5. Using CNN for Document Classification with Embeddings¶
The CBOW example used an Embedding layer to do simple classification. This example builds on that in three ways:
first by loading pretrained word embeddings, then
by finetuning these pretrained embeddings by classifying entire news articles, and
finally by using a convolutional neural network to capture the spatial relationships between words.
Transfer Learning
In machine learning, using a model trained on one task as an initializer for another task is called transfer learning.
In this example, we use the AG News dataset. To model the sequences of words in AG News, we introduce a variant of the Vocabulary class SequenceVocabulary, to bundle several tokens vital for modeling sequences. The Vectorizer demonstrates how to use this class.
Download GloVe Pretrained Embeddings - glove.6B.zip
5.1. Imports¶
import os
from argparse import Namespace
from collections import Counter
import json
import re
import string
import numpy as np
import pandas as pd
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torch.utils.data import Dataset, DataLoader
from tqdm.notebook import tqdm
5.2. Data Vectorization classes¶
5.2.1. The Standard Vocabulary Class¶
A Vocabulary object is able to look up an index for a token and vice versa, useful for constructing a one-hot vector for each word.
class Vocabulary(object):
"""Class to process text and extract vocabulary for mapping"""
def __init__(self, token_to_idx=None):
"""
Args:
token_to_idx (dict): a pre-existing map of tokens to indices
"""
if token_to_idx is None:
token_to_idx = {}
self._token_to_idx = token_to_idx
self._idx_to_token = {idx: token
for token, idx in self._token_to_idx.items()}
def to_serializable(self):
""" returns a dictionary that can be serialized """
return {'token_to_idx': self._token_to_idx}
@classmethod
def from_serializable(cls, contents):
""" instantiates the Vocabulary from a serialized dictionary """
return cls(**contents)
def add_token(self, token):
"""Update mapping dicts based on the token.
Args:
token (str): the item to add into the Vocabulary
Returns:
index (int): the integer corresponding to the token
"""
if token in self._token_to_idx:
index = self._token_to_idx[token]
else:
index = len(self._token_to_idx)
self._token_to_idx[token] = index
self._idx_to_token[index] = token
return index
def add_many(self, tokens):
"""Add a list of tokens into the Vocabulary
Args:
tokens (list): a list of string tokens
Returns:
indices (list): a list of indices corresponding to the tokens
"""
return [self.add_token(token) for token in tokens]
def lookup_token(self, token):
"""Retrieve the index associated with the token
Args:
token (str): the token to look up
Returns:
index (int): the index corresponding to the token
"""
return self._token_to_idx[token]
def lookup_index(self, index):
"""Return the token associated with the index
Args:
index (int): the index to look up
Returns:
token (str): the token corresponding to the index
Raises:
KeyError: if the index is not in the Vocabulary
"""
if index not in self._idx_to_token:
raise KeyError("the index (%d) is not in the Vocabulary" % index)
return self._idx_to_token[index]
def __str__(self):
return "<Vocabulary(size=%d)>" % len(self)
def __len__(self):
return len(self._token_to_idx)
5.2.2. SequenceVocabulary¶
When we deal with a sequence of words, it is often useful to have four special tokens
the
UNKtoken,the
MASKtoken,the
BEGINOFSEQUENCEtoken, andthe
ENDOFSEQUENCEtoken.
They serve three different purposes.
The
UNKtoken (short for unknown), allows the model to learn a representation for rare words so that it can accommodate words that it has never seen at test time.The
MASKtoken serves as a sentinel for Embedding layers and loss calculations when we have sequences of variable length (padding in the context of sequence modelling).Finally, the
BEGINOFSEQUENCEandENDOFSEQUENCEtokens give the neural network hints about the sequence boundaries.
The figure below shows the result of using these special tokens in the broader vectorization pipeline.
class SequenceVocabulary(Vocabulary):
def __init__(self, token_to_idx=None, unk_token="<UNK>",
mask_token="<MASK>", begin_seq_token="<BEGIN>",
end_seq_token="<END>"):
super(SequenceVocabulary, self).__init__(token_to_idx)
self._mask_token = mask_token
self._unk_token = unk_token
self._begin_seq_token = begin_seq_token
self._end_seq_token = end_seq_token
self.mask_index = self.add_token(self._mask_token)
self.unk_index = self.add_token(self._unk_token)
self.begin_seq_index = self.add_token(self._begin_seq_token)
self.end_seq_index = self.add_token(self._end_seq_token)
def to_serializable(self):
contents = super(SequenceVocabulary, self).to_serializable()
contents.update({'unk_token': self._unk_token,
'mask_token': self._mask_token,
'begin_seq_token': self._begin_seq_token,
'end_seq_token': self._end_seq_token})
return contents
def lookup_token(self, token):
"""Retrieve the index associated with the token
or the UNK index if token isn't present.
