import numpy as np
start_debugger_on_exception()
train_dataset = DataSetBert(data_file='./data/data_train/train.csv')
val_dataset = DataSetBert(data_file='./data/data_train/val.csv')
test_dataset = DataSetBert(data_file='./data/data_train/test.csv')
from torch.utils.data import DataLoader
device = torch.device('cuda:6')
train_dataloader = DataLoader(train_dataset, batch_size=11, shuffle=True)
val_dataloader = DataLoader(val_dataset, batch_size=11, shuffle=True)
test_dataloader = DataLoader(test_dataset, batch_size=11, shuffle=True)
model_config = BertConfig.from_pretrained('bert-base-chinese')
model_config.num_hidden_layers = 3
model = BertForSequenceClassification(model_config)
from transformers import BertTokenizer
tokenizer = BertTokenizer.from_pretrained('bert-base-chinese')
model.resize_token_embeddings(len(tokenizer))
model.config.pad_token_id = model.config.eos_token_id
model.config.max_position_embeddings = 1024
model.to(device)
model.train()
model.to(device)
import pdb
pdb.set_trace()
from transformers import AdamW
optimizer = AdamW(model.parameters(), lr=1e-5)
no_decay = ['bias', 'LayerNorm.weight']
optimizer_grouped_parameters = [{
'params': [
p for n, p in model.named_parameters()
if not any(nd in n for nd in no_decay)
],
