基于pytorch+transformers的文本分类_pytorch transformer 文本分类

文本分类作为NLP基础任务，是最常用的服务了，本文提供一个开箱即用的文本分类器。

模型model.py文件如下：

# File model.py
# -*- coding: utf-8 -*-
import torch
import os
from torch import nn
from transformers import BertForSequenceClassification, BertConfig

class BertModel(nn.Module):
    def __init__(self, num_labels):
        super(BertModel, self).__init__()
        self.bert = BertForSequenceClassification.from_pretrained("hfl/chinese-roberta-wwm-ext", num_labels=num_labels)
        self.device = torch.device("cuda")
        for param in self.bert.parameters():
            param.requires_grad = True  # 每个参数都要求梯度,也可以冻结一些层

    def forward(self, batch_seqs, batch_seq_masks, batch_seq_segments, labels):
        loss, logits = self.bert(input_ids = batch_seqs, attention_mask = batch_seq_masks, 
                              token_type_ids=batch_seq_segments, labels = labels)[:2]
        probabilities = nn.functional.softmax(logits, dim=-1)
        prob, pre_label = torch.max(probabilities, 1)
        return loss, pre_label, prob
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data.py文件如下：

# -*- coding: utf-8 -*-
from torch.utils.data import Dataset
from hanziconv import HanziConv
import pandas as pd
import torch

class DataPrecessForCLF(Dataset):
    def __init__(self, bert_tokenizer, df, max_char_len):
      
        self.y = torch.LongTensor(df["id"])
        self.max_seq_len = max_char_len
        df["sentence"] = df["sentence"].apply(lambda i: HanziConv.toSimplified(i))
        self.encoded_inputs = bert_tokenizer(df["sentence"].tolist(), padding="max_length", truncation=True,
                                             max_length=max_char_len, return_tensors="pt")

    def __len__(self):
        return len(self.y)

    def __getitem__(self, idx):
        assert len(self.encoded_inputs["input_ids"][idx]) == len(self.encoded_inputs["attention_mask"][idx]) == len(self.encoded_inputs["token_type_ids"][idx])
        return self.encoded_inputs["input_ids"][idx], self.encoded_inputs["attention_mask"][idx], self.encoded_inputs["token_type_ids"][idx], self.y[idx]
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utils.py文件如下：

# -*- coding: utf-8 -*-
import torch
import torch.nn as nn
import time
from tqdm import tqdm

def validate(model, dataloader):
    model.eval()
    device = model.device
    epoch_start = time.time()
    batch_time_avg = 0.0
    running_loss = 0.0
    correct_preds = 0
    
    for batch_idx, (batch_seqs, batch_seq_masks, batch_seq_segments, batch_labels) in enumerate(dataloader):
        batch_start = time.time()
        seqs, masks, segments, labels = batch_seqs.to(device), batch_seq_masks.to(device), batch_seq_segments.to(
            device), batch_labels.to(device)
        loss, pre_label, prob = model(seqs, masks, segments, labels)
        batch_time_avg += time.time() - batch_start
        running_loss += loss.item()
        correct_preds += (pre_label == labels).sum().item()
    epoch_time = time.time() - epoch_start
    epoch_loss = running_loss / len(dataloader)
    epoch_accuracy = correct_preds / len(dataloader.dataset)
    return epoch_time, epoch_loss, epoch_accuracy

def test(model, dataloader, inference=False):
    model.eval()
    device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')  # 设备
    time_start = time.time()
    batch_time = 0.0
    correct_preds = 0
    all_prob = []
    all_pred_label = []
    all_labels = []
    # Deactivate autograd for evaluation.
    with torch.no_grad():
        for (batch_seqs, batch_seq_masks, batch_seq_segments, batch_labels) in dataloader:
            batch_start = time.time()
            seqs, masks, segments, labels = batch_seqs.to(device), batch_seq_masks.to(device), batch_seq_segments.to(device), batch_labels.to(device)
            loss, pre_label, prob = model(seqs, masks, segments, labels)
            correct_preds += (pre_label == labels).sum().item()
            batch_time += time.time() - batch_start
            all_prob.extend(prob.cpu().numpy())
            all_pred_label.extend(pre_label.cpu().numpy())
            all_labels.extend(batch_labels)

    batch_time /= len(dataloader)
    total_time = time.time() - time_start
    if inference:
        return all_pred_label, all_prob, total_time
    accuracy = correct_preds / len(dataloader.dataset)
    return batch_time, total_time, accuracy

