Update files from the datasets library (from 1.18.0)

Release notes: https://github.com/huggingface/datasets/releases/tag/1.18.0

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README.md

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+---
+annotations_creators:
+- found
+language_creators:
+- found
+languages:
+- en
+licenses:
+- cc-by-4-0
+multilinguality:
+- monolingual
+size_categories:
+- 10M<n<100M
+source_datasets:
+- original
+task_categories:
+- other
+task_ids:
+- other-other-image-classification
+- other-other-image-captioning
+paperswithcode_id: redcaps
+pretty_name: RedCaps
+---
+
+# Dataset Card for RedCaps
+
+## Table of Contents
+- [Table of Contents](#table-of-contents)
+- [Dataset Description](#dataset-description)
+  - [Dataset Summary](#dataset-summary)
+  - [Dataset Preprocessing](#dataset-preprocessing)
+  - [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://redcaps.xyz/
+- **Repository:**
+- **Paper:** https://arxiv.org/abs/2111.11431
+- **Leaderboard:**
+- **Point of Contact:** kdexd@umich.edu
+
+### Dataset Summary
+
+RedCaps is a large-scale dataset of 12M image-text pairs collected from Reddit.
+Images and captions from Reddit depict and describe a wide variety of objects and scenes.
+The data is collected from a manually curated set of subreddits (350 total),
+which give coarse image labels and allow steering of the dataset composition
+without labeling individual instances. RedCaps data is created *by the people, for the people* – it contains everyday things that users like to share on social media, for example hobbies (r/crafts) and pets (r/shiba). Captions often contain specific and
+fine-grained descriptions (northern cardinal, taj mahal). Subreddit names provide relevant image
+labels (r/shiba) even when captions may not (mlem!), and sometimes may group many visually
+unrelated images through a common semantic meaning (r/perfectfit).
+
+### Dataset Preprocessing
+
+This dataset doesn't download the images locally by default. Instead, it exposes URLs to the images. To fetch the images, use the following code:
+
+```python
+from datasets import load_dataset
+from datasets.utils.file_utils import get_datasets_user_agent
+
+def fetch_images(batch, timeout):
+    import PIL.Image
+    import requests
+
+    images = []
+    for image_url in batch["image_url"]:
+        try:
+            image = PIL.Image.open(
+                requests.get(
+                    image_url,
+                    stream=True,
+                    headers={"user-agent": get_datasets_user_agent()},
+                    timeout=timeout,
+                ).raw
+              )
+        except requests.exceptions.ConnectionError:
+            image = None
+        images.append(image)
+    batch["image"] = images
+    return batch
+
+timeout = None
+num_proc = 4
+dset = load_dataset("red_caps", "rabbits_2017")
+dset = dset.map(fetch_images, batched=True, batch_size=100, fn_kwargs={"timeout": timeout}, num_proc=num_proc)
+```
+
+### Supported Tasks and Leaderboards
+
+From the paper:
+> We have used our dataset to train deep neural networks that perform image captioning, and
+that learn transferable visual representations for a variety of downstream visual recognition tasks
+(image classification, object detection, instance segmentation).
+
+> We anticipate that the dataset could be used for a variety of vision-and-language (V&L) tasks,
+such as image or text retrieval or text-to-image synthesis.
+
+### Languages
+
+All of the subreddits in RedCaps use English as their primary language.
+
+## Dataset Structure
+
+### Data Instances
+
+Each instance in RedCaps represents a single Reddit image post:
+
+```
+{
+  'image_id': 'bpzj7r',
+  'author': 'djasz1',
+  'image_url': 'https://i.redd.it/ho0wntksivy21.jpg',
+  'raw_caption': 'Found on a friend’s property in the Keys FL. She is now happily living in my house.',
+  'caption': 'found on a friend's property in the keys fl. she is now happily living in my house.', 'subreddit': 3,
+  'score': 72,
+  'created_utc': datetime.datetime(2019, 5, 18, 1, 36, 41),
+  'permalink': '/r/airplants/comments/bpzj7r/found_on_a_friends_property_in_the_keys_fl_she_is/', 'crosspost_parents': None
+}
+```
+
+### Data Fields
+
+- `image_id`: Unique alphanumeric ID of the image post (assigned by Reddit).
+- `author`: Reddit username of the image post author.
+- `image_url`: Static URL for downloading the image associated with the post.
+- `raw_caption`: Textual description of the image, written by the post author.
