wikitext_dataset

torchtune.datasets.wikitext_dataset(tokenizer: ModelTokenizer, source: str = 'EleutherAI/wikitext_document_level', subset: str = 'wikitext-103-v1', max_seq_len: Optional[int] = None, packed: bool = False, split: str = 'train', **load_dataset_kwargs: Dict[str, Any]) → Union[TextCompletionDataset, PackedDataset]

Support for family of datasets similar to wikitext, an unstructured text corpus consisting of fulls articles from Wikipedia.

Parameters:

• tokenizer (ModelTokenizer) – Tokenizer used by the model that implements the tokenize_messages method.

• source (str) – path to dataset repository on Hugging Face. For local datasets, define source as the data file type (e.g. “json”, “csv”, “text”) and pass in the filepath in data_files. See Hugging Face’s load_dataset (https://huggingface.co/docs/datasets/en/package_reference/loading_methods#datasets.load_dataset.path) for more details.

• subset (str) – name of subset of data to use, see the wikitext page for available subsets. Default is "wikitext-103-v1".

• max_seq_len (Optional[int]) – Maximum number of tokens in the returned input and label token id lists. Default is None, disabling truncation. We recommend setting this to the highest you can fit in memory and is supported by the model. For example, llama2-7B supports up to 4096 for sequence length.

• packed (bool) – Whether or not to pack the dataset to max_seq_len prior to training. Default is False.

• split (str) – split argument for datasets.load_dataset. You can use this argument to load a subset of a given split, e.g. split="train[:10%]". Default is “train”.

• **load_dataset_kwargs (Dict[str, Any]) – additional keyword arguments to pass to load_dataset.

Returns:

the configured TextCompletionDataset

or PackedDataset if packed=True

Return type:

Union[TextCompletionDataset, PackedDataset]