initial loader

dataset_infos.json

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+{"default": {"description": "ViGGO was designed for the task of data-to-text generation in chatbots (as opposed to task-oriented dialogue systems), with target responses being more conversational than information-seeking, yet constrained to the information presented in a meaning representation. The dataset, being relatively small and clean, can also serve for demonstrating transfer learning capabilities of neural models.\n", "citation": "@inproceedings{juraska-etal-2019-viggo,\n    title = \"{V}i{GGO}: A Video Game Corpus for Data-To-Text Generation in Open-Domain Conversation\",\n    author = \"Juraska, Juraj  and\n      Bowden, Kevin  and\n      Walker, Marilyn\",\n    booktitle = \"Proceedings of the 12th International Conference on Natural Language Generation\",\n    month = oct # \"{--}\" # nov,\n    year = \"2019\",\n    address = \"Tokyo, Japan\",\n    publisher = \"Association for Computational Linguistics\",\n    url = \"https://aclanthology.org/W19-8623\",\n    doi = \"10.18653/v1/W19-8623\",\n    pages = \"164--172\",\n}\n", "homepage": "https://nlds.soe.ucsc.edu/viggo", "license": "", "features": {"gem_id": {"dtype": "string", "id": null, "_type": "Value"}, "meaning_representation": {"dtype": "string", "id": null, "_type": "Value"}, "target": {"dtype": "string", "id": null, "_type": "Value"}, "references": [{"dtype": "string", "id": null, "_type": "Value"}]}, "post_processed": null, "supervised_keys": {"input": "meaning_representation", "output": "target"}, "task_templates": null, "builder_name": "viggo", "config_name": "default", "version": {"version_str": "1.0.0", "description": null, "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 2094490, "num_examples": 5103, "dataset_name": "viggo"}, "validation": {"name": "validation", "num_bytes": 285396, "num_examples": 714, "dataset_name": "viggo"}, "test": {"name": "test", "num_bytes": 415074, "num_examples": 1083, "dataset_name": "viggo"}, "challenge_train_1_percent": {"name": "challenge_train_1_percent", "num_bytes": 19585, "num_examples": 50, "dataset_name": "viggo"}}, "download_checksums": {"train.csv": {"num_bytes": 1370770, "checksum": "39d4bf7ef5b7b78c1c137d05d461e779884ad08cf53e0ee6cf63c3653c5f8aae"}, "validation.csv": {"num_bytes": 187448, "checksum": "c4d4b3b5075d84c1645cccd678e665e0d2a40226e3e9a383270a5c666006adbc"}, "test.csv": {"num_bytes": 268969, "checksum": "020f24199655b4c6f9246123263d2c6d3ea01189046150153dcc6cf8b914037a"}, "challenge_train_1_percent.csv": {"num_bytes": 11230, "checksum": "84a2540a71b5034c0d454899d6f68bfd2c0a48c7e3c6fb8e34723189bf818eaa"}}, "download_size": 1838417, "post_processing_size": null, "dataset_size": 2814545, "size_in_bytes": 4652962}}

viggo.py

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+import csv
+import json
+import os
+import datasets
+
+_CITATION = """\
+@inproceedings{juraska-etal-2019-viggo,
+    title = "{V}i{GGO}: A Video Game Corpus for Data-To-Text Generation in Open-Domain Conversation",
+    author = "Juraska, Juraj  and
+      Bowden, Kevin  and
+      Walker, Marilyn",
+    booktitle = "Proceedings of the 12th International Conference on Natural Language Generation",
+    month = oct # "{--}" # nov,
+    year = "2019",
+    address = "Tokyo, Japan",
+    publisher = "Association for Computational Linguistics",
+    url = "https://aclanthology.org/W19-8623",
+    doi = "10.18653/v1/W19-8623",
+    pages = "164--172",
+}
+"""
+
+_DESCRIPTION = """\
+ViGGO was designed for the task of data-to-text generation in chatbots (as opposed to task-oriented dialogue systems), with target responses being more conversational than information-seeking, yet constrained to the information presented in a meaning representation. The dataset, being relatively small and clean, can also serve for demonstrating transfer learning capabilities of neural models.
+"""
+
+_URLs = {
+    "train": "train.csv",
+    "validation": "validation.csv",
+    "test": "test.csv",
+    "challenge_train_1_percent": "challenge_train_1_percent.csv",
+    "challenge_train_1_percent": "challenge_train_1_percent.csv",
+    "challenge_train_1_percent": "challenge_train_1_percent.csv",
+    "challenge_train_1_percent": "challenge_train_1_percent.csv",
+    "challenge_train_1_percent": "challenge_train_1_percent.csv",
+    "challenge_train_1_percent": "challenge_train_1_percent.csv",
+}
+
+
+class Viggo(datasets.GeneratorBasedBuilder):
+    VERSION = datasets.Version("1.0.0")
+    DEFAULT_CONFIG_NAME = "viggo"
+
+    def _info(self):
+        features = datasets.Features(
+            {
+                "gem_id": datasets.Value("string"),
+                "meaning_representation": datasets.Value("string"),
+                "target": datasets.Value("string"),
+                "references": [datasets.Value("string")],
+            }
+        )
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=features,
+            supervised_keys=datasets.info.SupervisedKeysData(
+                input="meaning_representation", output="target"
+            ),
+            homepage="https://nlds.soe.ucsc.edu/viggo",
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        """Returns SplitGenerators."""
+        dl_dir = dl_manager.download_and_extract(_URLs)
+        return [
+            datasets.SplitGenerator(
+                name=spl, gen_kwargs={"filepath": dl_dir[spl], "split": spl}
+            )
+            for spl in _URLs.keys()
+        ]
+
+    def _generate_examples(self, filepath, split, filepaths=None, lang=None):
+        """Yields examples."""
+        with open(filepath, "r", encoding='utf-8-sig') as csvfile:
+            reader = csv.DictReader(csvfile)
+            for id_, row in enumerate(reader):
+                yield id_, {
+                    "gem_id": f"cs_restaurants-{split}-{id_}",
+                    "meaning_representation": row["mr"],
+                    "target": row["ref"],
+                    "references": [row["ref"]],
+                }