Upload multilingual_tacred.py

multilingual_tacred.py

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+# 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 supports a reduced version of the original TACRED JSON format with the following changes:
+- Removed fields: stanford_pos, stanford_ner, stanford_head, stanford_deprel, docid
+The motivation for this is that we want to support additional languages, for which these fields were not required
+or available. The reader expects the specification of a language-specific configuration specifying the variant
+(original or revised) and the language (as a two-letter iso code). The default config is 'original-en'.
+
+The 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)
+- obj_end to obj_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",
+]
+
+
+_NER_CLASS_LABELS = [
+    "LOCATION",
+    "ORGANIZATION",
+    "PERSON",
+    "DATE",
+    "MONEY",
+    "PERCENT",
+    "TIME",
+    "CAUSE_OF_DEATH",
+    "CITY",
+    "COUNTRY",
+    "CRIMINAL_CHARGE",
+    "EMAIL",
+    "HANDLE",
+    "IDEOLOGY",
+    "NATIONALITY",
+    "RELIGION",
+    "STATE_OR_PROVINCE",
+    "TITLE",
+    "URL",
+    "NUMBER",
+    "ORDINAL",
+    "MISC",
+    "DURATION",
+    "O",
+]
+
+
+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')`."
+            "Language-specific versions must be requested from git.nlp@dfki.de."
+        )
+
+    def _info(self):
+        features = datasets.Features(
+            {
+                "id": datasets.Value("string"),
+                "token": datasets.Sequence(datasets.Value("string")),
+                "subj_start": datasets.Value("int32"),
+                "subj_end": datasets.Value("int32"),
+                "subj_type": datasets.ClassLabel(names=_NER_CLASS_LABELS),
+                "obj_start": datasets.Value("int32"),
+                "obj_end": datasets.Value("int32"),
+                "obj_type": datasets.ClassLabel(names=_NER_CLASS_LABELS),
+                "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 = {}
+        variant, lang = self.config.name.split("-")
+        if variant == "revised":
+            patch_files = dl_manager.download_and_extract(_PATCH_URLs)
+
+        data_dir = os.path.abspath(os.path.expanduser(dl_manager.manual_dir))
+
+        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."""
+        # 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"],
+                    "token": [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"],
+                }