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README.md

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----
-language:
-- en
-paperswithcode_id: social-iqa
-pretty_name: Social Interaction QA
-dataset_info:
-  features:
-  - name: context
-    dtype: string
-  - name: question
-    dtype: string
-  - name: answerA
-    dtype: string
-  - name: answerB
-    dtype: string
-  - name: answerC
-    dtype: string
-  - name: label
-    dtype: string
-  splits:
-  - name: train
-    num_bytes: 6389954
-    num_examples: 33410
-  - name: validation
-    num_bytes: 376508
-    num_examples: 1954
-  download_size: 2198056
-  dataset_size: 6766462
----
-
-# Dataset Card for "social_i_qa"
-
-## 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://leaderboard.allenai.org/socialiqa/submissions/get-started](https://leaderboard.allenai.org/socialiqa/submissions/get-started)
-- **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:** 2.10 MB
-- **Size of the generated dataset:** 6.45 MB
-- **Total amount of disk used:** 8.55 MB
-
-### Dataset Summary
-
-We introduce Social IQa: Social Interaction QA, a new question-answering benchmark for testing social commonsense intelligence. Contrary to many prior benchmarks that focus on physical or taxonomic knowledge, Social IQa focuses on reasoning about people’s actions and their social implications. For example, given an action like "Jesse saw a concert" and a question like "Why did Jesse do this?", humans can easily infer that Jesse wanted "to see their favorite performer" or "to enjoy the music", and not "to see what's happening inside" or "to see if it works". The actions in Social IQa span a wide variety of social situations, and answer candidates contain both human-curated answers and adversarially-filtered machine-generated candidates. Social IQa contains over 37,000 QA pairs for evaluating models’ abilities to reason about the social implications of everyday events and situations. (Less)
-
-### 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
-
-#### default
-
-- **Size of downloaded dataset files:** 2.10 MB
-- **Size of the generated dataset:** 6.45 MB
-- **Total amount of disk used:** 8.55 MB
-
-An example of 'validation' looks as follows.
-```
-{
-    "answerA": "sympathetic",
-    "answerB": "like a person who was unable to help",
-    "answerC": "incredulous",
-    "context": "Sydney walked past a homeless woman asking for change but did not have any money they could give to her. Sydney felt bad afterwards.",
-    "label": "1",
-    "question": "How would you describe Sydney?"
-}
-```
-
-### Data Fields
-
-The data fields are the same among all splits.
-
-#### default
-- `context`: a `string` feature.
-- `question`: a `string` feature.
-- `answerA`: a `string` feature.
-- `answerB`: a `string` feature.
-- `answerC`: a `string` feature.
-- `label`: a `string` feature.
-
-### Data Splits
-
-| name  |train|validation|
-|-------|----:|---------:|
-|default|33410|      1954|
-
-## 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
-
-```
-
-```
-
-
-### Contributions
-
-Thanks to [@bhavitvyamalik](https://github.com/bhavitvyamalik), [@thomwolf](https://github.com/thomwolf), [@patrickvonplaten](https://github.com/patrickvonplaten), [@lewtun](https://github.com/lewtun) for adding this dataset.

dataset_infos.json

@@ -1 +0,0 @@
-{"default": {"description": "We introduce Social IQa: Social Interaction QA, a new question-answering benchmark for testing social commonsense intelligence. Contrary to many prior benchmarks that focus on physical or taxonomic knowledge, Social IQa focuses on reasoning about people\u2019s actions and their social implications. For example, given an action like \"Jesse saw a concert\" and a question like \"Why did Jesse do this?\", humans can easily infer that Jesse wanted \"to see their favorite performer\" or \"to enjoy the music\", and not \"to see what's happening inside\" or \"to see if it works\". The actions in Social IQa span a wide variety of social situations, and answer candidates contain both human-curated answers and adversarially-filtered machine-generated candidates. Social IQa contains over 37,000 QA pairs for evaluating models\u2019 abilities to reason about the social implications of everyday events and situations. (Less)\n", "citation": "\n", "homepage": "https://leaderboard.allenai.org/socialiqa/submissions/get-started", "license": "", "features": {"context": {"dtype": "string", "id": null, "_type": "Value"}, "question": {"dtype": "string", "id": null, "_type": "Value"}, "answerA": {"dtype": "string", "id": null, "_type": "Value"}, "answerB": {"dtype": "string", "id": null, "_type": "Value"}, "answerC": {"dtype": "string", "id": null, "_type": "Value"}, "label": {"dtype": "string", "id": null, "_type": "Value"}}, "supervised_keys": null, "builder_name": "social_i_qa", "config_name": "default", "version": {"version_str": "0.1.0", "description": null, "datasets_version_to_prepare": null, "major": 0, "minor": 1, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 6389954, "num_examples": 33410, "dataset_name": "social_i_qa"}, "validation": {"name": "validation", "num_bytes": 376508, "num_examples": 1954, "dataset_name": "social_i_qa"}}, "download_checksums": {"https://storage.googleapis.com/ai2-mosaic/public/socialiqa/socialiqa-train-dev.zip": {"num_bytes": 2198056, "checksum": "ee073914a0fc33265cfbcfc50ec20df9b2e07809c3f420f599138d1f394ef5c3"}}, "download_size": 2198056, "dataset_size": 6766462, "size_in_bytes": 8964518}}

