Delete loading script

conll2000.py

@@ -1,187 +0,0 @@
-# coding=utf-8
-# Copyright 2020 HuggingFace Datasets Authors.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-# Lint as: python3
-"""Introduction to the CoNLL-2000 Shared Task: Chunking"""
-
-import datasets
-
-
-logger = datasets.logging.get_logger(__name__)
-
-
-_CITATION = """\
-@inproceedings{tksbuchholz2000conll,
-   author     = "Tjong Kim Sang, Erik F. and Sabine Buchholz",
-   title      = "Introduction to the CoNLL-2000 Shared Task: Chunking",
-   editor     = "Claire Cardie and Walter Daelemans and Claire
-                 Nedellec and Tjong Kim Sang, Erik",
-   booktitle  = "Proceedings of CoNLL-2000 and LLL-2000",
-   publisher  = "Lisbon, Portugal",
-   pages      = "127--132",
-   year       = "2000"
-}
-"""
-
-_DESCRIPTION = """\
- Text chunking consists of dividing a text in syntactically correlated parts of words. For example, the sentence
- He reckons the current account deficit will narrow to only # 1.8 billion in September . can be divided as follows:
-[NP He ] [VP reckons ] [NP the current account deficit ] [VP will narrow ] [PP to ] [NP only # 1.8 billion ]
-[PP in ] [NP September ] .
-
-Text chunking is an intermediate step towards full parsing. It was the shared task for CoNLL-2000. Training and test
-data for this task is available. This data consists of the same partitions of the Wall Street Journal corpus (WSJ)
-as the widely used data for noun phrase chunking: sections 15-18 as training data (211727 tokens) and section 20 as
-test data (47377 tokens). The annotation of the data has been derived from the WSJ corpus by a program written by
-Sabine Buchholz from Tilburg University, The Netherlands.
-"""
-
-_URL = "https://github.com/teropa/nlp/raw/master/resources/corpora/conll2000/"
-_TRAINING_FILE = "train.txt"
-_TEST_FILE = "test.txt"
-
-
-class Conll2000(datasets.GeneratorBasedBuilder):
-    """Conll2000 dataset."""
-
-    def _info(self):
-        return datasets.DatasetInfo(
-            description=_DESCRIPTION,
-            features=datasets.Features(
-                {
-                    "id": datasets.Value("string"),
-                    "tokens": datasets.Sequence(datasets.Value("string")),
-                    "pos_tags": datasets.Sequence(
-                        datasets.features.ClassLabel(
-                            names=[
-                                "''",
-                                "#",
-                                "$",
-                                "(",
-                                ")",
-                                ",",
-                                ".",
-                                ":",
-                                "``",
-                                "CC",
-                                "CD",
-                                "DT",
-                                "EX",
-                                "FW",
-                                "IN",
-                                "JJ",
-                                "JJR",
-                                "JJS",
-                                "MD",
-                                "NN",
-                                "NNP",
-                                "NNPS",
-                                "NNS",
-                                "PDT",
-                                "POS",
-                                "PRP",
-                                "PRP$",
-                                "RB",
-                                "RBR",
-                                "RBS",
-                                "RP",
-                                "SYM",
-                                "TO",
-                                "UH",
-                                "VB",
-                                "VBD",
-                                "VBG",
-                                "VBN",
-                                "VBP",
-                                "VBZ",
-                                "WDT",
-                                "WP",
-                                "WP$",
-                                "WRB",
-                            ]
-                        )
-                    ),
-                    "chunk_tags": datasets.Sequence(
-                        datasets.features.ClassLabel(
-                            names=[
-                                "O",
-                                "B-ADJP",
-                                "I-ADJP",
-                                "B-ADVP",
-                                "I-ADVP",
-                                "B-CONJP",
-                                "I-CONJP",
-                                "B-INTJ",
-                                "I-INTJ",
-                                "B-LST",
-                                "I-LST",
-                                "B-NP",
-                                "I-NP",
-                                "B-PP",
-                                "I-PP",
-                                "B-PRT",
-                                "I-PRT",
-                                "B-SBAR",
-                                "I-SBAR",
-                                "B-UCP",
-                                "I-UCP",
-                                "B-VP",
-                                "I-VP",
-                            ]
-                        )
-                    ),
-                }
-            ),
-            supervised_keys=None,
-            homepage="https://www.clips.uantwerpen.be/conll2000/chunking/",
-            citation=_CITATION,
-        )
-
-    def _split_generators(self, dl_manager):
-        """Returns SplitGenerators."""
-        urls_to_download = {
-            "train": f"{_URL}{_TRAINING_FILE}",
-            "test": f"{_URL}{_TEST_FILE}",
-        }
-        downloaded_files = dl_manager.download_and_extract(urls_to_download)
-
-        return [
-            datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloaded_files["train"]}),
-            datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": downloaded_files["test"]}),
-        ]
-
-    def _generate_examples(self, filepath):
-        logger.info("⏳ Generating examples from = %s", filepath)
-        with open(filepath, encoding="utf-8") as f:
-            guid = 0
-            tokens = []
-            pos_tags = []
-            chunk_tags = []
-            for line in f:
-                if line == "" or line == "\n":
-                    if tokens:
-                        yield guid, {"id": str(guid), "tokens": tokens, "pos_tags": pos_tags, "chunk_tags": chunk_tags}
-                        guid += 1
-                        tokens = []
-                        pos_tags = []
-                        chunk_tags = []
-                else:
-                    # conll2000 tokens are space separated
-                    splits = line.split(" ")
-                    tokens.append(splits[0])
-                    pos_tags.append(splits[1])
-                    chunk_tags.append(splits[2].rstrip())
-            # last example
-            yield guid, {"id": str(guid), "tokens": tokens, "pos_tags": pos_tags, "chunk_tags": chunk_tags}