[CS224n #4] Sequence to Sequence Learning with Neural Networks (NeurIPS, 2014)

누렁이·2022년 10월 26일
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CS224n

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0. Summary

1. Introduction

  • Background

    • DNN의 장점 : parallel computation
      A surprising example of the power of DNNs is their ability to sort N N-bit numbers using only 2 hidden layers of quadratic size [27]. => 이게 엄청난 이유가 뭘까?
    • DNN 활용한 기존 연구의 한계점: sequence 활용한 적 X
      • 한정적인 task에 활용 (e.g., input, output이 고정된 사이즈의 vector로 인코딩)
      • 하지만, 많은 문제들은 길이를 알 수 없는 sequence로 뽑아야 해결할 수 있는 문제들이 많음 (e.g., speech recognition, machine translation, and question answering)

  • Goal

    • DNN 활용해서 sequential problem 해결하기

  • Challenge

    • 관련 문제는 sequence length가 고정되어 있지 않음. DNN은 고정된 input, output 을 요구함.
    • RNN은 long term dependecies 문제로 train 어려움.

  • Approach

    • LSTM 활용: long range temporal dependencies
      1) 한 개의 LSTM이 one timestep마다 input sequence를 읽어서 하나의 fixed-dimensional vector representaion을 얻는다.
      2) 또다른 LSTM이 해당 vector를 받아서 output sequence를 뽑는다.

  • Contribution

    • En -> Fr 번역에서 높은 성능을 보임
    • source sentence 입력 시 순서를 반대로 넣어줬더니 long sentence 도 무리 없이 opimization함.
    • LSTM은 다양한 길이의 input도 받을 수 있음. (긴 문장도 하나의 fixed vector로 만들기 때문에)

2. The model

  • 기존 RNN과 LSTM 비교
    • RNN
      • input, output간의 alignment (fixed size)를 알고 있다면 RNN을 활용해서 sequence mapping하는 거 간편함. (RNN은 아래 수식 이용해서 iterating하면서 output 구함)
    • LSTM
      • RNN은 long term dependencies 문제가 있음.
      • LSTM Objective function : input sequence가 주어졌을 때 output sequence 확률을 구하는데, 이 sequence 길이는 timestep마다 다름. LSTM은 input sequence의 last hidden state를 이용해서 고정된 vector를 얻는다. 그래서 sequence 사이즈가 달라도 괜찮음!
  • Proposed model
    • 1) 2개의 LSTM 사용(One for input sequence, another for output sequence) : model의 파라미터 수를 늘릴 수 있고 (계산 비용 늘어나지 X), language pair를 동시에 학습할 수 있음.
    • 2) 4 layer LSTM 사용: deep LSTM이 성능이 좋았음
    • 3) input sequence의 단어 순서를 reverse함 (c,b,a => d,e,f) : a의 번역 단어가 d인데, 순서를 반대로 해서 둘의 거리를 좁혀, SGD가 input, output간의 establish communication을 가능하게 한다. (=> 가까운게 더 좋겠지..?)
  • Objective Function
    • MT task를 위한 loss 구하기
      • S: source, T: target
      • training 이 끝나면, p분포를 가지고 가장 가능성 있는 translation을 도출

3. Experiments

  • task : WMT'14 English to French MT task

  • Metric : BLEU (Bilingual Evaluation Understudy) (n-gram 기반 기계 번역 결과와 사람이 직접 번역한 결과 비교)

  • Experiment setting

    • 1) Decoding: directly translate the input sentence without using a reference SMT system, 이때 beam search decoding 방식 적용

      • Greedy decoding: 해당 시점에서 가장 확률이 높은 후보 선택. 시간 복잡도에서는 좋은데, 정확도는 낮음. 1,2위 후보간 차이가 별로 없어도 1등에만 관심있기 때문.
      • Beam search: Greedy decoding 단점 보완. 해당 시점에서 나올 수 있는 경우(beam)의 수(k개)를 고려한 뒤 누적 확률이 가장 높은 경우를 선택.
        • 처음 start에서 k개 빔을 뽑고, 각 빔마다 또 k개의 빔을 뽑음. 누적으로 확률값을 계산해서 가장 높은 애만 남김. 마지막 를 만날 때까지 계속 반복. (k개를 어느정도로 설정하냐에 따라 성능 달라짐)

