Convert dataset to Parquet

Convert dataset to Parquet.

README.md

@@ -9,7 +9,6 @@ license:
 - mit
 multilinguality:
 - monolingual
-pretty_name: CommonsenseQA
 size_categories:
 - 1K<n<10K
 source_datasets:
@@ -19,6 +18,7 @@ task_categories:
 task_ids:
 - open-domain-qa
 paperswithcode_id: commonsenseqa
+pretty_name: CommonsenseQA
 dataset_info:
   features:
   - name: id
@@ -37,16 +37,25 @@ dataset_info:
     dtype: string
   splits:
   - name: train
-    num_bytes: 2209044
+    num_bytes: 2207794
     num_examples: 9741
   - name: validation
-    num_bytes: 274033
+    num_bytes: 273848
     num_examples: 1221
   - name: test
-    num_bytes: 258017
+    num_bytes: 257842
     num_examples: 1140
-  download_size: 4680691
-  dataset_size: 2741094
+  download_size: 1558570
+  dataset_size: 2739484
+configs:
+- config_name: default
+  data_files:
+  - split: train
+    path: data/train-*
+  - split: validation
+    path: data/validation-*
+  - split: test
+    path: data/test-*
 ---
 
 # Dataset Card for "commonsense_qa"

data/test-00000-of-00001.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:19efe93223d1712397aaa44b3adc07f4fa50349206a611ca4f33afbec661fa5e
+size 151227

data/train-00000-of-00001.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:b0449767ed986bfc2ca52b1244a46ef12f732756727f3cb0a4ab69ac8b3d282b
+size 1247103

data/validation-00000-of-00001.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:bdbd9bf9cc4d2349b24901038b2ab2f58e10e4e507ad2fd425dca55cd3cb6660
+size 160240

