Commit
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da70db2
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Parent(s):
39383d4
Convert dataset to Parquet (#5)
Browse files- Convert dataset to Parquet (c4f4109e71f2fd9522a4f6ff12615c89354d1b18)
- Delete loading script (6dc77dbfe0540d1ca2238cf3fd3bb69d23f297d4)
- Delete legacy dataset_infos.json (4a17b6c1fdf9e24b0585dd756de7ea489051e0a1)
- README.md +14 -5
- data/train-00000-of-00001.parquet +3 -0
- data/validation_matched-00000-of-00001.parquet +3 -0
- data/validation_mismatched-00000-of-00001.parquet +3 -0
- dataset_infos.json +0 -1
- multi_nli.py +0 -118
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:
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num_examples: 392702
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- name: validation_matched
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num_bytes:
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num_examples: 9815
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- name: validation_mismatched
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num_bytes:
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num_examples: 9832
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download_size:
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dataset_size:
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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)
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data/train-00000-of-00001.parquet
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version https://git-lfs.github.com/spec/v1
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oid sha256:1c1de03640b168e410aabfca19e7cc2f3dfcd7f0e126e935674e56fb102c4529
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size 213961663
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data/validation_matched-00000-of-00001.parquet
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version https://git-lfs.github.com/spec/v1
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oid sha256:350c26950b55f460b50d36c76aef87d64b49c78812d7abf7bf97e5fede10f186
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size 4938568
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data/validation_mismatched-00000-of-00001.parquet
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version https://git-lfs.github.com/spec/v1
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oid sha256:6b0e1231cebedd255000a7d1732af37ad0500902239db625e231ed77c0c8f2f8
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size 5104992
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dataset_infos.json
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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}}
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multi_nli.py
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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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# Lint as: python3
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"""The Multi-Genre NLI Corpus."""
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import json
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import os
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import datasets
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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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class MultiNli(datasets.GeneratorBasedBuilder):
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"""MultiNLI: The Stanford Question Answering Dataset. Version 1.1."""
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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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def _split_generators(self, dl_manager):
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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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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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def _generate_examples(self, filepath):
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"""Generate mnli examples"""
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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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}
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