+- `caption`: Cleaned version of "raw_caption" by us (see Q35).
+- `subreddit`: Name of subreddit where the post was submitted.
+- `score`: Net upvotes (discounting downvotes) received by the image post. This field is equal to `None` if the image post is a crosspost.
+- `created_utc`: Integer time epoch (in UTC) when the post was submitted to Reddit.
+- `permalink`: Partial URL of the Reddit post (https://reddit.com/<permalink>).
+- `crosspost_parents`: List of parent posts. This field is optional.
+
+
+### Data Splits
+
+All the data is contained in training set. The training set has nearly 12M (12,011,111) instances. 
+
+From the paper:
+> We intend our dataset to be primarily used for pre-training with one or more specific downstream task(s) in mind. Hence, all instances in our dataset would be used for training while
+the validation split is derived from downstream task(s). If users require a validation split, we
+recommend sampling it such that it follows the same subreddit distribution as entire dataset.
+
+## Dataset Creation
+
+### Curation Rationale
+
+From the paper:
+> Large datasets of image-text pairs are widely used for pre-training generic representations
+that transfer to a variety of downstream vision and vision-and-language tasks. Existing public
+datasets of this kind were curated from search engine results (SBU Captions [1]) or HTML
+alt-text from arbitrary web pages (Conceptual Captions [2, 31]). They performed complex
+data filtering to deal with noisy web data. Due to aggressive filtering, their data collection is
+inefficient and diversity is artificially supressed. We argue that the quality of data depends on
+its source, and the human intent behind its creation. In this work, we explore Reddit – a social
+media platform, for curating high quality data. We introduce RedCaps – a large dataset of
+12M image-text pairs from Reddit. While we expect the use-cases of RedCaps to be similar to
+existing datasets, we discuss how Reddit as a data source leads to fast and lightweight collection,
+better data quality, lets us easily steer the data distribution, and facilitates ethically responsible data curation.
+
+### Source Data
+
+#### Initial Data Collection and Normalization
+
+From the paper:
+> **Data Collection Pipeline**
+Reddit’s uniform structure allows us to parallelize data collection as independent tasks – each task
+involves collecting posts submitted to a single subreddit in one year. Our collection pipeline has three steps: (1) subreddit selection, (2) image post filtering, and (3) caption cleaning.
+**Step 1**. Subreddit selection: We collect data from a manually curated set of subreddits. Subreddits
+have their own rules, community norms, and moderators so curating subreddits allows us to steer the
+dataset’s composition without annotating individual instances. We select subreddits with a high volume of images posts, where images tend to be photographs (rather than memes, drawings, screenshots,
+etc) and post titles tend to describe image content (rather than making jokes, political commentary,
+etc). We do not select any NSFW, banned, or quarantined subreddits. We want to minimize the
+number of people that appear in RedCaps, so we omit subreddits whose primary purpose is to share or
+comment on images of people (such as celebrity pics or user selfies). We choose subreddits focused on
+general photography (r/pics, r/itookapicture), animals (r/axolotls, r/birdsofprey, r/dachshund),
+plants (r/roses, r/succulents), objects (r/classiccars, r/trains, r/mechanicalkeyboards), food
+(r/steak, r/macarons), scenery (r/cityporn1
+, r/desertporn), or activities (r/carpentry, r/kayaking).
+In total we collect data from 350 subreddits; the full list can be found in Appendix A.
+**Step 2**. Image post filtering: We use Pushshift [41] and Reddit [42, 43] APIs to download all image
+posts submitted to our selected subreddits from 2008–2020. Posts are collected at least six months
+after their creation to let upvotes stabilize. We only collect posts with images hosted on three domains:
+Reddit (i.redd.it), Imgur (i.imgur.com), and Flickr (staticflickr.com). Some image posts contain
+multiple images (gallery posts) – in this case we only collect the first image and associate it with
+the caption. We discard posts with < 2 upvotes to avoid unappealing content, and we discard posts
+marked NSFW (by their authors or subreddit moderators) to avoid pornographic or disturbing content.
+**Step 3**. Caption cleaning: We expect Reddit post titles to be less noisy than other large-scale
+sources of image captions such as alt-text [2, 31], so we apply minimal text cleaning. We lowercase
+captions and use ftfy [44] to remove character accents, emojis, and non-latin characters, following
+[29, 35, 36]. Then we apply simple pattern matching to discard all sub-strings enclosed in brackets
+((.*), [.*]). These sub-strings usually give non-semantic information: original content tags [oc],
+image resolutions (800x600 px), camera specs (shot with iPhone), self-promotion [Instagram:
+@user], and other references (link in comments). Finally, like [31] we replace social media
+handles (words starting with ‘@’) with a [USR] token to protect user privacy and reduce redundancy.
