Duplicate from derek-thomas/ScienceQA

Co-authored-by: Derek Thomas <derek-thomas@users.noreply.huggingface.co>

.gitattributes

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+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ckpt filter=lfs diff=lfs merge=lfs -text
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+*.safetensors filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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+*tfevents* filter=lfs diff=lfs merge=lfs -text
+# Audio files - uncompressed
+*.pcm filter=lfs diff=lfs merge=lfs -text
+*.sam filter=lfs diff=lfs merge=lfs -text
+*.raw filter=lfs diff=lfs merge=lfs -text
+# Audio files - compressed
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+# Image files - uncompressed
+*.bmp filter=lfs diff=lfs merge=lfs -text
+*.gif filter=lfs diff=lfs merge=lfs -text
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+# Image files - compressed
+*.jpg filter=lfs diff=lfs merge=lfs -text
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.gitignore

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+.ipynb_checkpoints
+.idea
+images/
+text/

README.md

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+---
+license: cc-by-sa-4.0
+annotations_creators:
+- expert-generated
+- found
+language:
+- en
+language_creators:
+- expert-generated
+- found
+multilinguality:
+- monolingual
+paperswithcode_id: scienceqa
+pretty_name: ScienceQA
+size_categories:
+- 10K<n<100K
+source_datasets:
+- original
+tags:
+- multi-modal-qa
+- science
+- chemistry
+- biology
+- physics
+- earth-science
+- engineering
+- geography
+- history
+- world-history
+- civics
+- economics
+- global-studies
+- grammar
+- writing
+- vocabulary
+- natural-science
+- language-science
+- social-science
+task_categories:
+- multiple-choice
+- question-answering
+- other
+- visual-question-answering
+- text-classification
+task_ids:
+- multiple-choice-qa
+- closed-domain-qa
+- open-domain-qa
+- visual-question-answering
+- multi-class-classification
+dataset_info:
+  features:
+  - name: image
+    dtype: image
+  - name: question
+    dtype: string
+  - name: choices
+    sequence: string
+  - name: answer
+    dtype: int8
+  - name: hint
+    dtype: string
+  - name: task
+    dtype: string
+  - name: grade
+    dtype: string
+  - name: subject
+    dtype: string
+  - name: topic
+    dtype: string
+  - name: category
+    dtype: string
+  - name: skill
+    dtype: string
+  - name: lecture
+    dtype: string
+  - name: solution
+    dtype: string
+  splits:
+  - name: train
+    num_bytes: 16416902
+    num_examples: 12726
+  - name: validation
+    num_bytes: 5404896
+    num_examples: 4241
+  - name: test
+    num_bytes: 5441676
+    num_examples: 4241
+  download_size: 0
+  dataset_size: 27263474
+---
+
+# Dataset Card Creation Guide
+
+## Table of Contents
+- [Dataset Card Creation Guide](#dataset-card-creation-guide)
+  - [Table of Contents](#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)
+      - [Initial Data Collection and Normalization](#initial-data-collection-and-normalization)
+      - [Who are the source language producers?](#who-are-the-source-language-producers)
+    - [Annotations](#annotations)
+      - [Annotation process](#annotation-process)
+      - [Who are the annotators?](#who-are-the-annotators)
+    - [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://scienceqa.github.io/index.html#home](https://scienceqa.github.io/index.html#home)
+- **Repository:** [https://github.com/lupantech/ScienceQA](https://github.com/lupantech/ScienceQA)
+- **Paper:** [https://arxiv.org/abs/2209.09513](https://arxiv.org/abs/2209.09513)
+- **Leaderboard:** [https://paperswithcode.com/dataset/scienceqa](https://paperswithcode.com/dataset/scienceqa)
+- **Point of Contact:** [Pan Lu](https://lupantech.github.io/) or file an issue on [Github](https://github.com/lupantech/ScienceQA/issues)
+
+### Dataset Summary
+
+Learn to Explain: Multimodal Reasoning via Thought Chains for Science Question Answering
+
+### Supported Tasks and Leaderboards
+
+Multi-modal Multiple Choice
+
+### Languages
+
+English
+
+## Dataset Structure
+
+### Data Instances
+
+Explore more samples [here](https://scienceqa.github.io/explore.html).
+
+``` json
+{'image': Image,
+ 'question': 'Which of these states is farthest north?',
+ 'choices': ['West Virginia', 'Louisiana', 'Arizona', 'Oklahoma'],
+ 'answer': 0,
+ 'hint': '',
+ 'task': 'closed choice',
+ 'grade': 'grade2',
+ 'subject': 'social science',
+ 'topic': 'geography',
+ 'category': 'Geography',
+ 'skill': 'Read a map: cardinal directions',
+ 'lecture': 'Maps have four cardinal directions, or main directions. Those directions are north, south, east, and west.\nA compass rose is a set of arrows that point to the cardinal directions. A compass rose usually shows only the first letter of each cardinal direction.\nThe north arrow points to the North Pole. On most maps, north is at the top of the map.',
