Convert dataset to Parquet

Convert dataset to Parquet.

README.md

@@ -54,16 +54,25 @@ dataset_info:
           '2': contradiction
   splits:
   - name: train
-    num_bytes: 410211586
+    num_bytes: 410210306
     num_examples: 392702
   - name: validation_matched
-    num_bytes: 10063939
+    num_bytes: 10063907
     num_examples: 9815
   - name: validation_mismatched
-    num_bytes: 10610221
+    num_bytes: 10610189
     num_examples: 9832
-  download_size: 226850426
-  dataset_size: 430885746
+  download_size: 224005223
+  dataset_size: 430884402
+configs:
+- config_name: default
+  data_files:
+  - split: train
+    path: data/train-*
+  - split: validation_matched
+    path: data/validation_matched-*
+  - split: validation_mismatched
+    path: data/validation_mismatched-*
 ---
 
 # Dataset Card for Multi-Genre Natural Language Inference (MultiNLI)

data/train-00000-of-00001.parquet

@@ -0,0 +1,3 @@
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+size 213961663

data/validation_matched-00000-of-00001.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:350c26950b55f460b50d36c76aef87d64b49c78812d7abf7bf97e5fede10f186
+size 4938568

data/validation_mismatched-00000-of-00001.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:6b0e1231cebedd255000a7d1732af37ad0500902239db625e231ed77c0c8f2f8
+size 5104992

dataset_infos.json

@@ -1 +1,86 @@
-{"default": {"description": "The Multi-Genre Natural Language Inference (MultiNLI) corpus is a\ncrowd-sourced collection of 433k sentence pairs annotated with textual\nentailment information. The corpus is modeled on the SNLI corpus, but differs in\nthat covers a range of genres of spoken and written text, and supports a\ndistinctive cross-genre generalization evaluation. The corpus served as the\nbasis for the shared task of the RepEval 2017 Workshop at EMNLP in Copenhagen.\n", "citation": "@InProceedings{N18-1101,\n  author = {Williams, Adina\n            and Nangia, Nikita\n            and Bowman, Samuel},\n  title = {A Broad-Coverage Challenge Corpus for\n           Sentence Understanding through Inference},\n  booktitle = {Proceedings of the 2018 Conference of\n               the North American Chapter of the\n               Association for Computational Linguistics:\n               Human Language Technologies, Volume 1 (Long\n               Papers)},\n  year = {2018},\n  publisher = {Association for Computational Linguistics},\n  pages = {1112--1122},\n  location = {New Orleans, Louisiana},\n  url = {http://aclweb.org/anthology/N18-1101}\n}\n", "homepage": "https://www.nyu.edu/projects/bowman/multinli/", "license": "", "features": {"promptID": {"dtype": "int32", "id": null, "_type": "Value"}, "pairID": {"dtype": "string", "id": null, "_type": "Value"}, "premise": {"dtype": "string", "id": null, "_type": "Value"}, "premise_binary_parse": {"dtype": "string", "id": null, "_type": "Value"}, "premise_parse": {"dtype": "string", "id": null, "_type": "Value"}, "hypothesis": {"dtype": "string", "id": null, "_type": "Value"}, "hypothesis_binary_parse": {"dtype": "string", "id": null, "_type": "Value"}, "hypothesis_parse": {"dtype": "string", "id": null, "_type": "Value"}, "genre": {"dtype": "string", "id": null, "_type": "Value"}, "label": {"num_classes": 3, "names": ["entailment", "neutral", "contradiction"], "names_file": null, "id": null, "_type": "ClassLabel"}}, "post_processed": null, "supervised_keys": null, "builder_name": "multi_nli", "config_name": "default", "version": {"version_str": "0.0.0", "description": null, "major": 0, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 410211586, "num_examples": 392702, "dataset_name": "multi_nli"}, "validation_matched": {"name": "validation_matched", "num_bytes": 10063939, "num_examples": 9815, "dataset_name": "multi_nli"}, "validation_mismatched": {"name": "validation_mismatched", "num_bytes": 10610221, "num_examples": 9832, "dataset_name": "multi_nli"}}, "download_checksums": {"https://cims.nyu.edu/~sbowman/multinli/multinli_1.0.zip": {"num_bytes": 226850426, "checksum": "049f507b9e36b1fcb756cfd5aeb3b7a0cfcb84bf023793652987f7e7e0957822"}}, "download_size": 226850426, "post_processing_size": null, "dataset_size": 430885746, "size_in_bytes": 657736172}}
+{
+    "default": {
+        "description": "The Multi-Genre Natural Language Inference (MultiNLI) corpus is a\ncrowd-sourced collection of 433k sentence pairs annotated with textual\nentailment information. The corpus is modeled on the SNLI corpus, but differs in\nthat covers a range of genres of spoken and written text, and supports a\ndistinctive cross-genre generalization evaluation. The corpus served as the\nbasis for the shared task of the RepEval 2017 Workshop at EMNLP in Copenhagen.\n",
+        "citation": "@InProceedings{N18-1101,\n  author = {Williams, Adina\n            and Nangia, Nikita\n            and Bowman, Samuel},\n  title = {A Broad-Coverage Challenge Corpus for\n           Sentence Understanding through Inference},\n  booktitle = {Proceedings of the 2018 Conference of\n               the North American Chapter of the\n               Association for Computational Linguistics:\n               Human Language Technologies, Volume 1 (Long\n               Papers)},\n  year = {2018},\n  publisher = {Association for Computational Linguistics},\n  pages = {1112--1122},\n  location = {New Orleans, Louisiana},\n  url = {http://aclweb.org/anthology/N18-1101}\n}\n",
+        "homepage": "https://www.nyu.edu/projects/bowman/multinli/",
+        "license": "",
+        "features": {
+            "promptID": {
+                "dtype": "int32",
+                "_type": "Value"
+            },
+            "pairID": {
+                "dtype": "string",
+                "_type": "Value"
+            },
+            "premise": {
+                "dtype": "string",
+                "_type": "Value"
+            },
+            "premise_binary_parse": {
+                "dtype": "string",
+                "_type": "Value"
+            },
+            "premise_parse": {
+                "dtype": "string",
+                "_type": "Value"
+            },
+            "hypothesis": {
+                "dtype": "string",
+                "_type": "Value"
+            },
+            "hypothesis_binary_parse": {
+                "dtype": "string",
+                "_type": "Value"
+            },
+            "hypothesis_parse": {
+                "dtype": "string",
+                "_type": "Value"
+            },
+            "genre": {
+                "dtype": "string",
+                "_type": "Value"
+            },
+            "label": {
+                "names": [
+                    "entailment",
+                    "neutral",
+                    "contradiction"
+                ],
+                "_type": "ClassLabel"
+            }
+        },
+        "builder_name": "multi_nli",
+        "dataset_name": "multi_nli",
+        "config_name": "default",
+        "version": {
+            "version_str": "0.0.0",
+            "major": 0,
+            "minor": 0,
+            "patch": 0
+        },
+        "splits": {
+            "train": {
+                "name": "train",
+                "num_bytes": 410210306,
+                "num_examples": 392702,
+                "dataset_name": null
+            },
+            "validation_matched": {
+                "name": "validation_matched",
+                "num_bytes": 10063907,
+                "num_examples": 9815,
+                "dataset_name": null
+            },
+            "validation_mismatched": {
+                "name": "validation_mismatched",
+                "num_bytes": 10610189,
+                "num_examples": 9832,
+                "dataset_name": null
+            }
+        },
+        "download_size": 224005223,
+        "dataset_size": 430884402,
+        "size_in_bytes": 654889625
+    }
+}