NLP data processing between `BucketIterator` and `build_vocab_from_iterator`
I am using AG News Dataset to train model for using text classification.
The part using TabularDataset to generate dataset from csv file.
import torchtext
import torch
from torchtext.legacy.data import Field, TabularDataset, BucketIterator, Iterator

import spacy

def des_tokenize(x):
  return x.split(' ')

def title_tokenize(x):
  return x.split(' ')

def category_tokenize(x):
  return x

device = torch.device(&quot;cuda&quot; if torch.cuda.is_available() else &quot;cpu&quot;)

CATEGORY = Field(tokenize=category_tokenize)
TITLE = Field(tokenize=title_tokenize, init_token='&lt;SOS&gt;', eos_token='&lt;EOS&gt;')
DES = Field(tokenize=des_tokenize, init_token='&lt;SOS&gt;', eos_token='&lt;EOS&gt;')

spacy_en = spacy.load('en_core_web_sm')

train_fields = [('id', None), ('category', CATEGORY), ('title', TITLE), ('description', DES)]

test_fields = [('title', TITLE), ('description', DES)]

train_data = TabularDataset(
              path = '/content/drive/MyDrive/summer2/train.csv',
              format = 'csv',
              fields = train_fields,
              skip_header = True)

test_data = TabularDataset(
              path = '/content/drive/MyDrive/summer2/test.csv',
              format = 'csv',
              fields = test_fields,
              skip_header = True)

After dataset being generated, choosing to use pre-train embedding model called
torchtext.vocab.GloVe to build vocab.
from torchtext.data.utils import get_tokenizer
from torchtext.vocab import build_vocab_from_iterator

train_batch_size = 10
test_batch_size = 1
max_length = 256

tokenizer = get_tokenizer('basic_english')

train_iter = torchtext.legacy.data.BucketIterator(
    train_data,
    batch_size=train_batch_size,
)

test_iter = torchtext.legacy.data.BucketIterator(
    test_data,
    batch_size=test_batch_size,
)


DES.build_vocab(
    train_data,
    vectors=torchtext.vocab.GloVe(name=&quot;6B&quot;, dim=50, max_vectors=50_000),
    max_size=50_000,
)

TITLE.build_vocab(
    train_data,
    vectors=torchtext.vocab.GloVe(name=&quot;6B&quot;, dim=50, max_vectors=50_000),
    max_size=50_000,
)


CATEGORY.build_vocab(train_data)

And the output looks great after create_batches function
def create_batches(self):
        self.batches = batch(self.data(), self.batch_size, self.batch_size_fn)

# Create batches - needs to be called before each loop.
train_iter.create_batches()

# Loop through BucketIterator.
print('PyTorchText BuketIterator\n')
for batch in train_iter.batches:

  # Let's check batch size.
  print('Batch size: %d\n'% len(batch))
  print('category\ttitle\tdescription'.ljust(10))
  
  # Print each example.
  for example in batch:
    print('%s \t %s \t %s'.ljust(10) % (example.category, example.title, example.description))
  print('\n')
  
