Convert dataset to Parquet

Convert dataset to Parquet.

README.md

@@ -39,16 +39,16 @@ dataset_info:
     dtype: string
   splits:
   - name: train
-    num_bytes: 6434909
+    num_bytes: 6434893
     num_examples: 6253
   - name: test
-    num_bytes: 843181
+    num_bytes: 843165
     num_examples: 811
   - name: validation
-    num_bytes: 689109
+    num_bytes: 689093
     num_examples: 661
-  download_size: 7755161
-  dataset_size: 7967199
+  download_size: 5167854
+  dataset_size: 7967151
 - config_name: extractive
   features:
   - name: query
@@ -69,6 +69,15 @@ dataset_info:
     num_examples: 661
   download_size: 7755161
   dataset_size: 7967199
+configs:
+- config_name: abstractive
+  data_files:
+  - split: train
+    path: abstractive/train-*
+  - split: test
+    path: abstractive/test-*
+  - split: validation
+    path: abstractive/validation-*
 ---
 
 # Dataset Card for AQuaMuSe

abstractive/test-00000-of-00001.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:0233791433515f5e906da847e3a808e157f8a1bb3891ae68126476f8c95ddf24
+size 543352

abstractive/train-00000-of-00001.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:13484e28ade720920ee06fb1e1c4e5be4e9bb7ce95e73625aef5883a667dc4ed
+size 4177513

abstractive/validation-00000-of-00001.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:7c1fa26da92681958b9ebe263b95918b144902bdb7dce3672b1cf9520317ecef
+size 446989

