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Jun 14, 2021
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updated text sentiment tutorial #1563

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108 changes: 17 additions & 91 deletions beginner_source/text_sentiment_ngrams_tutorial.py
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Original file line number Diff line number Diff line change
Expand Up @@ -49,32 +49,35 @@
#
# We have revisited the very basic components of the torchtext library, including vocab, word vectors, tokenizer. Those are the basic data processing building blocks for raw text string.
#
# Here is an example for typical NLP data processing with tokenizer and vocabulary. The first step is to build a vocabulary with the raw training dataset. Users can have a customized vocab by setting up arguments in the constructor of the Vocab class. For example, the minimum frequency ``min_freq`` for the tokens to be included.
# Here is an example for typical NLP data processing with tokenizer and vocabulary. The first step is to build a vocabulary with the raw training dataset. Here we use built in
# factory function `build_vocab_from_iterator` which accepts iterator that yield list or iterator of tokens. Users can also pass any special symbols to be added to the
# vocabulary.


from torchtext.data.utils import get_tokenizer
from collections import Counter
from torchtext.vocab import Vocab
from torchtext.vocab import build_vocab_from_iterator

tokenizer = get_tokenizer('basic_english')
train_iter = AG_NEWS(split='train')
counter = Counter()
for (label, line) in train_iter:
counter.update(tokenizer(line))
vocab = Vocab(counter, min_freq=1)

def yield_tokens(data_iter):
for _, text in data_iter:
yield tokenizer(text)

vocab = build_vocab_from_iterator(yield_tokens(train_iter), specials=["<unk>"])
vocab.set_default_index(vocab["<unk>"])

######################################################################
# The vocabulary block converts a list of tokens into integers.
#
# ::
#
# [vocab[token] for token in ['here', 'is', 'an', 'example']]
# >>> [476, 22, 31, 5298]
# vocab(['here', 'is', 'an', 'example'])
# >>> [475, 21, 30, 5286]
#
# Prepare the text processing pipeline with the tokenizer and vocabulary. The text and label pipelines will be used to process the raw data strings from the dataset iterators.

text_pipeline = lambda x: [vocab[token] for token in tokenizer(x)]
text_pipeline = lambda x: vocab(tokenizer(x))
label_pipeline = lambda x: int(x) - 1


Expand Down Expand Up @@ -246,6 +249,7 @@ def evaluate(dataloader):


from torch.utils.data.dataset import random_split
from torchtext.data.functional import to_map_style_dataset
# Hyperparameters
EPOCHS = 10 # epoch
LR = 5 # learning rate
Expand All @@ -256,8 +260,8 @@ def evaluate(dataloader):
scheduler = torch.optim.lr_scheduler.StepLR(optimizer, 1.0, gamma=0.1)
total_accu = None
train_iter, test_iter = AG_NEWS()
train_dataset = list(train_iter)
test_dataset = list(test_iter)
train_dataset = to_map_style_dataset(train_iter)
test_dataset = to_map_style_dataset(test_iter)
num_train = int(len(train_dataset) * 0.95)
split_train_, split_valid_ = \
random_split(train_dataset, [num_train, len(train_dataset) - num_train])
Expand Down Expand Up @@ -285,72 +289,6 @@ def evaluate(dataloader):
print('-' * 59)


######################################################################
# Running the model on GPU with the following printout:
#
# ::
#
# | epoch 1 | 500/ 1782 batches | accuracy 0.684
# | epoch 1 | 1000/ 1782 batches | accuracy 0.852
# | epoch 1 | 1500/ 1782 batches | accuracy 0.877
# -----------------------------------------------------------
# | end of epoch 1 | time: 8.33s | valid accuracy 0.867
# -----------------------------------------------------------
# | epoch 2 | 500/ 1782 batches | accuracy 0.895
# | epoch 2 | 1000/ 1782 batches | accuracy 0.900
# | epoch 2 | 1500/ 1782 batches | accuracy 0.903
# -----------------------------------------------------------
# | end of epoch 2 | time: 8.18s | valid accuracy 0.890
# -----------------------------------------------------------
# | epoch 3 | 500/ 1782 batches | accuracy 0.914
# | epoch 3 | 1000/ 1782 batches | accuracy 0.914
# | epoch 3 | 1500/ 1782 batches | accuracy 0.916
# -----------------------------------------------------------
# | end of epoch 3 | time: 8.20s | valid accuracy 0.897
# -----------------------------------------------------------
# | epoch 4 | 500/ 1782 batches | accuracy 0.926
# | epoch 4 | 1000/ 1782 batches | accuracy 0.924
# | epoch 4 | 1500/ 1782 batches | accuracy 0.921
# -----------------------------------------------------------
# | end of epoch 4 | time: 8.18s | valid accuracy 0.895
# -----------------------------------------------------------
# | epoch 5 | 500/ 1782 batches | accuracy 0.938
# | epoch 5 | 1000/ 1782 batches | accuracy 0.935
# | epoch 5 | 1500/ 1782 batches | accuracy 0.937
# -----------------------------------------------------------
# | end of epoch 5 | time: 8.16s | valid accuracy 0.902
# -----------------------------------------------------------
# | epoch 6 | 500/ 1782 batches | accuracy 0.939
# | epoch 6 | 1000/ 1782 batches | accuracy 0.939
# | epoch 6 | 1500/ 1782 batches | accuracy 0.938
# -----------------------------------------------------------
# | end of epoch 6 | time: 8.16s | valid accuracy 0.906
# -----------------------------------------------------------
# | epoch 7 | 500/ 1782 batches | accuracy 0.941
# | epoch 7 | 1000/ 1782 batches | accuracy 0.939
# | epoch 7 | 1500/ 1782 batches | accuracy 0.939
# -----------------------------------------------------------
# | end of epoch 7 | time: 8.19s | valid accuracy 0.903
# -----------------------------------------------------------
# | epoch 8 | 500/ 1782 batches | accuracy 0.942
# | epoch 8 | 1000/ 1782 batches | accuracy 0.941
# | epoch 8 | 1500/ 1782 batches | accuracy 0.942
# -----------------------------------------------------------
# | end of epoch 8 | time: 8.16s | valid accuracy 0.904
# -----------------------------------------------------------
# | epoch 9 | 500/ 1782 batches | accuracy 0.942
# | epoch 9 | 1000/ 1782 batches | accuracy 0.941
# | epoch 9 | 1500/ 1782 batches | accuracy 0.942
# -----------------------------------------------------------
# end of epoch 9 | time: 8.16s | valid accuracy 0.904
# -----------------------------------------------------------
# | epoch 10 | 500/ 1782 batches | accuracy 0.940
# | epoch 10 | 1000/ 1782 batches | accuracy 0.942
# | epoch 10 | 1500/ 1782 batches | accuracy 0.942
# -----------------------------------------------------------
# | end of epoch 10 | time: 8.15s | valid accuracy 0.904
# -----------------------------------------------------------


######################################################################
# Evaluate the model with test dataset
Expand All @@ -366,12 +304,7 @@ def evaluate(dataloader):
accu_test = evaluate(test_dataloader)
print('test accuracy {:8.3f}'.format(accu_test))

################################################
#
# ::
#
# test accuracy 0.906
#



######################################################################
Expand Down Expand Up @@ -409,10 +342,3 @@ def predict(text, text_pipeline):

print("This is a %s news" %ag_news_label[predict(ex_text_str, text_pipeline)])


################################################
#
# ::
#
# This is a Sports news
#