Upload fdRE.py

fdRE.py

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+import os
+
+import datasets
+from datasets.tasks import TextClassification
+
+
+_CITATION = """\
+        @author tianjie
+        fdRE
+        Chinese
+}
+"""
+
+_DESCRIPTION = """\
+     fdRE是一个中文的轴承故障诊断领域的关系抽取数据集
+     该数据集主要包含正向从属、反向从属以及无关三类标签
+"""
+
+_URL = "https://github.com/leonadase/FD_RE/archive/refs/heads/main.zip"
+
+
+class SemEval2010Task8(datasets.GeneratorBasedBuilder):
+    """The SemEval-2010 Task 8 focuses on Multi-way classification of semantic relations between pairs of nominals.
+    The task was designed to compare different approaches to semantic relation classification
+    and to provide a standard testbed for future research."""
+
+    VERSION = datasets.Version("1.0.0")
+
+    def _info(self):
+        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=datasets.Features(
+                {
+                    "sentence": datasets.Value("string"),
+                    "relation": datasets.ClassLabel(
+                        names=[
+                            "Part_Of(E1,E2)",
+                            "Part_Of(E2,E1)",
+                            "Other",
+                        ]
+                    ),
+                }
+            ),
+            # 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=datasets.info.SupervisedKeysData(input="sentence", output="relation"),
+            # Homepage of the dataset for documentation
+            citation=_CITATION,
+            task_templates=[TextClassification(text_column="sentence", label_column="relation")],
+        )
+
+    def _split_generators(self, dl_manager):
+        """Returns SplitGenerators."""
+        # dl_manager is a datasets.download.DownloadManager that can be used to
+        # download and extract URLs
+        dl_dir = dl_manager.download_and_extract(_URL)
+        data_dir = os.path.join(dl_dir, "fdRE")
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={
+                    "filepath": os.path.join(data_dir, "train.txt"),
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={
+                    "filepath": os.path.join(data_dir, "test.txt"),
+                },
+            ),
+        ]
+
+    def _generate_examples(self, filepath):
+        """Yields examples."""
+        with open(filepath, "r", encoding="us-ascii") as file:
+            lines = file.readlines()
+            num_lines_per_sample = 4
+
+            for i in range(0, len(lines), num_lines_per_sample):
+                idx = int(lines[i].split("\t")[0])
+                sentence = lines[i].split("\t")[1][1:-2]  # remove " at the start and "\n at the end
+                relation = lines[i + 1][:-1]  # remove \n at the end
+                yield idx, {
+                    "sentence": sentence,
+                    "relation": relation,
+                }