Using Google 1-billion benchmark data on PyTorch

[h56cho]

Hello,

I am new to NLP and I have some questions.

I downloaded the Google 1-billion benchmark dataset, and I am trying to use the dataset on PyTorch:

               
# Import packages 
import torch
import torch.nn as nn
import torch.nn.functional as F
from sklearn.model_selection import train_test_split
from torchtext.data import Field, BucketIterator, TabularDataset
from transformers import OpenAIGPTConfig, OpenAIGPTTokenizer, OpenAIGPTLMHeadModel
from transformers import AdamW, WarmupLinearSchedule
from scipy.spatial import distance
import spacy
import torchtext
from torchtext.data.utils import get_tokenizer
from torchtext.data import Field, BPTTIterator
import tensorflow as tf
#import lineflow as lf
#import lineflow.datasets as lfds
import math
import random
import numpy as np
import pandas as pd 
import time

# set hyperparameters for this experiment
bptt = 30
batch_size = 64
lr = 0.01 # learning rate
#criterion = nn.CrossEntropyLoss() # loss criterion
log_interval = 200
nlayer = 6

# define tokenizer
en = spacy.load('en')

def Sp_Tokenizer(text): 
    return [tok.text for tok in en.tokenizer(text)]

# define the English text field
TEXT = Field(tokenize = Sp_Tokenizer,
             init_token = '<sos>',
             eos_token = '<eos>',
             unk_token = '<unk>',
             pad_token = '<pad>',
             tokenizer_language = 'en',
             lower = True)

# loading Google 1 Billion Benchmark dataset
billion_google = open('/Users/dev/billion_google', encoding='utf-8').read()
billion_google_dict = {'English' : [line for line in billion_google]}
# convert billion_google into a pandas dataframe
billion_google_df = pd.DataFrame(billion_google_dict, columns=["English"])

# remove very long sentences
billion_google_df['eng_len'] = billion_google_df['English'].str.count(' ')
billion_google_df = billion_google_df.query('eng_len < 1025')

# create train and test set 
train_billion_google, test_billion_google = train_test_split(billion_google_df, test_size=0.2)
train_billion_google.to_csv("train_billion_google.csv", index=False)
test_billion_google.to_csv("test_billion_google.csv", index=False)

data_fields = [('English', TEXT)]
train_billion_google, test_billion_google = TabularDataset.splits(path='./', 
                                                                  train='train_billion_google.csv',
                                                                  validation='test_billion_google.csv', 
                                                                  format='csv', 
                                                                  fields=data_fields)

I also want to make a use of WikiText2 that is built in PyTorch:

train_Wiki2, val_Wiki2, test_Wiki2 = torchtext.datasets.WikiText2.splits(TEXT)

...and I want my train_billion_google to have the same structure as my train_Wiki2, more specifically, I want my train_billion_google to store a list of individual tokens under train_billion_google.examples[0].text.

How can I do this?

Thank you,

[bentrevett]

You should use the LanguageModelingDataset instead of the TabularDataset.

[h56cho]

Hello,
Thank you for your reply.
I am new to torchtext and I am unfamiliar with so many things.
How can I use LangaugeModelingDataset with splits?
I want to split my billion_google dataset into train_billion_google,val_billion_google, and test_billion_google, just as in the case of the WikiText2 dataset when I execute the line of code below:

train_Wiki2, val_Wiki2, test_Wiki2 = torchtext.datasets.WikiText2.splits(TEXT)

When I execute:

train_billion_google, val_billion_google, test_billion_google
                                            = LanguageModelingDataset.splits(path = '/Users/dev/billion_google', 
                                                encoding = 'utf-8', 
                                                text_field = TEXT)

I get the following error:

ValueError: not enough values to unpack (expected 3, got 0)

Thank you again,

[zhangguanheng66]

Thanks @bentrevett
We are re-writing several datasets, including wikitext2 dataset @h56cho .

In your case, it should be pretty simple to use our new pattern (take a look here). If you would like to add this billion_google dataset to torchtext, please open a PR and I'm happy to review/land it. Regarding splits function, the new pattern is more compatible with torch.utils.data and you should be able to use those functions there. Here is our example.

[h56cho]

Hello,

My background is not computer programming and I am having hard time understanding the posts.
If you can, could you please provide me with some codes as to how I can apply the splits function to billion_google?

I tried the code below and it's giving me an error...

train_billion_google, val_billion_google, test_billion_google
                                            = LanguageModelingDataset.splits(path = '/Users/dev/billion_google', 
                                                encoding = 'utf-8', 
                                                text_field = TEXT)
ValueError: not enough values to unpack (expected 3, got 0)

[zhangguanheng66]

@h56cho You could download the file, which is very large, and pass the path to LanguageModelingDataset.

[h56cho]

Hello,
Yes, I know that but my question is I don’t know how to split billion_google data into train, test, and validation sets. The splits function is not working for me, Python is complaining that there are not enough values to unpack.

[h56cho]

Hello,

I tried a slightly different approach and it's still not working for me...

# loading Google 1 Billion Benchmark dataset
billion_google = LanguageModelingDataset(path = '/Users/dev/billion_google', 
                                         encoding = 'utf-8', 
                                         text_field = TEXT_billion_google)

# note: I created empty files train_billion_google, val_billion_google, and test_billion_google
# under the appropriate directory before executing this code.
train_billion_google, val_billion_google, test_billion_google = billion_google.splits(path = '/Users/dev/',
                                                                                      train = 'train_billion_google',
                                                                                      validation = 'val_billion_google',
                                                                                      test = 'test_billion_google', 
                                                                                      text_field = TEXT_billion_google)

Python indicates that the train_billion_google set in this case contains only 13 tokens, which is clearly wrong. What am I doing wrong here? I can't split billion_google!

[zhangguanheng66]

Can you check the training file 'train_billion_google' ?

[h56cho]

Hello,

The file file train_billion_google is only 196 bytes large and when I open it, it's completely empty.

[zhangguanheng66]

So you didn't download it correctly.

[h56cho]

Hello,

I thought that I am supposed to manually make the empty files named train_billion_google, val_billion_google, test_billion_google before executing the line of code below:

# loading Google 1 Billion Benchmark dataset
billion_google = LanguageModelingDataset(path = '/Users/dev/billion_google', 
                                         encoding = 'utf-8', 
                                         text_field = TEXT_billion_google)

# note: I created empty files train_billion_google, val_billion_google, and test_billion_google
# under the appropriate directory before executing this code.
train_billion_google, val_billion_google, test_billion_google = billion_google.splits(path = '/Users/dev/',
                                                                                      train = 'train_billion_google',
                                                                                      validation = 'val_billion_google',
                                                                                      test = 'test_billion_google', 
                                                                                      text_field = TEXT_billion_google)

my "billion_google" file is full of texts, so I don't think I downloaded it in a wrong way (it was a successful download). The files train_billion_google, val_billion_google, test_billion_google are empty because I artificially made those 3 empty files and placed under the folder /User/dev/, because I thought I was supposed to do that to run the lines of code that I typed above.

again, how can I make the splits function to work?

Thank you,

[h56cho]

I don't get how I can use the "new pattern" in TorchText to make splits function to work.

[zhangguanheng66]

It quite confused me. To test your code, you should add "something" in train_billion_google file and see if it load the information as expected.

For the new pattern, it's not supposed to use splits. If you take a look at the PR I attached above, it should be pretty clear to put together a new dataset (a.k.a. billion_google).