+Due to such filtering, ≈12K (0.1%) captions in our dataset are empty strings. We do not discard them,
+as subreddit names alone provide meaningful supervision. Unlike CC-3M or CC-12M that discard
+captions without nouns or that don’t overlap image tags, we do not discard any instances in this step.
+Through this pipeline, we collect 13.4M instances from 350 subreddits. Our collection pipeline is
+less resource-intensive than existing datasets – we do not require webpage crawlers, search engines,
+or large databases of indexed webpages. RedCaps is easily extensible in the future by selecting more
+subreddits and collecting posts from future years. Next, we perform additional filtering to mitigate
+user privacy risks and harmful stereotypes in RedCaps, resulting in final size of 12M instances.
+
+#### Who are the source language producers?
+
+Reddit is the singular data source for RedCaps.
+
+### Annotations
+
+#### Annotation process
+
+The dataset is built using fully automatic data collection pipeline which doesn't require any human annotators.
+
+#### Who are the annotators?
+
+The annotation process doesn't require any human annotators.
+
+### Personal and Sensitive Information
+
+From the paper:
+> **Does the dataset relate to people?**
+The dataset pertains to people in that people wrote the captions and posted images to Reddit
+that we curate in RedCaps. We made specific design choices while curating RedCaps to avoid
+large quantities of images containing people:
+(a) We collect data from manually curated subreddits in which most contain primarily pertains
+to animals, objects, places, or activities. We exclude all subreddits whose primary purpose
+is to share and describe images of people (such as celebrity photos or user selfies).
+(b) We use an off-the-shelf face detector to find and remove images with potential presence of
+human faces. We manually checked 50K random images in RedCaps (Q16) and found 79
+images with identifiable human faces – the entire dataset may have ≈19K (0.15%) images
+with identifiable people. Refer Section 2.2 in the main paper.
+
+> **Is it possible to identify one or more natural persons, either directly or indirectly (i.e., in
+combination with other data) from the dataset?** 
+Yes, all instances in RedCaps include Reddit usernames of their post authors. This could be
+used to look up the Reddit user profile, and some Reddit users may have identifying information
+in their profiles. Some images may contain human faces which could be identified by
+appearance. However, note that all this information is already public on Reddit, and searching it
+in RedCaps is no easier than searching directly on Reddit.
+
+> **Were the individuals in question notified about the data collection?**
+No. Reddit users are anonymous by default, and are not required to share their personal contact
+information (email, phone numbers, etc.). Hence, the only way to notify the authors of RedCaps
+image posts is by sending them private messages on Reddit. This is practically difficult to do
+manually, and will be classified as spam and blocked by Reddit if attempted to programmatically
+send a templated message to millions of users.
+
+> **Did the individuals in question consent to the collection and use of their data?**
+Users did not explicitly consent to the use of their data in our dataset. However, by uploading
+their data on Reddit, they consent that it would appear on the Reddit plaform and will be
+accessible via the official Reddit API (which we use to collect RedCaps).
+
+> **If consent was obtained, were the consenting individuals provided with a mechanism to
+revoke their consent in the future or for certain uses?**
+Users have full control over the presence of their data in our dataset. If users wish to revoke
+their consent, they can delete the underlying Reddit post – it will be automatically removed
+dfrom RedCaps since we distributed images as URLs. Moreover, we provide an opt-out request
+form on our dataset website for anybody to request removal of an individual instance if it is
+potentially harmful (e.g. NSFW, violates privacy, harmful stereotypes, etc.).
+
+## Considerations for Using the Data
+
+### Social Impact of Dataset
+
+From the paper:
+> **Has an analysis of the potential impact of the dataset and its use on data subjects (e.g.,
+a data protection impact analysis) been conducted?**
+No.