dataset_infos.json

@@ -1 +1,126 @@
-{"abstractive": {"description": "AQuaMuSe is a novel scalable approach to automatically mine dual query based multi-document summarization datasets for extractive and abstractive summaries using question answering dataset (Google Natural Questions) and large document corpora (Common Crawl)", "citation": "@misc{kulkarni2020aquamuse,title={AQuaMuSe: Automatically Generating Datasets for Query-Based Multi-Document Summarization}, author={Sayali Kulkarni and Sheide Chammas and Wan Zhu and Fei Sha and Eugene Ie}, year={2020}, eprint={2010.12694}, archivePrefix={arXiv}, primaryClass={cs.CL}}", "homepage": "https://github.com/google-research-datasets/aquamuse", "license": "", "features": {"query": {"dtype": "string", "id": null, "_type": "Value"}, "input_urls": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}, "target": {"dtype": "string", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": null, "builder_name": "aquamuse", "config_name": "abstractive", "version": {"version_str": "2.3.0", "description": null, "major": 2, "minor": 3, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 6434909, "num_examples": 6253, "dataset_name": "aquamuse"}, "test": {"name": "test", "num_bytes": 843181, "num_examples": 811, "dataset_name": "aquamuse"}, "validation": {"name": "validation", "num_bytes": 689109, "num_examples": 661, "dataset_name": "aquamuse"}}, "download_checksums": {"https://github.com/google-research-datasets/aquamuse/raw/main/v2/aquamuse_v2.zip": {"num_bytes": 7755161, "checksum": "f2b4d9523031a986e545a7c0fdc8180670519696340d09179a39514fc76466d0"}}, "download_size": 7755161, "post_processing_size": null, "dataset_size": 7967199, "size_in_bytes": 15722360}, "extractive": {"description": "AQuaMuSe is a novel scalable approach to automatically mine dual query based multi-document summarization datasets for extractive and abstractive summaries using question answering dataset (Google Natural Questions) and large document corpora (Common Crawl)", "citation": "@misc{kulkarni2020aquamuse,title={AQuaMuSe: Automatically Generating Datasets for Query-Based Multi-Document Summarization}, author={Sayali Kulkarni and Sheide Chammas and Wan Zhu and Fei Sha and Eugene Ie}, year={2020}, eprint={2010.12694}, archivePrefix={arXiv}, primaryClass={cs.CL}}", "homepage": "https://github.com/google-research-datasets/aquamuse", "license": "", "features": {"query": {"dtype": "string", "id": null, "_type": "Value"}, "input_urls": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}, "target": {"dtype": "string", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": null, "builder_name": "aquamuse", "config_name": "extractive", "version": {"version_str": "2.3.0", "description": null, "major": 2, "minor": 3, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 6434909, "num_examples": 6253, "dataset_name": "aquamuse"}, "test": {"name": "test", "num_bytes": 843181, "num_examples": 811, "dataset_name": "aquamuse"}, "validation": {"name": "validation", "num_bytes": 689109, "num_examples": 661, "dataset_name": "aquamuse"}}, "download_checksums": {"https://github.com/google-research-datasets/aquamuse/raw/main/v2/aquamuse_v2.zip": {"num_bytes": 7755161, "checksum": "f2b4d9523031a986e545a7c0fdc8180670519696340d09179a39514fc76466d0"}}, "download_size": 7755161, "post_processing_size": null, "dataset_size": 7967199, "size_in_bytes": 15722360}}
+{
+    "abstractive": {
+        "description": "AQuaMuSe is a novel scalable approach to automatically mine dual query based multi-document summarization datasets for extractive and abstractive summaries using question answering dataset (Google Natural Questions) and large document corpora (Common Crawl)",
+        "citation": "@misc{kulkarni2020aquamuse,\n      title={AQuaMuSe: Automatically Generating Datasets for Query-Based Multi-Document Summarization},\n      author={Sayali Kulkarni and Sheide Chammas and Wan Zhu and Fei Sha and Eugene Ie},\n      year={2020},\n      eprint={2010.12694},\n      archivePrefix={arXiv},\n      primaryClass={cs.CL}\n}\n",
+        "homepage": "https://github.com/google-research-datasets/aquamuse",
+        "license": "",
+        "features": {
+            "query": {
+                "dtype": "string",
+                "_type": "Value"
+            },
+            "input_urls": {
+                "feature": {
+                    "dtype": "string",
+                    "_type": "Value"
+                },
+                "_type": "Sequence"
+            },
+            "target": {
+                "dtype": "string",
+                "_type": "Value"
+            }
+        },
+        "builder_name": "parquet",
+        "dataset_name": "aquamuse",
+        "config_name": "abstractive",
+        "version": {
+            "version_str": "2.3.0",
+            "major": 2,
+            "minor": 3,
+            "patch": 0
+        },
+        "splits": {
+            "train": {
+                "name": "train",
+                "num_bytes": 6434893,
+                "num_examples": 6253,
+                "dataset_name": null
+            },
+            "test": {
+                "name": "test",
+                "num_bytes": 843165,
+                "num_examples": 811,
+                "dataset_name": null
+            },
+            "validation": {
+                "name": "validation",
+                "num_bytes": 689093,
+                "num_examples": 661,
+                "dataset_name": null
+            }
+        },
+        "download_size": 5167854,
+        "dataset_size": 7967151,
+        "size_in_bytes": 13135005
+    },
+    "extractive": {
+        "description": "AQuaMuSe is a novel scalable approach to automatically mine dual query based multi-document summarization datasets for extractive and abstractive summaries using question answering dataset (Google Natural Questions) and large document corpora (Common Crawl)",
+        "citation": "@misc{kulkarni2020aquamuse,title={AQuaMuSe: Automatically Generating Datasets for Query-Based Multi-Document Summarization}, author={Sayali Kulkarni and Sheide Chammas and Wan Zhu and Fei Sha and Eugene Ie}, year={2020}, eprint={2010.12694}, archivePrefix={arXiv}, primaryClass={cs.CL}}",
+        "homepage": "https://github.com/google-research-datasets/aquamuse",
+        "license": "",
+        "features": {
+            "query": {
+                "dtype": "string",
+                "id": null,
+                "_type": "Value"
+            },
+            "input_urls": {
+                "feature": {
+                    "dtype": "string",
+                    "id": null,
+                    "_type": "Value"
+                },
+                "length": -1,
+                "id": null,
+                "_type": "Sequence"
+            },
+            "target": {
+                "dtype": "string",
+                "id": null,
+                "_type": "Value"
+            }
+        },
+        "post_processed": null,
+        "supervised_keys": null,
+        "builder_name": "aquamuse",
+        "config_name": "extractive",
+        "version": {
+            "version_str": "2.3.0",
+            "description": null,
+            "major": 2,
+            "minor": 3,
+            "patch": 0
+        },
+        "splits": {
+            "train": {
+                "name": "train",
+                "num_bytes": 6434909,
+                "num_examples": 6253,
+                "dataset_name": "aquamuse"
+            },
+            "test": {
+                "name": "test",
+                "num_bytes": 843181,
+                "num_examples": 811,
+                "dataset_name": "aquamuse"
+            },
+            "validation": {
+                "name": "validation",
+                "num_bytes": 689109,
+                "num_examples": 661,
+                "dataset_name": "aquamuse"
+            }
+        },
+        "download_checksums": {
+            "https://github.com/google-research-datasets/aquamuse/raw/main/v2/aquamuse_v2.zip": {
+                "num_bytes": 7755161,
+                "checksum": "f2b4d9523031a986e545a7c0fdc8180670519696340d09179a39514fc76466d0"
+            }
+        },
+        "download_size": 7755161,
+        "post_processing_size": null,
+        "dataset_size": 7967199,
+        "size_in_bytes": 15722360
+    }
+}