Convert dataset to Parquet

Convert dataset to Parquet.

README.md

@@ -23,6 +23,7 @@ task_ids:
 paperswithcode_id: anli
 pretty_name: Adversarial NLI
 dataset_info:
+  config_name: plain_text
   features:
   - name: uid
     dtype: string
@@ -39,37 +40,58 @@ dataset_info:
           '2': contradiction
   - name: reason
     dtype: string
-  config_name: plain_text
   splits:
   - name: train_r1
-    num_bytes: 8006920
+    num_bytes: 8006888
     num_examples: 16946
   - name: dev_r1
-    num_bytes: 573444
+    num_bytes: 573428
     num_examples: 1000
   - name: test_r1
-    num_bytes: 574933
+    num_bytes: 574917
     num_examples: 1000
   - name: train_r2
-    num_bytes: 20801661
+    num_bytes: 20801581
     num_examples: 45460
   - name: dev_r2
-    num_bytes: 556082
+    num_bytes: 556066
     num_examples: 1000
   - name: test_r2
-    num_bytes: 572655
+    num_bytes: 572639
     num_examples: 1000
   - name: train_r3
-    num_bytes: 44720895
+    num_bytes: 44720719
     num_examples: 100459
   - name: dev_r3
-    num_bytes: 663164
+    num_bytes: 663148
     num_examples: 1200
   - name: test_r3
-    num_bytes: 657602
+    num_bytes: 657586
     num_examples: 1200
-  download_size: 18621352
-  dataset_size: 77127356
+  download_size: 26286748
+  dataset_size: 77126972
+configs:
+- config_name: plain_text
+  data_files:
+  - split: train_r1
+    path: plain_text/train_r1-*
+  - split: dev_r1
+    path: plain_text/dev_r1-*
+  - split: test_r1
+    path: plain_text/test_r1-*
+  - split: train_r2
+    path: plain_text/train_r2-*
+  - split: dev_r2
+    path: plain_text/dev_r2-*
+  - split: test_r2
+    path: plain_text/test_r2-*
+  - split: train_r3
+    path: plain_text/train_r3-*
+  - split: dev_r3
+    path: plain_text/dev_r3-*
+  - split: test_r3
+    path: plain_text/test_r3-*
+  default: true
 ---
 
