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2b3226f | 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 42 43 44 45 46 47 48 49 50 51 | """Configuration for the BashkirRoBERTa Pre-LN masked-language model."""
from transformers import PretrainedConfig
class BashkirRobertaConfig(PretrainedConfig):
"""Keeps the exact architecture of the project's flagship checkpoint.
Despite the familiar name, this is not Hugging Face's post-LayerNorm
``RobertaConfig``: the encoder blocks here use pre-LayerNorm.
"""
model_type = "bashkir-roberta-preln"
def __init__(
self,
vocab_size=16_384,
hidden_size=640,
num_hidden_layers=8,
num_attention_heads=10,
intermediate_size=2_560,
max_position_embeddings=256,
hidden_dropout_prob=0.1,
pad_token_id=0,
bos_token_id=2,
eos_token_id=3,
cls_token_id=4,
sep_token_id=5,
mask_token_id=6,
tie_word_embeddings=True,
**kwargs,
):
super().__init__(
pad_token_id=pad_token_id,
bos_token_id=bos_token_id,
eos_token_id=eos_token_id,
cls_token_id=cls_token_id,
sep_token_id=sep_token_id,
mask_token_id=mask_token_id,
tie_word_embeddings=tie_word_embeddings,
**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.max_position_embeddings = max_position_embeddings
self.hidden_dropout_prob = hidden_dropout_prob
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