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README.md

@@ -1,316 +0,0 @@
----
-language:
-- en
-paperswithcode_id: scitail
-pretty_name: SciTail
-dataset_info:
-- config_name: snli_format
-  features:
-  - name: sentence1_binary_parse
-    dtype: string
-  - name: sentence1_parse
-    dtype: string
-  - name: sentence1
-    dtype: string
-  - name: sentence2_parse
-    dtype: string
-  - name: sentence2
-    dtype: string
-  - name: annotator_labels
-    sequence: string
-  - name: gold_label
-    dtype: string
-  splits:
-  - name: train
-    num_bytes: 22495833
-    num_examples: 23596
-  - name: test
-    num_bytes: 2008631
-    num_examples: 2126
-  - name: validation
-    num_bytes: 1266529
-    num_examples: 1304
-  download_size: 14174621
-  dataset_size: 25770993
-- config_name: tsv_format
-  features:
-  - name: premise
-    dtype: string
-  - name: hypothesis
-    dtype: string
-  - name: label
-    dtype: string
-  splits:
-  - name: train
-    num_bytes: 4618115
-    num_examples: 23097
-  - name: test
-    num_bytes: 411343
-    num_examples: 2126
-  - name: validation
-    num_bytes: 261086
-    num_examples: 1304
-  download_size: 14174621
-  dataset_size: 5290544
-- config_name: dgem_format
-  features:
-  - name: premise
-    dtype: string
-  - name: hypothesis
-    dtype: string
-  - name: label
-    dtype: string
-  - name: hypothesis_graph_structure
-    dtype: string
-  splits:
-  - name: train
-    num_bytes: 6832104
-    num_examples: 23088
-  - name: test
-    num_bytes: 608213
-    num_examples: 2126
-  - name: validation
-    num_bytes: 394040
-    num_examples: 1304
-  download_size: 14174621
-  dataset_size: 7834357
-- config_name: predictor_format
-  features:
-  - name: answer
-    dtype: string
-  - name: sentence2_structure
-    dtype: string
-  - name: sentence1
-    dtype: string
-  - name: sentence2
-    dtype: string
-  - name: gold_label
-    dtype: string
-  - name: question
-    dtype: string
-  splits:
-  - name: train
-    num_bytes: 8884823
-    num_examples: 23587
-  - name: test
-    num_bytes: 797161
-    num_examples: 2126
-  - name: validation
-    num_bytes: 511305
-    num_examples: 1304
-  download_size: 14174621
-  dataset_size: 10193289
----
-
-# Dataset Card for "scitail"
-
-## 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://allenai.org/data/scitail](https://allenai.org/data/scitail)
-- **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:** 54.07 MB
-- **Size of the generated dataset:** 46.82 MB
-- **Total amount of disk used:** 100.89 MB
-
-### Dataset Summary
-
-The SciTail dataset is an entailment dataset created from multiple-choice science exams and web sentences. Each question
-and the correct answer choice are converted into an assertive statement to form the hypothesis. We use information
-retrieval to obtain relevant text from a large text corpus of web sentences, and use these sentences as a premise P. We
-crowdsource the annotation of such premise-hypothesis pair as supports (entails) or not (neutral), in order to create
-the SciTail dataset. The dataset contains 27,026 examples with 10,101 examples with entails label and 16,925 examples
-with neutral label
-
-### Supported Tasks and Leaderboards
-
-[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
-
-### Languages
-
-[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
-
-## Dataset Structure
-
-### Data Instances
-
-#### dgem_format
-
-- **Size of downloaded dataset files:** 13.52 MB
-- **Size of the generated dataset:** 7.47 MB
-- **Total amount of disk used:** 20.99 MB
-
-An example of 'train' looks as follows.
-```
-
-```
-
-#### predictor_format
-
-- **Size of downloaded dataset files:** 13.52 MB
-- **Size of the generated dataset:** 9.72 MB
-- **Total amount of disk used:** 23.24 MB
-
-An example of 'validation' looks as follows.
-```
-
-```
-
-#### snli_format
-
-- **Size of downloaded dataset files:** 13.52 MB
-- **Size of the generated dataset:** 24.58 MB
-- **Total amount of disk used:** 38.10 MB
-
-An example of 'validation' looks as follows.
