Upload kptimes.py

kptimes.py

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+import json
+import datasets
+
+# _SPLIT = ['train', 'test', 'valid']
+_CITATION = """\
+@inproceedings{gallina2019kptimes,
+  title={KPTimes: A Large-Scale Dataset for Keyphrase Generation on News Documents},
+  author={Gallina, Ygor and Boudin, Florian and Daille, B{\'e}atrice},
+  booktitle={Proceedings of the 12th International Conference on Natural Language Generation},
+  pages={130--135},
+  year={2019}
+}
+"""
+
+_DESCRIPTION = """\
+
+"""
+
+_HOMEPAGE = "https://github.com/ygorg/KPTimes"
+
+# TODO: Add the licence for the dataset here if you can find it
+_LICENSE = "Apache License 2.0"
+
+# TODO: Add link to the official dataset URLs here
+
+_URLS = {
+    "test": "test.jsonl",
+    "train": "train.jsonl",
+    "valid": "valid.jsonl"
+}
+
+
+# TODO: Name of the dataset usually match the script name with CamelCase instead of snake_case
+class KPTimes(datasets.GeneratorBasedBuilder):
+    """TODO: Short description of my dataset."""
+
+    VERSION = datasets.Version("0.0.1")
+
+    BUILDER_CONFIGS = [
+        datasets.BuilderConfig(name="extraction", version=VERSION,
+                               description="This part of my dataset covers extraction"),
+        datasets.BuilderConfig(name="generation", version=VERSION,
+                               description="This part of my dataset covers generation"),
+        datasets.BuilderConfig(name="raw", version=VERSION, description="This part of my dataset covers the raw data"),
+    ]
+
+    DEFAULT_CONFIG_NAME = "extraction"
+
+    def _info(self):
+        if self.config.name == "extraction":  # This is the name of the configuration selected in BUILDER_CONFIGS above
+            features = datasets.Features(
+                {
+                    "id": datasets.Value("int64"),
+                    "document": datasets.features.Sequence(datasets.Value("string")),
+                    "doc_bio_tags": datasets.features.Sequence(datasets.Value("string"))
+
+                }
+            )
+        elif self.config.name == "generation":
+            features = datasets.Features(
+                {
+                    "id": datasets.Value("int64"),
+                    "document": datasets.features.Sequence(datasets.Value("string")),
+                    "extractive_keyphrases": datasets.features.Sequence(datasets.Value("string")),
+                    "abstractive_keyphrases": datasets.features.Sequence(datasets.Value("string"))
+
+                }
+            )
+        else:
+            features = datasets.Features(
+                {
+                    "id": datasets.Value("int64"),
+                    "document": datasets.features.Sequence(datasets.Value("string")),
+                    "doc_bio_tags": datasets.features.Sequence(datasets.Value("string")),
+                    "extractive_keyphrases": datasets.features.Sequence(datasets.Value("string")),
+                    "abstractive_keyphrases": datasets.features.Sequence(datasets.Value("string")),
+                    "other_metadata": datasets.features.Sequence(
+                        {
+                            "text": datasets.features.Sequence(datasets.Value("string")),
+                            "bio_tags": datasets.features.Sequence(datasets.Value("string"))
+                        }
+                    )
+
+                }
+            )
+        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,
+            homepage=_HOMEPAGE,
+            # License for the dataset if available
+            license=_LICENSE,
+            # Citation for the dataset
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+
+        data_dir = dl_manager.download_and_extract(_URLS)
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={
+                    "filepath": data_dir['train'],
+                    "split": "train",
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={
+                    "filepath": data_dir['test'],
+                    "split": "test"
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={
+                    "filepath": data_dir['valid'],
+                    "split": "valid",
+                },
+            ),
+        ]
+
+    # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
+    def _generate_examples(self, filepath, split):
+        with open(filepath, encoding="utf-8") as f:
+            for key, row in enumerate(f):
+                data = json.loads(row)
+                if self.config.name == "extraction":
+                    # Yields examples as (key, example) tuples
+                    yield key, {
+                        "id": data['paper_id'],
+                        "document": data["document"],
+                        "doc_bio_tags": data.get("doc_bio_tags")
+                    }
+                elif self.config.name == "generation":
+                    yield key, {
+                        "id": data['paper_id'],
+                        "document": data["document"],
+                        "extractive_keyphrases": data.get("extractive_keyphrases"),
+                        "abstractive_keyphrases": data.get("abstractive_keyphrases")
+                    }
+                else:
+                    yield key, {
+                        "id": data['paper_id'],
+                        "document": data["document"],
+                        "doc_bio_tags": data.get("doc_bio_tags"),
+                        "extractive_keyphrases": data.get("extractive_keyphrases"),
+                        "abstractive_keyphrases": data.get("abstractive_keyphrases"),
+                        "other_metadata": data["other_metadata"]
+                    }