Delete halvest_r.py

halvest_r.py

@@ -1,212 +0,0 @@
-# halvest-r.py
-
-import collections
-import gzip
-import json
-import os
-
-import datasets
-
-logger = datasets.logging.get_logger(__name__)
-
-_DESCRIPTION = "HALvest Raw"
-_URL = "https://huggingface.co/datasets/Madjakul/halvest"
-_LICENSE = """
-    The licence terms for HALvest-R strictly follows those of HAL.
-    Please refer to the below license when using this dataset.
-    - HAL license: https://doc.archives-ouvertes.fr/en/legal-aspects/
-    The corpus is extracted from the HAL's open archive which distributes scientific \
-    publications following open access principles. The corpus is made up of both \
-    creative commons licensed and copyrighted documents (distribution authorized on \
-    HAL by the publisher). This must be considered prior to using this dataset for any \
-    purpose, other than training deep learning models, data mining etc. We do not own \
-    any of the text from which these data has been extracted.
-"""
-_CITATION = """
-    @software{almanach_halvest_2024,
-    author = {Kulumba, Francis and Antoun, Wissam and Vimont, Guillaume and ROmary, Laurent},
-    title = {HALvest: Open Scientific Papers Harvested from HAL.},
-    month = {April},
-    year = {2024},
-    company = {Almanach},
-    url = {https://github.com/Madjakul/HALvesting}
-    }
-"""
-_BASE_DATA_PATH = "{language}/"
-_BASE_CHECKSUM_FILENAME = "checksum.sha256"
-
-
-def _languages():
-    """Creates the sorted dictionary of language codes, and language names."""
-    langs = {
-        "Albanian": "sq",
-        "Arabic": "ar",
-        "Armenian": "hy",
-        "Azerbaijani": "az",
-        "Basque": "eu",
-        "Bosnian": "bs",
-        "Breton": "br",
-        "Bulgarian": "bg",
-        "Catalan": "ca",
-        "Chinese": "zh",
-        "Corsican": "co",
-        "Croatian": "hr",
-        "Czech": "cs",
-        "Danish": "da",
-        "English": "en",
-        "Esperanto": "eo",
-        "Estonian": "et",
-        "Filipino": "tl",
-        "Finnish": "fi",
-        "French": "fr",
-        "Galician": "gl",
-        "German": "de",
-        "Greek": "el",
-        "Guarani": "gn",
-        "Hebrew": "he",
-        "Hindi": "hi",
-        "Hungarian": "hu",
-        "Indonesian": "id",
-        "Interlingue": "ie",
-        "Italian": "it",
-        "Japanese": "ja",
-        "Kazakh": "kk",
-        "Korean": "ko",
-        "Lithuanian": "lt",
-        "Macedonian": "mk",
-        "Marathi": "mr",
-        "Norwegian": "no",
-        "Occitan": "oc",
-        "Persian": "fa",
-        "Polish": "pl",
-        "Portuguese": "pt",
-        "Romanian": "ro",
-        "Russian": "ru",
-        "Serbian": "sr",
-        "Slovak": "sk",
-        "Slovenian": "sl",
-        "Spanish": "es",
-        "Swahili": "sw",
-        "Swedish": "sv",
-        "Tamil": "ta",
-        "Tetum": "tet",
-        "Thai": "th",
-        "Tibetan": "bo",
-        "Turkish": "tr",
-        "Turkmen": "tk",
-        "Ukrainian": "uk",
-        "Vietnamese": "vi",
-    }
-    langs = {v: k for k, v in langs.items()}
-    return collections.OrderedDict(sorted(langs.items()))
-
-
-class HALvest_RConfig(datasets.BuilderConfig):
-    """HALvest-R builder config.
-    Parameters
-    ----------
-    language: str
-        ISO 639 language code.
-    Attributes
-    ----------
-    base_data_path: str
-        f"{self.language}/".
-    """
-
-    def __init__(self, language: str, **kwargs):
-        if language not in _languages():
-            raise ValueError("Invalid language: %s " % language)
-
-        name = f"{language}"
-        description = f"""
-            Raw {_languages()[language]} HALvest-R dataset from February 2024.