+
+### Discussion of Biases
+
+From the paper:
+> **Harmful Stereotypes**: Another concern with
+Reddit data is that images or language may represent harmful stereotypes about gender, race, or other
+characteristics of people [48, 49, 51]. We select only non-NSFW subreddits with active moderation
+for collecting data. This stands in contrast to less curated uses of Reddit data, such as GPT-2 [35]
+whose training data includes at least 63K documents from banned or quarantined subreddits which
+may contain toxic language [53]. We attempt to further reduce harmful stereotypes in two ways:
+>  * **NSFW images**: We use the InceptionV3 [54] model from [55] to filter images detected as porn or hentai with confidence ≥ 0.9. Similar to face filtering, we estimated precision of our filtering and estimated amount of missed detections, shown in Table 1. The model detects 87K images with low
+precision (∼1%) – most detections are non-NSFW images with pink and beige hues.
+>  * **Potentially derogatory language**: We filter instances whose captions contain words or phrases from a common blocklist [56]. It is important to note that such coarse filtering might suppress language from marginalized groups reclaiming slurs [51]; however, as RedCaps is not intended to describe people, we believe this is a pragmatic tradeoff to avoid propagating harmful labels.
+
+> **Reddit demographics**: Reddit’s user demographics are not representative of the population at large.
+Compared to US adults, Reddit users skew male (69% vs 49%), young (58% 18-29 years old vs
+22%), college educated (36% vs 28%), and politically liberal (41% vs 25%) [57]. Reddit users
+are predominantly white (63%) [57], and 49% of desktop traffic to Reddit comes from the United
+States [58]. All of the subreddits in RedCaps use English as their primary language. Taken together,
+these demographic biases likely also bias the types of objects and places that appear in images on
+Reddit, and the language used to describe these images. We do not offer explicit countermeasures to
+these biases, but users of RedCaps should keep in mind that size doesn’t guarantee diversity [51].
+Subtler issues may also exist, such as imbalanced representation of demographic groups [59] or
+gender bias in object co-occurrence [60] or language [61]. These are hard to control in internet
+data, so we release RedCaps with explicit instructions on suitable use-cases; specifically requesting models not be trained to identify people, or make decisions that impact people. We document these instructions and other terms-of-use in a datasheet [45], provided in Appendix G.
+
+> **Does the dataset contain data that, if viewed directly, might be offensive, insulting, threatening, or might otherwise cause anxiety?**
+The scale of RedCaps means that we are unable to verify the contents of all images and
+captions. However we have tried to minimize the possibility that RedCaps contains data that
+might be offensive, insulting, threatening, or might cause anxiety via the following mitigations:
+(a) We manually curate the set of subreddits from which to collect data; we only chose
+subreddits that are not marked NSFW and which generally contain non-offensive content.
+(b) Within our curated subreddits, we did not include any posts marked NSFW.
+(c) We removed all instances whose captions contained any of the 400 potentially offensive
+words or phrases. Refer Section 2.2 in the main paper.
+(d) We remove all instances whose images were flagged NSFW by an off-the-shelf detector.
+We manually checked 50K random images in RedCaps and found one image containing
+nudity (exposed buttocks; no identifiable face). Refer Section 2.2 in the main paper
+
+> **Does the dataset identify any subpopulations (e.g., by age, gender)?**
+RedCaps does not explicitly identify any subpopulations. Since some images contain people
+and captions are free-form natural language written by Reddit users, it is possible that some
+captions may identify people appearing in individual images as part of a subpopulation.
+
+> **Were any ethical review processes conducted (e.g., by an institutional review board)?**
+We did not conduct a formal ethical review process via institutional review boards. However,
+as described in Section 2.2 of the main paper and Q16 we employed several filtering mechanisms
+to try and remove instances that could be problematic.
+
+### Other Known Limitations
+
+From the paper:
+> **Are there any errors, sources of noise, or redundancies in the dataset?**
+RedCaps is noisy by design since image-text pairs on the internet are noisy and unstructured.
+Some instances may also have duplicate images and captions – Reddit users may have shared
+the same image post in multiple subreddits. Such redundancies constitute a very small fraction
+of the dataset, and should have almost no effect in training large-scale models.
+
+> **Does the dataset contain data that might be considered confidential (e.g., data that is
+protected by legal privilege or by doctor-patient confidentiality, data that includes the
+content of individuals non-public communications)?**
+No, the subreddits included in RedCaps do not cover topics that may be considered confidential. All posts were publicly shared on Reddit prior to inclusion in RedCaps.
+
+## Additional Information
+
+### Dataset Curators
+
+From the paper:
+> Four researchers at the University of Michigan (affiliated as of 2021) have created RedCaps:
+Karan Desai, Gaurav Kaul, Zubin Aysola, and Justin Johnson.
