| from transformers import PretrainedConfig | |
| class BilmaConfig(PretrainedConfig): | |
| model_type = "bilma" | |
| def __init__( | |
| self, | |
| weights="spanish", | |
| num_attention_heads: int = 4, | |
| num_encoders: int = 2, | |
| max_length: int = 280, | |
| embedding_dim: int = 512, | |
| vocab_size: int = 28949, | |
| drop_rate: float = 0.1, | |
| **kwargs, | |
| ): | |
| if weights not in ["spanish", ""]: | |
| raise ValueError(f"`weights` must be 'spanish' or '', got {weights}.") | |
| if weights == "spanish": | |
| self.weights = weights | |
| self.num_attention_heads = 4 | |
| self.num_encoders = 2 | |
| self.max_length = 280 | |
| self.embedding_dim = 512 | |
| self.vocab_size = 28949 | |
| self.drop_rate = 0.1 | |
| super().__init__(**kwargs) | |
| return | |
| self.weights = weights | |
| self.num_attention_heads = num_attention_heads | |
| self.num_encoders = num_encoders | |
| self.max_length = max_length | |
| self.embedding_dim = embedding_dim | |
| self.vocab_size = vocab_size | |
| self.drop_rate = drop_rate | |
| super().__init__(**kwargs) |