# scheduler = get_linear_schedule_with_warmup( # optimizer, num_warmup_steps=100, num_training_steps=t_total # ) # AdamW 这个优化器是主流优化器 optimizer = AdamW(optimizer_grouped_parameters, lr=3e-5, eps=1e-8) scheduler
from transformers import get_linear_schedule_with_warmup # 训练轮数。BERT作者建议在2到4之间 epochs = 4 # 总训练步数是[批次数量]×[训练轮数](注意这和训练样本数量不同) total_steps = len(train_dataloader) * epochs # 创建学习率调度器 scheduler = get_linear_schedule_with_warmup(optimizer, nu...
import pandas as pd import numpy as np from tqdm.auto import tqdm import torch import torch.nn as nn from torch.utils.data import Dataset, DataLoader from transformers import BertTokenizerFast as BertTokenizer, BertModel, AdamW, get_linear_schedule_with_warmup import pytorch_lightning as pl from...
from transformers import get\_linear\_schedule\_with\_warmup # 训练轮数。BERT作者建议在2到4之间 epochs = 4 # 总训练步数是\[批次数量\]×\[训练轮数\](注意这和训练样本数量不同) total\_steps = len(train\_dataloader) * epochs # 创建学习率调度器 scheduler = get\_linear\_schedule\_with\...
from transformers import get_linear_schedule_with_warmup # 训练轮数。BERT作者建议在2到4之间 epochs = 4 # 总训练步数是[批次数量]×[训练轮数](注意这和训练样本数量不同) total_steps = len(train_dataloader) * epochs # 创建学习率调度器 ...
optimizer = AdamW(model.parameters(), lr=LR, correct_bias=False)scheduler = get_linear_schedule_with_warmup(optimizer, num_warmup_steps=WARMUP_STEPS, num_training_steps=t_total)forepochinrange(EPOCHS): loss.backward() optimizer.step() scheduler.step() 1 2 3 4 5 6 7 8 当保存和加载...
# 在预热阶段之后创建一个schedule,使其学习率从优化器中的初始lr线性降低到0 # 这里没使用预热,直接从初始学习率开始下降 scheduler = get_linear_schedule_with_warmup( optimizer, num_warmup_steps=0, # The number of steps for the warmup phase. num_training_steps=num_train_steps # The total ...
2. get_linear_schedule_with_warmup from transformers import BertConfig, AdamW, get_linear_schedule_with_warmup optimizer = AdamW(optimizer_grouped_parameters, lr=self.args.learning_rate, eps=self.args.adam_epsilon) scheduler = get_linear_schedule_with_warmup(optimizer, num_warmup_steps=self.arg...
from transformers import BertForSequenceClassification, AdamW, get_linear_schedule_with_warmup 接下来,准备数据和模型: # 准备训练和验证数据 train_texts = [...] # 输入你的训练文本列表 val_texts = [...] # 输入你的验证文本列表 labels = [...] # 输入你的标签列表,长度应与文本数量一致 # 实...
) model = AutoModelForSequenceClassification.from_pretrained("bert-base-cased", return_dict=True) optimizer = AdamW(params=model.parameters(), lr=args.lr, correct_bias=True) lr_scheduler = get_linear_schedule_with_warmup( optimizer=optimizer, num_warmup_steps=args.lr_warmup_iters, num_traini...