File size: 3,391 Bytes
2449566 | 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 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 | from dataclasses import dataclass, field
from typing import List, Optional, Any
import os
@dataclass
class Arguments:
train_data: str = field(
default=None, metadata={"help": "Path to training data"}
)
val_data: str = field(
default=None, metadata={"help": "Path to validation data"}
)
test_data: str = field(
default=None, metadata={"help": "Path to test data"}
)
syntactic_file: str = field(
default=None, metadata={"help": "Path to syntactic_file data"}
)
num_labels: int = field(default=2, metadata={"help": "Number of labels"})
batch_size: int = field(default=8)
val_batch_size: int = field(default=32)
max_len: int = field(
default=256,
metadata={
"help": "The maximum total input sequence length after tokenization for passage. Sequences longer "
"than this will be truncated, sequences shorter will be padded."
},
)
pad_to_multiple_of: int = field(default=2, metadata={"help": ""})
temperature: Optional[float] = field(default=2.0)
distill_temperature: Optional[float] = field(default=2.0)
knowledge_distillation: bool = field(default=True, metadata={"help": "Use knowledge distillation"})
finetune_hidden_states: bool = field(default=True)
output_attentions: bool = field(default=True)
teach_device: str = field(default='cuda:1')
student_device: str = field(default='cuda:0')
num_train_epochs: int = field(default=1)
learning_rate: float = field(default=1e-4)
weight_decay: float = field(default=0.01)
warmup_ratio: float = field(default=0.1)
geom_loss_weight: float = field(default=50)
hard_label_loss_weight: float = field(default=1.0)
teacher_layers_mapping: List[int] = field(default=list)
student_encoder_layers_finetuned: List[int] = field(default=list)
n_encoder_finetuned: int = field(default=6)
finetune_embedding: bool = field(default=False)
orthogonal: bool = field(default=True)
span_loss: bool = field(default=True)
span_weight_pooling: bool = field(default=True)
span_loss_weight: bool = field(default=True)
p: float = field(default=1.0)
hidden_loss_weights: List[float] = field(default=None)
teacher_embedding_dimension: int = field(default=1024)
output_dir: Optional[str] = field(default=None, metadata={"help": "Where to store the final model"})
teacher_model: str = field(default='')
teacher_tokenizer: str = field(default='')
student_model: str = field(default='google-bert/bert-base-uncased')
student_tokenizer: str = field(default='google-bert/bert-base-uncased')
hf_token: str = field(default='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')
load_student_tokenizer_kwargs: dict = field(default_factory=dict)
load_teacher_tokenizer_kwargs: dict = field(default_factory=dict)
def __post_init__(self):
if not os.path.exists(self.train_data):
raise FileNotFoundError(f"cannot find file: {self.train_data}, please set a true path")
if len(self.teacher_layers_mapping) != len(self.student_encoder_layers_finetuned):
raise ValueError("teacher_layers_mapping and student_encoder_layers_finetuned should have the same length")
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