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e0cf2f7d8e27dcba9f89b9128be27e0f5fb8a3e3
|
# Dataset Card for "pubmed_counterfactual"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
zxvix/pubmed_counterfactual
|
[
"region:us"
] |
2023-08-23T08:40:20+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "test", "path": "data/test-*"}]}], "dataset_info": {"features": [{"name": "MedlineCitation", "struct": [{"name": "PMID", "dtype": "int32"}, {"name": "DateCompleted", "struct": [{"name": "Year", "dtype": "int32"}, {"name": "Month", "dtype": "int32"}, {"name": "Day", "dtype": "int32"}]}, {"name": "NumberOfReferences", "dtype": "int32"}, {"name": "DateRevised", "struct": [{"name": "Year", "dtype": "int32"}, {"name": "Month", "dtype": "int32"}, {"name": "Day", "dtype": "int32"}]}, {"name": "Article", "struct": [{"name": "Abstract", "struct": [{"name": "AbstractText", "dtype": "string"}]}, {"name": "ArticleTitle", "dtype": "string"}, {"name": "AuthorList", "struct": [{"name": "Author", "sequence": [{"name": "LastName", "dtype": "string"}, {"name": "ForeName", "dtype": "string"}, {"name": "Initials", "dtype": "string"}, {"name": "CollectiveName", "dtype": "string"}]}]}, {"name": "Language", "dtype": "string"}, {"name": "GrantList", "struct": [{"name": "Grant", "sequence": [{"name": "GrantID", "dtype": "string"}, {"name": "Agency", "dtype": "string"}, {"name": "Country", "dtype": "string"}]}]}, {"name": "PublicationTypeList", "struct": [{"name": "PublicationType", "sequence": "string"}]}]}, {"name": "MedlineJournalInfo", "struct": [{"name": "Country", "dtype": "string"}]}, {"name": "ChemicalList", "struct": [{"name": "Chemical", "sequence": [{"name": "RegistryNumber", "dtype": "string"}, {"name": "NameOfSubstance", "dtype": "string"}]}]}, {"name": "CitationSubset", "dtype": "string"}, {"name": "MeshHeadingList", "struct": [{"name": "MeshHeading", "sequence": [{"name": "DescriptorName", "dtype": "string"}, {"name": "QualifierName", "dtype": "string"}]}]}]}, {"name": "PubmedData", "struct": [{"name": "ArticleIdList", "sequence": [{"name": "ArticleId", "sequence": "string"}]}, {"name": "PublicationStatus", "dtype": "string"}, {"name": "History", "struct": [{"name": "PubMedPubDate", "sequence": [{"name": "Year", "dtype": "int32"}, {"name": "Month", "dtype": "int32"}, {"name": "Day", "dtype": "int32"}]}]}, {"name": "ReferenceList", "sequence": [{"name": "Citation", "dtype": "string"}, {"name": "CitationId", "dtype": "int32"}]}]}, {"name": "text", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "original_text", "dtype": "string"}], "splits": [{"name": "test", "num_bytes": 4026750.12, "num_examples": 991}], "download_size": 2241988, "dataset_size": 4026750.12}}
|
2023-08-25T05:56:31+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "pubmed_counterfactual"
More Information needed
|
[
"# Dataset Card for \"pubmed_counterfactual\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"pubmed_counterfactual\"\n\nMore Information needed"
] |
[
6,
17
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"pubmed_counterfactual\"\n\nMore Information needed"
] |
fd568e28bfe3e2050cde4cd7f3db2300aefe7d99
|
This dataset contains questions and passages from Polish law.
The dataset was created by randomly searching for provisions and asking questions related to that provision, in the
style of SQuAD. As a result, the questions might be biassed towards the content of a specific provision.
The authors of this dataset are student from AGH University of Krakow, supervised by [Aleksander
Smywiński-Pohl](https://huggingface.co/apohllo), PhD.
