新闻文本分类-06 基于Bert的文本分类

上一章节采用Word2Vec做向量表示，通过TextCNN以及TextRNN的深度学习模型来做文本分类。这一章节采用bert微调，将最后一层的第一个token即[CLS]的隐藏向量作为句子的表示，然后输入到softmax层进行分类。

import logging
import random

import numpy as np
import torch

logging.basicConfig(level=logging.INFO, format='%(asctime)-15s %(levelname)s: %(message)s')

# set seed
seed = 666
random.seed(seed)
np.random.seed(seed)
torch.cuda.manual_seed(seed)
torch.manual_seed(seed)

# set cuda
gpu = 0
use_cuda = gpu >= 0 and torch.cuda.is_available()
if use_cuda:
    torch.cuda.set_device(gpu)
    device = torch.device("cuda", gpu)
else:
    device = torch.device("cpu")
logging.info("Use cuda: %s, gpu id: %d.", use_cuda, gpu)

result:

2020-08-04 14:09:06,452 INFO: Use cuda: True, gpu id: 0.

# split data to 10 fold
fold_num = 10
data_file = './input/train_set.csv'
import pandas as pd


def all_data2fold(fold_num, num=10000):
    fold_data = []
    f = pd.read_csv(data_file, sep='\t', encoding='UTF-8')
    texts = f['text'].tolist()[:num]
    labels = f['label'].tolist()[:num]

    total = len(labels)

    # 打乱index顺序，使all_texts和all_labels随机（一一对应）。
    index = list(range(total))
    np.random.shuffle(index)
    
    all_texts = []
    all_labels = []
    for i in index:
        all_texts.append(texts[i])
        all_labels.append(labels[i])

    # 创建key:value字典，其中key是label,value是label对应的index列表。
    label2id = {}
    for i in range(total):
        label = str(all_labels[i])
        if label not in label2id:
            label2id[label] = [i]
        else:
            label2id[label].append(i)

    # 将同一label划分为10折。
    all_index = [[] for _ in range(fold_num)]
    for label, data in label2id.items():
        # print(label, len(data))
        # 设data = 105
        # fold_num = 10
        # batch_size = 10
        # other = 5
        """
        i=0, cur_batch_size = 11, datch_data = [data[0], data[1], ... , data[10]]
        i=1, cur_batch_size = 11, datch_data = [data[10], data[11], ... , data[20]]
        ......
        i=5, cur_batch_size = 10, datch_data = [data[50], data[51], ... , data[59]]
        i=6, cur_batch_size = 10, datch_data = [data[60], data[61], ... , data[69]]
        ......
        i=9, cur_batch_size = 10, datch_data = [data[90], data[91], ... , data[99]]
        """
        batch_size = int(len(data) / fold_num)
        other = len(data) - batch_size * fold_num
        for i in range(fold_num):
#             cur_batch_size = batch_size + 1 if i < other else batch_size
#             # print(cur_batch_size)
#             batch_data = [data[i * batch_size + b] for b in range(cur_batch_size)]
            if i < other:
                cur_batch_size = batch_size + 1
                batch_data = [data[i * cur_batch_size + b] for b in range(cur_batch_size)]
            else:
                cur_batch_size = batch_size
                batch_data = [data[i * cur_batch_size + other + b] for b in range(cur_batch_size)]
            all_index[i].extend(batch_data)

    # 将10折分类后的all_index按照index分别划分到
    batch_size = int(total / fold_num)
    other_texts = []
    other_labels = []
    other_num = 0
    start = 0
    for fold in range(fold_num):
        num = len(all_index[fold])
        texts = [all_texts[i] for i in all_index[fold]]
        labels = [all_labels[i] for i in all_index[fold]]
        
        # 单折数量>10折平均数时，文本数截断至平均数batch_size,并降截断的文本分至other_texts,标签fold_labels同上。
        if num > batch_size:
            fold_texts = texts[:batch_size]
            other_texts.extend(texts[batch_size:])
            fold_labels = labels[:batch_size]
            other_labels.extend(labels[batch_size:])
            other_num += num - batch_size
        # 单折数量<10折平均数时，原有的文本加other_size中batch_size-num的文本数，标签fold_labels同上
        elif num < batch_size:
            end = start + batch_size - num
            fold_texts = texts + other_texts[start: end]
            fold_labels = labels + other_labels[start: end]
            start = end
        # 否则，文本数和标签数不变。
        else:
            fold_texts = texts
            fold_labels = labels

        assert batch_size == len(fold_labels)