def train(model, dataloader, optimizer, epoch_number, max_gradient_norm):
    model.train()
    device = model.device
    epoch_start = time.time()
    batch_time_avg = 0.0
    running_loss = 0.0
    correct_preds = 0
    tqdm_batch_iterator = tqdm(dataloader)
    for batch_index, (batch_seqs, batch_seq_masks, batch_seq_segments, batch_labels) in enumerate(tqdm_batch_iterator):
        batch_start = time.time()
        seqs, masks, segments, labels = batch_seqs.to(device), batch_seq_masks.to(device), batch_seq_segments.to(device), batch_labels.to(device)
        optimizer.zero_grad()
        loss, pre_label, prob = model(seqs, masks, segments, labels)
        loss.backward()
        nn.utils.clip_grad_norm_(model.parameters(), max_gradient_norm)
        optimizer.step()
        batch_time_avg += time.time() - batch_start
        running_loss += loss.item()
        correct_preds += (pre_label == labels).sum().item()
        description = "Avg. batch proc. time: {:.4f}s, loss: {:.4f}"\
                      .format(batch_time_avg/(batch_index+1), running_loss/(batch_index+1))
        tqdm_batch_iterator.set_description(description)
    epoch_time = time.time() - epoch_start
    epoch_loss = running_loss / len(dataloader)
    epoch_accuracy = correct_preds / len(dataloader.dataset)
    return epoch_time, epoch_loss, epoch_accuracy
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对于模型在真实数据上的训练脚本train.py

# -*- coding: utf-8 -*-
import os
import sys
import torch
from torch import nn
from torch.utils.data import DataLoader
from data import DataPrecessForCLF
from utils import train, validate
from model import BertModel
from transformers import BertTokenizer
from transformers.optimization import AdamW
import pandas as pd
from sklearn.preprocessing import LabelEncoder
import json
from sklearn.model_selection import train_test_split
le = LabelEncoder()

def main(train_file, target_dir,
         epochs=25,
         batch_size=16,
         lr=2e-05,
         patience=5,
         max_char_len=512,
         max_grad_norm=10.0,
         checkpoint=None):
    bert_tokenizer = BertTokenizer.from_pretrained("hfl/chinese-roberta-wwm-ext", do_lower_case=True)
    device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')  # 设备
    print(20 * "=", " Preparing for training ", 20 * "=")
    # 保存模型的路径
    if not os.path.exists(target_dir):
        os.makedirs(target_dir)
    # -------------------- Data loading ------------------- #
    data = pd.read_csv(train_file)
    data = data[["sentence", "label"]]
    data["sentence"] = data["sentence"].apply(lambda i: str(i))
    data["label"] = data["label"].apply(lambda i: str(i))
    data["id"] = torch.LongTensor(le.fit_transform(data["label"]))
    label = data[["id", "label"]]
    label = label.drop_duplicates()
    label_dict = {}
    for index, row in label.iterrows():
        label_dict[row["label"]] = row["id"]
    refund_map = dict(sorted(label_dict.items(), key=lambda d: d[0]))
    label_num = len(refund_map)
    with open(target_dir + '/label2id.json', "w", encoding="utf-8") as f:
        json.dump(refund_map, f, ensure_ascii=False, indent=4)
    print("the classification label num is {}".format(label_num))
    # train_dev_split
    df_train, df_dev, y_train, y_test = train_test_split(data, data["label"], test_size=0.2,
                                                    stratify=data["label"], random_state=666)
    df_train.reset_index(inplace=True, drop=True)
    df_dev.reset_index(inplace=True, drop=True)
    print("\t* Loading training data... , dataset size is {}".format(len(df_train)))
    train_data = DataPrecessForCLF(bert_tokenizer, df=df_train, max_char_len=max_char_len)
    train_loader = DataLoader(train_data, shuffle=True, batch_size=batch_size)
    print("\t* Loading validation data... , dataset size is {}".format(len(df_dev)))
    dev_data = DataPrecessForCLF(bert_tokenizer, df=df_dev, max_char_len=max_char_len)
    dev_loader = DataLoader(dev_data, shuffle=True, batch_size=batch_size)
    # -------------------- Model definition ------------------- #
    print("\t* Building model...")
    model = BertModel(label_num).to(device)
    param_optimizer = list(model.named_parameters())
    no_decay = ['bias', 'LayerNorm.bias', 'LayerNorm.weight']
    optimizer_grouped_parameters = [
            {
                    'params':[p for n, p in param_optimizer if not any(nd in n for nd in no_decay)],
                    'weight_decay':0.01
            },
            {
                    'params':[p for n, p in param_optimizer if any(nd in n for nd in no_decay)],
                    'weight_decay':0.0
            }
    ]
    optimizer = AdamW(optimizer_grouped_parameters, lr=lr)
    scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(optimizer, mode="max", 
                                                           factor=0.85, patience=0)
    best_score = 0.0
    start_epoch = 1
    # Data for loss curves plot
    epochs_count = []
    train_losses = []
    valid_losses = []
    # Continuing training from a checkpoint if one was given as argument
    if checkpoint:
        checkpoint = torch.load(checkpoint)
        start_epoch = checkpoint["epoch"] + 1
        best_score = checkpoint["best_score"]
        print("\t* Training will continue on existing model from epoch {}...".format(start_epoch))
        model.load_state_dict(checkpoint["model"])
        optimizer.load_state_dict(checkpoint["optimizer"])
        epochs_count = checkpoint["epochs_count"]
        train_losses = checkpoint["train_losses"]
        valid_losses = checkpoint["valid_losses"]
     # Compute loss and accuracy before starting (or resuming) training.
    _, valid_loss, valid_accuracy = validate(model, dev_loader)
    print("\t* Validation loss before training: {:.4f}, accuracy: {:.4f}%".format(valid_loss, (valid_accuracy*100)))
    # -------------------- Training epochs ------------------- #
    print("\n", 20 * "=", "Training Bert model on device: {}".format(device), 20 * "=")
    patience_counter = 0
    for epoch in range(start_epoch, epochs + 1):
        epochs_count.append(epoch)
        print("* Training epoch {}:".format(epoch))
        epoch_time, epoch_loss, epoch_accuracy = train(model, train_loader, optimizer, epoch, max_grad_norm)
        train_losses.append(epoch_loss)
        print("-> Training time: {:.4f}s, loss = {:.4f}, accuracy: {:.4f}%"
              .format(epoch_time, epoch_loss, (epoch_accuracy*100)))
        print("* Validation for epoch {}:".format(epoch))
        epoch_time, epoch_loss, epoch_accuracy = validate(model, dev_loader)
        valid_losses.append(epoch_loss)
        print("-> Valid. time: {:.4f}s, loss: {:.4f}, accuracy: {:.4f}%\n"
              .format(epoch_time, epoch_loss, (epoch_accuracy*100)))
        # Update the optimizer's learning rate with the scheduler.
        scheduler.step(epoch_accuracy)
        # Early stopping on validation accuracy.
        if epoch_accuracy < best_score:
            patience_counter += 1
        else:
            best_score = epoch_accuracy
            patience_counter = 0
            torch.save({"epoch": epoch, 
                        "model": model.state_dict(),
                        "best_score": best_score,
                        "epochs_count": epochs_count,
                        "train_losses": train_losses,
                        "valid_losses": valid_losses},
                        os.path.join(target_dir, "0704_nre.pth.tar"))
        if patience_counter >= patience:
            print("-> Early stopping: patience limit reached, stopping...")
            break