-```
-
-```
-
-#### tsv_format
-
-- **Size of downloaded dataset files:** 13.52 MB
-- **Size of the generated dataset:** 5.05 MB
-- **Total amount of disk used:** 18.56 MB
-
-An example of 'validation' looks as follows.
-```
-
-```
-
-### Data Fields
-
-The data fields are the same among all splits.
-
-#### dgem_format
-- `premise`: a `string` feature.
-- `hypothesis`: a `string` feature.
-- `label`: a `string` feature.
-- `hypothesis_graph_structure`: a `string` feature.
-
-#### predictor_format
-- `answer`: a `string` feature.
-- `sentence2_structure`: a `string` feature.
-- `sentence1`: a `string` feature.
-- `sentence2`: a `string` feature.
-- `gold_label`: a `string` feature.
-- `question`: a `string` feature.
-
-#### snli_format
-- `sentence1_binary_parse`: a `string` feature.
-- `sentence1_parse`: a `string` feature.
-- `sentence1`: a `string` feature.
-- `sentence2_parse`: a `string` feature.
-- `sentence2`: a `string` feature.
-- `annotator_labels`: a `list` of `string` features.
-- `gold_label`: a `string` feature.
-
-#### tsv_format
-- `premise`: a `string` feature.
-- `hypothesis`: a `string` feature.
-- `label`: a `string` feature.
-
-### Data Splits
-
-|      name      |train|validation|test|
-|----------------|----:|---------:|---:|
-|dgem_format     |23088|      1304|2126|
-|predictor_format|23587|      1304|2126|
-|snli_format     |23596|      1304|2126|
-|tsv_format      |23097|      1304|2126|
-
-## Dataset Creation
-
-### Curation Rationale
-
-[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
-
-### Source Data
-
-#### Initial Data Collection and Normalization
-
-[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
-
-#### Who are the source language producers?
-
-[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
-
-### Annotations
-
-#### Annotation process
-
-[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
-
-#### Who are the annotators?
-
-[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
-
-### Personal and Sensitive Information
-
-[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
-
-## Considerations for Using the Data
-
-### Social Impact of Dataset
-
-[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
-
-### Discussion of Biases
-
-[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
-
-### Other Known Limitations
-
-[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
-
-## 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
-
-[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
-
-### Citation Information
-
-```
-inproceedings{scitail,
-     Author = {Tushar Khot and Ashish Sabharwal and Peter Clark},
-     Booktitle = {AAAI},
-     Title = {{SciTail}: A Textual Entailment Dataset from Science Question Answering},
-     Year = {2018}
-}
-
-```
-
-
-### Contributions
-
-Thanks to [@patrickvonplaten](https://github.com/patrickvonplaten), [@mariamabarham](https://github.com/mariamabarham), [@lewtun](https://github.com/lewtun), [@thomwolf](https://github.com/thomwolf) for adding this dataset.

dataset_infos.json

@@ -1 +0,0 @@
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The dataset contains 27,026 examples with 10,101 examples with entails label and 16,925 examples \nwith neutral label\n", "citation": "inproceedings{scitail,\n     Author = {Tushar Khot and Ashish Sabharwal and Peter Clark},\n     Booktitle = {AAAI},\n     Title = {{SciTail}: A Textual Entailment Dataset from Science Question Answering},\n     Year = {2018}\n}\n", "homepage": "https://allenai.org/data/scitail", "license": "", "features": {"sentence1_binary_parse": {"dtype": "string", "id": null, "_type": "Value"}, "sentence1_parse": {"dtype": "string", "id": null, "_type": "Value"}, "sentence1": {"dtype": "string", "id": null, "_type": "Value"}, "sentence2_parse": {"dtype": "string", "id": null, "_type": "Value"}, "sentence2": {"dtype": "string", "id": null, "_type": "Value"}, "annotator_labels": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}, "gold_label": {"dtype": "string", "id": null, "_type": "Value"}}, "supervised_keys": null, "builder_name": "scitail", "config_name": "snli_format", "version": {"version_str": "1.1.0", "description": "", "datasets_version_to_prepare": null, "major": 1, "minor": 1, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 22495833, "num_examples": 23596, "dataset_name": "scitail"}, "test": {"name": "test", "num_bytes": 2008631, "num_examples": 2126, "dataset_name": "scitail"}, "validation": {"name": "validation", "num_bytes": 1266529, "num_examples": 1304, "dataset_name": "scitail"}}, "download_checksums": {"http://data.allenai.org.s3.amazonaws.com/downloads/SciTailV1.1.zip": {"num_bytes": 14174621, "checksum": "3fccd37350a94ca280b75998568df85fc2fc62843a3198d644fcbf858e6943d5"}}, "download_size": 14174621, "dataset_size": 25770993, "size_in_bytes": 39945614}, "tsv_format": {"description": "The SciTail dataset is an entailment dataset created from multiple-choice science exams and web sentences. 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scitail.py

@@ -1,298 +0,0 @@
-"""TODO(sciTail): Add a description here."""