        # shuffle，对10折文本和标签重新刷新。
        index = list(range(batch_size))
        np.random.shuffle(index)

        shuffle_fold_texts = []
        shuffle_fold_labels = []
        for i in index:
            shuffle_fold_texts.append(fold_texts[i])
            shuffle_fold_labels.append(fold_labels[i])

        data = {'label': shuffle_fold_labels, 'text': shuffle_fold_texts}
        fold_data.append(data)

    logging.info("Fold lens %s", str([len(data['label']) for data in fold_data]))

    return fold_data


fold_data = all_data2fold(10, num=200000)

result:

2020-08-04 14:19:36,069 INFO: Fold lens [20000, 20000, 20000, 20000, 20000, 20000, 20000, 20000, 20000, 20000]

# build train, dev, test data
fold_id = 9

# dev
# 取出第10折作为验证集
dev_data = fold_data[fold_id]

# train
# 将1-9折数据集作为训练集
train_texts = []
train_labels = []
for i in range(0, fold_id):
    data = fold_data[i]
    train_texts.extend(data['text'])
    train_labels.extend(data['label'])

train_data = {'label': train_labels, 'text': train_texts}

# test
# 读取测试集
test_data_file = './input/test_a.csv'
f = pd.read_csv(test_data_file, sep='\t', encoding='UTF-8')
texts = f['text'].tolist()
# 测试集label用0占位
test_data = {'label': [0] * len(texts), 'text': texts}

# build vocab
from collections import Counter
from transformers import BasicTokenizer

basic_tokenizer = BasicTokenizer()


class Vocab():
    def __init__(self, train_data):
        self.min_count = 5
        self.pad = 0
        self.unk = 1
        self._id2word = ['[PAD]', '[UNK]']
        self._id2extword = ['[PAD]', '[UNK]']

        self._id2label = []
        self.target_names = []

        self.build_vocab(train_data)

        reverse = lambda x: dict(zip(x, range(len(x))))
        self._word2id = reverse(self._id2word)
        self._label2id = reverse(self._id2label)

        logging.info("Build vocab: words %d, labels %d." % (self.word_size, self.label_size))

    def build_vocab(self, data):
        self.word_counter = Counter()
        
        # 计算词的个数
        for text in data['text']:
            words = text.split()
            for word in words:
                self.word_counter[word] += 1
        
        # most_common()将Counter({key1:value1, key2:value2, ...})改为[(key1, value1), (key2, value2)]形式。
        for word, count in self.word_counter.most_common():
            # 个数>min_count，添加到_id2word
            if count >= self.min_count:
                self._id2word.append(word)

        label2name = {0: '科技', 1: '股票', 2: '体育', 3: '娱乐', 4: '时政', 5: '社会', 6: '教育', 7: '财经',
                      8: '家居', 9: '游戏', 10: '房产', 11: '时尚', 12: '彩票', 13: '星座'}

        self.label_counter = Counter(data['label'])

        for label in range(len(self.label_counter)):
            count = self.label_counter[label]
            self._id2label.append(label)
            self.target_names.append(label2name[label])

    def load_pretrained_embs(self, embfile):
        with open(embfile, encoding='utf-8') as f:
            lines = f.readlines()
            items = lines[0].split()
            word_count, embedding_dim = int(items[0]), int(items[1])

        index = len(self._id2extword)
        embeddings = np.zeros((word_count + index, embedding_dim))
        for line in lines[1:]:
            values = line.split()
            self._id2extword.append(values[0])
            vector = np.array(values[1:], dtype='float64')
            embeddings[self.unk] += vector
            embeddings[index] = vector
            index += 1

        embeddings[self.unk] = embeddings[self.unk] / word_count
        embeddings = embeddings / np.std(embeddings)

        reverse = lambda x: dict(zip(x, range(len(x))))
        self._extword2id = reverse(self._id2extword)

        assert len(set(self._id2extword)) == len(self._id2extword)

        return embeddings

    def word2id(self, xs):
        if isinstance(xs, list):
            return [self._word2id.get(x, self.unk) for x in xs]
        return self._word2id.get(xs, self.unk)

    def extword2id(self, xs):
        if isinstance(xs, list):
            return [self._extword2id.get(x, self.unk) for x in xs]
        return self._extword2id.get(xs, self.unk)

    def label2id(self, xs):
        if isinstance(xs, list):
            return [self._label2id.get(x, self.unk) for x in xs]
        return self._label2id.get(xs, self.unk)