+ 'solution': 'To find the answer, look at the compass rose. Look at which way the north arrow is pointing. West Virginia is farthest north.'}
+```
+
+Some records might be missing any or all of image, lecture, solution. 
+
+### Data Fields
+
+- `image` : Contextual image
+- `question` : Prompt relating to the `lecture`
+- `choices` : Multiple choice answer with 1 correct to the `question`
+- `answer` : Index of choices corresponding to the correct answer
+- `hint` : Hint to help answer the `question`
+- `task` : Task description
+- `grade` : Grade level from K-12
+- `subject` : High level 
+- `topic` : natural-sciences, social-science, or language-science
+- `category` : A subcategory of `topic`
+- `skill` : A description of the task required
+- `lecture` : A relevant lecture that a `question` is generated from
+- `solution` : Instructions on how to solve the `question`
+
+
+Note that the descriptions can be initialized with the **Show Markdown Data Fields** output of the [Datasets Tagging app](https://huggingface.co/spaces/huggingface/datasets-tagging), you will then only need to refine the generated descriptions.
+
+### Data Splits
+- name: train
+    - num_bytes: 16416902
+    - num_examples: 12726
+- name: validation
+    - num_bytes: 5404896
+    - num_examples: 4241
+- name: test
+    - num_bytes: 5441676
+    - num_examples: 4241
+
+## Dataset Creation
+
+### Curation Rationale
+
+When answering a question, humans utilize the information available across different modalities to synthesize a consistent and complete chain of thought (CoT). This process is normally a black box in the case of deep learning models like large-scale language models. Recently, science question benchmarks have been used to diagnose the multi-hop reasoning ability and interpretability of an AI system. However, existing datasets fail to provide annotations for the answers, or are restricted to the textual-only modality, small scales, and limited domain diversity. To this end, we present Science Question Answering (ScienceQA).
+
+### Source Data
+
+ScienceQA is collected from elementary and high school science curricula.
+
+#### Initial Data Collection and Normalization
+
+See Below
+
+#### Who are the source language producers?
+
+See Below
+
+### Annotations
+
+Questions in the ScienceQA dataset are sourced from open resources managed by IXL Learning,
+an online learning platform curated by experts in the field of K-12 education. The dataset includes
+problems that align with California Common Core Content Standards. To construct ScienceQA, we
+downloaded the original science problems and then extracted individual components (e.g. questions,
+hints, images, options, answers, lectures, and solutions) from them based on heuristic rules.
+We manually removed invalid questions, such as questions that have only one choice, questions that
+contain faulty data, and questions that are duplicated, to comply with fair use and transformative
+use of the law. If there were multiple correct answers that applied, we kept only one correct answer.
+Also, we shuffled the answer options of each question to ensure the choices do not follow any
+specific pattern. To make the dataset easy to use, we then used semi-automated scripts to reformat
+the lectures and solutions. Therefore, special structures in the texts, such as tables and lists, are
+easily distinguishable from simple text passages. Similar to ImageNet, ReClor, and PMR datasets,
+ScienceQA is available for non-commercial research purposes only and the copyright belongs to
+the original authors. To ensure data quality, we developed a data exploration tool to review examples
+in the collected dataset, and incorrect annotations were further manually revised by experts. The tool
+can be accessed at https://scienceqa.github.io/explore.html.
+
+#### Annotation process
+
+See above
+
+#### Who are the annotators?
+
+See above
+
+### 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
+
+- Pan Lu1,3
+- Swaroop Mishra2,3
+- Tony Xia1
+- Liang Qiu1
+- Kai-Wei Chang1
+- Song-Chun Zhu1
+- Oyvind Tafjord3
+- Peter Clark3
+- Ashwin Kalyan3
+ 
+From: 
+1. University of California, Los Angeles    
+2. Arizona State University    
+3. Allen Institute for AI   
+
+
+
+### Licensing Information
+
+[Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)
+](https://creativecommons.org/licenses/by-nc-sa/4.0/)
+
+### Citation Information
+
+Provide the [BibTex](http://www.bibtex.org/)-formatted reference for the dataset. For example:
+```
+@inproceedings{lu2022learn,
+    title={Learn to Explain: Multimodal Reasoning via Thought Chains for Science Question Answering},
+    author={Lu, Pan and Mishra, Swaroop and Xia, Tony and Qiu, Liang and Chang, Kai-Wei and Zhu, Song-Chun and Tafjord, Oyvind and Clark, Peter and Ashwin Kalyan},
+    booktitle={The 36th Conference on Neural Information Processing Systems (NeurIPS)},
+    year={2022}
+}
+```
+### Contributions
+
+Thanks to [Derek Thomas](https://huggingface.co/derek-thomas) [@datavistics](https://github.com/datavistics) for adding this dataset.