-
-
-import csv
-import json
-import os
-import textwrap
-
-import datasets
-
-
-# TODO(sciTail): BibTeX citation
-_CITATION = """\
-inproceedings{scitail,
-     Author = {Tushar Khot and Ashish Sabharwal and Peter Clark},
-     Booktitle = {AAAI},
-     Title = {{SciTail}: A Textual Entailment Dataset from Science Question Answering},
-     Year = {2018}
-}
-"""
-
-# TODO(sciTail):
-_DESCRIPTION = """\
-The SciTail dataset is an entailment dataset created from multiple-choice science exams and web sentences. Each question
-and the correct answer choice are converted into an assertive statement to form the hypothesis. We use information
-retrieval to obtain relevant text from a large text corpus of web sentences, and use these sentences as a premise P. We
-crowdsource the annotation of such premise-hypothesis pair as supports (entails) or not (neutral), in order to create
-the SciTail dataset. The dataset contains 27,026 examples with 10,101 examples with entails label and 16,925 examples
-with neutral label
-"""
-
-_URL = "http://data.allenai.org.s3.amazonaws.com/downloads/SciTailV1.1.zip"
-
-
-class ScitailConfig(datasets.BuilderConfig):
-
-    """BuilderConfig for Xquad"""
-
-    def __init__(self, **kwargs):
-        """
-
-        Args:
-            **kwargs: keyword arguments forwarded to super.
-        """
-        super(ScitailConfig, self).__init__(version=datasets.Version("1.1.0", ""), **kwargs)
-
-
-class Scitail(datasets.GeneratorBasedBuilder):
-    """TODO(sciTail): Short description of my dataset."""
-
-    # TODO(sciTail): Set up version.
-    VERSION = datasets.Version("1.1.0")
-    BUILDER_CONFIGS = [
-        ScitailConfig(
-            name="snli_format",
-            description="JSONL format used by SNLI with a JSON object corresponding to each entailment example in each line.",
-        ),
-        ScitailConfig(
-            name="tsv_format", description="Tab-separated format with three columns: premise hypothesis label"
-        ),
-        ScitailConfig(
-            name="dgem_format",
-            description="Tab-separated format used by the DGEM model: premise hypothesis label hypothesis graph structure",
-        ),
-        ScitailConfig(
-            name="predictor_format",
-            description=textwrap.dedent(
-                """\
-          AllenNLP predictors work only with JSONL format. This folder contains the SciTail train/dev/test in JSONL format
-        so that it can be loaded into the predictors. Each line is a JSON object with the following keys:
-        gold_label : the example label from {entails, neutral}
-        sentence1: the premise
-        sentence2: the hypothesis
-        sentence2_structure: structure from the hypothesis """
-            ),
-        ),
-    ]
-
-    def _info(self):
-        # TODO(sciTail): Specifies the datasets.DatasetInfo object
-        if self.config.name == "snli_format":
-            return datasets.DatasetInfo(
-                # This is the description that will appear on the datasets page.
-                description=_DESCRIPTION,
-                # datasets.features.FeatureConnectors
-                features=datasets.Features(
-                    {
-                        "sentence1_binary_parse": datasets.Value("string"),
-                        "sentence1_parse": datasets.Value("string"),
-                        "sentence1": datasets.Value("string"),
-                        "sentence2_parse": datasets.Value("string"),
-                        "sentence2": datasets.Value("string"),
-                        "annotator_labels": datasets.features.Sequence(datasets.Value("string")),
-                        "gold_label": datasets.Value("string")
-                        # These are the features of your dataset like images, labels ...
-                    }
-                ),
-                # If there's a common (input, target) tuple from the features,
-                # specify them here. They'll be used if as_supervised=True in
-                # builder.as_dataset.