    @property
    def word_size(self):
        return len(self._id2word)

    @property
    def extword_size(self):
        return len(self._id2extword)

    @property
    def label_size(self):
        return len(self._id2label)


vocab = Vocab(train_data)

result:

2020-08-04 14:22:38,035 INFO: PyTorch version 1.3.1+cu100 available.

2020-08-04 14:24:31,666 INFO: Build vocab: words 5996, labels 14.

# build module
import torch.nn as nn
import torch.nn.functional as F


class Attention(nn.Module):
    def __init__(self, hidden_size):
        super(Attention, self).__init__()
        self.weight = nn.Parameter(torch.Tensor(hidden_size, hidden_size))
        self.weight.data.normal_(mean=0.0, std=0.05)

        self.bias = nn.Parameter(torch.Tensor(hidden_size))
        b = np.zeros(hidden_size, dtype=np.float32)
        self.bias.data.copy_(torch.from_numpy(b))

        self.query = nn.Parameter(torch.Tensor(hidden_size))
        self.query.data.normal_(mean=0.0, std=0.05)

    def forward(self, batch_hidden, batch_masks):
        # batch_hidden: b x len x hidden_size (2 * hidden_size of lstm)
        # batch_masks:  b x len

        # linear
        key = torch.matmul(batch_hidden, self.weight) + self.bias  # b x len x hidden

        # compute attention
        outputs = torch.matmul(key, self.query)  # b x len

        masked_outputs = outputs.masked_fill((1 - batch_masks).bool(), float(-1e32))

        attn_scores = F.softmax(masked_outputs, dim=1)  # b x len

        # 对于全零向量，-1e32的结果为 1/len, -inf为nan, 额外补0
        masked_attn_scores = attn_scores.masked_fill((1 - batch_masks).bool(), 0.0)

        # sum weighted sources
        batch_outputs = torch.bmm(masked_attn_scores.unsqueeze(1), key).squeeze(1)  # b x hidden

        return batch_outputs, attn_scores


# build word encoder
bert_path = './emb/bert-mini/'
dropout = 0.15

from transformers import BertModel


class WordBertEncoder(nn.Module):
    def __init__(self):
        super(WordBertEncoder, self).__init__()
        self.dropout = nn.Dropout(dropout)

        self.tokenizer = WhitespaceTokenizer()
        self.bert = BertModel.from_pretrained(bert_path)

        self.pooled = False
        logging.info('Build Bert encoder with pooled {}.'.format(self.pooled))

    def encode(self, tokens):
        tokens = self.tokenizer.tokenize(tokens)
        return tokens

    def get_bert_parameters(self):
        no_decay = ['bias', 'LayerNorm.weight']
        optimizer_parameters = [
            {'params': [p for n, p in self.bert.named_parameters() if not any(nd in n for nd in no_decay)],
             'weight_decay': 0.01},
            {'params': [p for n, p in self.bert.named_parameters() if any(nd in n for nd in no_decay)],
             'weight_decay': 0.0}
        ]
        return optimizer_parameters

    def forward(self, input_ids, token_type_ids):
        # input_ids: sen_num x bert_len
        # token_type_ids: sen_num  x bert_len

        # sen_num x bert_len x 256, sen_num x 256
        sequence_output, pooled_output = self.bert(input_ids=input_ids, token_type_ids=token_type_ids)

        if self.pooled:
            reps = pooled_output
        else:
            reps = sequence_output[:, 0, :]  # sen_num x 256

        if self.training:
            reps = self.dropout(reps)

        return reps


class WhitespaceTokenizer():
    """WhitespaceTokenizer with vocab."""

