Upload AMIsum.py

AMIsum.py

@@ -0,0 +1,90 @@
+import json
+import os
+
+import datasets
+
+
+class ERRNewsConfig(datasets.BuilderConfig):
+    def __init__(self, data_url, features, **kwargs):
+        super().__init__(version=datasets.Version("1.0.0"), **kwargs)
+        self.features = features
+        self.data_url = data_url
+
+
+class ERRNews(datasets.GeneratorBasedBuilder):
+    features = ["transcript", "summary", "id"]
+    data_url = "https://cs.taltech.ee/staff/heharm/AMIsum/"
+
+    BUILDER_CONFIGS = [
+        ERRNewsConfig(
+            name="full",
+            features=features,
+            data_url=data_url
+        )
+    ]
+
+    DEFAULT_CONFIG_NAME = "full"
+
+    def _info(self):
+        features = datasets.Features(
+            {
+                "transcript": datasets.Value("string"),
+                "summary": datasets.Value("string"),
+                "id": datasets.Value("string"),
+            })
+
+        description = """\
+        AMI Summarization Dataset (AMIsum) is a meeting summarization dataset, consisting of meeting transcripts \
+        and abstract summaries from the AMI Corpus: https://groups.inf.ed.ac.uk/ami/corpus/.
+        """
+
+        return datasets.DatasetInfo(
+            features=features,
+            description=description,
+            supervised_keys=None,
+            version=self.config.version,
+        )
+
+    def _split_generators(self, dl_manager):
+        """Returns SplitGenerators."""
+        train = "train.json"
+        test = "test.json"
+        val = "val.json"
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "file_path": dl_manager.download(self.config.data_url + train),
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                gen_kwargs={
+                    "file_path": dl_manager.download(self.config.data_url + val),
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={
+                    "file_path": dl_manager.download(self.config.data_url + test),
+                },
+            ),
+        ]
+
+    def create_dict(self, data):
+        res = dict()
+        for key in self.config.features:
+            res[key] = data[key]
+        return res
+
+    def _generate_examples(self, file_path):
+        with open(file_path) as f:
+            data = json.load(f)
+            for idx, transcript in enumerate(data["transcript"]):
+                id_ = data["id"][idx]
+                yield id_, {
+                    "transcript": transcript,
+                    "summary": data["summary"][idx],
+                    "id": id_,
+                }