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from transformers import PretrainedConfig |
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class BilmaConfig(PretrainedConfig): |
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model_type = "bilma" |
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def __init__( |
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self, |
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weights="VE", |
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include_top=True, |
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num_attention_heads: int = 4, |
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num_hidden_layers: int = 2, |
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seq_max_length: int = 280, |
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hidden_size: int = 512, |
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vocab_size: int = 29025, |
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hidden_dropout_prob: float = 0.1, |
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**kwargs, |
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): |
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countries = ["VE"] |
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if weights not in countries: |
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raise ValueError(f"`weights` must be one of {countries}, got {weights}.") |
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if weights is not None: |
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self.weights = weights |
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self.include_top = include_top |
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self.num_attention_heads = 4 |
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self.num_hidden_layers = 2 |
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self.seq_max_length = 280 |
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self.hidden_size = 512 |
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self.vocab_size = 29025 |
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self.hidden_dropout_prob = 0.1 |
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super().__init__(**kwargs) |
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return |
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self.weights = weights |
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self.include_top = include_top |
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self.num_attention_heads = num_attention_heads |
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self.num_hidden_layers = num_hidden_layers |
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self.seq_max_length = seq_max_length |
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self.hidden_size = hidden_size |
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self.vocab_size = vocab_size |
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self.hidden_dropout_prob = hidden_dropout_prob |
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super().__init__(**kwargs) |