    def __init__(self):
        vocab_file = bert_path + 'vocab.txt'
        self._token2id = self.load_vocab(vocab_file)
        self._id2token = {v: k for k, v in self._token2id.items()}
        self.max_len = 256
        self.unk = 1

        logging.info("Build Bert vocab with size %d." % (self.vocab_size))

    def load_vocab(self, vocab_file):
        f = open(vocab_file, 'r')
        lines = f.readlines()
        lines = list(map(lambda x: x.strip(), lines))
        vocab = dict(zip(lines, range(len(lines))))
        return vocab

    def tokenize(self, tokens):
        assert len(tokens) <= self.max_len - 2
        tokens = ["[CLS]"] + tokens + ["[SEP]"]
        output_tokens = self.token2id(tokens)
        return output_tokens

    def token2id(self, xs):
        if isinstance(xs, list):
            return [self._token2id.get(x, self.unk) for x in xs]
        return self._token2id.get(xs, self.unk)

    @property
    def vocab_size(self):
        return len(self._id2token)


# build sent encoder
sent_hidden_size = 256
sent_num_layers = 2


class SentEncoder(nn.Module):
    def __init__(self, sent_rep_size):
        super(SentEncoder, self).__init__()
        self.dropout = nn.Dropout(dropout)

        self.sent_lstm = nn.LSTM(
            input_size=sent_rep_size,
            hidden_size=sent_hidden_size,
            num_layers=sent_num_layers,
            batch_first=True,
            bidirectional=True
        )

    def forward(self, sent_reps, sent_masks):
        # sent_reps:  b x doc_len x sent_rep_size
        # sent_masks: b x doc_len

        sent_hiddens, _ = self.sent_lstm(sent_reps)  # b x doc_len x hidden*2
        sent_hiddens = sent_hiddens * sent_masks.unsqueeze(2)

        if self.training:
            sent_hiddens = self.dropout(sent_hiddens)

        return sent_hiddens

# build model
class Model(nn.Module):
    def __init__(self, vocab):
        super(Model, self).__init__()
        self.sent_rep_size = 256
        self.doc_rep_size = sent_hidden_size * 2
        self.all_parameters = {}
        parameters = []
        self.word_encoder = WordBertEncoder()
        bert_parameters = self.word_encoder.get_bert_parameters()

        self.sent_encoder = SentEncoder(self.sent_rep_size)
        self.sent_attention = Attention(self.doc_rep_size)
        parameters.extend(list(filter(lambda p: p.requires_grad, self.sent_encoder.parameters())))
        parameters.extend(list(filter(lambda p: p.requires_grad, self.sent_attention.parameters())))

        self.out = nn.Linear(self.doc_rep_size, vocab.label_size, bias=True)
        parameters.extend(list(filter(lambda p: p.requires_grad, self.out.parameters())))

        if use_cuda:
            self.to(device)

        if len(parameters) > 0:
            self.all_parameters["basic_parameters"] = parameters
        self.all_parameters["bert_parameters"] = bert_parameters

        logging.info('Build model with bert word encoder, lstm sent encoder.')

        para_num = sum([np.prod(list(p.size())) for p in self.parameters()])
        logging.info('Model param num: %.2f M.' % (para_num / 1e6))

    def forward(self, batch_inputs):
        # batch_inputs(batch_inputs1, batch_inputs2): b x doc_len x sent_len
        # batch_masks : b x doc_len x sent_len
        batch_inputs1, batch_inputs2, batch_masks = batch_inputs
        batch_size, max_doc_len, max_sent_len = batch_inputs1.shape[0], batch_inputs1.shape[1], batch_inputs1.shape[2]
        batch_inputs1 = batch_inputs1.view(batch_size * max_doc_len, max_sent_len)  # sen_num x sent_len
        batch_inputs2 = batch_inputs2.view(batch_size * max_doc_len, max_sent_len)  # sen_num x sent_len
        batch_masks = batch_masks.view(batch_size * max_doc_len, max_sent_len)  # sen_num x sent_len

        sent_reps = self.word_encoder(batch_inputs1, batch_inputs2)  # sen_num x sent_rep_size

        sent_reps = sent_reps.view(batch_size, max_doc_len, self.sent_rep_size)  # b x doc_len x sent_rep_size
        batch_masks = batch_masks.view(batch_size, max_doc_len, max_sent_len)  # b x doc_len x max_sent_len
        sent_masks = batch_masks.bool().any(2).float()  # b x doc_len

        sent_hiddens = self.sent_encoder(sent_reps, sent_masks)  # b x doc_len x doc_rep_size
        doc_reps, atten_scores = self.sent_attention(sent_hiddens, sent_masks)  # b x doc_rep_size

        batch_outputs = self.out(doc_reps)  # b x num_labels

        return batch_outputs
    
model = Model(vocab)

result:

