Delete tacred_dfki.py

tacred_dfki.py

@@ -1,304 +0,0 @@
-# coding=utf-8
-# Copyright 2022 The current dataset script contributor.
-#
-# 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.
-
-"""The TACRED Relation Classification dataset in various languages, DFKI format."""
-import itertools
-import json
-import os
-
-import datasets
-
-_CITATION = """\
-@inproceedings{zhang-etal-2017-position,
-    title = "Position-aware Attention and Supervised Data Improve Slot Filling",
-    author = "Zhang, Yuhao  and
-      Zhong, Victor  and
-      Chen, Danqi  and
-      Angeli, Gabor  and
-      Manning, Christopher D.",
-    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
-    month = sep,
-    year = "2017",
-    address = "Copenhagen, Denmark",
-    publisher = "Association for Computational Linguistics",
-    url = "https://www.aclweb.org/anthology/D17-1004",
-    doi = "10.18653/v1/D17-1004",
-    pages = "35--45",
-}
-
-@inproceedings{alt-etal-2020-tacred,
-    title = "{TACRED} Revisited: A Thorough Evaluation of the {TACRED} Relation Extraction Task",
-    author = "Alt, Christoph  and
-      Gabryszak, Aleksandra  and
-      Hennig, Leonhard",
-    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
-    month = jul,
-    year = "2020",
-    address = "Online",
-    publisher = "Association for Computational Linguistics",
-    url = "https://www.aclweb.org/anthology/2020.acl-main.142",
-    doi = "10.18653/v1/2020.acl-main.142",
-    pages = "1558--1569",
-}
-"""
-
-_DESCRIPTION = """\
-TACRED is a large-scale relation extraction dataset with 106,264 examples built over newswire
- and web text from the corpus used in the yearly TAC Knowledge Base Population (TAC KBP) challenges.
- Examples in TACRED cover 41 relation types as used in the TAC KBP challenges (e.g., per:schools_attended
- and org:members) or are labeled as no_relation if no defined relation is held. These examples are created
- by combining available human annotations from the TAC KBP challenges and crowdsourcing.
-
- Please see our EMNLP paper, or our EMNLP slides for full details.
-
-Note: There is currently a label-corrected version of the TACRED dataset, which you should consider using instead of
-the original version released in 2017. For more details on this new version, see the TACRED Revisited paper
-published at ACL 2020.
-
-NOTE: This Datasetreader does not support the original TACRED JSON format, but instead the DFKI version, with
-the following changes:
-- Removed fields: stanford_pos, stanford_ner, stanford_head, stanford_deprel, docid
-
-NOTE 2: This Datasetreader changes the offsets of the following fields, to conform with standard Python usage (see
-#_generate_examples()):
-- subj_end to subj_end + 1 (make end offset exclusive)
-- oj_end to oj_end + 1 (make end offset exclusive)
-"""
-
-_HOMEPAGE = "https://nlp.stanford.edu/projects/tacred/"
-
-_LICENSE = "LDC"
-
-_URL = "https://catalog.ldc.upenn.edu/LDC2018T24"
-
-# The HuggingFace dataset library don't host the datasets but only point to the original files
-# This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
-_PATCH_URLs = {
-    "dev": "https://raw.githubusercontent.com/DFKI-NLP/tacrev/master/patch/dev_patch.json",
-    "test": "https://raw.githubusercontent.com/DFKI-NLP/tacrev/master/patch/test_patch.json",
-}
-
-_VERSION = datasets.Version("1.0.0")
-
-_LANGS = [
-    "ar",
-    "de",
-    "en",
-    "es",
-    # "eu",
-    "fi",
-    "fr",
-    "hi",
-    "hu",
-    "ja",
-    "pl",
-    "ru",
-    "tr",
-    "zh",
-]
-
-_CLASS_LABELS = [
-    "no_relation",
-    "org:alternate_names",
-    "org:city_of_headquarters",
-    "org:country_of_headquarters",
-    "org:dissolved",
-    "org:founded",
-    "org:founded_by",
-    "org:member_of",
-    "org:members",
-    "org:number_of_employees/members",
-    "org:parents",
-    "org:political/religious_affiliation",
-    "org:shareholders",
-    "org:stateorprovince_of_headquarters",
-    "org:subsidiaries",
-    "org:top_members/employees",
-    "org:website",
-    "per:age",
-    "per:alternate_names",
-    "per:cause_of_death",
-    "per:charges",
-    "per:children",
-    "per:cities_of_residence",
-    "per:city_of_birth",
-    "per:city_of_death",
-    "per:countries_of_residence",
-    "per:country_of_birth",
-    "per:country_of_death",
-    "per:date_of_birth",
-    "per:date_of_death",
-    "per:employee_of",
-    "per:origin",
-    "per:other_family",
-    "per:parents",
-    "per:religion",
-    "per:schools_attended",
-    "per:siblings",
-    "per:spouse",
-    "per:stateorprovince_of_birth",
-    "per:stateorprovince_of_death",
-    "per:stateorprovinces_of_residence",
-    "per:title",
-]
-
-
-def convert_ptb_token(token: str) -> str:
-    """Convert PTB tokens to normal tokens"""
-    return {
-        "-lrb-": "(",
-        "-rrb-": ")",
-        "-lsb-": "[",
-        "-rsb-": "]",
-        "-lcb-": "{",
-        "-rcb-": "}",
-    }.get(token.lower(), token)
-
-
-class TacredDfkiConfig(datasets.BuilderConfig):
-    def __init__(self, **kwargs):
-        super(TacredDfkiConfig, self).__init__(version=_VERSION, **kwargs)
-
-
-class TacredDfki(datasets.GeneratorBasedBuilder):
-    """TACRED is a large-scale relation extraction dataset with 106,264 examples built over newswire
-    and web text from the corpus used in the yearly TAC Knowledge Base Population (TAC KBP) challenges."""
