Convert dataset to Parquet

Convert dataset to Parquet.

README.md

@@ -21,7 +21,7 @@ task_ids:
 paperswithcode_id: ambigqa
 pretty_name: 'AmbigQA: Answering Ambiguous Open-domain Questions'
 dataset_info:
-- config_name: light
+- config_name: full
   features:
   - name: id
     dtype: string
@@ -39,16 +39,32 @@ dataset_info:
         dtype: string
       - name: answer
         sequence: string
+  - name: viewed_doc_titles
+    sequence: string
+  - name: used_queries
+    sequence:
+    - name: query
+      dtype: string
+    - name: results
+      sequence:
+      - name: title
+        dtype: string
+      - name: snippet
+        dtype: string
+  - name: nq_answer
+    sequence: string
+  - name: nq_doc_title
+    dtype: string
   splits:
   - name: train
-    num_bytes: 2739732
+    num_bytes: 43538533
     num_examples: 10036
   - name: validation
-    num_bytes: 805808
+    num_bytes: 15383268
     num_examples: 2002
-  download_size: 19700900
-  dataset_size: 3545540
-- config_name: full
+  download_size: 30674462
+  dataset_size: 58921801
+- config_name: light
   features:
   - name: id
     dtype: string
@@ -66,31 +82,23 @@ dataset_info:
         dtype: string
       - name: answer
         sequence: string
-  - name: viewed_doc_titles
-    sequence: string
-  - name: used_queries
-    sequence:
-    - name: query
-      dtype: string
-    - name: results
-      sequence:
-      - name: title
-        dtype: string
-      - name: snippet
-        dtype: string
-  - name: nq_answer
-    sequence: string
-  - name: nq_doc_title
-    dtype: string
   splits:
   - name: train
-    num_bytes: 43538733
+    num_bytes: 2739732
     num_examples: 10036
   - name: validation
-    num_bytes: 15383368
+    num_bytes: 805808
     num_examples: 2002
   download_size: 19700900
-  dataset_size: 58922101
+  dataset_size: 3545540
+configs:
+- config_name: full
+  data_files:
+  - split: train
+    path: full/train-*
+  - split: validation
+    path: full/validation-*
+  default: true
 ---
 