2020-08-04 14:40:41,343 INFO: Build Bert vocab with size 5981.
2020-08-04 14:40:41,353 INFO: loading configuration file ./emb/bert-mini/config.json
2020-08-04 14:40:41,355 INFO: Model config BertConfig {
“attention_probs_dropout_prob”: 0.1,
“gradient_checkpointing”: false,
“hidden_act”: “gelu”,
“hidden_dropout_prob”: 0.1,
“hidden_size”: 256,
“initializer_range”: 0.02,
“intermediate_size”: 1024,
“layer_norm_eps”: 1e-12,
“max_position_embeddings”: 256,
“model_type”: “bert”,
“num_attention_heads”: 4,
“num_hidden_layers”: 4,
“pad_token_id”: 0,
“type_vocab_size”: 2,
“vocab_size”: 5981
}

2020-08-04 14:40:41,356 INFO: loading weights file ./emb/bert-mini/pytorch_model.bin
2020-08-04 14:40:41,536 INFO: All model checkpoint weights were used when initializing BertModel.

2020-08-04 14:40:41,538 INFO: All the weights of BertModel were initialized from the model checkpoint at ./emb/bert-mini/.
If your task is similar to the task the model of the ckeckpoint was trained on, you can already use BertModel for predictions without further training.
2020-08-04 14:40:41,539 INFO: Build Bert encoder with pooled False.
2020-08-04 14:45:21,991 INFO: Build model with bert word encoder, lstm sent encoder.
2020-08-04 14:45:21,994 INFO: Model param num: 7.72 M.

# build optimizer
learning_rate = 2e-4
bert_lr = 5e-5
decay = .75
decay_step = 1000
from transformers import AdamW, get_linear_schedule_with_warmup


class Optimizer:
    def __init__(self, model_parameters, steps):
        self.all_params = []
        self.optims = []
        self.schedulers = []

        for name, parameters in model_parameters.items():
            if name.startswith("basic"):
                optim = torch.optim.Adam(parameters, lr=learning_rate)
                self.optims.append(optim)

                l = lambda step: decay ** (step // decay_step)
                scheduler = torch.optim.lr_scheduler.LambdaLR(optim, lr_lambda=l)
                self.schedulers.append(scheduler)
                self.all_params.extend(parameters)
            elif name.startswith("bert"):
                optim_bert = AdamW(parameters, bert_lr, eps=1e-8)
                self.optims.append(optim_bert)

                scheduler_bert = get_linear_schedule_with_warmup(optim_bert, 0, steps)
                self.schedulers.append(scheduler_bert)

                for group in parameters:
                    for p in group['params']:
                        self.all_params.append(p)
            else:
                Exception("no nameed parameters.")

        self.num = len(self.optims)

    def step(self):
        for optim, scheduler in zip(self.optims, self.schedulers):
            optim.step()
            scheduler.step()
            optim.zero_grad()

    def zero_grad(self):
        for optim in self.optims:
            optim.zero_grad()

    def get_lr(self):
        lrs = tuple(map(lambda x: x.get_lr()[-1], self.schedulers))
        lr = ' %.5f' * self.num
        res = lr % lrs
        return res

# build dataset
def sentence_split(text, vocab, max_sent_len=256, max_segment=16):
    words = text.strip().split()
    document_len = len(words)

    index = list(range(0, document_len, max_sent_len))
    index.append(document_len)

    segments = []
    for i in range(len(index) - 1):
        segment = words[index[i]: index[i + 1]]
        assert len(segment) > 0
        segment = [word if word in vocab._id2word else '' for word in segment]
        segments.append([len(segment), segment])

    assert len(segments) > 0
    if len(segments) > max_segment:
        segment_ = int(max_segment / 2)
        return segments[:segment_] + segments[-segment_:]
    else:
        return segments


def get_examples(data, word_encoder, vocab, max_sent_len=256, max_segment=8):
    label2id = vocab.label2id
    examples = []

    for text, label in zip(data['text'], data['label']):
        # label
        id = label2id(label)

