复旦nlp实验室 nlp-beginner 任务二：基于深度学习的文本分类_复旦大学nlp 作业

任务二：基于深度学习的文本分类

熟悉Pytorch，用Pytorch重写《任务一》，实现CNN、RNN的文本分类；

1. 参考

   1. https://pytorch.org/
   2. Convolutional Neural Networks for Sentence Classification https://arxiv.org/abs/1408.5882
   3. https://machinelearningmastery.com/sequence-classification-lstm-recurrent-neural-networks-python-keras/

2. word embedding 的方式初始化

3. 随机embedding的初始化方式

4. 用glove 预训练的embedding进行初始化 https://nlp.stanford.edu/projects/glove/

5. 知识点：

   1. CNN/RNN的特征抽取
   2. 词嵌入
   3. Dropout

6. 时间：两周

main

数据集的加载与预处理都在main函数里面

import torch
import torch.nn as nn
from tqdm import tqdm, trange # tqdm模块来显示任务进度条
from torch.optim import Adam
from tensorboardX import SummaryWriter
import pandas as pd
import os
from torchtext.legacy import data
from torchtext.legacy.data import Iterator, BucketIterator
from torchtext.vocab import Vectors
import matplotlib.pyplot as plt
import numpy as np

from Model import RNN, CNN, LSTM

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

train_epochs = 5
batch_size = 512
learning_rate = 0.001
max_seq_length = 48
num_classes = 5
dropout_rate = 0.1
data_path = "data"
clip = 5

embed_size = 200
vectors = Vectors('glove.6B.200d.txt', 'C:/Users/Mechrevo/Desktop/AI/nlp-beginner/code-for-nlp-beginner-master/Task2-Text Classification (RNN&CNN)/embedding')
freeze = False

use_rnn = True
hidden_size = 256
num_layers = 1
bidirectional = True

use_lstm = False
num_filters = 200
kernel_sizes = [2, 3, 4]

def load_iters(batch_size=32, device="cpu", data_path='data', vectors=None):
    TEXT = data.Field(lower=True, batch_first=True, include_lengths=True)
    LABEL = data.LabelField(batch_first=True)
    train_fields = [(None, None), (None, None), ('text', TEXT), ('label', LABEL)]
    test_fields = [(None, None), (None, None), ('text', TEXT)]

    train_data = data.TabularDataset.splits(
        path=data_path,
        train='train.tsv',
        format='tsv',
        fields=train_fields,
        skip_header=True
    )[0]

    test_data = data.TabularDataset.splits(
        path='data',
        train='test.tsv',
        format='tsv',
        fields=test_fields,
        skip_header=True
    )[0]
    TEXT.build_vocab(train_data.text, vectors=vectors)
    LABEL.build_vocab(train_data.label)
    train_data, dev_data = train_data.split([0.8, 0.2])

    train_iter, dev_iter = BucketIterator.splits(
        (train_data, dev_data),
        batch_sizes=(batch_size, batch_size),
        device=device,
        sort_key=lambda x: len(x.text),
        sort_within_batch=True,
        repeat=False,
        shuffle=True
    )

    test_iter = Iterator(
        test_data,
        batch_size=batch_size,
        device=device,
        sort=False,
        sort_within_batch=False,
        repeat=False,
        shuffle=False
    )
    return train_iter, dev_iter, test_iter, TEXT, LABEL

if __name__ == "__main__":
    train_iter, dev_iter, test_iter, TEXT, LABEL = load_iters(batch_size, device, data_path, vectors)
    vocab_size = len(TEXT.vocab.itos)
    # build model
    if use_lstm:
        model = LSTM(vocab_size, embed_size, hidden_size, num_layers, num_classes, bidirectional, dropout_rate)
    elif use_rnn:
        model = RNN(vocab_size, embed_size, hidden_size, num_layers, num_classes, bidirectional, dropout_rate)
    else:
        model = CNN(vocab_size, embed_size, num_classes, num_filters, kernel_sizes, dropout_rate)
    if vectors is not None:
        model.embed.from_pretrained(TEXT.vocab.vectors, freeze=freeze)
    model.to(device)

    optimizer = Adam(model.parameters(), lr=learning_rate)
    loss_func = nn.CrossEntropyLoss()
    writer = SummaryWriter('logs', comment="rnn")
    loss_history = []
    for epoch in trange(train_epochs, desc="Epoch"):
        model.train()
        ep_loss = 0
        for step, batch in enumerate(tqdm(train_iter, desc="Iteration")):
            (inputs, lens), labels = batch.text, batch.label
            outputs = model(inputs, lens)
            loss = loss_func(outputs, labels)
            ep_loss += loss.item()

            model.zero_grad()
            loss.backward()
            nn.utils.clip_grad_norm_(model.parameters(), clip)
            optimizer.step()
            if step % 10 == 0:
                loss_history.append(loss.item())
            writer.add_scalar('Train_Loss', loss, epoch)
            if step % 10 == 0:
                tqdm.write('Epoch {}, Step {}, Loss {}'.format(epoch, step, loss.item()))