-
-    BUILDER_CONFIGS = [
-        TacredDfkiConfig(
-            name=f"{variant}-{lang}",
-            description=f"{'The revised TACRED (corrected labels in dev and test split)' if variant == 'revised' else 'The original TACRED'} examples in language '{lang}'.",
-        )
-        for (lang, variant) in itertools.product(_LANGS, ["original", "revised"])
-    ]
-
-    DEFAULT_CONFIG_NAME = "original-en"  # type: ignore
-
-    @property
-    def manual_download_instructions(self):
-        return (
-            "To use TACRED you have to download it manually. "
-            "It is available via the LDC at https://catalog.ldc.upenn.edu/LDC2018T24"
-            "Please extract all files in one folder and load the dataset with: "
-            "`datasets.load_dataset('tacred', data_dir='path/to/folder/folder_name')`."
-            "TODO: Language-specific versions must be downloaded from URL and extracted to PATH..."
-        )
-
-    def _info(self):
-        features = datasets.Features(
-            {
-                "id": datasets.Value("string"),
-                "tokens": datasets.Sequence(datasets.Value("string")),
-                "subj_start": datasets.Value("int32"),
-                "subj_end": datasets.Value("int32"),
-                "subj_type": datasets.Value("string"),
-                "obj_start": datasets.Value("int32"),
-                "obj_end": datasets.Value("int32"),
-                "obj_type": datasets.Value("string"),
-                "relation": datasets.ClassLabel(names=_CLASS_LABELS),
-            }
-        )
-
-        return datasets.DatasetInfo(
-            # This is the description that will appear on the datasets page.
-            description=_DESCRIPTION,
-            # This defines the different columns of the dataset and their types
-            features=features,  # Here we define them above because they are different between the two configurations
-            # If there's a common (input, target) tuple from the features,
-            # specify them here. They'll be used if as_supervised=True in
-            # builder.as_dataset.
-            supervised_keys=None,
-            # Homepage of the dataset for documentation
-            homepage=_HOMEPAGE,
-            # License for the dataset if available
-            license=_LICENSE,
-            # Citation for the dataset
-            citation=_CITATION,
-        )
-
-    def _split_generators(self, dl_manager):
-        """Returns SplitGenerators."""
-        # If several configurations are possible (listed in BUILDER_CONFIGS), the configuration selected by the user is in self.config.name
-
-        # dl_manager is a datasets.download.DownloadManager that can be used to download and extract URLs
-        # It can accept any type or nested list/dict and will give back the same structure with the url replaced with path to local files.
-        # By default the archives will be extracted and a path to a cached folder where they are extracted is returned instead of the archive
-        patch_files = {}
-        if self.config.name.startswith("revised-"):
-            patch_files = dl_manager.download_and_extract(_PATCH_URLs)
-
-        data_dir = os.path.abspath(os.path.expanduser(dl_manager.manual_dir))
-        lang = self.config.name.split("-")[1]
-
-        if not os.path.exists(data_dir):
-            raise FileNotFoundError(
-                "{} does not exist. Make sure you insert a manual dir via `datasets.load_dataset('DFKI-SLT/tacred_dfki', data_dir=...)` that includes the unzipped files from the TACRED_LDC zip. Manual download instructions: {}".format(
-                    data_dir, self.manual_download_instructions
-                )
-            )
-
-        return [
-            datasets.SplitGenerator(
-                name=datasets.Split.TRAIN,
-                gen_kwargs={
-                    "filepath": os.path.join(data_dir, lang, "train.json"),
-                    "patch_filepath": None,
-                },
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.TEST,
-                gen_kwargs={
-                    "filepath": os.path.join(data_dir, lang, "test.json"),
-                    "patch_filepath": patch_files.get("test"),
-                },
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.VALIDATION,
-                gen_kwargs={
-                    "filepath": os.path.join(data_dir, lang, "dev.json"),
-                    "patch_filepath": patch_files.get("dev"),
-                },
-            ),
-        ]
-
-    def _generate_examples(self, filepath, patch_filepath):
-        """Yields examples."""
-        # TODO: This method will receive as arguments the `gen_kwargs` defined in the previous `_split_generators` method.
-        # It is in charge of opening the given file and yielding (key, example) tuples from the dataset
-        # The key is not important, it's more here for legacy reason (legacy from tfds)
-        patch_examples = {}
-        if patch_filepath is not None:
-            with open(patch_filepath, encoding="utf-8") as f:
-                patch_examples = {example["id"]: example for example in json.load(f)}
-
-        with open(filepath, encoding="utf-8") as f:
-            data = json.load(f)
-            for example in data:
-                id_ = example["id"]
-
-                if id_ in patch_examples:
-                    example.update(patch_examples[id_])
-
-                yield id_, {
-                    "id": example["id"],
-                    "tokens": [convert_ptb_token(token) for token in example["token"]],
-                    "subj_start": example["subj_start"],
-                    "subj_end": example["subj_end"] + 1,  # make end offset exclusive
-                    "subj_type": example["subj_type"],
-                    "obj_start": example["obj_start"],
-                    "obj_end": example["obj_end"] + 1,  # make end offset exclusive
-                    "obj_type": example["obj_type"],
-                    "relation": example["relation"],
-                }