 # Dataset Card for "anli"

dataset_infos.json

@@ -1 +1,103 @@
-{"plain_text": {"description": "The Adversarial Natural Language Inference (ANLI) is a new large-scale NLI benchmark dataset, \nThe dataset is collected via an iterative, adversarial human-and-model-in-the-loop procedure.\nANLI is much more difficult than its predecessors including SNLI and MNLI.\nIt contains three rounds. Each round has train/dev/test splits.\n", "citation": "@InProceedings{nie2019adversarial,\n    title={Adversarial NLI: A New Benchmark for Natural Language Understanding},\n    author={Nie, Yixin \n                and Williams, Adina \n                and Dinan, Emily \n                and Bansal, Mohit \n                and Weston, Jason \n                and Kiela, Douwe},\n    booktitle = \"Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics\",\n    year = \"2020\",\n    publisher = \"Association for Computational Linguistics\",\n}\n", "homepage": "https://github.com/facebookresearch/anli/", "license": "", "features": {"uid": {"dtype": "string", "id": null, "_type": "Value"}, "premise": {"dtype": "string", "id": null, "_type": "Value"}, "hypothesis": {"dtype": "string", "id": null, "_type": "Value"}, "label": {"num_classes": 3, "names": ["entailment", "neutral", "contradiction"], "names_file": null, "id": null, "_type": "ClassLabel"}, "reason": {"dtype": "string", "id": null, "_type": "Value"}}, "supervised_keys": null, "builder_name": "anli", "config_name": "plain_text", "version": {"version_str": "0.1.0", "description": "", "datasets_version_to_prepare": null, "major": 0, "minor": 1, "patch": 0}, "splits": {"train_r1": {"name": "train_r1", "num_bytes": 8006920, "num_examples": 16946, "dataset_name": "anli"}, "dev_r1": {"name": "dev_r1", "num_bytes": 573444, "num_examples": 1000, "dataset_name": "anli"}, "test_r1": {"name": "test_r1", "num_bytes": 574933, "num_examples": 1000, "dataset_name": "anli"}, "train_r2": {"name": "train_r2", "num_bytes": 20801661, "num_examples": 45460, "dataset_name": "anli"}, "dev_r2": {"name": "dev_r2", "num_bytes": 556082, "num_examples": 1000, "dataset_name": "anli"}, "test_r2": {"name": "test_r2", "num_bytes": 572655, "num_examples": 1000, "dataset_name": "anli"}, "train_r3": {"name": "train_r3", "num_bytes": 44720895, "num_examples": 100459, "dataset_name": "anli"}, "dev_r3": {"name": "dev_r3", "num_bytes": 663164, "num_examples": 1200, "dataset_name": "anli"}, "test_r3": {"name": "test_r3", "num_bytes": 657602, "num_examples": 1200, "dataset_name": "anli"}}, "download_checksums": {"https://dl.fbaipublicfiles.com/anli/anli_v0.1.zip": {"num_bytes": 18621352, "checksum": "16ac929a7e90ecf9093deaec89cc81fe86a379265a5320a150028efe50c5cde8"}}, "download_size": 18621352, "dataset_size": 77127356, "size_in_bytes": 95748708}}
+{
+    "plain_text": {
+        "description": "The Adversarial Natural Language Inference (ANLI) is a new large-scale NLI benchmark dataset,\nThe dataset is collected via an iterative, adversarial human-and-model-in-the-loop procedure.\nANLI is much more difficult than its predecessors including SNLI and MNLI.\nIt contains three rounds. Each round has train/dev/test splits.\n",
+        "citation": "@InProceedings{nie2019adversarial,\n    title={Adversarial NLI: A New Benchmark for Natural Language Understanding},\n    author={Nie, Yixin\n                and Williams, Adina\n                and Dinan, Emily\n                and Bansal, Mohit\n                and Weston, Jason\n                and Kiela, Douwe},\n    booktitle = \"Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics\",\n    year = \"2020\",\n    publisher = \"Association for Computational Linguistics\",\n}\n",
+        "homepage": "https://github.com/facebookresearch/anli/",
+        "license": "",
+        "features": {
+            "uid": {
+                "dtype": "string",
+                "_type": "Value"
+            },
+            "premise": {
+                "dtype": "string",
+                "_type": "Value"
+            },
+            "hypothesis": {
+                "dtype": "string",
+                "_type": "Value"
+            },
+            "label": {
+                "names": [
+                    "entailment",
+                    "neutral",
+                    "contradiction"
+                ],
+                "_type": "ClassLabel"
+            },
+            "reason": {
+                "dtype": "string",
+                "_type": "Value"
+            }
+        },
+        "builder_name": "anli",
+        "dataset_name": "anli",
+        "config_name": "plain_text",
+        "version": {
+            "version_str": "0.1.0",
+            "description": "",
+            "major": 0,
+            "minor": 1,
+            "patch": 0
+        },
+        "splits": {
+            "train_r1": {
+                "name": "train_r1",
+                "num_bytes": 8006888,
+                "num_examples": 16946,
+                "dataset_name": null
+            },
+            "dev_r1": {
+                "name": "dev_r1",
+                "num_bytes": 573428,
+                "num_examples": 1000,
+                "dataset_name": null
+            },
+            "test_r1": {
+                "name": "test_r1",
+                "num_bytes": 574917,
+                "num_examples": 1000,
+                "dataset_name": null
+            },
+            "train_r2": {
+                "name": "train_r2",
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+                "num_examples": 45460,
+                "dataset_name": null
+            },
+            "dev_r2": {
+                "name": "dev_r2",
+                "num_bytes": 556066,
+                "num_examples": 1000,
+                "dataset_name": null
+            },
+            "test_r2": {
+                "name": "test_r2",
+                "num_bytes": 572639,
+                "num_examples": 1000,
+                "dataset_name": null
+            },
+            "train_r3": {
+                "name": "train_r3",
+                "num_bytes": 44720719,
+                "num_examples": 100459,
+                "dataset_name": null
+            },
+            "dev_r3": {
+                "name": "dev_r3",
+                "num_bytes": 663148,
+                "num_examples": 1200,
+                "dataset_name": null
+            },
+            "test_r3": {
+                "name": "test_r3",
+                "num_bytes": 657586,
+                "num_examples": 1200,
+                "dataset_name": null
+            }
+        },
+        "download_size": 26286748,
+        "dataset_size": 77126972,
+        "size_in_bytes": 103413720
+    }
+}

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