Task5 基于深度学习的文本分类2-3TextRNN

TextRNN
TextRNN利用RNN（循环神经网络）进行文本特征抽取，由于文本本身是一种序列，而LSTM天然适合建模序列数据。TextRNN将句子中每个词的词向量依次输入到双向双层LSTM，分别将两个方向最后一个有效位置的隐藏层拼接成一个向量作为文本的表示

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
word2vec_path = '../emb/word2vec.txt'
dropout = 0.15
word_hidden_size = 128
word_num_layers = 2


class WordLSTMEncoder(nn.Module):
    def __init__(self, vocab):
        super(WordLSTMEncoder, self).__init__()
        self.dropout = nn.Dropout(dropout)
        self.word_dims = 100

        self.word_embed = nn.Embedding(vocab.word_size, self.word_dims, padding_idx=0)

        extword_embed = vocab.load_pretrained_embs(word2vec_path)
        extword_size, word_dims = extword_embed.shape
        logging.info("Load extword embed: words %d, dims %d." % (extword_size, word_dims))

        self.extword_embed = nn.Embedding(extword_size, word_dims, padding_idx=0)
        self.extword_embed.weight.data.copy_(torch.from_numpy(extword_embed))
        self.extword_embed.weight.requires_grad = False

        input_size = self.word_dims

        self.word_lstm = nn.LSTM(
            input_size=input_size,
            hidden_size=word_hidden_size,
            num_layers=word_num_layers,
            batch_first=True,
            bidirectional=True
        )

    def forward(self, word_ids, extword_ids, batch_masks):
        # word_ids: sen_num x sent_len
        # extword_ids: sen_num x sent_len
        # batch_masks   sen_num x sent_len

        word_embed = self.word_embed(word_ids)  # sen_num x sent_len x 100
        extword_embed = self.extword_embed(extword_ids)
        batch_embed = word_embed + extword_embed

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

        hiddens, _ = self.word_lstm(batch_embed)  # sen_num x sent_len x  hidden*2
        hiddens = hiddens * batch_masks.unsqueeze(2)

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

        return hiddens


# 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 = word_hidden_size * 2
        self.doc_rep_size = sent_hidden_size * 2
        self.all_parameters = {}
        parameters = []
        self.word_encoder = WordLSTMEncoder(vocab)
        self.word_attention = Attention(self.sent_rep_size)
        parameters.extend(list(filter(lambda p: p.requires_grad, self.word_encoder.parameters())))
        parameters.extend(list(filter(lambda p: p.requires_grad, self.word_attention.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

        logging.info('Build model with lstm 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

        batch_hiddens = self.word_encoder(batch_inputs1, batch_inputs2,
                                          batch_masks)  # sen_num x sent_len x sent_rep_size
        sent_reps, atten_scores = self.word_attention(batch_hiddens, batch_masks)  # 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
decay = .75
decay_step = 1000


class Optimizer:
    def __init__(self, model_parameters):
        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)

            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, 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, max_segment)
        doc = []
        for sent_len, sent_words in sents_words:
            word_ids = vocab.word2id(sent_words)
            extword_ids = vocab.extword2id(sent_words)
            doc.append([sent_len, word_ids, extword_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]

    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 = 200

test_batch_size = 16
train_batch_size = 16

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


class Trainer():
    def __init__(self, model, vocab):
        self.model = model
        self.report = True

        self.train_data = get_examples(train_data, vocab)
        self.batch_num = int(np.ceil(len(self.train_data) / float(train_batch_size)))
        self.dev_data = get_examples(dev_data, vocab)
        self.test_data = get_examples(test_data, vocab)

        # criterion
        self.criterion = nn.CrossEntropyLoss()

        # label name
        self.target_names = vocab.target_names

        # optimizer
        self.optimizer = Optimizer(model.all_parameters)

        # 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()

        y_pred = []
        y_true = []
        with torch.no_grad():
            for batch_data in data_iter(self.dev_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()