If you use the dataset, please cite the following article:
```
@article{kobylinski2023poleval,
title={PolEval 2022/23 Challenge Tasks and Results},
author={Kobylinski, {\L}ukasz and Ogrodniczuk, Maciej and Rybak, Piotr and Przyby{\l}a, Piotr and Pezik, Piotr and Miko{\l}ajczyk, Agnieszka and Janowski, Wojciech and Marcinczuk, Micha{\l} and Smywinski-Pohl, Aleksander},
year={2023},
journal={Proceedings of the 18th Conference on Computer Science and Intelligence Systems},
pages={1243–1250}
}
```
|
piotr-rybak/legal-questions
|
[
"language:pl",
"region:us"
] |
2023-08-23T08:57:44+00:00
|
{"language": ["pl"], "library_name": "transformers"}
|
2023-12-14T10:01:28+00:00
|
[] |
[
"pl"
] |
TAGS
#language-Polish #region-us
|
This dataset contains questions and passages from Polish law.
The dataset was created by randomly searching for provisions and asking questions related to that provision, in the
style of SQuAD. As a result, the questions might be biassed towards the content of a specific provision.
The authors of this dataset are student from AGH University of Krakow, supervised by Aleksander
Smywiński-Pohl, PhD.
If you use the dataset, please cite the following article:
|
[] |
[
"TAGS\n#language-Polish #region-us \n"
] |
[
11
] |
[
"passage: TAGS\n#language-Polish #region-us \n"
] |
23de0cdc718d4e00e818e61a61989e289faa4559
|
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<h2 style="text-align: left;"><strong>What Is <a href="https://rangii.jimdosite.com/">Rangii Toenail Fungus</a>?</strong></h2>
<p><a href="https://sway.office.com/TErqXTWzIirvIsfm?ref=Link&loc=mysways">Rangii</a> Toenail Fungus is a revolutionary 20-in-1 nail and feet-improving formula that offers exceptional benefits to users. The product is designed to provide comprehensive support to healthy nails and feet, and it is a top choice for individuals dealing with toenail fungus and brittle nails.</p>
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<h2 style="text-align: left;"><strong>How Does It <a href="https://rangii.company.site/">Rangii Toenail Fungus</a> Work?</strong></h2>
<p><a href="https://rangii-toenail-fungus.clubeo.com/page/rangii-clinically-porven-get-healthy-toes-beautiful-nails-toenail-fungus-remover-serum-spam-or-legit.html">Rangii Toenail Fungus</a> is specifically designed to combat toenail fungus and brittle nails. The key to its effectiveness lies in its ability to target the main cause of toenail fungus – T. Rubrum.</p>
<p>T. Rubrum is a type of fungus that is responsible for the majority of toenail fungus cases. It thrives in warm, moist environments such as shoes and socks and can spread quickly if left untreated. Rangii Toenail Fungus is designed to attack this fungus at its source by targeting and eliminating it from the nails.</p>
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<h3 style="text-align: left;"><strong style="font-family: georgia;">MUST SEE: <a href="https://www.healthsupplement24x7.com/get-rangii"><span style="background-color: #ffe599; color: red;">“Critical News Rangii Report – They Will Never Tell You This”</span></a></strong></h3>
<div class="separator" style="clear: both; text-align: center;"><a style="margin-left: 1em; margin-right: 1em;" href="https://www.healthsupplement24x7.com/get-rangii"><img src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhJeVoX2upe3lQe5q1x0lt8bxoh1Jt6S8DSW1OCLRARpgpqbzpvISSVrWx72rheU2KPkrTRU4_TaYpyNRqZ3ez27e_cc6d-wNJjEaUzxagGiVLy-u2f4ngsAflC7kiFx1Q6cOM1s2sg4VEwhCiT7ueM3XOLNZ4XmrAufwsgRx-_PGobvifctLi6fdKxxozh/w640-h290/Rangii%20Toenail%20Fungus%202.png" alt="" width="640" height="290" border="0" data-original-height="702" data-original-width="1550" /></a></div>
