Convert dataset to Parquet

Convert dataset to Parquet.

README.md

@@ -42,8 +42,15 @@ dataset_info:
   - name: validation
     num_bytes: 168004403
     num_examples: 930062
-  download_size: 1482064429
+  download_size: 2496835395
   dataset_size: 3824869475
+configs:
+- config_name: default
+  data_files:
+  - split: train
+    path: data/train-*
+  - split: validation
+    path: data/validation-*
 ---
 
 # Dataset Card for "asnq"

data/train-00000-of-00008.parquet

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+size 299139770

data/train-00001-of-00008.parquet

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+size 298212695

data/train-00002-of-00008.parquet

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+size 299688447

data/train-00003-of-00008.parquet

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+size 299173033

data/train-00004-of-00008.parquet

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+size 299751866

data/train-00005-of-00008.parquet

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+size 299035267

data/train-00006-of-00008.parquet

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+size 299086683

data/train-00007-of-00008.parquet

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+size 299236601

data/validation-00000-of-00001.parquet

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+version https://git-lfs.github.com/spec/v1
+oid sha256:c400a3e7bbd1b1f184bbc3bda3747b3946ccfa29e76c7ce2841613601515fed6
+size 103511033

dataset_infos.json

@@ -1 +1,59 @@
-{"default": {"description": "ASNQ is a dataset for answer sentence selection derived from\nGoogle's Natural Questions (NQ) dataset (Kwiatkowski et al. 2019).\n\nEach example contains a question, candidate sentence, label indicating whether or not\nthe sentence answers the question, and two additional features --\nsentence_in_long_answer and short_answer_in_sentence indicating whether ot not the\ncandidate sentence is contained in the long_answer and if the short_answer is in the candidate sentence.\n\nFor more details please see\nhttps://arxiv.org/pdf/1911.04118.pdf\n\nand\n\nhttps://research.google/pubs/pub47761/\n", "citation": "@article{garg2019tanda,\n    title={TANDA: Transfer and Adapt Pre-Trained Transformer Models for Answer Sentence Selection},\n    author={Siddhant Garg and Thuy Vu and Alessandro Moschitti},\n    year={2019},\n    eprint={1911.04118},\n}\n", "homepage": "https://github.com/alexa/wqa_tanda#answer-sentence-natural-questions-asnq", "license": "", "features": {"question": {"dtype": "string", "id": null, "_type": "Value"}, "sentence": {"dtype": "string", "id": null, "_type": "Value"}, "label": {"num_classes": 2, "names": ["neg", "pos"], "id": null, "_type": "ClassLabel"}, "sentence_in_long_answer": {"dtype": "bool", "id": null, "_type": "Value"}, "short_answer_in_sentence": {"dtype": "bool", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": null, "task_templates": null, "builder_name": "asnq", "config_name": "default", "version": {"version_str": "1.0.0", "description": null, "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 3656865072, "num_examples": 20377568, "dataset_name": "asnq"}, "validation": {"name": "validation", "num_bytes": 168004403, "num_examples": 930062, "dataset_name": "asnq"}}, "download_checksums": {"data/asnq.zip": {"num_bytes": 1482064429, "checksum": "4d1b3ee889be3d2a090d075a7494cee339f6fcfae6bc16ce5d06ad66874e591c"}}, "download_size": 1482064429, "post_processing_size": null, "dataset_size": 3824869475, "size_in_bytes": 5306933904}}
+{
+    "default": {
+        "description": "ASNQ is a dataset for answer sentence selection derived from\nGoogle's Natural Questions (NQ) dataset (Kwiatkowski et al. 2019).\n\nEach example contains a question, candidate sentence, label indicating whether or not\nthe sentence answers the question, and two additional features --\nsentence_in_long_answer and short_answer_in_sentence indicating whether ot not the\ncandidate sentence is contained in the long_answer and if the short_answer is in the candidate sentence.\n\nFor more details please see\nhttps://arxiv.org/pdf/1911.04118.pdf\n\nand\n\nhttps://research.google/pubs/pub47761/\n",
+        "citation": "@article{garg2019tanda,\n    title={TANDA: Transfer and Adapt Pre-Trained Transformer Models for Answer Sentence Selection},\n    author={Siddhant Garg and Thuy Vu and Alessandro Moschitti},\n    year={2019},\n    eprint={1911.04118},\n}\n",
+        "homepage": "https://github.com/alexa/wqa_tanda#answer-sentence-natural-questions-asnq",
+        "license": "",
+        "features": {
+            "question": {
+                "dtype": "string",
+                "_type": "Value"
+            },
+            "sentence": {
+                "dtype": "string",
+                "_type": "Value"
+            },
+            "label": {
+                "names": [
+                    "neg",
+                    "pos"
+                ],
+                "_type": "ClassLabel"
+            },
+            "sentence_in_long_answer": {
+                "dtype": "bool",
+                "_type": "Value"
+            },
+            "short_answer_in_sentence": {
+                "dtype": "bool",
+                "_type": "Value"
+            }
+        },
+        "builder_name": "parquet",
+        "dataset_name": "asnq",
+        "config_name": "default",
+        "version": {
+            "version_str": "1.0.0",
+            "major": 1,
+            "minor": 0,
+            "patch": 0
+        },
+        "splits": {
+            "train": {
+                "name": "train",
+                "num_bytes": 3656865072,
+                "num_examples": 20377568,
+                "dataset_name": null
+            },
+            "validation": {
+                "name": "validation",
+                "num_bytes": 168004403,
+                "num_examples": 930062,
+                "dataset_name": null
+            }
+        },
+        "download_size": 2496835395,
+        "dataset_size": 3824869475,
+        "size_in_bytes": 6321704870
+    }
+}