| 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](https://mega.nz/#F!EZZD0YwJ!9_PlEQzdMVLaNdKv_ICNVQ!cc4RgQQZ) | |
| 2. Remove blacklisted URLs. | |
| ``` | |
| python blacklist_urls.py <path to the dowloaded deduplicated URLs> <filename for clean urls. e.g. clean_urls.txt> | |
| ``` | |
| 3. Download the content from the clean urls with [openwebtext's utilities](https://github.com/eukaryote31/openwebtext/blob/master/download.py). | |
| 4. 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 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> | |
| ``` | |
| Additional cleanup (e.g. remove documents less than 512 characters or dataset specific cleaning like stories, realnews datasets) can be done using `cleanup_fix_dataset.py`. More details can be found by running `python cleanup_fix_dataset.py --help`. | |
| 2. Using LSH, find possible duplicates and store then in a file for later processing. The code supports saving and loading fingerprints for recurrent deduplications, and is also multithreaded for faster processing. More details are can be found by `python find_duplicate.py --help`. | |
| ``` | |
| 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> | |
| ``` | |
| 3. 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> | |
| ``` | |
| 4. 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> | |
| ``` | |
| 5. Shuffle the dataset. | |
| ``` | |
| shuf <cleaned deduped data file> -o train_data.json | |
| ``` | |
| # Deduplicating ngrams | |
| To deduplicate the downstream tasks (e.g. lambada, squad) from the training dataset, we run the following command. | |
| ``` | |
| python filter_ngrams.py --tasks <name of the task, e.g. lambada, squad> --dedup_dataset <training dataset to deduplicate> <json key> --output <output training dataset> | |
| ``` | |
| We use 13-grams by default 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. These parameters can be changed using corresponding arguments. | |
| Only for the lambada task, we need to provide the path, `--lambada_path <path of the lambada test data>`. | |
| Several other features (e.g. save and load dictionary) have been added, look at `python filter_ngrams.py --help` for details. | |