default/social_i_qa-train.parquet

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default/social_i_qa-validation.parquet

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social_i_qa.py

@@ -1,100 +0,0 @@
-"""TODO(social_i_qa): Add a description here."""
-
-
-import json
-import os
-
-import datasets
-
-
-# TODO(social_i_qa): BibTeX citation
-_CITATION = """
-"""
-
-# TODO(social_i_qa):
-_DESCRIPTION = """\
-We introduce Social IQa: Social Interaction QA, a new question-answering benchmark for testing social commonsense intelligence. Contrary to many prior benchmarks that focus on physical or taxonomic knowledge, Social IQa focuses on reasoning about people’s actions and their social implications. For example, given an action like "Jesse saw a concert" and a question like "Why did Jesse do this?", humans can easily infer that Jesse wanted "to see their favorite performer" or "to enjoy the music", and not "to see what's happening inside" or "to see if it works". The actions in Social IQa span a wide variety of social situations, and answer candidates contain both human-curated answers and adversarially-filtered machine-generated candidates. Social IQa contains over 37,000 QA pairs for evaluating models’ abilities to reason about the social implications of everyday events and situations. (Less)
-"""
-_URL = "https://storage.googleapis.com/ai2-mosaic/public/socialiqa/socialiqa-train-dev.zip"
-
-
-class SocialIQa(datasets.GeneratorBasedBuilder):
-    """TODO(social_i_qa): Short description of my dataset."""
-
-    # TODO(social_i_qa): Set up version.
-    VERSION = datasets.Version("0.1.0")
-
-    def _info(self):
-        # TODO(social_i_qa): Specifies the datasets.DatasetInfo object
-        return datasets.DatasetInfo(
-            # This is the description that will appear on the datasets page.
-            description=_DESCRIPTION,
-            # datasets.features.FeatureConnectors
-            features=datasets.Features(
-                {
-                    # These are the features of your dataset like images, labels ...
-                    "context": datasets.Value("string"),
-                    "question": datasets.Value("string"),
-                    "answerA": datasets.Value("string"),
-                    "answerB": datasets.Value("string"),
-                    "answerC": datasets.Value("string"),
-                    "label": datasets.Value("string"),
-                }
-            ),
-            # 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://leaderboard.allenai.org/socialiqa/submissions/get-started",
-            citation=_CITATION,
-        )
-
-    def _split_generators(self, dl_manager):
-        """Returns SplitGenerators."""
-        # TODO(social_i_qa): 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)
-        dl_dir = os.path.join(dl_dir, "socialiqa-train-dev")
-        return [
-            datasets.SplitGenerator(
-                name=datasets.Split.TRAIN,
-                # These kwargs will be passed to _generate_examples
-                gen_kwargs={
-                    "filepath": os.path.join(dl_dir, "train.jsonl"),
-                    "labelpath": os.path.join(dl_dir, "train-labels.lst"),
-                },
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.VALIDATION,
-                # These kwargs will be passed to _generate_examples
-                gen_kwargs={
-                    "filepath": os.path.join(dl_dir, "dev.jsonl"),
-                    "labelpath": os.path.join(dl_dir, "dev-labels.lst"),
-                },
-            ),
-        ]
-
-    def _generate_examples(self, filepath, labelpath):
-        """Yields examples."""
-        # TODO(social_i_qa): Yields (key, example) tuples from the dataset
-        with open(labelpath, encoding="utf-8") as f:
-            labels = [label.strip() for label in f]
-        with open(filepath, encoding="utf-8") as f1:
-            for id_, row in enumerate(f1):
-                data = json.loads(row)
-                label = labels[id_]
-                context = data["context"]
-                answerA = data["answerA"]
-                answerB = data["answerB"]
-                answerC = data["answerC"]
-                question = data["question"]
-                yield id_, {
-                    "context": context,
-                    "question": question,
-                    "answerA": answerA,
-                    "answerB": answerB,
-                    "answerC": answerC,
-                    "label": label,
-                }