File size: 1,488 Bytes
a29a500 6d646da a29a500 6d646da a29a500 6d646da a29a500 6d646da | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 | from transformers import PretrainedConfig
class MyBertConfig(PretrainedConfig):
model_type = "mybert"
def __init__(
self,
vocab_size=28996,
hidden_size=768,
num_hidden_layers=12,
num_attention_heads=12,
intermediate_size=2048,
max_position_embeddings=2048,
hidden_dropout_prob=0.0,
attention_probs_dropout_prob=0.0,
layer_norm_eps=1e-5,
initializer_range=0.02,
rope_theta=10000.0,
use_bias=False,
sparse_prediction=True,
pad_token_id=0,
tie_word_embeddings=True,
**kwargs,
):
super().__init__(pad_token_id=pad_token_id,
tie_word_embeddings=tie_word_embeddings, **kwargs)
assert hidden_size % num_attention_heads == 0
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.max_position_embeddings = max_position_embeddings
self.hidden_dropout_prob = hidden_dropout_prob
self.attention_probs_dropout_prob = attention_probs_dropout_prob
self.layer_norm_eps = layer_norm_eps
self.initializer_range = initializer_range
self.rope_theta = rope_theta
self.use_bias = use_bias
self.sparse_prediction = sparse_prediction |