Delete loading script

best2009.py

@@ -1,138 +0,0 @@
-import os
-from functools import reduce
-from pathlib import Path
-
-import datasets
-
-
-_CITATION = """\
-@inproceedings{kosawat2009best,
-  title={BEST 2009: Thai word segmentation software contest},
-  author={Kosawat, Krit and Boriboon, Monthika and Chootrakool, Patcharika and Chotimongkol, Ananlada and Klaithin, Supon and Kongyoung, Sarawoot and Kriengket, Kanyanut and Phaholphinyo, Sitthaa and Purodakananda, Sumonmas and Thanakulwarapas, Tipraporn and others},
-  booktitle={2009 Eighth International Symposium on Natural Language Processing},
-  pages={83--88},
-  year={2009},
-  organization={IEEE}
-}
-@inproceedings{boriboon2009best,
-  title={Best corpus development and analysis},
-  author={Boriboon, Monthika and Kriengket, Kanyanut and Chootrakool, Patcharika and Phaholphinyo, Sitthaa and Purodakananda, Sumonmas and Thanakulwarapas, Tipraporn and Kosawat, Krit},
-  booktitle={2009 International Conference on Asian Language Processing},
-  pages={322--327},
-  year={2009},
-  organization={IEEE}
-}
-"""
-
-_LICENSE = "CC-BY-NC-SA 3.0"
-
-_DESCRIPTION = """\
-`best2009` is a Thai word-tokenization dataset from encyclopedia, novels, news and articles by
-[NECTEC](https://www.nectec.or.th/) (148,995/2,252 lines of train/test). It was created for
-[BEST 2010: Word Tokenization Competition](https://thailang.nectec.or.th/archive/indexa290.html?q=node/10).
-The test set answers are not provided publicly.
-"""
-
-
-class Best2009Config(datasets.BuilderConfig):
-    def __init__(self, **kwargs):
-        """BuilderConfig
-
-        Args:
-          **kwargs: keyword arguments forwarded to super.
-        """
-        super(Best2009Config, self).__init__(**kwargs)
-
-
-class Best2009(datasets.GeneratorBasedBuilder):
-
-    _DOWNLOAD_URL = "https://archive.org/download/best_dataset/data.zip"
-    _TRAIN_FOLDER = "train"
-    _TEST_FOLDER = "test"
-
-    _USELESS_TAGS = {"<NE>": "", "</NE>": "", "<AB>": "", "</AB>": ""}
-    # character type mapping from https://github.com/rkcosmos/deepcut/blob/master/deepcut/utils.py
-    _CHAR_TYPES_DICT = {
-        "กขฃคฆงจชซญฎฏฐฑฒณดตถทธนบปพฟภมยรลวศษสฬอ": "c",
-        "ฅฉผฟฌหฮ": "n",
-        "ะาำิีืึุู": "v",  # า ะ ำ ิ ี ึ ื ั ู ุ
-        "เแโใไ": "w",
-        "่้๊๋": "t",  # วรรณยุกต์ ่ ้ ๊ ๋
-        "์ๆฯ.": "s",  # ์  ๆ ฯ .
-        "0123456789๑๒๓๔๕๖๗๘๙": "d",
-        '"': "q",
-        "‘": "q",
-        "’": "q",
-        "'": "q",
-        " ": "p",
-        "abcdefghijklmnopqrstuvwxyz": "s_e",
-        "ABCDEFGHIJKLMNOPQRSTUVWXYZ": "b_e",
-    }
-    _CHAR_TYPE_FLATTEN = {}
-    for ks, v in _CHAR_TYPES_DICT.items():
-        for k in ks:
-            _CHAR_TYPE_FLATTEN[k] = v
-    _CHAR_TYPES = ["b_e", "c", "d", "n", "o", "p", "q", "s", "s_e", "t", "v", "w"]
-
-    BUILDER_CONFIGS = [
-        Best2009Config(
-            name="best2009",
-            version=datasets.Version("1.0.0"),
-            description=_DESCRIPTION,
-        ),
-    ]
-
-    def _info(self):
-        return datasets.DatasetInfo(
-            description=_DESCRIPTION,
-            features=datasets.Features(
-                {
-                    "fname": datasets.Value("string"),
-                    "char": datasets.Sequence(datasets.Value("string")),
-                    "char_type": datasets.Sequence(datasets.features.ClassLabel(names=self._CHAR_TYPES)),
-                    "is_beginning": datasets.Sequence(datasets.features.ClassLabel(names=["neg", "pos"])),
-                }
-            ),
-            supervised_keys=None,
-            homepage="https://aiforthai.in.th/",
-            citation=_CITATION,
-            license=_LICENSE,
-        )
-
-    def _split_generators(self, dl_manager):
-        arch_path = dl_manager.download_and_extract(self._DOWNLOAD_URL)
-        data_dir = os.path.join(arch_path, "data")
-        return [
-            datasets.SplitGenerator(
-                name=datasets.Split.TRAIN,
-                gen_kwargs={"filepath": os.path.join(data_dir, self._TRAIN_FOLDER), "split": "train"},
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.TEST,
-                gen_kwargs={"filepath": os.path.join(data_dir, self._TEST_FOLDER), "split": "train"},
-            ),
-        ]
-
-    def _generate_examples(self, filepath, split):
-        for file_idx, fname in enumerate(sorted(Path(filepath).rglob("*.txt"))):
-            with open(fname, encoding="utf-8") as f:
-                for line_idx, line in enumerate(f):
-                    chars = []
-                    char_types = []
-                    is_beginnings = []
-                    # replace useless tokens
-                    line = reduce(lambda a, kv: a.replace(*kv), self._USELESS_TAGS.items(), line)
-                    # tokens are pipe separated
-                    splits = line.split("|")
-                    for token in splits:
-                        for i in range(len(token)):
-                            chars.append(token[i])
-                            char_types.append(self._CHAR_TYPE_FLATTEN.get(token[i], "o"))
-                            is_beginning = 1 if i == 0 else 0
-                            is_beginnings.append(is_beginning)
-                    yield f"{file_idx}_{line_idx}", {
-                        "fname": fname.name,
-                        "char": chars,
-                        "char_type": char_types,
-                        "is_beginning": is_beginnings if split == "train" else [0 for i in range(len(chars))],
-                    }