initial loader

dataset_infos.json

@@ -1 +1 @@
-{"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"}, "challenge_train_2_percent": {"name": "challenge_train_2_percent", "num_bytes": 43525, "num_examples": 103, "dataset_name": "viggo"}, "challenge_train_5_percent": {"name": "challenge_train_5_percent", "num_bytes": 111501, "num_examples": 256, "dataset_name": "viggo"}, "challenge_train_10_percent": {"name": "challenge_train_10_percent", "num_bytes": 215005, "num_examples": 510, "dataset_name": "viggo"}, "challenge_train_20_percent": {"name": "challenge_train_20_percent", "num_bytes": 441134, "num_examples": 1021, "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"}, "challenge_train_2_percent.csv": {"num_bytes": 25113, "checksum": "17f3f49f21d31f616e570ed10441f3cfa7af4feef0789c0a182b1ac0c0ac47c1"}, "challenge_train_5_percent.csv": {"num_bytes": 64269, "checksum": "6c497a8bcb545ac55151c836a6cd5a3ae0d702d8394298d85154a41624d0e6dd"}, "challenge_train_10_percent.csv": {"num_bytes": 123238, "checksum": "1717d1481a285eb71cc4f0dff9766e6620419a7a206a81a97555b5c51c765e77"}, "challenge_train_20_percent.csv": {"num_bytes": 252901, "checksum": "7e02fe7c2be951d171142eb0568c433e5fb458c7332abf906d2e2ff2ac85049e"}}, "download_size": 2303938, "post_processing_size": null, "dataset_size": 3625710, "size_in_bytes": 5929648}}
+{"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": 2048563, "num_examples": 5103, "dataset_name": "viggo"}, "validation": {"name": "validation", "num_bytes": 278970, "num_examples": 714, "dataset_name": "viggo"}, "test": {"name": "test", "num_bytes": 405327, "num_examples": 1083, "dataset_name": "viggo"}, "challenge_train_1_percent": {"name": "challenge_train_1_percent", "num_bytes": 19135, "num_examples": 50, "dataset_name": "viggo"}, "challenge_train_2_percent": {"name": "challenge_train_2_percent", "num_bytes": 42598, "num_examples": 103, "dataset_name": "viggo"}, "challenge_train_5_percent": {"name": "challenge_train_5_percent", "num_bytes": 109197, "num_examples": 256, "dataset_name": "viggo"}, "challenge_train_10_percent": {"name": "challenge_train_10_percent", "num_bytes": 210415, "num_examples": 510, "dataset_name": "viggo"}, "challenge_train_20_percent": {"name": "challenge_train_20_percent", "num_bytes": 431945, "num_examples": 1021, "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"}, "challenge_train_2_percent.csv": {"num_bytes": 25113, "checksum": "17f3f49f21d31f616e570ed10441f3cfa7af4feef0789c0a182b1ac0c0ac47c1"}, "challenge_train_5_percent.csv": {"num_bytes": 64269, "checksum": "6c497a8bcb545ac55151c836a6cd5a3ae0d702d8394298d85154a41624d0e6dd"}, "challenge_train_10_percent.csv": {"num_bytes": 123238, "checksum": "1717d1481a285eb71cc4f0dff9766e6620419a7a206a81a97555b5c51c765e77"}, "challenge_train_20_percent.csv": {"num_bytes": 252901, "checksum": "7e02fe7c2be951d171142eb0568c433e5fb458c7332abf906d2e2ff2ac85049e"}}, "download_size": 2303938, "post_processing_size": null, "dataset_size": 3546150, "size_in_bytes": 5850088}}

viggo.py

@@ -75,7 +75,7 @@ class Viggo(datasets.GeneratorBasedBuilder):
             reader = csv.DictReader(csvfile)
             for id_, row in enumerate(reader):
                 yield id_, {
-                    "gem_id": f"cs_restaurants-{split}-{id_}",
+                    "gem_id": f"viggo-{split}-{id_}",
                     "meaning_representation": row["mr"],
                     "target": row["ref"],
                     "references": [row["ref"]],