    • 2) Rescoring: rescore the n-best lists of an SMT baseline

      [원문] We also used the LSTM to rescore the 1000-best lists produced by the baseline system [29]. To rescore an n-best list, we computed the log probability of every hypothesis with our LSTM and took an even average with their score and the LSTM’s score.
      https://m.blog.naver.com/sooftware/221816126290

    • 3) Reversing

      • [원문] While we do not have a complete explanation to this phenomenon, we believe that it is caused by the introduction of many short term dependencies to the dataset. Normally, when we concatenate a source sentence with a target sentence, each word in the source sentence is far from its corresponding word in the target sentence.
      • 왜 이게 좋은지 정확히 설명할 순 없지만, 우리는 dataset의 short term dependencies 때문이라고 생각한다.(source와 target사이 거리가 머니까)

4. Result

  • 번역 성능
    SOTA 못찍었지만, large MT task에서 phrase-based SMT baseline보다 성능이 뛰어난 최초의 pure neural translation system 이다!

  • Long sentences 성능
  • order of words 중요

5. Conclusion

  • 최초의 DNN 활용한 seq2seq 모델. 두 개의 LSTM 모델 사용!
  • SOTA (statistical model)의 성능은 뛰어넘지 못했다는 한계점은 있음.
  • Long sentence 잘한다.
  • reverse 한 거 좋았다.
  • 이후 연구
    • Transformer (Attention is all you need) : long-term dependency 문제가 여전히 있어서 이를 극복

6. Implementation

  • Teacher forcing : training 할 때, decode 과정에서 다음 입력값에 예측값을 주는 게 아니라 정답 target값을 줘서 교정을 해준다. 그럼 학습 초기에 안정적으로 학습할 수 있다고 함.
#!/usr/bin/env python
# coding: utf-8

# In[ ]:


# https://github.com/bentrevett/pytorch-seq2seq
# https://pytorch.org/tutorials/intermediate/seq2seq_translation_tutorial.html


# # 1 - Sequence to Sequence Learning with Neural Networks
# 
# [Sequence to Sequence Learning with Neural Networks](https://arxiv.org/abs/1409.3215) paper. 

# In[1]:


import re

import torch
import torch.nn as nn
import torch.optim as optim

#from torchtext.legacy.datasets import Multi30k
from torchtext.legacy.data import Example,Dataset,Field, BucketIterator

import spacy
import numpy as np

import random
import math
import time

from tqdm import tqdm


# In[2]:


SEED = 1234

random.seed(SEED)
np.random.seed(SEED)
torch.manual_seed(SEED)
torch.cuda.manual_seed(SEED)
torch.backends.cudnn.deterministic = True


# # 1. Preparing Data

# # dataset 출처 : https://github.com/jungyeul/korean-parallel-corpora
# 
# # gz 푸는 방법
# import tarfile
# tar = tarfile.open("korean-english-park.train.tar.gz")
# tar.extractall()
# tar.close()

# In[3]:


with open("korean-english-park.train.en", "r") as f:
    train_en = f.readlines()
    
with open("korean-english-park.test.en", "r") as f:
    test_en = f.readlines()
    
with open("korean-english-park.train.ko", "r") as f:
    train_ko = f.readlines()
    
with open("korean-english-park.test.ko", "r") as f:
    test_ko = f.readlines()


# In[4]:


train_en[0], train_ko[0]


# In[60]:


test_en[0], test_ko[0]


# In[5]:


spacy_kor = spacy.load('ko_core_news_sm')
spacy_en = spacy.load('en_core_web_sm')


# In[ ]:





# ### Tokenizer function
# - These can be passed to torchtext and will take in the sentence as a string and return the sentence as a list of tokens.
# - We copy this by reversing the sentence after it has been transformed into a list of tokens.

# In[6]:


def clean_text(text):
    """
    remove special characters from the input sentence to normalize it
    Args:
        text: (string) text string which may contain special character
    Returns:
        normalized sentence
    """
    text = re.sub('[-=+,#/\?:^$.@*\"※~&%ㆍ!』\\‘|\(\)\[\]\<\>`…》]', '', text)
    return text


# In[7]:


def tokenize_kor(text):
    """
    Tokenizes korean text from a string into a list of strings (tokens) and reverses it
    """
    return [tok.text for tok in spacy_kor.tokenizer(text)][::-1] 

def tokenize_en(text):
    """
    Tokenizes English text from a string into a list of strings (tokens)
    """
    return [tok.text for tok in spacy_en.tokenizer(text)]


# In[9]:


[tok.text for tok in spacy_kor.tokenizer("나는 바보입니다 왜 이러세요")][::-1]


# ### Torchtext Fields
# - handle how data should be processed
# - field also appends the "start of sequence" and "end of sequence" tokens via the init_token and eos_token arguments, and converts all words to lowercase.