+
+### Licensing Information
+
+The image metadata is licensed under CC-BY 4.0 license. Additionally, uses of this dataset are subject to Reddit API terms (https://www.reddit.com/wiki/
+api-terms) and users must comply with Reddit User Agreeement, Content Policy,
+and Privacy Policy – all accessible at https://www.redditinc.com/policies.
+
+From the paper:
+> RedCaps should only be used for non-commercial research. RedCaps should not be used for any tasks that involve identifying features related to people (facial recognition, gender, age, ethnicity identification, etc.) or make decisions that impact people (mortgages, job applications, criminal sentences; or moderation decisions about user-uploaded data that could result in bans from a website). Any commercial and for-profit uses of RedCaps are restricted – it should not be used to train models that will be deployed in production systems as part of a product offered by businesses or government agencies.
+
+
+### Citation Information
+
+```
+@misc{desai2021redcaps,
+      title={RedCaps: web-curated image-text data created by the people, for the people},
+      author={Karan Desai and Gaurav Kaul and Zubin Aysola and Justin Johnson},
+      year={2021},
+      eprint={2111.11431},
+      archivePrefix={arXiv},
+      primaryClass={cs.CV}
+}
+```
+
+### Contributions
+
+Thanks to [@mariosasko](https://github.com/mariosasko) for adding this dataset.

dataset_infos.json

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+{"all": {"description": "RedCaps is a large-scale dataset of 12M image-text pairs collected from Reddit.\nImages and captions from Reddit depict and describe a wide variety of objects and scenes.\nThe data is collected from a manually curated set of subreddits (350 total),\nwhich give coarse image labels and allow steering of the dataset composition\nwithout labeling individual instances.\n", "citation": "@misc{desai2021redcaps,\n      title={RedCaps: web-curated image-text data created by the people, for the people},\n      author={Karan Desai and Gaurav Kaul and Zubin Aysola and Justin Johnson},\n      year={2021},\n      eprint={2111.11431},\n      archivePrefix={arXiv},\n      primaryClass={cs.CV}\n}\n", "homepage": "https://redcaps.xyz/", "license": "CC BY 4.0", "features": {"image_id": {"dtype": "string", "id": null, "_type": "Value"}, "author": {"dtype": "string", "id": null, "_type": "Value"}, "image_url": {"dtype": "string", "id": null, "_type": "Value"}, "raw_caption": {"dtype": "string", "id": null, "_type": "Value"}, "caption": {"dtype": "string", "id": null, "_type": "Value"}, "subreddit": {"num_classes": 350, "names": ["abandonedporn", "abandoned", "absoluteunits", "airplants", "alltheanimals", "amateurphotography", "amateurroomporn", "animalporn", "antiques", "antkeeping", "ants", "aquariums", "architectureporn", "artefactporn", "astronomy", "astrophotography", "australiancattledog", "australianshepherd", "autumnporn", "averagebattlestations", "awwducational", "awwnverts", "axolotls", "backpacking", "backyardchickens", "baking", "ballpython", "barista", "bassfishing", "battlestations", "bbq", "beagle", "beardeddragons", "beekeeping", "beerandpizza", "beerporn", "beerwithaview", "beginnerwoodworking", "bengalcats", "bento", "bernesemountaindogs", "berries", "bettafish", "bicycling", "bikecommuting", "birding", "birdphotography", "birdpics", "birdsofprey", "birds", "blackcats", "blacksmith", "bladesmith", "boatporn", "bonsai", "bookporn", 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"dumpsterdiving", "earthporn", "eatsandwiches", "embroidery", "entomology", "equestrian", "espresso", "exposureporn", "eyebleach", "f1porn", "farming", "femalelivingspace", "fermentation", "ferrets", "fireporn", "fishing", "fish", "flowers", "flyfishing", "foodporn", "food", "foraging", "fossilporn", "fountainpens", "foxes", "frenchbulldogs", "frogs", "gardening", "gardenwild", "geckos", "gemstones", "geologyporn", "germanshepherds", "glutenfree", "goldenretrievers", "goldfish", "gold", "greatpyrenees", "grilledcheese", "grilling", "guineapigs", "gunporn", "guns", "hamsters", "handtools", "healthyfood", "hedgehog", "helicopters", "herpetology", "hiking", "homestead", "horses", "hotpeppers", "houseplants", "houseporn", "husky", "icecreamery", "indoorgarden", "infrastructureporn", "insects", "instantpot", "interestingasfuck", "interiordesign", "itookapicture", "jellyfish", "jewelry", "kayakfishing", "kayaking", "ketorecipes", "knifeporn", "knives", "labrador", "leathercraft", 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"post_processing_size": null, "dataset_size": 3235627787, "size_in_bytes": 4297535968}}

dummy/all/1.0.0/dummy_data.zip

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+version https://git-lfs.github.com/spec/v1
+oid sha256:82dcacecd82f25c1fbfae629fba39f2a51126be7d9c1edea4435431214bfb66f
+size 302735

red_caps.py

@@ -0,0 +1,367 @@
+# coding=utf-8
+# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
+#
+# 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.
