Datasets:
guardian_authorship

Tasks:

Text Classification

Sub-tasks: multi-class-classification topic-classification

Languages: English

Multilinguality: monolingual

Size Categories: 1K<n<10K

Language Creators: found

Annotations Creators: found

Source Datasets: original

License: unknown

Dataset card Files Files and versions Community

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The dataset preview is not available for this split.

Cannot load the dataset split (in streaming mode) to extract the first rows.

Error code:   StreamingRowsError
Exception:    KeyError
Message:      "There is no item named 'Guardian/Guardian_original/Books' in the archive"
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/first_rows.py", line 570, in compute_first_rows_response
                  rows = get_rows(
                File "/src/services/worker/src/worker/job_runners/first_rows.py", line 161, in decorator
                  return func(*args, **kwargs)
                File "/src/services/worker/src/worker/job_runners/first_rows.py", line 217, in get_rows
                  rows_plus_one = list(itertools.islice(ds, rows_max_number + 1))
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 937, in __iter__
                  for key, example in ex_iterable:
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 113, in __iter__
                  yield from self.generate_examples_fn(**self.kwargs)
                File "/tmp/modules-cache/datasets_modules/datasets/guardian_authorship/8c5f5675c8658367fcec31c02ac32be3a671d3eee703862e92b84b6b61e4fb38/guardian_authorship.py", line 316, in _generate_examples
                  with open(path_2_article, "r", encoding="utf8", errors="ignore") as f:
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/streaming.py", line 70, in wrapper
                  return function(*args, use_auth_token=use_auth_token, **kwargs)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/download/streaming_download_manager.py", line 495, in xopen
                  file_obj = fsspec.open(file, mode=mode, *args, **kwargs).open()
                File "/src/services/worker/.venv/lib/python3.9/site-packages/fsspec/core.py", line 134, in open
                  return self.__enter__()
                File "/src/services/worker/.venv/lib/python3.9/site-packages/fsspec/core.py", line 102, in __enter__
                  f = self.fs.open(self.path, mode=mode)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/fsspec/spec.py", line 1151, in open
                  f = self._open(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/fsspec/implementations/zip.py", line 122, in _open
                  out = self.zip.open(path, mode.strip("b"))
                File "/usr/local/lib/python3.9/zipfile.py", line 1511, in open
                  zinfo = self.getinfo(name)
                File "/usr/local/lib/python3.9/zipfile.py", line 1438, in getinfo
                  raise KeyError(
              KeyError: "There is no item named 'Guardian/Guardian_original/Books' in the archive"

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Dataset Card for "guardian_authorship"

Dataset Summary

A dataset cross-topic authorship attribution. The dataset is provided by Stamatatos 2013. 1- The cross-topic scenarios are based on Table-4 in Stamatatos 2017 (Ex. cross_topic_1 => row 1:P S U&W ). 2- The cross-genre scenarios are based on Table-5 in the same paper. (Ex. cross_genre_1 => row 1:B P S&U&W).

3- The same-topic/genre scenario is created by grouping all the datasts as follows. For ex., to use same_topic and split the data 60-40 use: train_ds = load_dataset('guardian_authorship', name="cross_topic_<<#>>", split='train[:60%]+validation[:60%]+test[:60%]') tests_ds = load_dataset('guardian_authorship', name="cross_topic_<<#>>", split='train[-40%:]+validation[-40%:]+test[-40%:]')

IMPORTANT: train+validation+test[:60%] will generate the wrong splits because the data is imbalanced

See https://huggingface.co/docs/datasets/splits.html for detailed/more examples

Supported Tasks and Leaderboards

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Languages

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Dataset Structure

Data Instances

cross_genre_1

Size of downloaded dataset files: 2.96 MB
Size of the generated dataset: 2.61 MB
Total amount of disk used: 5.57 MB

An example of 'train' looks as follows.

{
    "article": "File 1a\n",
    "author": 0,
    "topic": 4
}

cross_genre_2

Size of downloaded dataset files: 2.96 MB
Size of the generated dataset: 2.61 MB
Total amount of disk used: 5.57 MB

An example of 'validation' looks as follows.

{
    "article": "File 1a\n",
    "author": 0,
    "topic": 1
}

cross_genre_3

Size of downloaded dataset files: 2.96 MB
Size of the generated dataset: 2.61 MB
Total amount of disk used: 5.57 MB

An example of 'validation' looks as follows.

{
    "article": "File 1a\n",
    "author": 0,
    "topic": 2
}

cross_genre_4

Size of downloaded dataset files: 2.96 MB
Size of the generated dataset: 2.61 MB
Total amount of disk used: 5.57 MB

An example of 'validation' looks as follows.

{
    "article": "File 1a\n",
    "author": 0,
    "topic": 3
}

cross_topic_1

Size of downloaded dataset files: 2.96 MB
Size of the generated dataset: 2.23 MB
Total amount of disk used: 5.18 MB

An example of 'validation' looks as follows.

{
    "article": "File 1a\n",
    "author": 0,
    "topic": 1
}

Data Fields

The data fields are the same among all splits.

cross_genre_1

author: a classification label, with possible values including catherinebennett (0), georgemonbiot (1), hugoyoung (2), jonathanfreedland (3), martinkettle (4).
topic: a classification label, with possible values including Politics (0), Society (1), UK (2), World (3), Books (4).
article: a string feature.

cross_genre_2

author: a classification label, with possible values including catherinebennett (0), georgemonbiot (1), hugoyoung (2), jonathanfreedland (3), martinkettle (4).
topic: a classification label, with possible values including Politics (0), Society (1), UK (2), World (3), Books (4).
article: a string feature.

cross_genre_3

author: a classification label, with possible values including catherinebennett (0), georgemonbiot (1), hugoyoung (2), jonathanfreedland (3), martinkettle (4).
topic: a classification label, with possible values including Politics (0), Society (1), UK (2), World (3), Books (4).
article: a string feature.

cross_genre_4

author: a classification label, with possible values including catherinebennett (0), georgemonbiot (1), hugoyoung (2), jonathanfreedland (3), martinkettle (4).
topic: a classification label, with possible values including Politics (0), Society (1), UK (2), World (3), Books (4).
article: a string feature.

cross_topic_1

author: a classification label, with possible values including catherinebennett (0), georgemonbiot (1), hugoyoung (2), jonathanfreedland (3), martinkettle (4).
topic: a classification label, with possible values including Politics (0), Society (1), UK (2), World (3), Books (4).
article: a string feature.

Data Splits

name	train	validation	test
cross_genre_1	63	112	269
cross_genre_2	63	62	319
cross_genre_3	63	90	291
cross_genre_4	63	117	264
cross_topic_1	112	62	207

Dataset Creation

Curation Rationale

More Information Needed

Source Data

Initial Data Collection and Normalization

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Who are the source language producers?

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Annotations

Considerations for Using the Data

Social Impact of Dataset

More Information Needed

Discussion of Biases

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Other Known Limitations

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Additional Information

Dataset Curators

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Licensing Information

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Citation Information

@article{article,
    author = {Stamatatos, Efstathios},
    year = {2013},
    month = {01},
    pages = {421-439},
    title = {On the robustness of authorship attribution based on character n-gram features},
    volume = {21},
    journal = {Journal of Law and Policy}
}

@inproceedings{stamatatos2017authorship,
    title={Authorship attribution using text distortion},
    author={Stamatatos, Efstathios},
    booktitle={Proc. of the 15th Conf. of the European Chapter of the Association for Computational Linguistics},
    volume={1}
    pages={1138--1149},
    year={2017}
}