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tutorial/ScienceQA.py

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+import json
+from pathlib import Path
+
+import datasets
+
+_DESCRIPTION = """Science Question Answering (ScienceQA), a new benchmark that consists of 21,208 multimodal 
+multiple choice questions with a diverse set of science topics and annotations of their answers 
+with corresponding lectures and explanations. 
+The lecture and explanation provide general external knowledge and specific reasons,
+respectively, for arriving at the correct answer."""
+
+# Lets use the project page instead of the github repo
+_HOMEPAGE = "https://scienceqa.github.io"
+
+_CITATION = """\
+@inproceedings{lu2022learn,
+    title={Learn to Explain: Multimodal Reasoning via Thought Chains for Science Question Answering},
+    author={Lu, Pan and Mishra, Swaroop and Xia, Tony and Qiu, Liang and Chang, Kai-Wei and Zhu, Song-Chun and Tafjord, Oyvind and Clark, Peter and Ashwin Kalyan},
+    booktitle={The 36th Conference on Neural Information Processing Systems (NeurIPS)},
+    year={2022}
+}
+"""
+
+_LICENSE = "Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)"
+
+
+class ScienceQA(datasets.GeneratorBasedBuilder):
+    """Science Question Answering (ScienceQA), a new benchmark that consists of 21,208 multimodal
+multiple choice questions with a diverse set of science topics and annotations of their answers
+with corresponding lectures and explanations.
+The lecture and explanation provide general external knowledge and specific reasons,
+respectively, for arriving at the correct answer."""
+
+    VERSION = datasets.Version("1.0.0")
+
+    def _info(self):
+        return datasets.DatasetInfo(
+                description=_DESCRIPTION,
+                features=datasets.Features(
+                        {
+                            "image": datasets.Image(),
+                            "question": datasets.Value("string"),
+                            "choices": datasets.features.Sequence(datasets.Value("string")),
+                            "answer": datasets.Value("int8"),
+                            "hint": datasets.Value("string"),
+                            "task": datasets.Value("string"),
+                            "grade": datasets.Value("string"),
+                            "subject": datasets.Value("string"),
+                            "topic": datasets.Value("string"),
+                            "category": datasets.Value("string"),
+                            "skill": datasets.Value("string"),
+                            "lecture": datasets.Value("string"),
+                            "solution": datasets.Value("string")
+                            }
+                        ),
+                homepage=_HOMEPAGE,
+                citation=_CITATION,
+                license=_LICENSE,
+                )
+
+    def _split_generators(self, dl_manager):
+        text_path = Path.cwd() / 'text' / 'problems.json'
+        image_dir = Path.cwd() / 'images'
+        return [
+            datasets.SplitGenerator(
+                    name=datasets.Split.TRAIN,
+                    # These kwargs will be passed to _generate_examples
+                    gen_kwargs={
+                        "text_path": text_path,
+                        "image_dir": image_dir,
+                        "split": "train",
+                        },
+                    ),
+            datasets.SplitGenerator(
+                    name=datasets.Split.VALIDATION,
+                    # These kwargs will be passed to _generate_examples
+                    gen_kwargs={
+                        "text_path": text_path,
+                        "image_dir": image_dir,
+                        "split": "val",
+                        },
+                    ),
+            datasets.SplitGenerator(
+                    name=datasets.Split.TEST,
+                    # These kwargs will be passed to _generate_examples
+                    gen_kwargs={
+                        "text_path": text_path,
+                        "image_dir": image_dir,
+                        "split": "test"
+                        },
+                    ),
+            ]
+
+    # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
+    def _generate_examples(self, text_path, image_dir, split):
+        with open(text_path, encoding="utf-8") as f:
+            # Load all the text. Note that if this was HUGE, we would need to find a better way to load the json
+            data = json.load(f)
+            ignore_keys = ['image', 'split']
+
+            # Get image_id from its annoying location
+            for image_id, row in data.items():
+                # Only look for the rows in our split
+                if row['split'] == split:
+
+                    # Note, not all rows have images.
+                    # Get all the image data we need
+                    if row['image']:
+                        image_path = image_dir / split / image_id / 'image.png'
+                        image_bytes = image_path.read_bytes()
+                        image_dict = {'path': str(image_path), 'bytes': image_bytes}
+                    else:
+                        image_dict = None
+
+                    # Keep only the keys we need
+                    relevant_row = {k: v for k, v in row.items() if k not in ignore_keys}
+
+                    return_dict = {
+                        'image': image_dict,
+                        **relevant_row
+                        }
+                    yield image_id, return_dict

tutorial/download.sh

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+#!/bin/bash
+# Modified from the original here: https://github.com/lupantech/ScienceQA/blob/main/tools/download.sh
+
+cd images
+
+if [ -d "train" ];
+then
+  echo "Already downloaded train"
+else
+  ls -alF
+  wget https://scienceqa.s3.us-west-1.amazonaws.com/images/train.zip
+  unzip -q train.zip
+  rm train.zip
+fi
+
+if [ -d "val" ];
+then
+  echo "Already downloaded val"
+else
+  ls -alF
+  wget https://scienceqa.s3.us-west-1.amazonaws.com/images/val.zip
+  unzip -q val.zip
+  rm val.zip
+fi
+
+if [ -d "test" ];
+then
+  echo "Already downloaded test"
+else
+  ls -alF
+  wget https://scienceqa.s3.us-west-1.amazonaws.com/images/test.zip
+  unzip -q test.zip
+  rm test.zip
+fi
+
+echo "Completed downloads!"