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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) |