        # evaluating
        model.eval()
        with torch.no_grad():
            corr_num = 0
            err_num = 0
            for batch in dev_iter:
                (inputs, lens), labels = batch.text, batch.label
                outputs = model(inputs, lens)
                corr_num += (outputs.argmax(1) == labels).sum().item()
                err_num += (outputs.argmax(1) != labels).sum().item()
            tqdm.write('Epoch {}, Accuracy {}'.format(epoch, corr_num / (corr_num + err_num)))
    if use_lstm:
        plt.title('LSTM Model')
    elif use_rnn:
        plt.title('RNN Model')
    else:
        plt.title('CNN Model')
    plt.plot(np.arange(len(loss_history)), np.array(loss_history))
    plt.xlabel('Iterations')
    plt.ylabel('Training Loss')
    plt.show()
    # predicting
    model.eval()
    with torch.no_grad():
        predicts = []
        for batch in test_iter:
            inputs, lens = batch.text
            outputs = model(inputs, lens)
            predicts.extend(outputs.argmax(1).cpu().numpy())
        test_data = pd.read_csv(os.path.join(data_path, 'test.tsv'), sep='\t')
        test_data["Sentiment"] = predicts
        test_data[['PhraseId', 'Sentiment']].set_index('PhraseId').to_csv('result.csv')

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model

Models : LSTM RNN CNN

import torch
import torch.nn as nn
import torch.nn.functional as F
import math

class RNN(nn.Module):
    def __init__(self, vocab_size, embed_size, hidden_size, num_layers, num_classes,
                 bidirectional=True, dropout_rate=0.3):
        super(RNN, self).__init__()
        self.hidden_size = hidden_size
        self.num_layers = num_layers
        self.embed = nn.Embedding(vocab_size, embed_size)
        self.rnn = nn.RNN(embed_size, hidden_size, num_layers, batch_first=True, bidirectional=bidirectional)
        self.bidirectional = bidirectional
        if not bidirectional:
            self.fc = nn.Linear(hidden_size, num_classes)
        else:
            self.fc = nn.Linear(hidden_size * 2, num_classes)
        self.dropout = nn.Dropout(dropout_rate)
        self.init()

    def init(self):
        std = 1.0 / math.sqrt(self.hidden_size)
        for w in self.parameters():
            w.data.uniform_(-std, std)

    def forward(self, x, lens):
        embeddings = self.embed(x)
        output, _ = self.rnn(embeddings)

        real_output = output[range(len(lens)), lens - 1]
        out = self.fc(self.dropout(real_output))
        return out
class LSTM(nn.Module):
    def __init__(self, vocab_size, embed_size, hidden_size, num_layers, num_classes,
                 bidirectional=True, dropout_rate=0.3):
        super(LSTM, self).__init__()
        self.hidden_size = hidden_size
        self.num_layers = num_layers
        self.embed = nn.Embedding(vocab_size, embed_size)
        self.rnn = nn.LSTM(embed_size, hidden_size, num_layers, batch_first=True, bidirectional=bidirectional)
        self.bidirectional = bidirectional
        if not bidirectional:
            self.fc = nn.Linear(hidden_size, num_classes)
        else:
            self.fc = nn.Linear(hidden_size * 2, num_classes)
        self.dropout = nn.Dropout(dropout_rate)
        self.init()

    def init(self):
        std = 1.0 / math.sqrt(self.hidden_size)
        for w in self.parameters():
            w.data.uniform_(-std, std)

    def forward(self, x, lens):
        embeddings = self.embed(x)
        output, _ = self.rnn(embeddings)

        real_output = output[range(len(lens)), lens - 1]
        out = self.fc(self.dropout(real_output))
        return out
class CNN(nn.Module):
    def __int__(self, vocab_size, embed_size, num_classes, num_filters=100, kernel_size=[2, 3, 4], dropout_rate=0.3):
        super(CNN, self).__init__()
        self.embed = nn.Embedding(vocab_size, embed_size)
        self.convs = nn.ModuleList([
            nn.Conv2d(1, num_filters, (k, embed_size), padding=(k - 1, 0))
            for k in kernel_size
        ])
        self.fc = nn.Linear(len(kernel_size) * num_filters, num_classes)
        self.dropout = nn.Dropout(dropout_rate)

    def conv_and_pool(self, x, conv):
        x = F.relu(conv(x).squeeze(3))
        x_max = F.max_pool1d(x, x.size(2)).squeeze(2)
        return x_max

    def forward(self, x, lens):
        embed = self.embed(x).unsqueeze(1)