+"""RedCaps dataset."""
+
+
+import collections
+import json
+import os
+import re
+
+import datasets
+
+
+_CITATION = """\
+@misc{desai2021redcaps,
+      title={RedCaps: web-curated image-text data created by the people, for the people},
+      author={Karan Desai and Gaurav Kaul and Zubin Aysola and Justin Johnson},
+      year={2021},
+      eprint={2111.11431},
+      archivePrefix={arXiv},
+      primaryClass={cs.CV}
+}
+"""
+
+_DESCRIPTION = """\
+RedCaps is a large-scale dataset of 12M image-text pairs collected from Reddit.
+Images and captions from Reddit depict and describe a wide variety of objects and scenes.
+The data is collected from a manually curated set of subreddits (350 total),
+which give coarse image labels and allow steering of the dataset composition
+without labeling individual instances.
+"""
+
+_HOMEPAGE = "https://redcaps.xyz/"
+
+_LICENSE = "CC BY 4.0"
+
+_URL = "https://www.dropbox.com/s/cqtdpsl4hewlli1/redcaps_v1.0_annotations.zip?dl=1"
+
+_SUBREDDITS_WITH_YEAR = """\
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+savagegarden_2019 savagegarden_2020 scotch_2017 scotch_2018 scotch_2019 scotch_2020 seaporn_2017 seaporn_2018
+seaporn_2019 seaporn_2020 seriouseats_2017 seriouseats_2018 seriouseats_2019 seriouseats_2020 sewing_2017 sewing_2018
+sewing_2019 sewing_2020 sharks_2017 sharks_2018 sharks_2019 sharks_2020 shiba_2017 shiba_2018 shiba_2019 shiba_2020
+shihtzu_2017 shihtzu_2018 shihtzu_2019 shihtzu_2020 shrimptank_2017 shrimptank_2018 shrimptank_2019 shrimptank_2020
+siamesecats_2017 siamesecats_2018 siamesecats_2019 siamesecats_2020 siberiancats_2017 siberiancats_2018
+siberiancats_2019 siberiancats_2020 silverbugs_2017 silverbugs_2018 silverbugs_2019 silverbugs_2020 skyporn_2017
+skyporn_2018 skyporn_2019 skyporn_2020 sloths_2017 sloths_2018 sloths_2019 sloths_2020 smoking_2017 smoking_2018
+smoking_2019 smoking_2020 snails_2017 snails_2018 snails_2019 snails_2020 snakes_2017 snakes_2018 snakes_2019
+snakes_2020 sneakers_2017 sneakers_2018 sneakers_2019 sneakers_2020 sneks_2017 sneks_2018 sneks_2019 sneks_2020
+somethingimade_2017 somethingimade_2018 somethingimade_2019 somethingimade_2020 soup_2017 soup_2018 soup_2019 soup_2020
+sourdough_2017 sourdough_2018 sourdough_2019 sourdough_2020 sousvide_2017 sousvide_2018 sousvide_2019 sousvide_2020
+spaceporn_2017 spaceporn_2018 spaceporn_2019 spaceporn_2020 spicy_2017 spicy_2018 spicy_2019 spicy_2020 spiderbro_2017
+spiderbro_2018 spiderbro_2019 spiderbro_2020 spiders_2017 spiders_2018 spiders_2019 spiders_2020 squirrels_2017
+squirrels_2018 squirrels_2019 squirrels_2020 steak_2017 steak_2018 steak_2019 steak_2020 streetphotography_2017
+streetphotography_2018 streetphotography_2019 streetphotography_2020 succulents_2017 succulents_2018 succulents_2019
+succulents_2020 superbowl_2017 superbowl_2018 superbowl_2019 superbowl_2020 supermodelcats_2017 supermodelcats_2018
+supermodelcats_2019 supermodelcats_2020 sushi_2017 sushi_2018 sushi_2019 sushi_2020 tacos_2017 tacos_2018 tacos_2019
+tacos_2020 tarantulas_2017 tarantulas_2018 tarantulas_2019 tarantulas_2020 tastyfood_2017 tastyfood_2018 tastyfood_2019
+tastyfood_2020 teaporn_2017 teaporn_2018 teaporn_2019 teaporn_2020 tea_2017 tea_2018 tea_2019 tea_2020 tequila_2017
+tequila_2018 tequila_2019 tequila_2020 terrariums_2017 terrariums_2018 terrariums_2019 terrariums_2020
+thedepthsbelow_2017 thedepthsbelow_2018 thedepthsbelow_2019 thedepthsbelow_2020 thriftstorehauls_2017
+thriftstorehauls_2018 thriftstorehauls_2019 thriftstorehauls_2020 tinyanimalsonfingers_2017 tinyanimalsonfingers_2018