Args:
token (str): the token to look up
Returns:
index (int): the index corresponding to the token
Notes:
`unk_index` needs to be >=0 (having been added into the Vocabulary)
for the UNK functionality
"""
if self.unk_index >= 0:
return self._token_to_idx.get(token, self.unk_index)
else:
return self._token_to_idx[token]
5.2.3. The Vectorizer¶
The Vectorizer, instantiates and encapsulates the use of the SequenceVocabulary. In this example, the Vectorizer follows the pattern we demonstrated before of restricting the total set of words allowed in the Vocabulary by counting and thresholding on a certain frequency (cutoff). The core purpose of this action is to improve the signal quality for the model and limit the memory model’s memory usage by removing noisy, lowfrequency words.
After instantiation, the Vectorizer’s vectorize() method takes as input a news title and
returns a vector that is as long as the longest title in the dataset. It has two key behaviors.
The first is that it stores the maximum sequence length locally. Normally, the dataset tracks the maximum sequence length, and at inference time, the length of the test sequence is taken as the length of the vector, for example in a Recurrent Neural Network (RNN) model. However, because we have a CNN model, it is important to maintain a static size even at inference time.
The second key behavior, it outputs a zeropadded vector of integers (indices), which represent the words in the sequence.
Additionally, this vector of integers has the integer for the BEGINOFSEQUENCE token prepended to the beginning and the integer for the ENDOFSEQUENCE token appended to the end.
From the classifier’s perspective, these special tokens provide evidence of the sequence boundaries, allowing it to react to words near the boundary differently than to words near the center.
class NewsVectorizer(object):
""" The Vectorizer which coordinates the Vocabularies and puts them to use"""
def __init__(self, title_vocab, category_vocab):
self.title_vocab = title_vocab
self.category_vocab = category_vocab
def vectorize(self, title, vector_length=-1):
"""
Args:
title (str): the string of words separated by a space
vector_length (int): an argument for forcing the length of index vector
Returns:
the vetorized title (numpy.array)
"""
indices = [self.title_vocab.begin_seq_index]
indices.extend(self.title_vocab.lookup_token(token)
for token in title.split(" "))
indices.append(self.title_vocab.end_seq_index)
if vector_length < 0:
vector_length = len(indices)
out_vector = np.zeros(vector_length, dtype=np.int64)
out_vector[:len(indices)] = indices
out_vector[len(indices):] = self.title_vocab.mask_index
return out_vector
@classmethod
def from_dataframe(cls, news_df, cutoff=25):
"""Instantiate the vectorizer from the dataset dataframe
Args:
news_df (pandas.DataFrame): the target dataset
cutoff (int): frequency threshold for including in Vocabulary
Returns:
an instance of the NewsVectorizer
"""
category_vocab = Vocabulary()
for category in sorted(set(news_df.category)):
category_vocab.add_token(category)
word_counts = Counter()
for title in news_df.title:
for token in title.split(" "):
if token not in string.punctuation:
word_counts[token] += 1
title_vocab = SequenceVocabulary()
for word, word_count in word_counts.items():
if word_count >= cutoff:
title_vocab.add_token(word)
return cls(title_vocab, category_vocab)
@classmethod
def from_serializable(cls, contents):
title_vocab = \
SequenceVocabulary.from_serializable(contents['title_vocab'])
category_vocab = \
Vocabulary.from_serializable(contents['category_vocab'])
return cls(title_vocab=title_vocab, category_vocab=category_vocab)
def to_serializable(self):
return {'title_vocab': self.title_vocab.to_serializable(),
'category_vocab': self.category_vocab.to_serializable()}
5.2.4. The Dataset¶
class NewsDataset(Dataset):
def __init__(self, news_df, vectorizer):
"""
Args:
news_df (pandas.DataFrame): the dataset
vectorizer (NewsVectorizer): vectorizer instatiated from dataset
"""
self.news_df = news_df
self._vectorizer = vectorizer
# +1 if only using begin_seq, +2 if using both begin and end seq tokens
measure_len = lambda context: len(context.split(" "))
self._max_seq_length = max(map(measure_len, news_df.title)) + 2
self.train_df = self.news_df[self.news_df.split=='train']
self.train_size = len(self.train_df)
self.val_df = self.news_df[self.news_df.split=='val']
self.validation_size = len(self.val_df)
self.test_df = self.news_df[self.news_df.split=='test']
self.test_size = len(self.test_df)
self._lookup_dict = {'train': (self.train_df, self.train_size),
'val': (self.val_df, self.validation_size),
'test': (self.test_df, self.test_size)}
self.set_split('train')
# Class weights
class_counts = news_df.category.value_counts().to_dict()
def sort_key(item):
return self._vectorizer.category_vocab.lookup_token(item[0])
sorted_counts = sorted(class_counts.items(), key=sort_key)
frequencies = [count for _, count in sorted_counts]
self.class_weights = 1.0 / torch.tensor(frequencies, dtype=torch.float32)
@classmethod
def load_dataset_and_make_vectorizer(cls, news_csv):
"""Load dataset and make a new vectorizer from scratch
Args:
news_csv (str): location of the dataset
Returns:
an instance of NewsDataset
"""
news_df = pd.read_csv(news_csv)
train_news_df = news_df[news_df.split=='train']
return cls(news_df, NewsVectorizer.from_dataframe(train_news_df))
@classmethod
def load_dataset_and_load_vectorizer(cls, news_csv, vectorizer_filepath):
"""Load dataset and the corresponding vectorizer.
