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albertvillanova HF staff commited on
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da70db2
1 Parent(s): 39383d4

Convert dataset to Parquet (#5)

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- Convert dataset to Parquet (c4f4109e71f2fd9522a4f6ff12615c89354d1b18)
- Delete loading script (6dc77dbfe0540d1ca2238cf3fd3bb69d23f297d4)
- Delete legacy dataset_infos.json (4a17b6c1fdf9e24b0585dd756de7ea489051e0a1)

README.md CHANGED
@@ -54,16 +54,25 @@ dataset_info:
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  '2': contradiction
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  splits:
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  - name: train
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- num_bytes: 410211586
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  num_examples: 392702
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  - name: validation_matched
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- num_bytes: 10063939
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  num_examples: 9815
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  - name: validation_mismatched
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- num_bytes: 10610221
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  num_examples: 9832
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- download_size: 226850426
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- dataset_size: 430885746
 
 
 
 
 
 
 
 
 
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  ---
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  # Dataset Card for Multi-Genre Natural Language Inference (MultiNLI)
 
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  '2': contradiction
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  splits:
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  - name: train
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+ num_bytes: 410210306
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  num_examples: 392702
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  - name: validation_matched
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+ num_bytes: 10063907
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  num_examples: 9815
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  - name: validation_mismatched
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+ num_bytes: 10610189
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  num_examples: 9832
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+ download_size: 224005223
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+ dataset_size: 430884402
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+ configs:
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+ - config_name: default
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+ data_files:
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+ - split: train
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+ path: data/train-*
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+ - split: validation_matched
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+ path: data/validation_matched-*
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+ - split: validation_mismatched
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+ path: data/validation_mismatched-*
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  ---
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  # Dataset Card for Multi-Genre Natural Language Inference (MultiNLI)
data/train-00000-of-00001.parquet ADDED
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+ size 213961663
data/validation_matched-00000-of-00001.parquet ADDED
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data/validation_mismatched-00000-of-00001.parquet ADDED
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+ size 5104992
dataset_infos.json DELETED
@@ -1 +0,0 @@
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- {"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 DELETED
@@ -1,118 +0,0 @@
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- # coding=utf-8
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- # Copyright 2020 The TensorFlow Datasets Authors and the HuggingFace Datasets Authors.
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- #
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- # Licensed under the Apache License, Version 2.0 (the "License");
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- # you may not use this file except in compliance with the License.
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- # You may obtain a copy of the License at
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- #
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- # http://www.apache.org/licenses/LICENSE-2.0
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- #
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- # Unless required by applicable law or agreed to in writing, software
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- # distributed under the License is distributed on an "AS IS" BASIS,
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- # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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- # See the License for the specific language governing permissions and
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- # limitations under the License.
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-
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- # Lint as: python3
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- """The Multi-Genre NLI Corpus."""
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-
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-
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- import json
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- import os
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-
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- import datasets
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-
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-
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- _CITATION = """\
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- @InProceedings{N18-1101,
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- author = {Williams, Adina
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- and Nangia, Nikita
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- and Bowman, Samuel},
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- title = {A Broad-Coverage Challenge Corpus for
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- Sentence Understanding through Inference},
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- booktitle = {Proceedings of the 2018 Conference of
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- the North American Chapter of the
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- Association for Computational Linguistics:
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- Human Language Technologies, Volume 1 (Long
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- Papers)},
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- year = {2018},
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- publisher = {Association for Computational Linguistics},
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- pages = {1112--1122},
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- location = {New Orleans, Louisiana},
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- url = {http://aclweb.org/anthology/N18-1101}
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- }
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- """
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-
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- _DESCRIPTION = """\
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- The Multi-Genre Natural Language Inference (MultiNLI) corpus is a
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- crowd-sourced collection of 433k sentence pairs annotated with textual
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- entailment information. The corpus is modeled on the SNLI corpus, but differs in
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- that covers a range of genres of spoken and written text, and supports a
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- distinctive cross-genre generalization evaluation. The corpus served as the
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- basis for the shared task of the RepEval 2017 Workshop at EMNLP in Copenhagen.
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- """
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-
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-
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- class MultiNli(datasets.GeneratorBasedBuilder):
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- """MultiNLI: The Stanford Question Answering Dataset. Version 1.1."""
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-
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- def _info(self):
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- return datasets.DatasetInfo(
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- description=_DESCRIPTION,
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- features=datasets.Features(
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- {
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- "promptID": datasets.Value("int32"),
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- "pairID": datasets.Value("string"),
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- "premise": datasets.Value("string"),
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- "premise_binary_parse": datasets.Value("string"), # parses in unlabeled binary-branching format
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- "premise_parse": datasets.Value("string"), # sentence as parsed by the Stanford PCFG Parser 3.5.2
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- "hypothesis": datasets.Value("string"),
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- "hypothesis_binary_parse": datasets.Value("string"), # parses in unlabeled binary-branching format
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- "hypothesis_parse": datasets.Value(
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- "string"
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- ), # sentence as parsed by the Stanford PCFG Parser 3.5.2
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- "genre": datasets.Value("string"),
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- "label": datasets.features.ClassLabel(names=["entailment", "neutral", "contradiction"]),
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- }
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- ),
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- # No default supervised_keys (as we have to pass both premise
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- # and hypothesis as input).
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- supervised_keys=None,
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- homepage="https://www.nyu.edu/projects/bowman/multinli/",
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- citation=_CITATION,
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- )
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-
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- def _split_generators(self, dl_manager):
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-
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- downloaded_dir = dl_manager.download_and_extract("https://cims.nyu.edu/~sbowman/multinli/multinli_1.0.zip")
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- mnli_path = os.path.join(downloaded_dir, "multinli_1.0")
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- train_path = os.path.join(mnli_path, "multinli_1.0_train.jsonl")
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- matched_validation_path = os.path.join(mnli_path, "multinli_1.0_dev_matched.jsonl")
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- mismatched_validation_path = os.path.join(mnli_path, "multinli_1.0_dev_mismatched.jsonl")
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-
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- return [
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- datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": train_path}),
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- datasets.SplitGenerator(name="validation_matched", gen_kwargs={"filepath": matched_validation_path}),
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- datasets.SplitGenerator(name="validation_mismatched", gen_kwargs={"filepath": mismatched_validation_path}),
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- ]
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-
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- def _generate_examples(self, filepath):
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- """Generate mnli examples"""
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-
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- with open(filepath, encoding="utf-8") as f:
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- for id_, row in enumerate(f):
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- data = json.loads(row)
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- if data["gold_label"] == "-":
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- continue
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- yield id_, {
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- "promptID": data["promptID"],
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- "pairID": data["pairID"],
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- "premise": data["sentence1"],
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- "premise_binary_parse": data["sentence1_binary_parse"],
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- "premise_parse": data["sentence1_parse"],
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- "hypothesis": data["sentence2"],
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- "hypothesis_binary_parse": data["sentence2_binary_parse"],
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- "hypothesis_parse": data["sentence2_parse"],
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- "genre": data["genre"],
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- "label": data["gold_label"],
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- }