Upload fact2020.py

fact2020.py

@@ -0,0 +1,104 @@
+# coding=utf-8
+#
+#
+
+# Lint as: python3
+"""Overview of FACT at IberLEF 2020: Events Detection and Classification"""
+
+import os
+
+import datasets
+
+
+logger = datasets.logging.get_logger(__name__)
+
+
+_CITATION = """\
+@inproceedings{fact2020,
+    title = "Overview of FACT at IberLEF 2020: Events Detection and Classification",
+    author = "Rosa, Aiala and Chiruzzo, Luis and Wonsever, Dina and Malcuori, Marisa and Curell, Hortènsia and Castellón, Irene and Vázquez, Gloria and Fernández-Montraveta, Ana and Góngora, Santiago and Alonso, Laura",
+    booktitle = "Proceedings of the Seventh Conference on Natural Language Learning at {HLT}-{NAACL} 2003",
+    year = "2020",
+    url = "https://www.aclweb.org/anthology/W03-0419",
+}
+"""
+
+_DESCRIPTION = """\
+In this paper we present the second edition of the FACT shared task (Factuality Annotation and Classification
+Task), included in IberLEF2020. The main objective of this task is to advance in the study of the factuality of
+the events mentioned in texts. This year, the FACT task includes a subtask on event identification in addition
+to the factuality classification subtask. We describe the submitted systems as well as the corpus used, which is
+the same used in FACT 2019 but extended by adding annotations for nominal events.
+"""
+
+_URL = "https://huggingface.co/datasets/filevich/fact2020/raw/main/"
+_URLS = {
+  "train": _URL + "train.json",
+  "validation": _URL + "validation.json",
+  "test": _URL + "test-task1.json",
+}
+
+_TRAINING_FILE = "train.txt"
+_DEV_FILE = "valid.txt"
+_TEST_FILE = "test.txt"
+
+
+class Fact2020Config(datasets.BuilderConfig):
+    """BuilderConfig for Fact2020"""
+
+    def __init__(self, **kwargs):
+        """BuilderConfig forFact2020.
+        Args:
+          **kwargs: keyword arguments forwarded to super.
+        """
+        super(Fact2020Config, self).__init__(**kwargs)
+
+
+class Fact2020(datasets.GeneratorBasedBuilder):
+    """Fact2020 dataset."""
+
+    BUILDER_CONFIGS = [
+        Fact2020Config(name="fact2020", version=datasets.Version("1.0.0"), description="Fact2020 dataset"),
+    ]
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features(
+                {
+                    "id": datasets.Value("string"),
+                    "tokens": datasets.Sequence(datasets.Value("string")),
+                    "fact_tags": datasets.Sequence(
+                        datasets.features.ClassLabel(
+                            names=[
+                                "O",
+                                "F",
+                                "CF",
+                                "U",
+                            ]
+                        )
+                    ),
+                }
+            ),
+            supervised_keys=None,
+            homepage="https://ceur-ws.org/Vol-2664/fact_overview.pdf",
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        urls_to_download = self._URLS
+        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.VALIDATION, gen_kwargs={"filepath": downloaded_files["validation"]}),
+            datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": downloaded_files["test"]}),
+        ]
+
+    def _generate_examples(self, filepath):
+        logger.info("⏳ Generating examples from = %s", filepath)
+        
+        import json
+        with open(filepath, encoding="utf-8") as f:
+          data = json.load(f)
+          return [(i, {"id": str(i), **d}) for i,d in enumerate(data)]
+