`, *optional*, defaults to 16384): |
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Vocabulary size of the LTG-BERT model. Defines the number of different tokens that can be represented by the |
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`inputs_ids` passed when calling [`LtgBertModel`]. |
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hidden_size (`int`, *optional*, defaults to 768): |
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Dimensionality of the encoder layers and the pooler layer. |
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num_hidden_layers (`int`, *optional*, defaults to 12): |
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Number of hidden layers in the Transformer encoder. |
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num_attention_heads (`int`, *optional*, defaults to 12): |
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Number of attention heads for each attention layer in the Transformer encoder. |
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intermediate_size (`int`, *optional*, defaults to 2048): |
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Dimensionality of the "intermediate" (often named feed-forward) layer in the Transformer encoder. |
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hidden_dropout_prob (`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 (`float`, *optional*, defaults to 0.1): |
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The dropout ratio for the attention probabilities. |
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max_position_embeddings (`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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layer_norm_eps (`float`, *optional*, defaults to 1e-12): |
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The epsilon used by the layer normalization layers. |
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classifier_dropout (`float`, *optional*): |
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The dropout ratio for the classification head. |
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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=16384, |
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attention_probs_dropout_prob=0.1, |
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hidden_dropout_prob=0.1, |
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hidden_size=768, |
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intermediate_size=2048, |
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max_position_embeddings=512, |
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position_bucket_size=32, |
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num_attention_heads=12, |
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num_hidden_layers=12, |
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layer_norm_eps=1.0e-7, |
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pad_token_id=4, |
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output_all_encoded_layers=True, |
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classifier_dropout=None, |
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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.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_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.output_all_encoded_layers = output_all_encoded_layers |
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self.position_bucket_size = position_bucket_size |
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self.layer_norm_eps = layer_norm_eps |
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self.classifier_dropout = classifier_dropout |
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