dataset_infos.json

@@ -1 +1,71 @@
-{"default": {"description": "CommonsenseQA is a new multiple-choice question answering dataset that requires different types of commonsense knowledge\nto predict the correct answers . It contains 12,102 questions with one correct answer and four distractor answers.\nThe dataset is provided in two major training/validation/testing set splits: \"Random split\" which is the main evaluation\nsplit, and \"Question token split\", see paper for details.\n", "citation": "@inproceedings{talmor-etal-2019-commonsenseqa,\n    title = \"{C}ommonsense{QA}: A Question Answering Challenge Targeting Commonsense Knowledge\",\n    author = \"Talmor, Alon  and\n      Herzig, Jonathan  and\n      Lourie, Nicholas  and\n      Berant, Jonathan\",\n    booktitle = \"Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)\",\n    month = jun,\n    year = \"2019\",\n    address = \"Minneapolis, Minnesota\",\n    publisher = \"Association for Computational Linguistics\",\n    url = \"https://aclanthology.org/N19-1421\",\n    doi = \"10.18653/v1/N19-1421\",\n    pages = \"4149--4158\",\n    archivePrefix = \"arXiv\",\n    eprint        = \"1811.00937\",\n    primaryClass  = \"cs\",\n}\n", "homepage": "https://www.tau-nlp.org/commonsenseqa", "license": "", "features": {"id": {"dtype": "string", "id": null, "_type": "Value"}, "question": {"dtype": "string", "id": null, "_type": "Value"}, "question_concept": {"dtype": "string", "id": null, "_type": "Value"}, "choices": {"feature": {"label": {"dtype": "string", "id": null, "_type": "Value"}, "text": {"dtype": "string", "id": null, "_type": "Value"}}, "length": -1, "id": null, "_type": "Sequence"}, "answerKey": {"dtype": "string", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": null, "task_templates": null, "builder_name": "commonsense_qa", "config_name": "default", "version": {"version_str": "1.0.0", "description": null, "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 2209044, "num_examples": 9741, "dataset_name": "commonsense_qa"}, "validation": {"name": "validation", "num_bytes": 274033, "num_examples": 1221, "dataset_name": "commonsense_qa"}, "test": {"name": "test", "num_bytes": 258017, "num_examples": 1140, "dataset_name": "commonsense_qa"}}, "download_checksums": {"https://s3.amazonaws.com/commensenseqa/train_rand_split.jsonl": {"num_bytes": 3785890, "checksum": "58ffa3c8472410e24b8c43f423d89c8a003d8284698a6ed7874355dedd09a2fb"}, "https://s3.amazonaws.com/commensenseqa/dev_rand_split.jsonl": {"num_bytes": 471653, "checksum": "3210497fdaae614ac085d9eb873dd7f4d49b6f965a93adadc803e1229fd8a02a"}, "https://s3.amazonaws.com/commensenseqa/test_rand_split_no_answers.jsonl": {"num_bytes": 423148, "checksum": "b426896d71a9cd064cf01cfaf6e920817c51701ef66028883ac1af2e73ad5f29"}}, "download_size": 4680691, "post_processing_size": null, "dataset_size": 2741094, "size_in_bytes": 7421785}}
+{
+    "default": {
+        "description": "CommonsenseQA is a new multiple-choice question answering dataset that requires different types of commonsense knowledge\nto predict the correct answers . It contains 12,102 questions with one correct answer and four distractor answers.\nThe dataset is provided in two major training/validation/testing set splits: \"Random split\" which is the main evaluation\nsplit, and \"Question token split\", see paper for details.\n",
+        "citation": "@inproceedings{talmor-etal-2019-commonsenseqa,\n    title = \"{C}ommonsense{QA}: A Question Answering Challenge Targeting Commonsense Knowledge\",\n    author = \"Talmor, Alon  and\n      Herzig, Jonathan  and\n      Lourie, Nicholas  and\n      Berant, Jonathan\",\n    booktitle = \"Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)\",\n    month = jun,\n    year = \"2019\",\n    address = \"Minneapolis, Minnesota\",\n    publisher = \"Association for Computational Linguistics\",\n    url = \"https://aclanthology.org/N19-1421\",\n    doi = \"10.18653/v1/N19-1421\",\n    pages = \"4149--4158\",\n    archivePrefix = \"arXiv\",\n    eprint        = \"1811.00937\",\n    primaryClass  = \"cs\",\n}\n",
+        "homepage": "https://www.tau-nlp.org/commonsenseqa",
+        "license": "",
+        "features": {
+            "id": {
+                "dtype": "string",
+                "_type": "Value"
+            },
+            "question": {
+                "dtype": "string",
+                "_type": "Value"
+            },
+            "question_concept": {
+                "dtype": "string",
+                "_type": "Value"
+            },
+            "choices": {
+                "feature": {
+                    "label": {
+                        "dtype": "string",
+                        "_type": "Value"
+                    },
+                    "text": {
+                        "dtype": "string",
+                        "_type": "Value"
+                    }
+                },
+                "_type": "Sequence"
+            },
+            "answerKey": {
+                "dtype": "string",
+                "_type": "Value"
+            }
+        },
+        "builder_name": "commonsense_qa",
+        "dataset_name": "commonsense_qa",
+        "config_name": "default",
+        "version": {
+            "version_str": "1.0.0",
+            "major": 1,
+            "minor": 0,
+            "patch": 0
+        },
+        "splits": {
+            "train": {
+                "name": "train",
+                "num_bytes": 2207794,
+                "num_examples": 9741,
+                "dataset_name": null
+            },
+            "validation": {
+                "name": "validation",
+                "num_bytes": 273848,
+                "num_examples": 1221,
+                "dataset_name": null
+            },
+            "test": {
+                "name": "test",
+                "num_bytes": 257842,
+                "num_examples": 1140,
+                "dataset_name": null
+            }
+        },
+        "download_size": 1558570,
+        "dataset_size": 2739484,
+        "size_in_bytes": 4298054
+    }
+}