The dataset viewer is not available for this subset.
Cannot get the split names for the config 'default' of the dataset.
Exception:    HfHubHTTPError
Message:      500 Server Error: Internal Server Error for url: https://huggingface.co/api/datasets/pg19 (Request ID: Root=1-65dfaaf4-04a049702d3614df029b5261;e82073d4-2640-4c9b-9b94-ac133fe6e6fe)

Internal Error - We're working hard to fix this as soon as possible!
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/config/split_names.py", line 68, in compute_split_names_from_streaming_response
                  for split in get_dataset_split_names(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/inspect.py", line 571, in get_dataset_split_names
                  info = get_dataset_config_info(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/inspect.py", line 477, in get_dataset_config_info
                  builder = load_dataset_builder(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py", line 2220, in load_dataset_builder
                  dataset_module = dataset_module_factory(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py", line 1871, in dataset_module_factory
                  raise e1 from None
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py", line 1816, in dataset_module_factory
                  raise e
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py", line 1790, in dataset_module_factory
                  dataset_info = hf_api.dataset_info(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/huggingface_hub/utils/_validators.py", line 118, in _inner_fn
                  return fn(*args, **kwargs)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/huggingface_hub/hf_api.py", line 2148, in dataset_info
                  hf_raise_for_status(r)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/huggingface_hub/utils/_errors.py", line 333, in hf_raise_for_status
                  raise HfHubHTTPError(str(e), response=response) from e
              huggingface_hub.utils._errors.HfHubHTTPError: 500 Server Error: Internal Server Error for url: https://huggingface.co/api/datasets/pg19 (Request ID: Root=1-65dfaaf4-04a049702d3614df029b5261;e82073d4-2640-4c9b-9b94-ac133fe6e6fe)
              
              Internal Error - We're working hard to fix this as soon as possible!

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Dataset Card for "pg19"

Dataset Summary

This repository contains the PG-19 language modeling benchmark. It includes a set of books extracted from the Project Gutenberg books library, that were published before 1919. It also contains metadata of book titles and publication dates.

PG-19 is over double the size of the Billion Word benchmark and contains documents that are 20X longer, on average, than the WikiText long-range language modelling benchmark. Books are partitioned into a train, validation, and test set. Book metadata is stored in metadata.csv which contains (book_id, short_book_title, publication_date).

Unlike prior benchmarks, we do not constrain the vocabulary size --- i.e. mapping rare words to an UNK token --- but instead release the data as an open-vocabulary benchmark. The only processing of the text that has been applied is the removal of boilerplate license text, and the mapping of offensive discriminatory words as specified by Ofcom to placeholder tokens. Users are free to model the data at the character-level, subword-level, or via any mechanism that can model an arbitrary string of text. To compare models we propose to continue measuring the word-level perplexity, by calculating the total likelihood of the dataset (via any chosen subword vocabulary or character-based scheme) divided by the number of tokens --- specified below in the dataset statistics table. One could use this dataset for benchmarking long-range language models, or use it to pre-train for other natural language processing tasks which require long-range reasoning, such as LAMBADA or NarrativeQA. We would not recommend using this dataset to train a general-purpose language model, e.g. for applications to a production-system dialogue agent, due to the dated linguistic style of old texts and the inherent biases present in historical writing.

Supported Tasks and Leaderboards

More Information Needed

Languages

More Information Needed

Dataset Structure

Data Instances

default

  • Size of downloaded dataset files: 11.74 GB
  • Size of the generated dataset: 11.51 GB
  • Total amount of disk used: 23.25 GB

An example of 'train' looks as follows.

This example was too long and was cropped:

{
    "publication_date": 1907,
    "short_book_title": "La Fiammetta by Giovanni Boccaccio",
    "text": "\"\\n\\n\\n\\nProduced by Ted Garvin, Dave Morgan and PG Distributed Proofreaders\\n\\n\\n\\n\\nLA FIAMMETTA\\n\\nBY\\n\\nGIOVANNI BOCCACCIO\\n...",
    "url": "http://www.gutenberg.org/ebooks/10006"
}

Data Fields

The data fields are the same among all splits.

default

  • short_book_title: a string feature.
  • publication_date: a int32 feature.
  • url: a string feature.
  • text: a string feature.

Data Splits

name train validation test
default 28602 50 100

Dataset Creation

Curation Rationale

More Information Needed

Source Data

Initial Data Collection and Normalization

More Information Needed

Who are the source language producers?

More Information Needed

Annotations

Annotation process

More Information Needed

Who are the annotators?

More Information Needed

Personal and Sensitive Information

More Information Needed

Considerations for Using the Data

Social Impact of Dataset

More Information Needed

Discussion of Biases

More Information Needed

Other Known Limitations

More Information Needed

Additional Information

Dataset Curators

More Information Needed

Licensing Information

The dataset is licensed under Apache License, Version 2.0.

Citation Information

@article{raecompressive2019,
  author = {Rae, Jack W and Potapenko, Anna and Jayakumar, Siddhant M and
            Hillier, Chloe and Lillicrap, Timothy P},
  title = {Compressive Transformers for Long-Range Sequence Modelling},
  journal = {arXiv preprint},
  url = {https://arxiv.org/abs/1911.05507},
  year = {2019},
}

Contributions

Thanks to @thomwolf, @lewtun, @lucidrains, @lhoestq for adding this dataset.

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