File size: 2,281 Bytes
ff83b1d 7a48cdd ff83b1d |
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 52 53 |
from transformers import PretrainedConfig
class CobaldParserConfig(PretrainedConfig):
model_type = "cobald_parser"
def __init__(
self,
encoder_model_name: str = None,
null_classifier_hidden_size: int = 0,
lemma_classifier_hidden_size: int = 0,
morphology_classifier_hidden_size: int = 0,
dependency_classifier_hidden_size: int = 0,
misc_classifier_hidden_size: int = 0,
deepslot_classifier_hidden_size: int = 0,
semclass_classifier_hidden_size: int = 0,
activation: str = 'relu',
dropout: float = 0.1,
consecutive_null_limit: int = 0,
vocabulary: dict[dict[int, str]] = {},
# LoRA params (новое!)
use_lora: bool = False,
lora_r: int = 8,
lora_alpha: int = 16,
lora_dropout: float = 0.05,
lora_target_modules: list = None,
**kwargs
):
print("DEBUG (encoder): use_lora:", use_lora)
print("DEBUG (encoder): lora_target_modules:", lora_target_modules)
self.encoder_model_name = encoder_model_name
self.null_classifier_hidden_size = null_classifier_hidden_size
self.consecutive_null_limit = consecutive_null_limit
self.lemma_classifier_hidden_size = lemma_classifier_hidden_size
self.morphology_classifier_hidden_size = morphology_classifier_hidden_size
self.dependency_classifier_hidden_size = dependency_classifier_hidden_size
self.misc_classifier_hidden_size = misc_classifier_hidden_size
self.deepslot_classifier_hidden_size = deepslot_classifier_hidden_size
self.semclass_classifier_hidden_size = semclass_classifier_hidden_size
self.activation = activation
self.dropout = dropout
# The serialized config stores mappings as strings,
# e.g. {"0": "acl", "1": "conj"}, so we have to convert them to int.
self.use_lora = use_lora
self.lora_r = lora_r
self.lora_alpha = lora_alpha
self.lora_dropout = lora_dropout
self.lora_target_modules = lora_target_modules
self.vocabulary = {
column: {int(k): v for k, v in labels.items()}
for column, labels in vocabulary.items()
}
super().__init__(**kwargs) |