        # words
        sents_words = sentence_split(text, vocab, max_sent_len-2, max_segment)
        doc = []
        for sent_len, sent_words in sents_words:
            token_ids = word_encoder.encode(sent_words)
            sent_len = len(token_ids)
            token_type_ids = [0] * sent_len
            doc.append([sent_len, token_ids, token_type_ids])
        examples.append([id, len(doc), doc])

    logging.info('Total %d docs.' % len(examples))
    return examples

# build loader

def batch_slice(data, batch_size):
    batch_num = int(np.ceil(len(data) / float(batch_size)))
    for i in range(batch_num):
        cur_batch_size = batch_size if i < batch_num - 1 else len(data) - batch_size * i
        docs = [data[i * batch_size + b] for b in range(cur_batch_size)]

        yield docs


def data_iter(data, batch_size, shuffle=True, noise=1.0):
    """
    randomly permute data, then sort by source length, and partition into batches
    ensure that the length of  sentences in each batch
    """

    batched_data = []
    if shuffle:
        np.random.shuffle(data)

        lengths = [example[1] for example in data]
        noisy_lengths = [- (l + np.random.uniform(- noise, noise)) for l in lengths]
        sorted_indices = np.argsort(noisy_lengths).tolist()
        sorted_data = [data[i] for i in sorted_indices]
    else:
        sorted_data =data
        
    batched_data.extend(list(batch_slice(sorted_data, batch_size)))

    if shuffle:
        np.random.shuffle(batched_data)

    for batch in batched_data:
        yield batch

# some function
from sklearn.metrics import f1_score, precision_score, recall_score


def get_score(y_ture, y_pred):
    y_ture = np.array(y_ture)
    y_pred = np.array(y_pred)
    f1 = f1_score(y_ture, y_pred, average='macro') * 100
    p = precision_score(y_ture, y_pred, average='macro') * 100
    r = recall_score(y_ture, y_pred, average='macro') * 100

    return str((reformat(p, 2), reformat(r, 2), reformat(f1, 2))), reformat(f1, 2)


def reformat(num, n):
    return float(format(num, '0.' + str(n) + 'f'))

# build trainer

import time
from sklearn.metrics import classification_report

clip = 5.0
epochs = 1
early_stops = 3
log_interval = 50

test_batch_size = 16
train_batch_size = 16

save_model = './bert.bin'
save_test = './bert.csv'

class Trainer():
    def __init__(self, model, vocab):
        self.model = model
        self.report = True
        
        self.train_data = get_examples(train_data, model.word_encoder, vocab)
        self.batch_num = int(np.ceil(len(self.train_data) / float(train_batch_size)))
        self.dev_data = get_examples(dev_data, model.word_encoder, vocab)
        self.test_data = get_examples(test_data, model.word_encoder, vocab)

        # criterion
        self.criterion = nn.CrossEntropyLoss()

        # label name
        self.target_names = vocab.target_names

        # optimizer
        self.optimizer = Optimizer(model.all_parameters, steps=self.batch_num * epochs)

        # count
        self.step = 0
        self.early_stop = -1
        self.best_train_f1, self.best_dev_f1 = 0, 0
        self.last_epoch = epochs

    def train(self):
        logging.info('Start training...')
        for epoch in range(1, epochs + 1):
            train_f1 = self._train(epoch)

            dev_f1 = self._eval(epoch)

            if self.best_dev_f1 <= dev_f1:
                logging.info(
                    "Exceed history dev = %.2f, current dev = %.2f" % (self.best_dev_f1, dev_f1))
                torch.save(self.model.state_dict(), save_model)

                self.best_train_f1 = train_f1
                self.best_dev_f1 = dev_f1
                self.early_stop = 0
            else:
                self.early_stop += 1
                if self.early_stop == early_stops:
                    logging.info(
                        "Eearly stop in epoch %d, best train: %.2f, dev: %.2f" % (
                            epoch - early_stops, self.best_train_f1, self.best_dev_f1))
                    self.last_epoch = epoch
                    break
    def test(self):
        self.model.load_state_dict(torch.load(save_model))
        self._eval(self.last_epoch + 1, test=True)

    def _train(self, epoch):
        self.optimizer.zero_grad()
        self.model.train()