 # Dataset Card for AmbigQA: Answering Ambiguous Open-domain Questions

dataset_infos.json

@@ -1 +1,217 @@
-{"light": {"description": "AmbigNQ, a dataset covering 14,042 questions from NQ-open, an existing open-domain QA benchmark. We find that over half of the questions in NQ-open are ambiguous. The types of ambiguity are diverse and sometimes subtle, many of which are only apparent after examining evidence provided by a very large text corpus.  AMBIGNQ, a dataset with\n14,042 annotations on NQ-OPEN questions containing diverse types of ambiguity.\nWe provide two distributions of our new dataset AmbigNQ: a full version with all annotation metadata and a light version with only inputs and outputs.\n", "citation": "@inproceedings{ min2020ambigqa,\n    title={ {A}mbig{QA}: Answering Ambiguous Open-domain Questions },\n    author={ Min, Sewon and Michael, Julian and Hajishirzi, Hannaneh and Zettlemoyer, Luke },\n    booktitle={ EMNLP },\n    year={2020}\n}\n", "homepage": "https://nlp.cs.washington.edu/ambigqa/", "license": "CC BY-SA 3.0", "features": {"id": {"dtype": "string", "id": null, "_type": "Value"}, "question": {"dtype": "string", "id": null, "_type": "Value"}, "annotations": {"feature": {"type": {"dtype": "string", "id": null, "_type": "Value"}, "answer": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}, "qaPairs": {"feature": {"question": {"dtype": "string", "id": null, "_type": "Value"}, "answer": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}}, "length": -1, "id": null, "_type": "Sequence"}}, "length": -1, "id": null, "_type": "Sequence"}}, "post_processed": null, "supervised_keys": null, "builder_name": "ambig_qa", "config_name": "light", "version": {"version_str": "1.0.0", "description": null, "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 2739732, "num_examples": 10036, "dataset_name": "ambig_qa"}, "validation": {"name": "validation", "num_bytes": 805808, "num_examples": 2002, "dataset_name": "ambig_qa"}}, "download_checksums": {"https://nlp.cs.washington.edu/ambigqa/data/ambignq_light.zip": {"num_bytes": 1061383, "checksum": "3f5dada69dec05cef1533a64945cd7bafde1aa94b0cdd6fa9a22f881206220db"}, "https://nlp.cs.washington.edu/ambigqa/data/ambignq.zip": {"num_bytes": 18639517, "checksum": "e85cec5909f076c6f584322c7f05cae44dcacaec93758c110a26fcceaa8da0ce"}}, "download_size": 19700900, "post_processing_size": null, "dataset_size": 3545540, "size_in_bytes": 23246440}, "full": {"description": "AmbigNQ, a dataset covering 14,042 questions from NQ-open, an existing open-domain QA benchmark. 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AMBIGNQ, a dataset with\n14,042 annotations on NQ-OPEN questions containing diverse types of ambiguity.\nWe provide two distributions of our new dataset AmbigNQ: a full version with all annotation metadata and a light version with only inputs and outputs.\n", "citation": "@inproceedings{ min2020ambigqa,\n    title={ {A}mbig{QA}: Answering Ambiguous Open-domain Questions },\n    author={ Min, Sewon and Michael, Julian and Hajishirzi, Hannaneh and Zettlemoyer, Luke },\n    booktitle={ EMNLP },\n    year={2020}\n}\n", "homepage": "https://nlp.cs.washington.edu/ambigqa/", "license": "CC BY-SA 3.0", "features": {"id": {"dtype": "string", "id": null, "_type": "Value"}, "question": {"dtype": "string", "id": null, "_type": "Value"}, "annotations": {"feature": {"type": {"dtype": "string", "id": null, "_type": "Value"}, "answer": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}, "qaPairs": {"feature": {"question": {"dtype": "string", "id": null, "_type": "Value"}, "answer": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}}, "length": -1, "id": null, "_type": "Sequence"}}, "length": -1, "id": null, "_type": "Sequence"}, "viewed_doc_titles": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}, "used_queries": {"feature": {"query": {"dtype": "string", "id": null, "_type": "Value"}, "results": {"feature": {"title": {"dtype": "string", "id": null, "_type": "Value"}, "snippet": {"dtype": "string", "id": null, "_type": "Value"}}, "length": -1, "id": null, "_type": "Sequence"}}, "length": -1, "id": null, "_type": "Sequence"}, "nq_answer": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}, "nq_doc_title": {"dtype": "string", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": null, "builder_name": "ambig_qa", "config_name": "full", "version": {"version_str": "1.0.0", "description": null, "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 43538733, "num_examples": 10036, "dataset_name": "ambig_qa"}, "validation": {"name": "validation", "num_bytes": 15383368, "num_examples": 2002, "dataset_name": "ambig_qa"}}, "download_checksums": {"https://nlp.cs.washington.edu/ambigqa/data/ambignq_light.zip": {"num_bytes": 1061383, "checksum": "3f5dada69dec05cef1533a64945cd7bafde1aa94b0cdd6fa9a22f881206220db"}, "https://nlp.cs.washington.edu/ambigqa/data/ambignq.zip": {"num_bytes": 18639517, "checksum": "e85cec5909f076c6f584322c7f05cae44dcacaec93758c110a26fcceaa8da0ce"}}, "download_size": 19700900, "post_processing_size": null, "dataset_size": 58922101, "size_in_bytes": 78623001}}
+{
+    "light": {
+        "description": "AmbigNQ, a dataset covering 14,042 questions from NQ-open, an existing open-domain QA benchmark. We find that over half of the questions in NQ-open are ambiguous. The types of ambiguity are diverse and sometimes subtle, many of which are only apparent after examining evidence provided by a very large text corpus.  AMBIGNQ, a dataset with\n14,042 annotations on NQ-OPEN questions containing diverse types of ambiguity.\nWe provide two distributions of our new dataset AmbigNQ: a full version with all annotation metadata and a light version with only inputs and outputs.\n",
+        "citation": "@inproceedings{ min2020ambigqa,\n    title={ {A}mbig{QA}: Answering Ambiguous Open-domain Questions },\n    author={ Min, Sewon and Michael, Julian and Hajishirzi, Hannaneh and Zettlemoyer, Luke },\n    booktitle={ EMNLP },\n    year={2020}\n}\n",
+        "homepage": "https://nlp.cs.washington.edu/ambigqa/",
+        "license": "CC BY-SA 3.0",
+        "features": {
+            "id": {
+                "dtype": "string",
+                "id": null,
+                "_type": "Value"
+            },
+            "question": {