if __name__ == "__main__":
    main(os.path.join(base_path, "model_data/train.csv"),
     os.path.join(base_path, "checkpoint"))

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对模型进行测试集评测test.py

# -*- coding: utf-8 -*-
import torch
from sys import platform
from torch.utils.data import DataLoader
from transformers import BertTokenizer
import pandas as pd
from data import DataPrecessForCLF
from utils import test
from model import BertModel
import os
import json
from sklearn import metrics
with open("checkpoint/label2id.json", "r", encoding="utf-8") as f:
    label2id = json.load(f)
id2label = {v: k for k, v in label2id.items()}
def get_label(key):
    return id2label.get(int(key))
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')  # 设备
bert_tokenizer = BertTokenizer.from_pretrained("hfl/chinese-roberta-wwm-ext", do_lower_case=True)
def file_test_prf(test_file, pretrained_file, batch_size=8):
    checkpoint = torch.load(pretrained_file, map_location=device)
    df_test = pd.read_csv(test_file)
    df_test["id"] = 0
    inference_data = DataPrecessForCLF(bert_tokenizer, df=df_test, max_char_len=512)
    inference_loader = DataLoader(inference_data, shuffle=False, batch_size=batch_size)
    model = BertModel(num_labels=len(label2id)).to(device)
    model.load_state_dict(checkpoint["model"])
    max_index, max_prob, total_time = test(model, inference_loader, inference=True)
    all_pred_label = list(map(get_label, max_index))
    df_test["pred_label"] = all_pred_label
    df_test["prob"] = max_prob
    useful_label = list(label2id.keys())
    useful_label.remove("其他") # 其他作为副采样的数据可以不参与评测
    print(metrics.classification_report(list(df_test["label"]), all_pred_label, labels=useful_label, digits=3))
    df_test.to_csv(os.path.join(base_path, "result_files/test_result.csv"), index=False)

if __name__ == "__main__":
    file_test_prf(os.path.join(base_path, "model_data/test.csv"), 
    os.path.join(base_path, "checkpoint/clf.pth.tar"))

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其中train.csv 和 test.csv只需要满足拥有sentence和label列即可，sample如下：

sentence,label
今天天气不错，天气
今天大盘三大指数全部收红，股票
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requirements.txt如下，以上脚本基于python3.8运行无误：

torch==1.9.1
transformers==4.12.5
hanziconv==0.3.2
pandas==1.4.2
tqdm==4.64.0
scikit-learn==1.1.1
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