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README.md

The following steps show how to prepare training dataset to train the mode.

Libraries to install

    pip install ftfy langdetect numpy torch pandas nltk sentencepiece boto3 tqdm regex bs4 newspaper3k htmlmin tldextract 
    git clone https://github.com/mattilyra/LSH
    cd LSH
    python setup.py install

Download the dataset

  1. Download the deduplicated URLs from jcpeterson
  2. Remove blacklisted URLs.
python blacklist_urls.py <path to the dowloaded deduplicated URLs> <filename for clean urls. e.g. clean_urls.txt>

  1. Download the content from the clean urls with openwebtext's utilities.

  2. Merge the contents into one loose json file with 1 json per newline of the format {'text': text, 'url': unique_url}. It is important for the url to be unique.

Prepare the data for GPT-2 training:

  1. Perform ftfy, english detection and remove documents with less than 128 tokens. This step can be sharded and run on shards.
python cleanup_dataset.py <input data file> <output cleaned data filename>
  1. Using LSH, find possible duplicates and store then in a file for later processing. This step can NOT be sharded and usually takes 12 to 24 hours for OpenWebText dataset. The code supports saving and loading fingerprints for recurrent deduplications.
python find_duplicates.py --inputs <pairlist list of input cleaned data files and keys, e.g. cc.json cc_id news.json news_id> --output <output possible duplicate urls filename>
  1. Based on similarity measure defind inside function is_similar (default: 0.9), group urls that are similar. Basically, for each group, only one url we should keep and remove the rest.
python group_duplicate_urls.py <possible duplicate urls file> <output file containing similar urls>
  1. Remove similar documents that were detected in the last step.
python remove_group_duplicates.py <file containing simialr documents> <cleaned data file> <outputfile containing deduplicate data>
  1. Shuffle the dataset.
shuf <cleaned deduped data file> -o train_data.json

Deduplicating ngrams

To deduplicate the downstream tasks from the training dataset, we run the following command.

python filter_ngrams.py <down stream task dataset> <training dataset to deduplicate> <output training dataset>

We use 13-grams for the deduplication. When we find a 13-gram match in a training document, we split the document into two pieces and remove the 13-gram along with 200 characters from the both side of the 13-gram. We also remove any splitted document with less than 200 characters or if a document got splitted more than 10 times.