  # Only look at first batch. Reuse this code in training models.
  break

Output looks like
PyTorchText BuketIterator

Batch size: 10

category    title   description
2    ['UPDATE', '1-Open-Rejuvenated', 'Haas', 'reaches', 'last', 'eight']    ['Germany', '#39;s', 'Tommy', 'Haas', 'continued', 'his', 'resurgence', 'with', 'a', '7-6', '6-1', '7-5', 'victory', 'over', 'Czech', 'teenager', 'Tomas', 'Berdych', 'on', 'Tuesday', 'to', 'reach', 'the', 'quarter-finals', 'of', 'the', 'US', 'Open', 'for', 'the', 'first', 'time.']
3    ['Japan', '#39;s', 'Nikkei', 'Average,', 'Topix', 'Advance;', 'Toyota,', 'Advantest', 'Gain']   ['Japan', '#39;s', 'Nikkei', '225', 'Stock', 'Average', 'rose', '56.74,', 'or', '0.5', 'percent,', 'to', '11,139.97', 'at', '9:01', 'am', 'in', 'Tokyo.', 'The', 'broader', 'Topix', 'index', 'gained', '5.35,', 'or', '0.5', 'percent,', 'to', '1132.']
2    ['Wildcats', 'on', 'the', 'rise', 'with', 'Santos']     ['The', 'University', 'of', 'New', &quot;Hampshire's&quot;, 'impressive', '51-40', 'road', 'victory', 'over', '10th-ranked', 'Villanova', 'Saturday', 'night', 'vaulted', 'the', 'Wildcats', 'three', 'spots', 'to', 'ninth', 'in', 'this', &quot;week's&quot;, 'Sports', 'Network', '1-AA', 'football', 'poll,', 'while', 'dropping', 'Villanova', 'to', '14th.']
1    ['Cracking', 'under', 'the', 'strain']      ['Severe', 'cracks', 'surfaced', 'inside', 'the', 'Israeli', 'government', 'this', 'week', 'as', 'its', 'senior', 'law', 'officers', 'publicly', 'fell', 'out', 'with', 'the', 'defence', 'establishment', 'and', 'the', 'Foreign', 'Ministry', 'over', 'the', 'country', '#39;s', 'future', 'strategy', 'in', 'the', 'face', 'of', 'the', 'July', 'verdict', 'of', 'the', 'International', '']
1    ['Arab', 'League', 'to', 'hold', 'emergency', 'meeting']    ['The', 'Arab', 'League', 'says', 'it', 'will', 'hold', 'an', 'emergency', 'session', 'to', 'discuss', 'the', 'violence', 'in', 'Gaza,', 'which', 'has', 'claimed', 'at', 'least', '56', 'Palestinians', 'this', 'week.']
2    ['Holmes', 'to', 'decide', 'on', 'double']      ['Kelly', 'Holmes', 'has', 'still', 'to', 'confirm', 'whether', 'she', 'will', 'attempt', 'to', 'repeat', 'her', 'Olympic', 'double', 'at', 'this', 'weekend', '#39;s', 'World', 'Athletics', 'Final', 'after', 'clearing', 'the', 'first', 'hurdle', 'with', 'a', 'victory', 'in', 'the', '1500m', 'yesterday.']
2    ['NBA', 'suspends', 'nine', 'players,', 'Artest', 'for', 'rest', 'of', 'season']    ['NBA', 'on', 'Sunday', 'suspended', 'nine', 'players', 'for', 'involving', 'in', 'a', 'melee', 'during', 'Friday', '#39;s', 'game', 'between', 'Detorit', 'Pistons', 'and', 'Indiana', 'Pacers,', 'with', 'Ron', 'Artest', 'suspended', 'for', 'the', 'rest', 'of', 'the', 'season,', '73', 'games.']
2    ['On', 'the', 'Far', 'Side', 'of', 'the', 'Field,', 'a', 'Familiar', 'Face']    ['Perhaps', 'there', 'will', 'be', 'a', 'moment', 'during', &quot;Sunday's&quot;, 'game', 'between', 'the', 'Giants', 'and', 'the', 'Redskins', 'when', 'a', 'coach', 'and', 'his', 'former', 'franchise', 'quarterback', 'will', 'do', 'a', 'double', 'take.']
3    ['', '#39;QUIET', '#39;', 'RULE', 'MAY', 'CHANGE']      ['The', 'Securities', 'and', 'Exchange', 'Commission', 'wants', 'to', 'scrap', 'a', '1933', 'rule', 'that', 'forces', 'a', 'strict', '', 'quot;quiet', 'period', 'quot;', 'on', 'all', 'talk', 'about', 'a', 'company', 'just', 'prior', 'to', 'its', 'stock', 'being', 'sold', 'initially', 'to', 'the', 'public.']
2    ['Denehy', 'boosts', 'Walpole', '']     ['Danvers', 'coach', 'thought', 'he', 'had', 'the', 'perfect', 'game', 'plan', 'against', 'Walpole', 'last', 'night', 'in', 'the', 'Division', '2', 'playoffs', 'at', 'Endicott', 'College.', 'It', 'was', 'the', 'same', 'game', 'plan', 'that', 'earned', 'his', 'team', 'its', 'first', 'playoff', 'berth', 'in', '63', 'years.']

The question is that what if I use build_vocab_from_iterator to create iterator ?
build_vocab_from_iterator
Does the function has same meaning between my part using BucketIterator ?
Also, I think using Pretrained Word Embeddings GloVe is better than FastText in this work, because the model needs to classify the description is which types.

After all, the solution which I just post can train the model.
And it had better to use stopwords from library to has better accuracy.
