Convert dataset to Parquet

Convert dataset to Parquet.

README.md

@@ -1,5 +1,4 @@
 ---
-pretty_name: SQuAD2.0
 annotations_creators:
 - crowdsourced
 language_creators:
@@ -20,23 +19,9 @@ task_ids:
 - open-domain-qa
 - extractive-qa
 paperswithcode_id: squad
-train-eval-index:
-- config: squad_v2
-  task: question-answering
-  task_id: extractive_question_answering
-  splits:
-    train_split: train
-    eval_split: validation
-  col_mapping:
-    question: question
-    context: context
-    answers:
-      text: text
-      answer_start: answer_start
-  metrics:
-  - type: squad_v2
-    name: SQuAD v2
+pretty_name: SQuAD2.0
 dataset_info:
+  config_name: squad_v2
   features:
   - name: id
     dtype: string
@@ -52,16 +37,39 @@ dataset_info:
       dtype: string
     - name: answer_start
       dtype: int32
-  config_name: squad_v2
   splits:
   - name: train
-    num_bytes: 116699950
+    num_bytes: 116732025
     num_examples: 130319
   - name: validation
-    num_bytes: 11660302
+    num_bytes: 11661091
     num_examples: 11873
-  download_size: 46494161
-  dataset_size: 128360252
+  download_size: 17720493
+  dataset_size: 128393116
+configs:
+- config_name: squad_v2
+  data_files:
+  - split: train
+    path: squad_v2/train-*
+  - split: validation
+    path: squad_v2/validation-*
+  default: true
+train-eval-index:
+- config: squad_v2
+  task: question-answering
+  task_id: extractive_question_answering
+  splits:
+    train_split: train
+    eval_split: validation
+  col_mapping:
+    question: question
+    context: context
+    answers:
+      text: text
+      answer_start: answer_start
+  metrics:
+  - type: squad_v2
+    name: SQuAD v2
 ---
 
 # Dataset Card for "squad_v2"

dataset_infos.json

@@ -1 +1,70 @@
-{"squad_v2": {"description": "combines the 100,000 questions in SQuAD1.1 with over 50,000 unanswerable questions written adversarially by crowdworkers\n to look similar to answerable ones. To do well on SQuAD2.0, systems must not only answer questions when possible, but\n also determine when no answer is supported by the paragraph and abstain from answering.\n", "citation": "@article{2016arXiv160605250R,\n       author = {{Rajpurkar}, Pranav and {Zhang}, Jian and {Lopyrev},\n                 Konstantin and {Liang}, Percy},\n        title = \"{SQuAD: 100,000+ Questions for Machine Comprehension of Text}\",\n      journal = {arXiv e-prints},\n         year = 2016,\n          eid = {arXiv:1606.05250},\n        pages = {arXiv:1606.05250},\narchivePrefix = {arXiv},\n       eprint = {1606.05250},\n}\n", "homepage": "https://rajpurkar.github.io/SQuAD-explorer/", "license": "", "features": {"id": {"dtype": "string", "id": null, "_type": "Value"}, "title": {"dtype": "string", "id": null, "_type": "Value"}, "context": {"dtype": "string", "id": null, "_type": "Value"}, "question": {"dtype": "string", "id": null, "_type": "Value"}, "answers": {"feature": {"text": {"dtype": "string", "id": null, "_type": "Value"}, "answer_start": {"dtype": "int32", "id": null, "_type": "Value"}}, "length": -1, "id": null, "_type": "Sequence"}}, "post_processed": null, "supervised_keys": null, "task_templates": [{"task": "question-answering-extractive", "question_column": "question", "context_column": "context", "answers_column": "answers"}], "builder_name": "squad_v2", "config_name": "squad_v2", "version": {"version_str": "2.0.0", "description": null, "major": 2, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 116699950, "num_examples": 130319, "dataset_name": "squad_v2"}, "validation": {"name": "validation", "num_bytes": 11660302, "num_examples": 11873, "dataset_name": "squad_v2"}}, "download_checksums": {"https://rajpurkar.github.io/SQuAD-explorer/dataset/train-v2.0.json": {"num_bytes": 42123633, "checksum": "68dcfbb971bd3e96d5b46c7177b16c1a4e7d4bdef19fb204502738552dede002"}, "https://rajpurkar.github.io/SQuAD-explorer/dataset/dev-v2.0.json": {"num_bytes": 4370528, "checksum": "80a5225e94905956a6446d296ca1093975c4d3b3260f1d6c8f68bc2ab77182d8"}}, "download_size": 46494161, "post_processing_size": null, "dataset_size": 128360252, "size_in_bytes": 174854413}}
+{
+    "squad_v2": {
+        "description": "combines the 100,000 questions in SQuAD1.1 with over 50,000 unanswerable questions written adversarially by crowdworkers\n to look similar to answerable ones. To do well on SQuAD2.0, systems must not only answer questions when possible, but\n also determine when no answer is supported by the paragraph and abstain from answering.\n",
+        "citation": "@article{2016arXiv160605250R,\n       author = {{Rajpurkar}, Pranav and {Zhang}, Jian and {Lopyrev},\n                 Konstantin and {Liang}, Percy},\n        title = \"{SQuAD: 100,000+ Questions for Machine Comprehension of Text}\",\n      journal = {arXiv e-prints},\n         year = 2016,\n          eid = {arXiv:1606.05250},\n        pages = {arXiv:1606.05250},\narchivePrefix = {arXiv},\n       eprint = {1606.05250},\n}\n",
+        "homepage": "https://rajpurkar.github.io/SQuAD-explorer/",
+        "license": "",
+        "features": {
+            "id": {
+                "dtype": "string",
+                "_type": "Value"
+            },
+            "title": {
+                "dtype": "string",
+                "_type": "Value"
+            },
+            "context": {
+                "dtype": "string",
+                "_type": "Value"
+            },
+            "question": {
+                "dtype": "string",
+                "_type": "Value"
+            },
+            "answers": {
+                "feature": {
+                    "text": {
+                        "dtype": "string",
+                        "_type": "Value"
+                    },
+                    "answer_start": {
+                        "dtype": "int32",
+                        "_type": "Value"
+                    }
+                },
+                "_type": "Sequence"
+            }
+        },
+        "task_templates": [
+            {
+                "task": "question-answering-extractive"
+            }
+        ],
+        "builder_name": "squad_v2",
+        "dataset_name": "squad_v2",
+        "config_name": "squad_v2",
+        "version": {
+            "version_str": "2.0.0",
+            "major": 2,
+            "minor": 0,
+            "patch": 0
+        },
+        "splits": {
+            "train": {
+                "name": "train",
+                "num_bytes": 116732025,
+                "num_examples": 130319,
+                "dataset_name": null
+            },
+            "validation": {
+                "name": "validation",
+                "num_bytes": 11661091,
+                "num_examples": 11873,
+                "dataset_name": null
+            }
+        },
+        "download_size": 17720493,
+        "dataset_size": 128393116,
+        "size_in_bytes": 146113609
+    }
+}

squad_v2/train-00000-of-00001.parquet

@@ -0,0 +1,3 @@
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+size 16369982

squad_v2/validation-00000-of-00001.parquet

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+oid sha256:0560174ab095c5ac0a8c8dc8da05f1625453c45a77e4ce9cabc6947ddfdd24cb
+size 1350511