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README.md

@@ -1,243 +0,0 @@
----
-annotations_creators:
-- crowdsourced
-language_creators:
-- crowdsourced
-- found
-language:
-- en
-license:
-- cc-by-3.0
-- cc-by-sa-3.0
-- mit
-- other
-license_details: Open Portion of the American National Corpus
-multilinguality:
-- monolingual
-size_categories:
-- 100K<n<1M
-source_datasets:
-- original
-task_categories:
-- text-classification
-task_ids:
-- natural-language-inference
-- multi-input-text-classification
-paperswithcode_id: multinli
-pretty_name: Multi-Genre Natural Language Inference
-dataset_info:
-  features:
-  - name: promptID
-    dtype: int32
-  - name: pairID
-    dtype: string
-  - name: premise
-    dtype: string
-  - name: premise_binary_parse
-    dtype: string
-  - name: premise_parse
-    dtype: string
-  - name: hypothesis
-    dtype: string
-  - name: hypothesis_binary_parse
-    dtype: string
-  - name: hypothesis_parse
-    dtype: string
-  - name: genre
-    dtype: string
-  - name: label
-    dtype:
-      class_label:
-        names:
-          0: entailment
-          1: neutral
-          2: contradiction
-  splits:
-  - name: train
-    num_bytes: 410211586
-    num_examples: 392702
-  - name: validation_matched
-    num_bytes: 10063939
-    num_examples: 9815
-  - name: validation_mismatched
-    num_bytes: 10610221
-    num_examples: 9832
-  download_size: 226850426
-  dataset_size: 430885746
----
-
-# Dataset Card for Multi-Genre Natural Language Inference (MultiNLI)
-
-## Table of Contents
-- [Dataset Description](#dataset-description)
-  - [Dataset Summary](#dataset-summary)
-  - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
-  - [Languages](#languages)
-- [Dataset Structure](#dataset-structure)
-  - [Data Instances](#data-instances)
-  - [Data Fields](#data-fields)
-  - [Data Splits](#data-splits)
-- [Dataset Creation](#dataset-creation)
-  - [Curation Rationale](#curation-rationale)
-  - [Source Data](#source-data)
-  - [Annotations](#annotations)
-  - [Personal and Sensitive Information](#personal-and-sensitive-information)
-- [Considerations for Using the Data](#considerations-for-using-the-data)
-  - [Social Impact of Dataset](#social-impact-of-dataset)
-  - [Discussion of Biases](#discussion-of-biases)
-  - [Other Known Limitations](#other-known-limitations)
-- [Additional Information](#additional-information)
-  - [Dataset Curators](#dataset-curators)
-  - [Licensing Information](#licensing-information)
-  - [Citation Information](#citation-information)
-  - [Contributions](#contributions)
-
-## Dataset Description
-
-- **Homepage:** [https://www.nyu.edu/projects/bowman/multinli/](https://www.nyu.edu/projects/bowman/multinli/)
-- **Repository:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
-- **Paper:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
-- **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
-- **Size of downloaded dataset files:** 216.34 MB
-- **Size of the generated dataset:** 73.39 MB
-- **Total amount of disk used:** 289.74 MB
-
-### Dataset Summary
-
-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.
-
-### Supported Tasks and Leaderboards
-
-[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
-
-### Languages
-
-The dataset contains samples in English only.
-
-## Dataset Structure
-
-### Data Instances
-
-- **Size of downloaded dataset files:** 216.34 MB
-- **Size of the generated dataset:** 73.39 MB
-- **Total amount of disk used:** 289.74 MB
-
-Example of a data instance:
-
-```
-{
-    "promptID": 31193,
-    "pairID": "31193n",
-    "premise": "Conceptually cream skimming has two basic dimensions - product and geography.",
-    "premise_binary_parse": "( ( Conceptually ( cream skimming ) ) ( ( has ( ( ( two ( basic dimensions ) ) - ) ( ( product and ) geography ) ) ) . ) )",
-    "premise_parse": "(ROOT (S (NP (JJ Conceptually) (NN cream) (NN skimming)) (VP (VBZ has) (NP (NP (CD two) (JJ basic) (NNS dimensions)) (: -) (NP (NN product) (CC and) (NN geography)))) (. .)))",
-    "hypothesis": "Product and geography are what make cream skimming work. ",
-    "hypothesis_binary_parse": "( ( ( Product and ) geography ) ( ( are ( what ( make ( cream ( skimming work ) ) ) ) ) . ) )",
-    "hypothesis_parse": "(ROOT (S (NP (NN Product) (CC and) (NN geography)) (VP (VBP are) (SBAR (WHNP (WP what)) (S (VP (VBP make) (NP (NP (NN cream)) (VP (VBG skimming) (NP (NN work)))))))) (. .)))",
-    "genre": "government",
-    "label": 1
-}
-```
-
-### Data Fields
-
-The data fields are the same among all splits.
-
-- `promptID`: Unique identifier for prompt
-- `pairID`: Unique identifier for pair
-- `{premise,hypothesis}`: combination of `premise` and `hypothesis`
-- `{premise,hypothesis} parse`: Each sentence as parsed by the Stanford PCFG Parser 3.5.2
-- `{premise,hypothesis} binary parse`: parses in unlabeled binary-branching format
-- `genre`: a `string` feature.
-- `label`: a classification label, with possible values including `entailment` (0), `neutral` (1), `contradiction` (2). Dataset instances which don't have any gold label are marked with -1 label. Make sure you filter them before starting the training using `datasets.Dataset.filter`.
-
-### Data Splits
-
-|train |validation_matched|validation_mismatched|
-|-----:|-----------------:|--------------------:|
-|392702|              9815|                 9832|
-
-## Dataset Creation
-
-### Curation Rationale
-
-They constructed MultiNLI so as to make it possible to explicitly evaluate models both on the quality of their sentence representations within the training domain and on their ability to derive reasonable representations in unfamiliar domains.
-
-### Source Data
-
-#### Initial Data Collection and Normalization
-
-They created each sentence pair by selecting a premise sentence from a preexisting text source and asked a human annotator to compose a novel sentence to pair with it as a hypothesis.
-
-#### Who are the source language producers?
-
-[More Information Needed]
-
-### Annotations
-
-#### Annotation process
-
-[More Information Needed]
-
-#### Who are the annotators?
-
-[More Information Needed]
-
-### Personal and Sensitive Information
-
-[More Information Needed]
-
-## Considerations for Using the Data
-
-### Social Impact of Dataset
-
-[More Information Needed]
-
-### Discussion of Biases
-
-[More Information Needed]
-
-### Other Known Limitations
-
-[More Information Needed]
-
-## Additional Information
-
-### Dataset Curators
-
-[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
-
-### Licensing Information
-
-The majority of the corpus is released under the OANC’s license, which allows all content to be freely used, modified, and shared under permissive terms. The data in the FICTION section falls under several permissive licenses; Seven Swords is available under a Creative Commons Share-Alike 3.0 Unported License, and with the explicit permission of the author, Living History and Password Incorrect are available under Creative Commons Attribution 3.0 Unported Licenses; the remaining works of fiction are in the public domain in the United States (but may be licensed differently elsewhere).
-
-### Citation Information
-
-```
-@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"
-}
-```
-
-### Contributions
-
-Thanks to [@bhavitvyamalik](https://github.com/bhavitvyamalik), [@patrickvonplaten](https://github.com/patrickvonplaten), [@thomwolf](https://github.com/thomwolf), [@mariamabarham](https://github.com/mariamabarham) for adding this dataset.

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}}

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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"],
-                }