cerebras.modelzoo.data_preparation.data_preprocessing.utils#

Functions

`append_eos_to_multiple_semantic_regions`
`check_fim_special_tokens`
`chunk`	Since we do character-level FIM we need to detokenize, determine boundaries to split, and re-tokenize after splitting.
`create_features_auto_lm_vsl`	Given a list of VSL sequences, generate input features and labels.
`dump_args`	Write the input params to file.
`dump_result`	Write outputs of execution
`fim`	Takes in an array of input_ids, mask, and labels, and performs the FIM operation to re-arrange into PSM and SPM format with some probability
`find_token_range`
`format_fim`	Takes in list of prefix/middle/suffix token lists, along with respective FIM (or AR) formats.
`get_files`	Get all files of given filetypes from input directory.
`get_params`	Retrieve configuration parameters :returns:
`get_parser`	Argparser definition for command line arguments from user.
`get_size`	Recursively finds size of objects
`get_tokenizer_vocab`
`handle_bos_token_default`	When performing FIM, we tokenize each chunk again after splitting.
`has_valid_extension`
`listdir_or_file`
`pad_helper`	Helper for padding.
`set_defaults`
`split_text_and_tokenize`	Function to split the text into smaller sequences of length max_tok_len and then tokenize each of the smaller sequences.
`truncate_helper`	The goal of our truncation scheme is to avoid removing tokens from the middle section.
`truncate_or_pad_helper`	Since we perform FIM at character-level, we potentially split characters in the middle of a word.
`update_params`	Update config parameters with CLI arguments
`validate_tokens`
`wikitext_detokenizer`	Detokenizer for wikitext.

cerebras.modelzoo.data_preparation.data_preprocessing.tokenflow.tokenizer.GenericTokenizer

cerebras.modelzoo.data_preparation.data_preprocessing.utils.append_eos_to_multiple_semantic_regions