        start_time = time.time()
        epoch_start_time = time.time()
        overall_losses = 0
        losses = 0
        batch_idx = 1
        y_pred = []
        y_true = []
        for batch_data in data_iter(self.train_data, train_batch_size, shuffle=True):
            torch.cuda.empty_cache()
            batch_inputs, batch_labels = self.batch2tensor(batch_data)
            batch_outputs = self.model(batch_inputs)
            loss = self.criterion(batch_outputs, batch_labels)
            loss.backward()

            loss_value = loss.detach().cpu().item()
            losses += loss_value
            overall_losses += loss_value

            y_pred.extend(torch.max(batch_outputs, dim=1)[1].cpu().numpy().tolist())
            y_true.extend(batch_labels.cpu().numpy().tolist())

            nn.utils.clip_grad_norm_(self.optimizer.all_params, max_norm=clip)
            for optimizer, scheduler in zip(self.optimizer.optims, self.optimizer.schedulers):
                optimizer.step()
                scheduler.step()
            self.optimizer.zero_grad()

            self.step += 1

            if batch_idx % log_interval == 0:
                elapsed = time.time() - start_time

                lrs = self.optimizer.get_lr()
                logging.info(
                    '| epoch {:3d} | step {:3d} | batch {:3d}/{:3d} | lr{} | loss {:.4f} | s/batch {:.2f}'.format(
                        epoch, self.step, batch_idx, self.batch_num, lrs,
                        losses / log_interval,
                        elapsed / log_interval))

                losses = 0
                start_time = time.time()

            batch_idx += 1

        overall_losses /= self.batch_num
        during_time = time.time() - epoch_start_time

        # reformat
        overall_losses = reformat(overall_losses, 4)
        score, f1 = get_score(y_true, y_pred)

        logging.info(
            '| epoch {:3d} | score {} | f1 {} | loss {:.4f} | time {:.2f}'.format(epoch, score, f1,
                                                                                  overall_losses,
                                                                                  during_time))
        if set(y_true) == set(y_pred) and self.report:
            report = classification_report(y_true, y_pred, digits=4, target_names=self.target_names)
            logging.info('\n' + report)

        return f1

    def _eval(self, epoch, test=False):
        self.model.eval()
        start_time = time.time()
        data = self.test_data if test else self.dev_data
        y_pred = []
        y_true = []
        with torch.no_grad():
            for batch_data in data_iter(data, test_batch_size, shuffle=False):
                torch.cuda.empty_cache()
                batch_inputs, batch_labels = self.batch2tensor(batch_data)
                batch_outputs = self.model(batch_inputs)
                y_pred.extend(torch.max(batch_outputs, dim=1)[1].cpu().numpy().tolist())
                y_true.extend(batch_labels.cpu().numpy().tolist())

            score, f1 = get_score(y_true, y_pred)

            during_time = time.time() - start_time
            
            if test:
                df = pd.DataFrame({'label': y_pred})
                df.to_csv(save_test, index=False, sep=',')
            else:
                logging.info(
                    '| epoch {:3d} | dev | score {} | f1 {} | time {:.2f}'.format(epoch, score, f1,
                                                                              during_time))
                if set(y_true) == set(y_pred) and self.report:
                    report = classification_report(y_true, y_pred, digits=4, target_names=self.target_names)
                    logging.info('\n' + report)

        return f1

    def batch2tensor(self, batch_data):
        '''
            [[label, doc_len, [[sent_len, [sent_id0, ...], [sent_id1, ...]], ...]]
        '''
        batch_size = len(batch_data)
        doc_labels = []
        doc_lens = []
        doc_max_sent_len = []
        for doc_data in batch_data:
            doc_labels.append(doc_data[0])
            doc_lens.append(doc_data[1])
            sent_lens = [sent_data[0] for sent_data in doc_data[2]]
            max_sent_len = max(sent_lens)
            doc_max_sent_len.append(max_sent_len)

        max_doc_len = max(doc_lens)
        max_sent_len = max(doc_max_sent_len)

        batch_inputs1 = torch.zeros((batch_size, max_doc_len, max_sent_len), dtype=torch.int64)
        batch_inputs2 = torch.zeros((batch_size, max_doc_len, max_sent_len), dtype=torch.int64)
        batch_masks = torch.zeros((batch_size, max_doc_len, max_sent_len), dtype=torch.float32)
        batch_labels = torch.LongTensor(doc_labels)