Used in the case in the vectorizer has been cached for re-use
Args:
news_csv (str): location of the dataset
vectorizer_filepath (str): location of the saved vectorizer
Returns:
an instance of NewsDataset
"""
news_df = pd.read_csv(news_csv)
vectorizer = cls.load_vectorizer_only(vectorizer_filepath)
return cls(news_csv, vectorizer)
@staticmethod
def load_vectorizer_only(vectorizer_filepath):
"""a static method for loading the vectorizer from file
Args:
vectorizer_filepath (str): the location of the serialized vectorizer
Returns:
an instance of NewsVectorizer
"""
with open(vectorizer_filepath) as fp:
return NewsVectorizer.from_serializable(json.load(fp))
def save_vectorizer(self, vectorizer_filepath):
"""saves the vectorizer to disk using json
Args:
vectorizer_filepath (str): the location to save the vectorizer
"""
with open(vectorizer_filepath, "w") as fp:
json.dump(self._vectorizer.to_serializable(), fp)
def get_vectorizer(self):
""" returns the vectorizer """
return self._vectorizer
def set_split(self, split="train"):
""" selects the splits in the dataset using a column in the dataframe """
self._target_split = split
self._target_df, self._target_size = self._lookup_dict[split]
def __len__(self):
return self._target_size
def __getitem__(self, index):
"""the primary entry point method for PyTorch datasets
Args:
index (int): the index to the data point
Returns:
a dictionary holding the data point's features (x_data) and label (y_target)
"""
row = self._target_df.iloc[index]
title_vector = \
self._vectorizer.vectorize(row.title, self._max_seq_length)
category_index = \
self._vectorizer.category_vocab.lookup_token(row.category)
return {'x_data': title_vector,
'y_target': category_index}
def get_num_batches(self, batch_size):
"""Given a batch size, return the number of batches in the dataset
Args:
batch_size (int)
Returns:
number of batches in the dataset
"""
return len(self) // batch_size
def generate_batches(dataset, batch_size, shuffle=True,
drop_last=True, device="cpu"):
"""
A generator function which wraps the PyTorch DataLoader. It will
ensure each tensor is on the write device location.
"""
dataloader = DataLoader(dataset=dataset, batch_size=batch_size,
shuffle=shuffle, drop_last=drop_last)
for data_dict in dataloader:
out_data_dict = {}
for name, tensor in data_dict.items():
out_data_dict[name] = data_dict[name].to(device)
yield out_data_dict
dataset = NewsDataset.load_dataset_and_make_vectorizer(args.news_csv)
#dataset.save_vectorizer(args.vectorizer_file)
dataloader = DataLoader(dataset=dataset, batch_size=2,
shuffle=True, drop_last=True)
for data_dict in dataloader:
for name, tensor in data_dict.items():
print(name)
print(data_dict[name])
x_data
tensor([[ 2, 647, 721, 1, 148, 214, 142, 428, 1, 3, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0],
[ 2, 507, 12, 1656, 426, 1, 3, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0]])
y_target
tensor([1, 0])
x_data
tensor([[ 2, 1597, 1, 273, 12, 1344, 9, 698, 1680, 3, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0],
[ 2, 166, 1411, 183, 1, 3, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0]])
y_target
tensor([0, 0])
x_data
tensor([[ 2, 854, 1463, 546, 2673, 3085, 1461, 3, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0],
[ 2, 2838, 429, 79, 2087, 2614, 428, 1, 3, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0]])
y_target
tensor([3, 2])
x_data
tensor([[ 2, 2124, 45, 1687, 1022, 1, 3008, 1, 2424, 3, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0],
[ 2, 1, 835, 1, 1, 207, 3, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0]])
y_target
tensor([1, 1])
x_data
tensor([[ 2, 1, 45, 1, 809, 696, 2692, 1, 1, 3, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0],
[ 2, 1, 796, 187, 208, 3096, 2565, 2119, 548, 1674, 671, 3,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0]])
y_target
tensor([1, 2])
x_data
tensor([[ 2, 1, 1445, 1421, 1, 3, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0],
[ 2, 1, 1, 9, 1117, 1943, 61, 1812, 3, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0]])
y_target
tensor([2, 1])
x_data
tensor([[ 2, 2308, 474, 693, 51, 2303, 914, 428, 1, 3, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0],
[ 2, 455, 1, 1, 32, 1, 2161, 428, 1, 3, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0]])