        conv_results = [self.conv_and_pool(embed, conv) for conv in self.convs]

        out = torch.cat(conv_results, 1)
        return self.fc(self.dropout(out))
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自己用抱抱脸的bert模型重写了一遍：

import torch
from transformers import AdamW, AutoTokenizer, AutoModelForSequenceClassification
from datasets import load_dataset
from transformers import DataCollatorWithPadding
from transformers import TrainingArguments
import numpy as np
from torch.utils.data import DataLoader
from transformers import AdamW
from transformers import get_scheduler
from transformers import Trainer
from tqdm.auto import tqdm
import os
os.environ["KMP_DUPLICATE_LIB_OK"]  =  "TRUE"
import matplotlib.pyplot as plt
from datasets import load_metric

def tokenize_function(example):
    return tokenizer(example["Phrase"], truncation=True)

def compute_metrics(eval_preds):
    metric = load_metric("accuracy")
    logits, labels = eval_preds
    predictions = np.argmax(logits, axis=-1)
    return metric.compute(predictions=predictions, references=labels)

if __name__ == "__main__":
    data_files = {"train": "data/train.tsv", "validation" : "data/validation.tsv"
        ,"test": "data/test.tsv"}
    data = load_dataset("csv", data_files=data_files,delimiter="\t")
    checkpoint = "distilbert-base-uncased"
    tokenizer = AutoTokenizer.from_pretrained(checkpoint)
    model = AutoModelForSequenceClassification.from_pretrained(checkpoint, num_labels=5)
    tokenized_datasets = data.map(tokenize_function, batched=True)

    tokenized_datasets = tokenized_datasets.remove_columns(["PhraseId", "SentenceId"])
    tokenized_datasets = tokenized_datasets.rename_column("Sentiment", "labels")
    tokenized_datasets = tokenized_datasets.remove_columns(["Phrase"])
    tokenized_datasets.set_format("torch")

    data_collator = DataCollatorWithPadding(tokenizer=tokenizer)

    training_args = TrainingArguments("test-trainer")

    train_dataloader = DataLoader(
        tokenized_datasets["train"], shuffle=True, batch_size=16, collate_fn=data_collator
    )
    eval_dataloader = DataLoader(
        tokenized_datasets["validation"], batch_size=16, collate_fn=data_collator
    )

    optimizer = AdamW(model.parameters(), lr=0.0001)

    num_epochs = 1
    num_training_steps = num_epochs * len(train_dataloader)
    lr_scheduler = get_scheduler(
        "linear",
        optimizer=optimizer,
        num_warmup_steps=0,
        num_training_steps=num_training_steps,
    )
    print(num_training_steps)

    device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
    model.to(device)

    trainer = Trainer(
        model,
        training_args,
        train_dataset=tokenized_datasets["train"],
        eval_dataset=tokenized_datasets["validation"],
        data_collator=data_collator,
        tokenizer=tokenizer,
    )

    progress_bar = tqdm(range(num_training_steps))

    #model.train()
    loss_list = []
    for epoch in range(num_epochs):
        for idx, batch in enumerate(train_dataloader):
            batch = {k: v.to(device) for k, v in batch.items()}
            outputs = model(**batch)
            loss = outputs.loss
            if idx % 100 == 0:
                loss_list.append(loss.item())
                tqdm.write('step:{}, loss :{}'.format(idx/100, loss.item() ) )
            loss.backward()

            optimizer.step()
            lr_scheduler.step()
            optimizer.zero_grad()
            progress_bar.update(1)

    plt.plot(np.arange(len(loss_list)), np.array(loss_list))
    plt.xlabel('Iterations')
    plt.ylabel('Training Loss')
    plt.title('distilled-bert-uncased')
    plt.show()

    metric = load_metric("accuracy")
    model.eval()
    for batch in eval_dataloader:
        batch = {k: v.to(device) for k, v in batch.items()}
        with torch.no_grad():
            outputs = model(**batch)

        logits = outputs.logits
        predictions = torch.argmax(logits, dim=-1)
        metric.add_batch(predictions=predictions, references=batch["labels"])

    metric.compute()
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训练结果

LSTM model训练结果：

RNN model训练结果：

bert的结果，可以看到还是非常的拉胯，无法收敛

总结

LSTM和RNN模型在此数据集上差距不是很大…但是RNN出现了异常值，可能是因为有脏数据（噪声过多，很烂的数据集）

之后试试用transformer bert等现在用的更多的模型来跑一跑试试，效果可能会有一定提升（已使用bert，并无提升，看了一下数据集应该是因为数据集噪声太多而导致无法收敛的）

数据集太小，just for fun.

了解了用torchtext读数据和构建trivial神经网络的过程

Pytorch yyds！

抱抱脸 yyds！