# In[10]:


SRC = Field(tokenize = tokenize_kor, 
            init_token = '<sos>', 
            eos_token = '<eos>', 
            lower = True)

TRG = Field(tokenize = tokenize_en, 
            init_token = '<sos>', 
            eos_token = '<eos>', 
            lower = True)


# In[11]:


def convert_to_dataset(SRC, TRG, train_src, train_tg):

    # convert each row of DataFrame to torchtext 'Example' containing 'kor' and 'eng' Fields
    list_of_examples = [Example.fromlist([clean_text(ko),clean_text(en)],
                                     fields=[('src', SRC), ('trg', TRG)]) for ko,en in zip(train_src,train_tg)]
    
    # construct torchtext 'Dataset' using torchtext 'Example' list
    dataset = Dataset(examples=list_of_examples, fields=[('src', SRC), ('trg', TRG)])

    return dataset


# In[102]:


train_data = convert_to_dataset(SRC, TRG, train_ko[:10000], train_en[:10000])
val_data = convert_to_dataset(SRC, TRG, test_ko,test_en)
test_data = convert_to_dataset(SRC, TRG, test_ko,test_en)


# In[103]:


print(f"Number of training examples: {len(train_data.examples)}")
print(f"Number of testing examples: {len(test_data.examples)}")


# In[104]:


print(vars(train_data.examples[0]))


# ### Build Vocab
# - The vocabulary is used to associate each unique token with an index (an integer). 
# - The vocabularies of the source and target languages are distinct.
# - Using the min_freq argument, we only allow tokens that appear at least 2 times to appear in our vocabulary. 
# - Tokens that appear only once are converted into an <unk> (unknown) token.
# - our vocabulary should only be built from the training set and not the validation/test set.

# In[105]:


SRC.build_vocab(train_data, min_freq = 1)
TRG.build_vocab(train_data, min_freq = 1)


# In[106]:


print(f"Unique tokens in source (kor) vocabulary: {len(SRC.vocab)}")
print(f"Unique tokens in target (en) vocabulary: {len(TRG.vocab)}")


# ### Iterator
# : BucketIterator instead of the standard Iterator as it creates batches in such a way that it minimizes the amount of padding in both the source and target sentences.

# In[17]:


device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')


# In[107]:


BATCH_SIZE = 128

train_iterator,val_iterator, test_iterator = BucketIterator.splits(
    (train_data, val_data, test_data), 
    batch_size = BATCH_SIZE, 
    device = device
    ,sort=False)


# # 2. Building the Seq2Seq Model

# ### Encoder
# : 2 layer LSTM. (The paper we are implementing uses a 4-layer LSTM, but in the interest of training time we cut this down to 2-layers.)
# 
# $$\begin{align*}
# (h_t^1, c_t^1) &= \text{EncoderLSTM}^1(e(x_t), (h_{t-1}^1, c_{t-1}^1))\\
# (h_t^2, c_t^2) &= \text{EncoderLSTM}^2(h_t^1, (h_{t-1}^2, c_{t-1}^2))
# \end{align*}$$
# 
# ![](https://github.com/bentrevett/pytorch-seq2seq/blob/master/assets/seq2seq2.png?raw=1)

# In[108]:


class Encoder(nn.Module):
    def __init__(self, input_dim, emb_dim, hid_dim, n_layers, dropout):
        super().__init__()
        
        self.hid_dim = hid_dim
        self.n_layers = n_layers
        
        self.embedding = nn.Embedding(input_dim, emb_dim)
        
        self.rnn = nn.LSTM(emb_dim, hid_dim, n_layers, dropout = dropout)
        
        self.dropout = nn.Dropout(dropout)
        
    def forward(self, src):
        
        #src = [src len, batch size]
        
        embedded = self.dropout(self.embedding(src))
        