+tinyanimalsonfingers_2019 tinyanimalsonfingers_2020 tonightsdinner_2017 tonightsdinner_2018 tonightsdinner_2019
+tonightsdinner_2020 toolporn_2017 toolporn_2018 toolporn_2019 toolporn_2020 tools_2017 tools_2018 tools_2019 tools_2020
+torties_2017 torties_2018 torties_2019 torties_2020 tortoise_2017 tortoise_2018 tortoise_2019 tortoise_2020
+tractors_2017 tractors_2018 tractors_2019 tractors_2020 trailrunning_2017 trailrunning_2018 trailrunning_2019
+trailrunning_2020 trains_2017 trains_2018 trains_2019 trains_2020 trucks_2017 trucks_2018 trucks_2019 trucks_2020
+turtle_2017 turtle_2018 turtle_2019 turtle_2020 underwaterphotography_2017 underwaterphotography_2018
+underwaterphotography_2019 underwaterphotography_2020 upcycling_2017 upcycling_2018 upcycling_2019 upcycling_2020
+urbanexploration_2017 urbanexploration_2018 urbanexploration_2019 urbanexploration_2020 urbanhell_2017 urbanhell_2018
+urbanhell_2019 urbanhell_2020 veganfoodporn_2017 veganfoodporn_2018 veganfoodporn_2019 veganfoodporn_2020
+veganrecipes_2017 veganrecipes_2018 veganrecipes_2019 veganrecipes_2020 vegetablegardening_2017 vegetablegardening_2018
+vegetablegardening_2019 vegetablegardening_2020 vegetarian_2017 vegetarian_2018 vegetarian_2019 vegetarian_2020
+villageporn_2017 villageporn_2018 villageporn_2019 villageporn_2020 vintageaudio_2017 vintageaudio_2018
+vintageaudio_2019 vintageaudio_2020 vintage_2017 vintage_2018 vintage_2019 vintage_2020 vinyl_2017 vinyl_2018
+vinyl_2019 vinyl_2020 volumeeating_2017 volumeeating_2018 volumeeating_2019 volumeeating_2020 watches_2017 watches_2018
+watches_2019 watches_2020 waterporn_2017 waterporn_2018 waterporn_2019 waterporn_2020 weatherporn_2017 weatherporn_2018
+weatherporn_2019 weatherporn_2020 wewantplates_2017 wewantplates_2018 wewantplates_2019 wewantplates_2020
+wildernessbackpacking_2017 wildernessbackpacking_2018 wildernessbackpacking_2019 wildernessbackpacking_2020
+wildlifephotography_2017 wildlifephotography_2018 wildlifephotography_2019 wildlifephotography_2020 wine_2017 wine_2018
+wine_2019 wine_2020 winterporn_2017 winterporn_2018 winterporn_2019 winterporn_2020 woodcarving_2017 woodcarving_2018
+woodcarving_2019 woodcarving_2020 woodworking_2017 woodworking_2018 woodworking_2019 woodworking_2020 workbenches_2017
+workbenches_2018 workbenches_2019 workbenches_2020 workspaces_2017 workspaces_2018 workspaces_2019 workspaces_2020
+yarnaddicts_2017 yarnaddicts_2018 yarnaddicts_2019 yarnaddicts_2020 zerowaste_2017 zerowaste_2018 zerowaste_2019
+zerowaste_2020
+"""
+_SUBREDDITS_WITH_YEAR = _SUBREDDITS_WITH_YEAR.strip().split()
+
+_SUBREDDIT_TO_YEAR = collections.defaultdict(list)
+for subreddit_with_year in _SUBREDDITS_WITH_YEAR:
+    subreddit, year = subreddit_with_year.split("_")
+    _SUBREDDIT_TO_YEAR[subreddit].append(year)
+
+_SUBREDDITS = list(_SUBREDDIT_TO_YEAR.keys())
+
+
+def _config_name_to_subreddits_with_year(config_name):
+    if config_name == "all":
+        return _SUBREDDITS_WITH_YEAR
+    elif re.match(r".*_\d{4}$", config_name):
+        return [config_name]
+    else:
+        return [f"{config_name}_{year}" for year in _SUBREDDIT_TO_YEAR[config_name]]
+
+
+def _config_name_to_description(config_name):
+    if config_name == "all":
+        return "Contains data from all the subreddits"
+    else:
+        if re.match(r".*_\d{4}$", config_name):
+            subreddit, year = config_name.split("_")
+            year_str = "2008 - 2017" if year == "2017" else year
+        else:
+            subreddit = config_name
