NLP-新闻文本分类(六、基于深度学习的文本分类3_class sentencoder(nn.module):

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)
 
 
 
# split data to 10 fold
fold_num = 10
data_file = '../data/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)
 
    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])
 
    label2id = {}
    for i in range(total):
        label = str(all_labels[i])
        if label not in label2id:
            label2id[label] = [i]
        else:
            label2id[label].append(i)
 
    all_index = [[] for _ in range(fold_num)]
    for label, data in label2id.items():
        # print(label, len(data))
        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)]
            all_index[i].extend(batch_data)
 
    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]]
 
        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
        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
        else:
            fold_texts = texts
            fold_labels = labels
 
        assert batch_size == len(fold_labels)
 
        # shuffle
        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)
 
 
 
# build train, dev, test data
fold_id = 9
 
# dev
dev_data = fold_data[fold_id]
 
# train
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
test_data_file = '../data/test_a.csv'
f = pd.read_csv(test_data_file, sep='\t', encoding='UTF-8')
texts = f['text'].tolist()
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()
 
        for text in data['text']:
            words = text.split()
            for word in words:
                self.word_counter[word] += 1
 
        for word, count in self.word_counter.most_common():
            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)
 
 
 
# 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)
 
 
 
# 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 '<UNK>' 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
 
 
 
 
# 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()
 
 
# test
trainer.test()