        #embedded = [src len, batch size, emb dim]
        
        outputs, (hidden, cell) = self.rnn(embedded)
        
        #outputs = [src len, batch size, hid dim * n directions]
        #hidden = [n layers * n directions, batch size, hid dim]
        #cell = [n layers * n directions, batch size, hid dim]
        
        #outputs are always from the top hidden layer
        
        return hidden, cell


# ### Decoder
# 
# : a 2-layer (4 in the paper) LSTM.
# 
# ![](https://github.com/bentrevett/pytorch-seq2seq/blob/master/assets/seq2seq3.png?raw=1)

# In[109]:


class Decoder(nn.Module):
    def __init__(self, output_dim, emb_dim, hid_dim, n_layers, dropout):
        super().__init__()
        
        self.output_dim = output_dim
        self.hid_dim = hid_dim
        self.n_layers = n_layers
        
        self.embedding = nn.Embedding(output_dim, emb_dim)
        
        self.rnn = nn.LSTM(emb_dim, hid_dim, n_layers, dropout = dropout)
        
        self.fc_out = nn.Linear(hid_dim, output_dim)
        
        self.dropout = nn.Dropout(dropout)
        
    def forward(self, input, hidden, cell):
        
        #input = [batch size]
        #hidden = [n layers * n directions, batch size, hid dim]
        #cell = [n layers * n directions, batch size, hid dim]
        
        #n directions in the decoder will both always be 1, therefore:
        #hidden = [n layers, batch size, hid dim]
        #context = [n layers, batch size, hid dim]
        
        input = input.unsqueeze(0)
        
        #input = [1, batch size]
        
        embedded = self.dropout(self.embedding(input))
        
        #embedded = [1, batch size, emb dim]
                
        output, (hidden, cell) = self.rnn(embedded, (hidden, cell))
        
        #output = [seq len, batch size, hid dim * n directions]
        #hidden = [n layers * n directions, batch size, hid dim]
        #cell = [n layers * n directions, batch size, hid dim]
        
        #seq len and n directions will always be 1 in the decoder, therefore:
        #output = [1, batch size, hid dim]
        #hidden = [n layers, batch size, hid dim]
        #cell = [n layers, batch size, hid dim]
        
        prediction = self.fc_out(output.squeeze(0))
        
        #prediction = [batch size, output dim]
        
        return prediction, hidden, cell


# ### Seq2Seq
# 
# For the final part of the implemenetation, we'll implement the seq2seq model. This will handle: 
# - receiving the input/source sentence
# - using the encoder to produce the context vectors 
# - using the decoder to produce the predicted output/target sentence
# 
# ![](https://github.com/bentrevett/pytorch-seq2seq/blob/master/assets/seq2seq4.png?raw=1)
# 
# The teacher forcing ratio is used when training our model.   
# 
# **Note**: our decoder loop starts at 1, not 0. This means the 0th element of our `outputs` tensor remains all zeros. So our `trg` and `outputs` look something like:
# 
# $$\begin{align*}
# \text{trg} = [<sos>, &y_1, y_2, y_3, <eos>]\\
# \text{outputs} = [0, &\hat{y}_1, \hat{y}_2, \hat{y}_3, <eos>]
# \end{align*}$$
# 
# Later on when we calculate the loss, we cut off the first element of each tensor to get:
# 
# $$\begin{align*}
# \text{trg} = [&y_1, y_2, y_3, <eos>]\\
# \text{outputs} = [&\hat{y}_1, \hat{y}_2, \hat{y}_3, <eos>]
# \end{align*}$$

# In[110]:


class Seq2Seq(nn.Module):
    def __init__(self, encoder, decoder, device):
        super().__init__()
        
        self.encoder = encoder
        self.decoder = decoder
        self.device = device
        
        assert encoder.hid_dim == decoder.hid_dim,             "Hidden dimensions of encoder and decoder must be equal!"
        assert encoder.n_layers == decoder.n_layers,             "Encoder and decoder must have equal number of layers!"
        
    def forward(self, src, trg, teacher_forcing_ratio = 0.5):
        
        #src = [src len, batch size]
        #trg = [trg len, batch size]
        #teacher_forcing_ratio is probability to use teacher forcing
        #e.g. if teacher_forcing_ratio is 0.75 we use ground-truth inputs 75% of the time
        
        batch_size = trg.shape[1]
        trg_len = trg.shape[0]
        trg_vocab_size = self.decoder.output_dim
        
