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from transformers.configuration_utils import PretrainedConfig
class NorT5Config(PretrainedConfig):
"""Configuration class to store the configuration of a `NorT5`.
"""
def __init__(
self,
vocab_size=50000,
attention_probs_dropout_prob=0.1,
hidden_dropout_prob=0.1,
hidden_size=768,
intermediate_size=2048,
max_position_embeddings=512,
position_bucket_size=32,
num_attention_heads=12,
num_hidden_layers=12,
layer_norm_eps=1.0e-7,
output_all_encoded_layers=True,
pad_token_id=3,
cls_token_id=1,
sep_token_id=2,
bos_token_id=5,
eos_token_id=6,
**kwargs,
):
super().__init__(**kwargs)
self.vocab_size = vocab_size
self.hidden_size = hidden_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.intermediate_size = intermediate_size
self.hidden_dropout_prob = hidden_dropout_prob
self.attention_probs_dropout_prob = attention_probs_dropout_prob
self.max_position_embeddings = max_position_embeddings
self.output_all_encoded_layers = output_all_encoded_layers
self.position_bucket_size = position_bucket_size
self.layer_norm_eps = layer_norm_eps
self.pad_token_id = pad_token_id
self.cls_token_id = cls_token_id
self.sep_token_id = sep_token_id
self.bos_token_id = bos_token_id
self.eos_token_id = eos_token_id
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