-        """
-        super(HALvest_RConfig, self).__init__(
-            name=name, description=description, **kwargs
-        )
-        self.language = language
-        self.base_data_path = _BASE_DATA_PATH.format(language=language)
-
-
-class HALvest_R(datasets.GeneratorBasedBuilder):
-    """HALvest Raw: Open Scientific Papers Harvested from HAL (Unfiltered)."""
-
-    BUILDER_CONFIGS = [
-        HALvest_RConfig(language=language, version=datasets.Version("0.1.0"))
-        for language in _languages()
-    ]
-    BUILDER_CONFIG_CLASS = HALvest_RConfig
-
-    def _info(self):
-        return datasets.DatasetInfo(
-            description=_DESCRIPTION,
-            features=datasets.Features(
-                {
-                    "halid": datasets.Value("string"),
-                    "lang": datasets.Value("string"),
-                    "domain": datasets.Sequence("string"),
-                    "timestamp": datasets.Value("string"),
-                    "year": datasets.Value("string"),
-                    "url": datasets.Value("string"),
-                    "text": datasets.Value("string"),
-                    "token_count": datasets.Value("int32"),
-                    "rps_doc_frac_all_caps_words": datasets.Value("float64"),
-                    "rps_doc_frac_lines_end_with_ellipsis": datasets.Value("float64"),
-                    "rps_doc_frac_no_alph_words": datasets.Value("float64"),
-                    "rps_doc_lorem_ipsum": datasets.Value("float64"),
-                    "rps_doc_mean_word_length": datasets.Value("float64"),
-                    "rps_doc_stop_word_fraction": datasets.Value("float64"),
-                    "rps_doc_symbol_to_word_ratio": datasets.Value("float64"),
-                    "rps_doc_frac_unique_words": datasets.Value("float64"),
-                    "rps_doc_unigram_entropy": datasets.Value("float64"),
-                    "rps_doc_word_count": datasets.Value("int64"),
-                    "doc_frac_lines_ending_with_terminal_punctution_mark": datasets.Value("float64"),
-                    "rps_lines_frac_start_with_bulletpoint": datasets.Value("float64"),
-                    "rps_doc_num_sentences": datasets.Value("int64"),
-                    "rps_frac_chars_in_dupe_5grams": datasets.Value("float64"),
-                    "rps_frac_chars_in_dupe_6grams": datasets.Value("float64"),
-                    "rps_frac_chars_in_dupe_7grams": datasets.Value("float64"),
-                    "rps_frac_chars_in_dupe_8grams": datasets.Value("float64"),
-                    "rps_frac_chars_in_dupe_9grams": datasets.Value("float64"),
-                    "rps_frac_chars_in_dupe_10grams": datasets.Value("float64"),
-                    "kenlm_pp": datasets.Value("float64"),
-                }
-            ),
-            supervised_keys=None,
-            homepage=_URL,
-            citation=_CITATION,
-            license=_LICENSE,
-        )
-
-    def _split_generators(self, dl_manager):
-        checksum_path = os.path.join(
-            self.config.base_data_path, _BASE_CHECKSUM_FILENAME
-        )
-        checksum_file = dl_manager.download(checksum_path)
-
-        with open(checksum_file, encoding="utf-8") as f:
-            data_filenames = [line.split("\t")[1] for line in f if line]
-            data_urls = [
-                os.path.join(self.config.base_data_path, data_filename.rstrip("\n"))
-                for data_filename in data_filenames
-            ]
-
-        downloaded_files = dl_manager.download(
-            [url for url in data_urls if url.endswith(".gz")]
-        )
-
-        return [
-            datasets.SplitGenerator(
-                name=datasets.Split.TRAIN, gen_kwargs={"filepaths": downloaded_files}
-            )
-        ]
-
-    def _generate_examples(self, filepaths):
-        id_ = 0
-        for filepath in filepaths:
-            logger.info("Generating examples from = %s", filepath)
-            with gzip.open(open(filepath, "rb"), "rt", encoding="utf-8") as f:
-                for line in f:
-                    js_line = json.loads(line)
-                    yield id_, js_line
-                    id_ += 1