        #tensor to store decoder outputs
        outputs = torch.zeros(trg_len, batch_size, trg_vocab_size).to(self.device)
        
        #last hidden state of the encoder is used as the initial hidden state of the decoder
        hidden, cell = self.encoder(src)
        
        #first input to the decoder is the <sos> tokens
        input = trg[0,:]
        
        for t in range(1, trg_len):
            
            #insert input token embedding, previous hidden and previous cell states
            #receive output tensor (predictions) and new hidden and cell states
            output, hidden, cell = self.decoder(input, hidden, cell)
            
            #place predictions in a tensor holding predictions for each token
            outputs[t] = output
            
            #decide if we are going to use teacher forcing or not
            teacher_force = random.random() < teacher_forcing_ratio
            
            #get the highest predicted token from our predictions
            top1 = output.argmax(1) 
            
            #if teacher forcing, use actual next token as next input
            #if not, use predicted token
            input = trg[t] if teacher_force else top1
        
        return outputs


# # 3. Training the Seq2Seq Model

# In[111]:


INPUT_DIM = len(SRC.vocab)
OUTPUT_DIM = len(TRG.vocab)
ENC_EMB_DIM = 256
DEC_EMB_DIM = 256
HID_DIM = 512
N_LAYERS = 2
ENC_DROPOUT = 0.5
DEC_DROPOUT = 0.5

enc = Encoder(INPUT_DIM, ENC_EMB_DIM, HID_DIM, N_LAYERS, ENC_DROPOUT)
dec = Decoder(OUTPUT_DIM, DEC_EMB_DIM, HID_DIM, N_LAYERS, DEC_DROPOUT)

model = Seq2Seq(enc, dec, device).to(device)


# In[112]:


def init_weights(m):
    for name, param in m.named_parameters():
        nn.init.uniform_(param.data, -0.08, 0.08)
        
model.apply(init_weights)


# In[113]:


def count_parameters(model):
    return sum(p.numel() for p in model.parameters() if p.requires_grad)

print(f'The model has {count_parameters(model):,} trainable parameters')


# In[114]:


optimizer = optim.Adam(model.parameters())


# In[115]:


TRG_PAD_IDX = TRG.vocab.stoi[TRG.pad_token]
criterion = nn.CrossEntropyLoss(ignore_index = TRG_PAD_IDX)


# ### train loop

# In[116]:


def train(model, iterator, optimizer, criterion, clip):
    
    model.train()
    
    epoch_loss = 0
    
    for i, batch in enumerate(iterator):
        
        src = batch.src
        trg = batch.trg
        
        optimizer.zero_grad()
        
        output = model(src, trg)
        
        #trg = [trg len, batch size]
        #output = [trg len, batch size, output dim]
        
        output_dim = output.shape[-1]
        
        output = output[1:].view(-1, output_dim)
        trg = trg[1:].view(-1)
        
        #trg = [(trg len - 1) * batch size]
        #output = [(trg len - 1) * batch size, output dim]
        
        loss = criterion(output, trg)
        
        loss.backward()
        
        torch.nn.utils.clip_grad_norm_(model.parameters(), clip)
        
        optimizer.step()
        
        epoch_loss += loss.item()
        
    return epoch_loss / len(iterator)


# ### evaluation loop

# In[117]:


def evaluate(model, iterator, criterion):
    
    model.eval()
    
    epoch_loss = 0
    
    with torch.no_grad():
    
        for i, batch in enumerate(iterator):

            src = batch.src
            trg = batch.trg

            output = model(src, trg, 0) #turn off teacher forcing

            #trg = [trg len, batch size]
            #output = [trg len, batch size, output dim]

            output_dim = output.shape[-1]
            
            output = output[1:].view(-1, output_dim)
            trg = trg[1:].view(-1)

            #trg = [(trg len - 1) * batch size]
            #output = [(trg len - 1) * batch size, output dim]

            loss = criterion(output, trg)
            
            epoch_loss += loss.item()
        
    return epoch_loss / len(iterator)


# In[118]:


def epoch_time(start_time, end_time):
    elapsed_time = end_time - start_time
    elapsed_mins = int(elapsed_time / 60)
    elapsed_secs = int(elapsed_time - (elapsed_mins * 60))
    return elapsed_mins, elapsed_secs