y_target
tensor([2, 3])
x_data
tensor([[ 2, 2658, 12, 1944, 98, 1580, 1, 3, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0],
[ 2, 1128, 1, 1129, 1, 98, 114, 629, 3, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0]])
y_target
tensor([3, 1])
x_data
tensor([[ 2, 60, 1141, 1, 640, 119, 340, 951, 94, 88, 1, 3,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0],
[ 2, 758, 37, 949, 2355, 1013, 2549, 3, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0]])
y_target
tensor([0, 1])
x_data
tensor([[ 2, 3194, 3195, 12, 1, 76, 37, 698, 3, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0],
[ 2, 2080, 2771, 1, 729, 32, 1090, 548, 287, 3, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0]])
y_target
tensor([2, 2])
x_data
tensor([[ 2, 2269, 1, 754, 548, 3353, 2716, 3, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0],
[ 2, 1448, 12, 365, 1, 3, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0]])
y_target
tensor([3, 0])
x_data
tensor([[ 2, 590, 1, 3190, 1774, 608, 3194, 3195, 428, 1, 3, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0],
[ 2, 1, 45, 1, 3, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0]])
y_target
tensor([2, 0])
x_data
tensor([[ 2, 550, 369, 1, 51, 92, 1, 37, 470, 741, 3, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0],
[ 2, 105, 1, 45, 105, 1, 16, 3, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0]])
y_target
tensor([0, 1])
x_data
tensor([[ 2, 433, 1458, 131, 1, 861, 302, 88, 1, 3, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0],
[ 2, 3251, 972, 37, 18, 37, 1, 3, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0]])
y_target
tensor([3, 2])
x_data
tensor([[ 2, 104, 1352, 466, 1278, 418, 1, 1, 28, 1, 1, 3,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0],
[ 2, 597, 391, 1036, 9, 1918, 32, 1081, 1002, 3, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0]])
y_target
tensor([0, 3])
x_data
tensor([[ 2, 1307, 1, 1, 3, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0],
[ 2, 105, 1, 157, 1, 1, 3, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0]])
y_target
tensor([1, 1])
x_data
tensor([[ 2, 224, 1268, 756, 32, 829, 12, 1, 88, 1, 3, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0],
[ 2, 1166, 544, 166, 2025, 1, 168, 1, 1, 3, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0]])
y_target
tensor([1, 1])
x_data
tensor([[ 2, 2031, 1, 1, 856, 1905, 297, 2973, 1764, 1, 3, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0],
[ 2, 3149, 1, 1243, 45, 1260, 98, 36, 3, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0]])
y_target
tensor([1, 3])
x_data
tensor([[ 2, 1, 1, 2844, 32, 1094, 1, 1, 1, 88, 1, 3,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0],
[ 2, 121, 1118, 1, 45, 2176, 548, 3071, 3, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0]])
y_target
tensor([1, 3])
x_data
tensor([[ 2, 479, 85, 1, 1, 28, 32, 385, 15, 3, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0],
[ 2, 1994, 3348, 492, 1662, 13, 2572, 3, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0]])
y_target
tensor([0, 3])
x_data
tensor([[ 2, 829, 2483, 2019, 315, 9, 2071, 1331, 769, 2973, 1764, 1,
3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0],
[ 2, 9, 1778, 1, 1, 884, 1, 3, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0]])
y_target
tensor([1, 1])
x_data
tensor([[ 2, 2998, 61, 105, 784, 79, 1, 202, 729, 140, 32, 2651,
3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0],
[ 2, 702, 1, 333, 45, 124, 37, 169, 3, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0]])
y_target
tensor([2, 0])
x_data
tensor([[ 2, 1, 1, 32, 2646, 548, 2431, 267, 1, 3382, 57, 1,
37, 1910, 2646, 3346, 3, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0],
[ 2, 2936, 1260, 1251, 1851, 1, 1, 3, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0]])
y_target
tensor([3, 3])
x_data
tensor([[ 2, 3369, 2774, 650, 1877, 37, 1, 1302, 3, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0],
[ 2, 1405, 1, 2174, 357, 439, 2060, 1, 1, 3, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0]])
y_target
tensor([3, 0])
x_data
tensor([[ 2, 1, 166, 1, 1589, 116, 1, 1, 3, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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