import numpy as np import random import torch import matplotlib.pylab as plt from torch.nn.utils import clip_grad_norm_ from torch.utils.data import TensorDataset, DataLoader, RandomSampler, SequentialSampler from transformers import BertTokenizer, BertForSequenceClassification, AdamW from transformers ...
1.2BertTokenizer进行编码,将每一句转成数字 1tokenizer = BertTokenizer.from_pretrained('bert-base-chinese', cache_dir="E:/transformer_file/")2print(pos_text[2])3print(tokenizer.tokenize(pos_text[2]))4print(tokenizer.encode(pos_text[2]))5print(tokenizer.convert_ids_to_tokens(tokenizer.encode(...
2.2 BertTokenizer进行编码,将每一句转成数字 model_name='bert-base-chinese' cache_dir='./sample_data/' tokenizer=BertTokenizer.from_pretrained(model_name,cache_dir=cache_dir) print(pos_text[2]) print(tokenizer.tokenize(pos_text[2])) print(tokenizer.encode(pos_text[2])) print(tokenizer.conver...
Pytorch-使⽤Bert预训练模型微调中⽂⽂本分类 渣渣本跑不动，以下代码运⾏在Google Colab上。neg.txt和pos.txt各5000条酒店评论，每条评论⼀⾏。安装transformers库 !pip install transformers 导包，设定超参数 1import numpy as np 2import random 3import torch 4import matplotlib.pyplot as plt 5from...
(output): BertSelfOutput( (dense): Linear(in_features=768, out_features=768, bias=True) (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True) (dropout): Dropout(p=0.1, inplace=False) ) ) (intermediate): BertIntermediate( (dense): Linear(in_features=768, out_features...
Pytorch-使用Bert预训练模型微调中文文本分类 行。 1. 导包和设定超参数 importnumpyasnp importrandom importtorch importmatplotlib.pylabasplt fromtorch.nn.utilsimportclip_grad_norm_ fromtorch.utils.dataimportTensorDataset,DataLoader,RandomSampler,SequentialSampler...
Pytorch-使⽤Bert预训练模型微调中⽂⽂本分类 渣渣本跑不动，以下代码运⾏在Google Colab上。neg.txt和pos.txt各5000条酒店评论，每条评论⼀⾏。安装transformers库 !pip install transformers 导包，设定超参数 1import numpy as np 2import random 3import torch 4import matplotlib.pyplot as plt 5from...