+                "dtype": "string",
+                "id": null,
+                "_type": "Value"
+            },
+            "annotations": {
+                "feature": {
+                    "type": {
+                        "dtype": "string",
+                        "id": null,
+                        "_type": "Value"
+                    },
+                    "answer": {
+                        "feature": {
+                            "dtype": "string",
+                            "id": null,
+                            "_type": "Value"
+                        },
+                        "length": -1,
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+                        "_type": "Sequence"
+                    },
+                    "qaPairs": {
+                        "feature": {
+                            "question": {
+                                "dtype": "string",
+                                "id": null,
+                                "_type": "Value"
+                            },
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+                            }
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+                    }
+                },
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+            }
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+        "post_processed": null,
+        "supervised_keys": null,
+        "builder_name": "ambig_qa",
+        "config_name": "light",
+        "version": {
+            "version_str": "1.0.0",
+            "description": null,
+            "major": 1,
+            "minor": 0,
+            "patch": 0
+        },
+        "splits": {
+            "train": {
+                "name": "train",
+                "num_bytes": 2739732,
+                "num_examples": 10036,
+                "dataset_name": "ambig_qa"
+            },
+            "validation": {
+                "name": "validation",
+                "num_bytes": 805808,
+                "num_examples": 2002,
+                "dataset_name": "ambig_qa"
+            }
+        },
+        "download_checksums": {
+            "https://nlp.cs.washington.edu/ambigqa/data/ambignq_light.zip": {
+                "num_bytes": 1061383,
+                "checksum": "3f5dada69dec05cef1533a64945cd7bafde1aa94b0cdd6fa9a22f881206220db"
+            },
+            "https://nlp.cs.washington.edu/ambigqa/data/ambignq.zip": {
+                "num_bytes": 18639517,
+                "checksum": "e85cec5909f076c6f584322c7f05cae44dcacaec93758c110a26fcceaa8da0ce"
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+        "download_size": 19700900,
+        "post_processing_size": null,
+        "dataset_size": 3545540,
+        "size_in_bytes": 23246440
+    },
+    "full": {
+        "description": "AmbigNQ, a dataset covering 14,042 questions from NQ-open, an existing open-domain QA benchmark. We find that over half of the questions in NQ-open are ambiguous. The types of ambiguity are diverse and sometimes subtle, many of which are only apparent after examining evidence provided by a very large text corpus.  AMBIGNQ, a dataset with\n14,042 annotations on NQ-OPEN questions containing diverse types of ambiguity.\nWe provide two distributions of our new dataset AmbigNQ: a full version with all annotation metadata and a light version with only inputs and outputs.\n",
+        "citation": "@inproceedings{ min2020ambigqa,\n    title={ {A}mbig{QA}: Answering Ambiguous Open-domain Questions },\n    author={ Min, Sewon and Michael, Julian and Hajishirzi, Hannaneh and Zettlemoyer, Luke },\n    booktitle={ EMNLP },\n    year={2020}\n}\n",
+        "homepage": "https://nlp.cs.washington.edu/ambigqa/",
+        "license": "CC BY-SA 3.0",
+        "features": {
+            "id": {
+                "dtype": "string",
+                "_type": "Value"
+            },
+            "question": {
+                "dtype": "string",
+                "_type": "Value"
+            },
+            "annotations": {
+                "feature": {
+                    "type": {
+                        "dtype": "string",
+                        "_type": "Value"
+                    },
+                    "answer": {
+                        "feature": {
+                            "dtype": "string",
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+                        "feature": {
+                            "question": {
+                                "dtype": "string",
+                                "_type": "Value"
+                            },
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+                                "feature": {
+                                    "dtype": "string",
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+                            }
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+                "feature": {
+                    "dtype": "string",
+                    "_type": "Value"
+                },
+                "_type": "Sequence"
+            },
+            "used_queries": {
+                "feature": {
+                    "query": {
+                        "dtype": "string",
+                        "_type": "Value"
+                    },
+                    "results": {
+                        "feature": {
+                            "title": {
+                                "dtype": "string",
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+            },
+            "nq_doc_title": {
+                "dtype": "string",
+                "_type": "Value"
+            }
+        },
+        "builder_name": "parquet",
+        "dataset_name": "ambig_qa",
+        "config_name": "full",
+        "version": {
+            "version_str": "1.0.0",
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+                "name": "validation",
+                "num_bytes": 15383268,
+                "num_examples": 2002,
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+            }
+        },
+        "download_size": 30674462,
+        "dataset_size": 58921801,
+        "size_in_bytes": 89596263
+    }
+}

full/train-00000-of-00001.parquet

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full/validation-00000-of-00001.parquet

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