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

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+oid sha256:350c26950b55f460b50d36c76aef87d64b49c78812d7abf7bf97e5fede10f186
+size 4938568

data/validation_mismatched-00000-of-00001.parquet

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+version https://git-lfs.github.com/spec/v1
+oid sha256:6b0e1231cebedd255000a7d1732af37ad0500902239db625e231ed77c0c8f2f8
+size 5104992

dataset_infos.json

@@ -1 +0,0 @@
-{"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}}

multi_nli.py

@@ -1,118 +0,0 @@
-# coding=utf-8
-# Copyright 2020 The TensorFlow Datasets Authors and the HuggingFace Datasets Authors.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-# Lint as: python3
-"""The Multi-Genre NLI Corpus."""
-
-
-import json
-import os
-
-import datasets
-
-
-_CITATION = """\
-@InProceedings{N18-1101,
-  author = {Williams, Adina
-            and Nangia, Nikita
-            and Bowman, Samuel},
-  title = {A Broad-Coverage Challenge Corpus for
-           Sentence Understanding through Inference},
-  booktitle = {Proceedings of the 2018 Conference of
-               the North American Chapter of the
-               Association for Computational Linguistics:
-               Human Language Technologies, Volume 1 (Long
-               Papers)},
-  year = {2018},
-  publisher = {Association for Computational Linguistics},
-  pages = {1112--1122},
-  location = {New Orleans, Louisiana},
-  url = {http://aclweb.org/anthology/N18-1101}
-}
-"""
-
-_DESCRIPTION = """\
-The Multi-Genre Natural Language Inference (MultiNLI) corpus is a
-crowd-sourced collection of 433k sentence pairs annotated with textual
-entailment information. The corpus is modeled on the SNLI corpus, but differs in
-that covers a range of genres of spoken and written text, and supports a
-distinctive cross-genre generalization evaluation. The corpus served as the
-basis for the shared task of the RepEval 2017 Workshop at EMNLP in Copenhagen.
-"""
-
-
-class MultiNli(datasets.GeneratorBasedBuilder):
-    """MultiNLI: The Stanford Question Answering Dataset. Version 1.1."""
-
-    def _info(self):
-        return datasets.DatasetInfo(
-            description=_DESCRIPTION,
-            features=datasets.Features(
-                {
-                    "promptID": datasets.Value("int32"),
-                    "pairID": datasets.Value("string"),
-                    "premise": datasets.Value("string"),
-                    "premise_binary_parse": datasets.Value("string"),  # parses in unlabeled binary-branching format
-                    "premise_parse": datasets.Value("string"),  # sentence as parsed by the Stanford PCFG Parser 3.5.2
-                    "hypothesis": datasets.Value("string"),
-                    "hypothesis_binary_parse": datasets.Value("string"),  # parses in unlabeled binary-branching format
-                    "hypothesis_parse": datasets.Value(
-                        "string"
-                    ),  # sentence as parsed by the Stanford PCFG Parser 3.5.2
-                    "genre": datasets.Value("string"),
-                    "label": datasets.features.ClassLabel(names=["entailment", "neutral", "contradiction"]),
-                }
-            ),
-            # No default supervised_keys (as we have to pass both premise
-            # and hypothesis as input).
-            supervised_keys=None,
-            homepage="https://www.nyu.edu/projects/bowman/multinli/",
-            citation=_CITATION,
-        )
-
-    def _split_generators(self, dl_manager):
-
-        downloaded_dir = dl_manager.download_and_extract("https://cims.nyu.edu/~sbowman/multinli/multinli_1.0.zip")
-        mnli_path = os.path.join(downloaded_dir, "multinli_1.0")
-        train_path = os.path.join(mnli_path, "multinli_1.0_train.jsonl")
-        matched_validation_path = os.path.join(mnli_path, "multinli_1.0_dev_matched.jsonl")
-        mismatched_validation_path = os.path.join(mnli_path, "multinli_1.0_dev_mismatched.jsonl")
-
-        return [
-            datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": train_path}),
-            datasets.SplitGenerator(name="validation_matched", gen_kwargs={"filepath": matched_validation_path}),
-            datasets.SplitGenerator(name="validation_mismatched", gen_kwargs={"filepath": mismatched_validation_path}),
-        ]
-
-    def _generate_examples(self, filepath):
-        """Generate mnli examples"""
-
-        with open(filepath, encoding="utf-8") as f:
-            for id_, row in enumerate(f):
-                data = json.loads(row)
-                if data["gold_label"] == "-":
-                    continue
-                yield id_, {
-                    "promptID": data["promptID"],
-                    "pairID": data["pairID"],
-                    "premise": data["sentence1"],
-                    "premise_binary_parse": data["sentence1_binary_parse"],
-                    "premise_parse": data["sentence1_parse"],
-                    "hypothesis": data["sentence2"],
-                    "hypothesis_binary_parse": data["sentence2_binary_parse"],
-                    "hypothesis_parse": data["sentence2_parse"],
-                    "genre": data["genre"],
-                    "label": data["gold_label"],
-                }