Upload tacred.py

tacred.py

@@ -0,0 +1,257 @@
+"""TODO: Add a description here."""
+
+
+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",
+}
+"""
+
+# TODO: Add description of the dataset here
+# You can copy an official description
+_DESCRIPTION = """\
+This new dataset is designed to solve this great NLP task and is crafted with a lot of care.
+"""
+
+# TODO: Add a link to an official homepage for the dataset here
+_HOMEPAGE = ""
+
+# TODO: Add the licence for the dataset here if you can find it
+_LICENSE = ""
+
+# TODO: Add link to the official dataset URLs here
+# 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",
+}
+
+_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)
+
+
+# TODO: Name of the dataset usually match the script name with CamelCase instead of snake_case
+class TACRED(datasets.GeneratorBasedBuilder):
+    """TODO: Short description of my dataset."""
+
+    # This is an example of a dataset with multiple configurations.
+    # If you don't want/need to define several sub-sets in your dataset,
+    # just remove the BUILDER_CONFIG_CLASS and the BUILDER_CONFIGS attributes.
+
+    # If you need to make complex sub-parts in the datasets with configurable options
+    # You can create your own builder configuration class to store attribute, inheriting from datasets.BuilderConfig
+    # BUILDER_CONFIG_CLASS = MyBuilderConfig
+
+    # You will be able to load one or the other configurations in the following list with
+    # data = datasets.load_dataset('my_dataset', 'first_domain')
+    # data = datasets.load_dataset('my_dataset', 'second_domain')
+    BUILDER_CONFIGS = [
+        datasets.BuilderConfig(
+            name="original", version=datasets.Version("1.0.0"), description="The original TACRED."
+        ),
+        datasets.BuilderConfig(
+            name="revised",
+            version=datasets.Version("1.0.0"),
+            description="The revised TACRED (corrected labels in dev and test split).",
+        ),
+    ]
+
+    DEFAULT_CONFIG_NAME = "original"  # 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')`"
+        )
+
+    def _info(self):
+        features = datasets.Features(
+            {
+                "tokens": datasets.Sequence(datasets.Value("string")),
+                "head_start": datasets.Value("int32"),
+                "head_end": datasets.Value("int32"),
+                "tail_start": datasets.Value("int32"),
+                "tail_end": datasets.Value("int32"),
+                "label": 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."""
+        # TODO: This method is tasked with downloading/extracting the data and defining the splits depending on the configuration
+        # 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 == "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('tacred', 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, "train.json"),
+                    "patch_filepath": None,
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={
+                    "filepath": os.path.join(data_dir, "test.json"),
+                    "patch_filepath": patch_files.get("test"),
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                gen_kwargs={
+                    "filepath": os.path.join(data_dir, "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_, {
+                    "tokens": [convert_ptb_token(token) for token in example["token"]],
+                    "head_start": example["subj_start"],
+                    "head_end": example["subj_end"] + 1,  # make end offset exclusive
+                    "tail_start": example["obj_start"],
+                    "tail_end": example["obj_end"] + 1,  # make end offset exclusive
+                    "label": example["relation"],
+                }