aqua_rat / dataset_infos.json
{"raw": {"description": "A large-scale dataset consisting of approximately 100,000 algebraic word problems. \nThe solution to each question is explained step-by-step using natural language. \nThis data is used to train a program generation model that learns to generate the explanation, \nwhile generating the program that solves the question.\n", "citation": "@InProceedings{ACL,\ntitle = {Program induction by rationale generation: Learning to solve and explain algebraic word problems},\nauthors={Ling, Wang and Yogatama, Dani and Dyer, Chris and Blunsom, Phil},\nyear={2017}\n}\n", "homepage": "https://github.com/deepmind/AQuA", "license": "Copyright 2017 Google Inc.\n\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing, software\ndistributed under the License is distributed on an \"AS IS\" BASIS,\nWITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\nSee the License for the specific language governing permissions and\nlimitations under the License.\n", "features": {"question": {"dtype": "string", "id": null, "_type": "Value"}, "options": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}, "rationale": {"dtype": "string", "id": null, "_type": "Value"}, "correct": {"dtype": "string", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": null, "builder_name": "aqua_rat", "config_name": "raw", "version": {"version_str": "0.0.0", "description": null, "major": 0, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 42333259, "num_examples": 97467, "dataset_name": "aqua_rat"}, "test": {"name": "test", "num_bytes": 116779, "num_examples": 254, "dataset_name": "aqua_rat"}, "validation": {"name": "validation", "num_bytes": 118636, "num_examples": 254, "dataset_name": "aqua_rat"}}, "download_checksums": {"https://raw.githubusercontent.com/deepmind/AQuA/master/train.json": {"num_bytes": 47570935, "checksum": "35b70c7cb1660c34d302bb932dfd3656ce3d05e8c982c797767fec31d7d1e0ba"}, "https://raw.githubusercontent.com/deepmind/AQuA/master/dev.json": {"num_bytes": 132008, "checksum": "4a52301d8a49ae0b286f8e697f9c5da1b109b247b825b47b4c6de34312dceec5"}, "https://raw.githubusercontent.com/deepmind/AQuA/master/test.json": {"num_bytes": 130192, "checksum": "de41581881ef95c5588c44ddc701c77fd480839241dd422a0d181262af24520c"}}, "download_size": 47833135, "post_processing_size": null, "dataset_size": 42568674, "size_in_bytes": 90401809}, "tokenized": {"description": "A large-scale dataset consisting of approximately 100,000 algebraic word problems. \nThe solution to each question is explained step-by-step using natural language. \nThis data is used to train a program generation model that learns to generate the explanation, \nwhile generating the program that solves the question.\n", "citation": "@InProceedings{ACL,\ntitle = {Program induction by rationale generation: Learning to solve and explain algebraic word problems},\nauthors={Ling, Wang and Yogatama, Dani and Dyer, Chris and Blunsom, Phil},\nyear={2017}\n}\n", "homepage": "https://github.com/deepmind/AQuA", "license": "Copyright 2017 Google Inc.\n\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing, software\ndistributed under the License is distributed on an \"AS IS\" BASIS,\nWITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\nSee the License for the specific language governing permissions and\nlimitations under the License.\n", "features": {"question": {"dtype": "string", "id": null, "_type": "Value"}, "options": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}, "rationale": {"dtype": "string", "id": null, "_type": "Value"}, "correct": {"dtype": "string", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": null, "builder_name": "aqua_rat", "config_name": "tokenized", "version": {"version_str": "0.0.0", "description": null, "major": 0, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 46493843, "num_examples": 97467, "dataset_name": "aqua_rat"}, "test": {"name": "test", "num_bytes": 126283, "num_examples": 254, "dataset_name": "aqua_rat"}, "validation": {"name": "validation", "num_bytes": 128873, "num_examples": 254, "dataset_name": "aqua_rat"}}, "download_checksums": {"https://raw.githubusercontent.com/deepmind/AQuA/master/train.tok.json": {"num_bytes": 51721975, "checksum": "5b2fbd7e2216455c7c419bf7e6628d5f3d88a2c96fb412d965b97b85bf96cbab"}, "https://raw.githubusercontent.com/deepmind/AQuA/master/dev.tok.json": {"num_bytes": 142236, "checksum": "16baf52dde06481b0cf92150d6e4caae2d80446138aba0362a30e9254d1b8658"}, "https://raw.githubusercontent.com/deepmind/AQuA/master/test.tok.json": {"num_bytes": 139683, "checksum": "08e0cb7dad55ac0d4ef314c5fd2335e118e21749c4be9cd4ef0e5b27f11630d8"}}, "download_size": 52003894, "post_processing_size": null, "dataset_size": 46748999, "size_in_bytes": 98752893}}