使用 pytorch 进行 BERT 文本分类

Question

我正在尝试借助此代码构建用于文本分类的 BERT 模型 [https://towardsdatascience.com/bert-text-classification-using-pytorch-723dfb8b6b5b]。我的数据集包含两列（标签、文本）。标签可以具有三个值 (0,1,2)。代码运行没有任何错误，但是混淆矩阵的所有值都是 0。我的代码有问题吗？

import matplotlib.pyplot as plt
import pandas as pd
import torch
from torchtext.data import Field, TabularDataset, BucketIterator, Iterator
import torch.nn as nn
from transformers import BertTokenizer, BertForSequenceClassification
import torch.optim as optim
from sklearn.metrics import accuracy_score, classification_report, confusion_matrix

import seaborn as sns

torch.manual_seed(42)
device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')

tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')

MAX_SEQ_LEN = 128
PAD_INDEX = tokenizer.convert_tokens_to_ids(tokenizer.pad_token)
UNK_INDEX = tokenizer.convert_tokens_to_ids(tokenizer.unk_token)


label_field = Field(sequential=False, use_vocab=False, batch_first=True, dtype=torch.float)
text_field = Field(use_vocab=False, tokenize=tokenizer.encode, lower=False, include_lengths=False, batch_first=True, fix_length=MAX_SEQ_LEN, pad_token=PAD_INDEX, unk_t>
fields = [('label', label_field), ('text', text_field)]
CLASSIFICATION_REPORT = "classification_report.jsonl"


train, valid, test = TabularDataset.splits(path='', train='train.csv', validation='validate.csv', test='test.csv', format='CSV', fields=fields, skip_header=True)

train_iter = BucketIterator(train, batch_size=16, sort_key=lambda x: len(x.text), device=device, train=True, sort=True, sort_within_batch=True)
valid_iter = BucketIterator(valid, batch_size=16, sort_key=lambda x: len(x.text), device=device, train=True, sort=True, sort_within_batch=True)
test_iter = Iterator(test, batch_size=16, device=device, train=False, shuffle=False, sort=False)

class BERT(nn.Module):
        def __init__(self):
                super(BERT, self).__init__()
                options_name = "bert-base-uncased"
                self.encoder = BertForSequenceClassification.from_pretrained(options_name, num_labels = 3)

        def forward(self, text, label):
                loss, text_fea = self.encoder(text, labels=label)[:2]
                return loss, text_fea

def train(model, optimizer, criterion = nn.BCELoss(), train_loader = train_iter, valid_loader = valid_iter, num_epochs = 5, eval_every = len(train_iter) // 2, file_pat>        running_loss = 0.0
        valid_running_loss = 0.0
        global_step = 0
        train_loss_list = []
        valid_loss_list = []
        global_steps_list = []

        model.train()

        for epoch in range(num_epochs):
                for (label, text), _ in train_loader:
                        label = label.type(torch.LongTensor)
                        label = label.to(device)
                        text = text.type(torch.LongTensor)
                        text = text.to(device)
                        output = model(text, label)
                        loss, _ = output
                        optimizer.zero_grad()
                        loss.backward()
                        optimizer.step()
                        running_loss += loss.item()
                        global_step += 1
                        if global_step % eval_every == 0:
                                model.eval()
                                with torch.no_grad():
                                        for (label, text), _ in valid_loader:
                                                label = label.type(torch.LongTensor)
                                                label = label.to(device)
                                                text = text.type(torch.LongTensor)
                                                text = text.to(device)
                                                output = model(text, label)
                                                loss, _ = output
                                                valid_running_loss += loss.item()

                                average_train_loss = running_loss / eval_every
                                average_valid_loss = valid_running_loss / len(valid_loader)
                                train_loss_list.append(average_train_loss)
                                valid_loss_list.append(average_valid_loss)
                                global_steps_list.append(global_step)


                                # resetting running values
                                running_loss = 0.0
                                valid_running_loss = 0.0
                                model.train()

                                # print progress
                                print('Epoch [{}/{}], Step [{}/{}], Train Loss: {:.4f}, Valid Loss: {:.4f}'.format(epoch+1, num_epochs, global_step, num_epochs*len(tra>
                                if best_valid_loss > average_valid_loss:
                                        best_valid_loss = average_valid_loss
        print('Finished Training!')

model = BERT().to(device)
optimizer = optim.Adam(model.parameters(), lr=2e-5)

train(model=model, optimizer=optimizer)


def evaluate(model, test_loader):
        y_pred = []
        y_true = []
        model.eval()
        with torch.no_grad():
                for (label, text), _ in test_loader:
                        label = label.type(torch.LongTensor)
                        label = label.to(device)
                        text = text.type(torch.LongTensor)
                        text = text.to(device)
                        output = model(text, label)

                        _, output = output
                        y_pred.extend(torch.argmax(output, 2).tolist())
                        y_true.extend(label.tolist())
        print('Classification Report:')
        print(classification_report(y_true, y_pred, labels=[0,1,2], digits=4))
best_model = BERT().to(device)
evaluate(best_model, test_iter)

Answer 1

您正在使用 criterion = nn.BCELoss()，二元交叉熵用于多 class class化问题，“标签可以具有三个值 (0,1, 2)”。为 multiclass classification.

使用合适的损失函数

使用 pytorch 进行 BERT 文本分类

BERT text clasisification using pytorch

python

nlp

classification

pytorch

bert-language-model