-                supervised_keys=None,
-                # Homepage of the dataset for documentation
-                homepage="https://allenai.org/data/scitail",
-                citation=_CITATION,
-            )
-        elif self.config.name == "tsv_format":
-            return datasets.DatasetInfo(
-                # This is the description that will appear on the datasets page.
-                description=_DESCRIPTION,
-                # datasets.features.FeatureConnectors
-                features=datasets.Features(
-                    {
-                        "premise": datasets.Value("string"),
-                        "hypothesis": datasets.Value("string"),
-                        "label": datasets.Value("string")
-                        # These are the features of your dataset like images, labels ...
-                    }
-                ),
-                # If there's a common (input, target) tuple from the features,
-                # specify them here. They'll be used if as_supervised=True in
-                # builder.as_dataset.
-                supervised_keys=None,
-                # Homepage of the dataset for documentation
-                homepage="https://allenai.org/data/scitail",
-                citation=_CITATION,
-            )
-        elif self.config.name == "predictor_format":
-            return datasets.DatasetInfo(
-                # This is the description that will appear on the datasets page.
-                description=_DESCRIPTION,
-                # datasets.features.FeatureConnectors
-                features=datasets.Features(
-                    {
-                        "answer": datasets.Value("string"),
-                        "sentence2_structure": datasets.Value("string"),
-                        "sentence1": datasets.Value("string"),
-                        "sentence2": datasets.Value("string"),
-                        "gold_label": datasets.Value("string"),
-                        "question": datasets.Value("string")
-                        # These are the features of your dataset like images, labels ...
-                    }
-                ),
-                # If there's a common (input, target) tuple from the features,
-                # specify them here. They'll be used if as_supervised=True in
-                # builder.as_dataset.
-                supervised_keys=None,
-                # Homepage of the dataset for documentation
-                homepage="https://allenai.org/data/scitail",
-                citation=_CITATION,
-            )
-        elif self.config.name == "dgem_format":
-            return datasets.DatasetInfo(
-                # This is the description that will appear on the datasets page.
-                description=_DESCRIPTION,
-                # datasets.features.FeatureConnectors
-                features=datasets.Features(
-                    {
-                        "premise": datasets.Value("string"),
-                        "hypothesis": datasets.Value("string"),
-                        "label": datasets.Value("string"),
-                        "hypothesis_graph_structure": datasets.Value("string")
-                        # These are the features of your dataset like images, labels ...
-                    }
-                ),
-                # If there's a common (input, target) tuple from the features,
-                # specify them here. They'll be used if as_supervised=True in
-                # builder.as_dataset.
-                supervised_keys=None,
-                # Homepage of the dataset for documentation
-                homepage="https://allenai.org/data/scitail",
-                citation=_CITATION,
-            )
-
-    def _split_generators(self, dl_manager):
-        """Returns SplitGenerators."""
-        # TODO(sciTail): Downloads the data and defines the splits
-        # dl_manager is a datasets.download.DownloadManager that can be used to
-        # download and extract URLs
-        dl_dir = dl_manager.download_and_extract(_URL)
-        data_dir = os.path.join(dl_dir, "SciTailV1.1")
-        snli = os.path.join(data_dir, "snli_format")
-        dgem = os.path.join(data_dir, "dgem_format")
-        tsv = os.path.join(data_dir, "tsv_format")
-        predictor = os.path.join(data_dir, "predictor_format")
-        if self.config.name == "snli_format":
-            return [
-                datasets.SplitGenerator(
-                    name=datasets.Split.TRAIN,
-                    # These kwargs will be passed to _generate_examples
-                    gen_kwargs={"filepath": os.path.join(snli, "scitail_1.0_train.txt")},
-                ),
-                datasets.SplitGenerator(
-                    name=datasets.Split.TEST,
-                    # These kwargs will be passed to _generate_examples
-                    gen_kwargs={"filepath": os.path.join(snli, "scitail_1.0_test.txt")},
-                ),
-                datasets.SplitGenerator(
-                    name=datasets.Split.VALIDATION,
-                    # These kwargs will be passed to _generate_examples
