Delete _cacapo.py

_cacapo.py

@@ -1,132 +0,0 @@
-#!/usr/bin/env python3
-
-"""
-The script used to load the dataset from the original source.
-"""
-
-import os
-import xml.etree.cElementTree as ET
-from collections import defaultdict
-from glob import glob
-from os.path import join as pjoin
-from pathlib import Path
-
-import datasets
-
-_CITATION = """\
-@inproceedings{van2020cacapo,
-  title={The CACAPO dataset: A multilingual, multi-domain dataset for neural pipeline and end-to-end data-to-text generation},
-  author={van der Lee, Chris and Emmery, Chris and Wubben, Sander and Krahmer, Emiel},
-  booktitle={Proceedings of the 13th International Conference on Natural Language Generation},
-  pages={68--79},
-  year={2020}
-}
-"""
-
-_DESCRIPTION = """\
-CACAPO is a data-to-text dataset that contains sentences from news reports for the sports, weather, stock, and incidents domain in English and Dutch, aligned with relevant attribute-value paired data. This is the first data-to-text dataset based on "naturally occurring" human-written texts (i.e., texts that were not collected in a task-based setting), that covers various domains, as well as multiple languages. """
-_URL = "https://github.com/TallChris91/CACAPO-Dataset"
-_LICENSE = "CC BY 4.0"
-
-def et_to_dict(tree):
-    dct = {tree.tag: {} if tree.attrib else None}
-    children = list(tree)
-    if children:
-        dd = defaultdict(list)
-        for dc in map(et_to_dict, children):
-            for k, v in dc.items():
-                dd[k].append(v)
-        dct = {tree.tag: dd}
-    if tree.attrib:
-        dct[tree.tag].update((k, v) for k, v in tree.attrib.items())
-    if tree.text:
-        text = tree.text.strip()
-        if children or tree.attrib:
-            if text:
-                dct[tree.tag]["text"] = text
-        else:
-            dct[tree.tag] = text
-    return dct
-
-
-def parse_entry(entry):
-    res = {}
-    otriple_set_list = entry["originaltripleset"]
-    res["original_triple_sets"] = [{"otriple_set": otriple_set["otriple"]} for otriple_set in otriple_set_list]
-    mtriple_set_list = entry["modifiedtripleset"]
-    res["modified_triple_sets"] = [{"mtriple_set": mtriple_set["mtriple"]} for mtriple_set in mtriple_set_list]
-    res["category"] = entry["category"]
-    res["eid"] = entry["eid"]
-    res["size"] = int(entry["size"])
-    res["lex"] = {
-        "comment": [ex.get("comment", "") for ex in entry.get("lex", [])],
-        "lid": [ex.get("lid", "") for ex in entry.get("lex", [])],
-        "text": [ex.get("text", "") for ex in entry.get("lex", [])],
-    }
-    return res
-
-
-def xml_file_to_examples(filename):
-    tree = ET.parse(filename).getroot()
-
-    examples = et_to_dict(tree)["benchmark"]["entries"][0]["entry"]
-    return [parse_entry(entry) for entry in examples]
-
-
-class CACAPO(datasets.GeneratorBasedBuilder):
-    VERSION = datasets.Version("1.0.0")
-
-    def _info(self):
-        return datasets.DatasetInfo(
-            description=_DESCRIPTION,
-            features=datasets.Features({
-                "category": datasets.Value("string"),
-                "lang": datasets.Value("string"),
-                "size": datasets.Value("int32"),
-                "eid": datasets.Value("string"),
-                "original_triple_sets": datasets.Sequence(
-                    {"otriple_set": datasets.Sequence(datasets.Value("string"))}
-                ),
-                "modified_triple_sets": datasets.Sequence(
-                    {"mtriple_set": datasets.Sequence(datasets.Value("string"))}
-                ),
-                "lex": datasets.Sequence(
-                    {
-                        "comment": datasets.Value("string"),
-                        "lid": datasets.Value("string"),
-                        "text": datasets.Value("string"),
-                    }
-                ),
-            }),
-            supervised_keys=None,
-            homepage=_URL,
-            citation=_CITATION,
-            license=_LICENSE,
-        )
-
-    def _split_generators(self, dl_manager):
-        """Returns SplitGenerators."""
-        return [
-            datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filedirs": ["Incidents", "Sports", "Stocks", "Weather"], "split" : "train"}),
-            datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filedirs": ["Incidents", "Sports", "Stocks", "Weather"], "split" : "dev"}),
-            datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filedirs": ["Incidents", "Sports", "Stocks", "Weather"], "split" : "test"}),
-        ]
-
-    def _generate_examples(self, filedirs, split):
-        """Yields examples."""
-        id_ = 0
-    
-        for lang in ["en", "nl"]:
-            for filedir in filedirs:
-                xml_file = os.path.join(lang, filedir, f"WebNLGFormat{split.title()}.xml")
-
-                for exple_dict in xml_file_to_examples(xml_file):
-                    exple_dict["category"] = filedir
-                    exple_dict["lang"] = lang
-                    id_ += 1
-                    yield id_, exple_dict
-
-
-if __name__ == '__main__':
-    dataset = datasets.load_dataset(__file__)
-    dataset.push_to_hub("kasnerz/cacapo")