        for b in range(batch_size):
            for sent_idx in range(doc_lens[b]):
                sent_data = batch_data[b][2][sent_idx]
                for word_idx in range(sent_data[0]):
                    batch_inputs1[b, sent_idx, word_idx] = sent_data[1][word_idx]
                    batch_inputs2[b, sent_idx, word_idx] = sent_data[2][word_idx]
                    batch_masks[b, sent_idx, word_idx] = 1

        if use_cuda:
            batch_inputs1 = batch_inputs1.to(device)
            batch_inputs2 = batch_inputs2.to(device)
            batch_masks = batch_masks.to(device)
            batch_labels = batch_labels.to(device)

        return (batch_inputs1, batch_inputs2, batch_masks), batch_labels

# train
trainer = Trainer(model, vocab)
trainer.train()

result:

2020-08-04 15:07:59,017 INFO: Total 180000 docs.
2020-08-04 15:10:20,178 INFO: Total 20000 docs.
2020-08-04 15:16:11,610 INFO: Total 50000 docs.
2020-08-04 15:16:11,611 INFO: Start training...
2020-08-04 15:16:56,929 INFO: | epoch   1 | step  50 | batch  50/11250 | lr 0.00020 0.00005 | loss 2.0790 | s/batch 0.91
2020-08-04 15:17:37,935 INFO: | epoch   1 | step 100 | batch 100/11250 | lr 0.00020 0.00005 | loss 1.1971 | s/batch 0.82
2020-08-04 15:18:24,089 INFO: | epoch   1 | step 150 | batch 150/11250 | lr 0.00020 0.00005 | loss 0.9736 | s/batch 0.92
2020-08-04 15:19:07,020 INFO: | epoch   1 | step 200 | batch 200/11250 | lr 0.00020 0.00005 | loss 0.7862 | s/batch 0.86
......
2020-08-04 17:59:22,058 INFO: | epoch   1 | score (91.02, 89.05, 90.0) | f1 90.0 | loss 0.2538 | time 9790.18
2020-08-04 17:59:22,490 INFO: 
              precision    recall  f1-score   support

          科技     0.9208    0.9323    0.9265     35027
          股票     0.9275    0.9397    0.9336     33251
          体育     0.9785    0.9816    0.9801     28283
          娱乐     0.9364    0.9535    0.9449     19920
          时政     0.8787    0.8942    0.8864     13515
          社会     0.8706    0.8631    0.8669     11009
          教育     0.9366    0.9206    0.9285      8987
          财经     0.8694    0.8078    0.8375      7957
          家居     0.8992    0.8883    0.8937      7063
          游戏     0.9115    0.8762    0.8935      5291
          房产     0.9295    0.8963    0.9126      4428
          时尚     0.8857    0.8432    0.8639      2812
          彩票     0.9201    0.8566    0.8872      1639
          星座     0.8785    0.8130    0.8444       818

    accuracy                         0.9237    180000
   macro avg     0.9102    0.8905    0.9000    180000
weighted avg     0.9235    0.9237    0.9235    180000
2020-08-04 18:13:58,969 INFO: | epoch   1 | dev | score (94.03, 94.12, 94.06) | f1 94.06 | time 876.48
2020-08-04 18:13:59,022 INFO: 
              precision    recall  f1-score   support

          科技     0.9517    0.9514    0.9515      3891
          股票     0.9547    0.9580    0.9564      3694
          体育     0.9879    0.9889    0.9884      3142
          娱乐     0.9574    0.9751    0.9662      2213
          时政     0.9242    0.9181    0.9211      1501
          社会     0.9374    0.8814    0.9086      1223
          教育     0.9437    0.9569    0.9502       998
          财经     0.8875    0.8835    0.8855       884
          家居     0.9385    0.9349    0.9367       784
          游戏     0.9057    0.9489    0.9268       587
          房产     0.9734    0.9654    0.9694       492
          时尚     0.9486    0.9248    0.9365       319
          彩票     0.9180    0.9231    0.9205       182
          星座     0.9355    0.9667    0.9508        90

    accuracy                         0.9506     20000
   macro avg     0.9403    0.9412    0.9406     20000
weighted avg     0.9506    0.9506    0.9506     20000

2020-08-04 18:13:59,023 INFO: Exceed history dev = 0.00, current dev = 94.06