+            year_str = ", ".join(
+                ["2008 - 2017" if year == "2017" else year for year in _SUBREDDIT_TO_YEAR[config_name]]
+            )
+        return f"Contains data from the {subreddit} subreddit posted in {year_str}"
+
+
+class RedCapsConfig(datasets.BuilderConfig):
+    """BuilderConfig for RedCaps."""
+
+    def __init__(self, name, **kwargs):
+        """BuilderConfig for RedCaps.
+
+        Args:
+          **kwargs: keyword arguments forwarded to super.
+        """
+        assert "description" not in kwargs
+        kwargs["description"] = _config_name_to_description(name)
+        super(RedCapsConfig, self).__init__(version=datasets.Version("1.0.0", ""), name=name, **kwargs)
+
+
+class RedCaps(datasets.GeneratorBasedBuilder):
+    """RedCaps dataset."""
+
+    BUILDER_CONFIGS = [
+        RedCapsConfig("all"),
+    ]
+    BUILDER_CONFIGS += [RedCapsConfig(subreddit) for subreddit in _SUBREDDITS]
+    BUILDER_CONFIGS += [RedCapsConfig(subreddit_with_year) for subreddit_with_year in _SUBREDDITS_WITH_YEAR]
+
+    DEFAULT_CONFIG_NAME = "all"
+
+    def _info(self):
+        features = datasets.Features(
+            {
+                "image_id": datasets.Value("string"),
+                "author": datasets.Value("string"),
+                "image_url": datasets.Value("string"),
+                "raw_caption": datasets.Value("string"),
+                "caption": datasets.Value("string"),
+                "subreddit": datasets.ClassLabel(names=_SUBREDDITS),
+                "score": datasets.Value("int32"),
+                "created_utc": datasets.Value("timestamp[s, tz=UTC]"),
+                "permalink": datasets.Value("string"),
+                "crosspost_parents": datasets.Sequence(datasets.Value("string")),
+            }
+        )
+
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=features,
+            supervised_keys=None,
+            homepage=_HOMEPAGE,
+            license=_LICENSE,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        annotations_dir = dl_manager.download_and_extract(_URL)
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "annotations_dir": annotations_dir,
+                    "subreddits": _config_name_to_subreddits_with_year(self.config.name),
+                },
+            ),
+        ]
+
+    def _generate_examples(self, annotations_dir, subreddits):
+        annotations_dir = os.path.join(annotations_dir, "annotations")
+        idx = 0
+        for subreddit in subreddits:
+            subreddit_file = os.path.join(annotations_dir, subreddit + ".json")
+            with open(subreddit_file, encoding="utf-8") as f:
+                data = json.load(f)
+                for annot in data["annotations"]:
+                    yield idx, {
+                        "image_id": annot["image_id"],
+                        "author": annot["author"],
+                        "image_url": annot["url"],
+                        "raw_caption": annot["raw_caption"],
+                        "caption": annot["caption"],
+                        "subreddit": annot["subreddit"],
+                        "score": annot["score"] if "score" in annot else None,
+                        "created_utc": annot["created_utc"],
+                        "permalink": annot["permalink"],
+                        "crosspost_parents": annot["crosspost_parents"]
+                        if "crosspost_parents" in annot and annot["crosspost_parents"]
+                        else None,
+                    }
+                    idx += 1