-                    gen_kwargs={"filepath": os.path.join(snli, "scitail_1.0_dev.txt")},
-                ),
-            ]
-        elif self.config.name == "tsv_format":
-            return [
-                datasets.SplitGenerator(
-                    name=datasets.Split.TRAIN,
-                    # These kwargs will be passed to _generate_examples
-                    gen_kwargs={"filepath": os.path.join(tsv, "scitail_1.0_train.tsv")},
-                ),
-                datasets.SplitGenerator(
-                    name=datasets.Split.TEST,
-                    # These kwargs will be passed to _generate_examples
-                    gen_kwargs={"filepath": os.path.join(tsv, "scitail_1.0_test.tsv")},
-                ),
-                datasets.SplitGenerator(
-                    name=datasets.Split.VALIDATION,
-                    # These kwargs will be passed to _generate_examples
-                    gen_kwargs={"filepath": os.path.join(tsv, "scitail_1.0_dev.tsv")},
-                ),
-            ]
-        elif self.config.name == "predictor_format":
-            return [
-                datasets.SplitGenerator(
-                    name=datasets.Split.TRAIN,
-                    # These kwargs will be passed to _generate_examples
-                    gen_kwargs={"filepath": os.path.join(predictor, "scitail_1.0_structure_train.jsonl")},
-                ),
-                datasets.SplitGenerator(
-                    name=datasets.Split.TEST,
-                    # These kwargs will be passed to _generate_examples
-                    gen_kwargs={"filepath": os.path.join(predictor, "scitail_1.0_structure_test.jsonl")},
-                ),
-                datasets.SplitGenerator(
-                    name=datasets.Split.VALIDATION,
-                    # These kwargs will be passed to _generate_examples
-                    gen_kwargs={"filepath": os.path.join(predictor, "scitail_1.0_structure_dev.jsonl")},
-                ),
-            ]
-        elif self.config.name == "dgem_format":
-            return [
-                datasets.SplitGenerator(
-                    name=datasets.Split.TRAIN,
-                    # These kwargs will be passed to _generate_examples
-                    gen_kwargs={"filepath": os.path.join(dgem, "scitail_1.0_structure_train.tsv")},
-                ),
-                datasets.SplitGenerator(
-                    name=datasets.Split.TEST,
-                    # These kwargs will be passed to _generate_examples
-                    gen_kwargs={"filepath": os.path.join(dgem, "scitail_1.0_structure_test.tsv")},
-                ),
-                datasets.SplitGenerator(
-                    name=datasets.Split.VALIDATION,
-                    # These kwargs will be passed to _generate_examples
-                    gen_kwargs={"filepath": os.path.join(dgem, "scitail_1.0_structure_dev.tsv")},
-                ),
-            ]
-
-    def _generate_examples(self, filepath):
-        """Yields examples."""
-        # TODO(sciTail): Yields (key, example) tuples from the dataset
-        with open(filepath, encoding="utf-8") as f:
-            if self.config.name == "snli_format":
-                for id_, row in enumerate(f):
-                    data = json.loads(row)
-
-                    yield id_, {
-                        "sentence1_binary_parse": data["sentence1_binary_parse"],
-                        "sentence1_parse": data["sentence1_parse"],
-                        "sentence1": data["sentence1"],
-                        "sentence2_parse": data["sentence2_parse"],
-                        "sentence2": data["sentence2"],
-                        "annotator_labels": data["annotator_labels"],
-                        "gold_label": data["gold_label"],
-                    }
-            elif self.config.name == "tsv_format":
-                data = csv.reader(f, delimiter="\t")
-                for id_, row in enumerate(data):
-                    yield id_, {"premise": row[0], "hypothesis": row[1], "label": row[2]}
-            elif self.config.name == "dgem_format":
-                data = csv.reader(f, delimiter="\t")
-                for id_, row in enumerate(data):
-                    yield id_, {
-                        "premise": row[0],
-                        "hypothesis": row[1],
-                        "label": row[2],
-                        "hypothesis_graph_structure": row[3],
-                    }
-            elif self.config.name == "predictor_format":
-                for id_, row in enumerate(f):
-                    data = json.loads(row)
-                    yield id_, {
-                        "answer": data["answer"],
-                        "sentence2_structure": data["sentence2_structure"],
-                        "sentence1": data["sentence1"],
-                        "sentence2": data["sentence2"],
-                        "gold_label": data["gold_label"],
-                        "question": data["question"],
-                    }

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