# In[119]:


import matplotlib.pyplot as plt
plt.switch_backend('agg')
import matplotlib.ticker as ticker
import numpy as np


def showPlot(points):
    plt.figure()
    fig, ax = plt.subplots()
    # this locator puts ticks at regular intervals
    loc = ticker.MultipleLocator()#base=0.2
    ax.yaxis.set_major_locator(loc)
    plt.plot(points)


# In[120]:


N_EPOCHS = 10
CLIP = 1

best_valid_loss = float('inf')
print_every=3 
plot_every=3

start = time.time()
plot_losses = []
print_loss_total = 0  # Reset every print_every
plot_loss_total = 0  # Reset every plot_every

for epoch in tqdm(range(N_EPOCHS)):
    
    start_time = time.time()
    
    train_loss = train(model, train_iterator, optimizer, criterion, CLIP)
    #valid_loss = evaluate(model, valid_iterator, criterion)
    print_loss_total += train_loss
    plot_loss_total += train_loss
    
    end_time = time.time()
    epoch_mins, epoch_secs = epoch_time(start_time, end_time)
    
#     if epoch % print_every == 0:
#         print_loss_avg = print_loss_total / print_every
#         print_loss_total = 0
#         print('%s (%d %d%%) %.4f' % (timeSince(start, iter / n_iters),
#                                      iter, iter / n_iters * 100, print_loss_avg))

    if epoch % plot_every == 0:
        plot_loss_avg = plot_loss_total / plot_every
        plot_losses.append(plot_loss_avg)
        plot_loss_total = 0
        
showPlot(plot_losses)
        
    
#     if valid_loss < best_valid_loss:
#         best_valid_loss = valid_loss
#         torch.save(model.state_dict(), 'tut1-model.pt')
    
#     print(f'Epoch: {epoch+1:02} | Time: {epoch_mins}m {epoch_secs}s')
#     print(f'\tTrain Loss: {train_loss:.3f} | Train PPL: {math.exp(train_loss):7.3f}')
#     #print(f'\t Val. Loss: {valid_loss:.3f} |  Val. PPL: {math.exp(valid_loss):7.3f}')


# In[121]:


#model.load_state_dict(torch.load('tut1-model.pt'))
test_loss = evaluate(model, test_iterator, criterion)

print(f'| Test Loss: {test_loss:.3f} | Test PPL: {math.exp(test_loss):7.3f} |')


# In[ ]:


### 결과 출력


# In[157]:


for i, batch in enumerate(test_iterator):

    src = batch.src
    trg = batch.trg

    output = model(src, trg, 0) #turn off teacher forcing


# In[191]:


src = batch.src
trg = batch.trg

output = model(src, trg, 0) #turn off teacher forcing

for src_,trg_,out_ in zip(src.transpose(1,0),trg.transpose(1,0),output.transpose(1,0)):
    src_list = []
    trg_list = []
    pred_list = []
    
    
    for token in src_:
        word = SRC.vocab.itos[token]
        if word not in ['<sos>','<pad>','<eos>']:
            src_list.append(word) 
            
    for token in trg_:
        word = TRG.vocab.itos[token]
        if word not in ['<sos>','<pad>','<eos>']:
            trg_list.append(word) 
            
    
    for token in out_:
        topv, topi = token.data.topk(1)
        word = TRG.vocab.itos[topi.item()]
        if word not in ['<sos>','<pad>','<eos>']:
            pred_list.append(word) 
        
    print("src : ", src_list[::-1])
    print("trg : ", trg_list)
    print("predict : ", pred_list)  
    print()


# ### in detail

# In[68]:


val_iterator


# In[76]:


for i, batch in enumerate(val_iterator):
    src = batch.src
    trg = batch.trg


# In[79]:


i


# In[82]:


src.size(), trg.size()


# In[102]:


output = model(src, trg)
output_dim = output.shape[-1]

output = output[1:].view(-1, output_dim)
trg = trg[1:].view(-1)


# In[109]:


topv, topi = output[0].data.topk(1)


# In[110]:


topi.item()


# In[117]:


TRG.vocab.itos[topi.item()]

Reference

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[CS224n #3] Topic Modeling - LSA/LDA/ BERTopic

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[CS224n #5] Dense Passage Retrieval for Open-Domain Question Answering (EMNLP, 2020)

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