# Pre-processing steps ### 1. run `convert_space_format.py` Convert the string `` to `SPACE` ### 2. run `create_negtive_examples.py` We use the next file of the current file as its negative examples, which is apparently rational. Specifically, for each instruction in the current positive file, we randomly choose a line in its next file and select one of two instructions in the line as its negative example. ### 3. run `merge_examples_to_json.py` We dump the positive and negative examples with their corresponding labels into several json files. Each json file contains 20m lines of examples. ### 4. run `check_length.py` We will specify the length padded to when we use the tokenizer, `tokenizer.enable_padding(..., length=)`. So we need to know the longest sentences in the dataset. ### 5. run `count_word_for_vocab.py` Similarly, we also need to specify the size of vocabulary when we train the tokenizer, `WordLevelTrainer(vocab_size=, ...)`. So we need to know how many characters in the dataset.