<h2 style="text-align: left;"><strong>The Potion of Potency: A Symphony of Natural and Effective Ingredients In <a href="https://rangii.webflow.io/">Rangii</a><br /></strong></h2>
<p><a href="https://bitbucket.org/rangii-toenail-fungus/rangii-toenail-fungus/issues/1/rangii-clinically-porven-get-healthy-toes">Rangii</a> Serum is a powerhouse of natural and organic ingredients, each carefully selected for its ability to support healthy nails and beautiful feet. Here's a breakdown of the all-natural, powerful blend of ingredients:</p>
<p><strong>Witch Hazel, Scots Pine, and Horsetail Extract:</strong> These plant extracts have strong anti-inflammatory, antimicrobial, and astringent properties, ideal for fighting nail and foot infections and reducing swelling.</p>
<p><strong>Gotu Kola:</strong> This ancient herb is known for its wound-healing abilities and has been shown to improve blood circulation, promoting healthy nail and foot growth.</p>
<p><strong>Rosemary & Pelargonium Graveolens:</strong> These essential oils have antimicrobial and antifungal properties, helping to combat common nail and foot issues like athlete's foot and fungal nail infections.</p>
<p><strong>Glycerin:</strong> A natural humectant, glycerin helps to lock in moisture, preventing dryness and brittleness in nails and feet.</p>
<p><strong>Lemon Peel Extract & Aloe Vera:</strong> These natural ingredients are rich in antioxidants and have soothing properties, which can help reduce irritation and inflammation.</p>
<p><strong>Organic Green Tea & Hops:</strong> Packed with antioxidants, these ingredients help fight off free radicals and promote overall nail and foot health.</p>
<p><strong>Vitamin C and E:</strong> These essential vitamins promote collagen production, improve circulation, and protect against environmental damage, resulting in healthier nails and feet.</p>
<p><strong>Hyaluronic Acid:</strong> Known for its incredible moisturizing capabilities, it helps keep nails and feet hydrated and plump.</p>
<p><strong>Jojoba Seed Oil and Sage Leaf Extract:</strong> These natural oils provide nourishment and hydration, supporting the overall health of your nails and feet.</p>
<p><strong>MSM:</strong> Methylsulfonylmethane (MSM) is an organic sulfur compound that supports collagen production, contributing to the strength and resilience of nails and feet.</p>
<h3 style="text-align: left;"><a href="https://www.healthsupplement24x7.com/get-rangii"><span style="background-color: #ffe599;"><span style="color: red;"><span style="font-family: georgia;"><strong>To Learn More about Rangii Ingredients in Detail, Click Here to Head to Its Official Website</strong></span></span></span></a></h3>
<div class="separator" style="clear: both; text-align: center;"><a style="margin-left: 1em; margin-right: 1em;" href="https://www.healthsupplement24x7.com/get-rangii"><img src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhF2D2GsgFuIXywzKgLFuO-T0FgJQecanaMLMZzrnHSTjRKlSgkQ7QFcroAnceHuTzCZRGQsyazPXBVXWeVm5iTWmbgF3XmPXXODsQyUms8tI2Eldiw3jXilGWO9EubwHlQ5XyfO6r3I1Y27kLvMIXY3LO_v9lHBJbIB9ppFnJRMKBAFtaUhsONpdNehIFn/w640-h342/Rangii%20Toenail%20Fungus%204.png" alt="" width="640" height="342" border="0" data-original-height="749" data-original-width="1400" /></a></div>
<h2 style="text-align: left;"><strong><a href="https://rangii.mystrikingly.com/">Rangii Toenail Fungus</a> Real Benefits</strong></h2>
<p><a href="https://haitiliberte.com/advert/rangii-clinically-porven-get-healthy-toes-beautiful-nails-toenail-fungus-remover-serumspam-or-legit/">Rangii Toenail Fungus</a> Serum offers various advantages that cater to nail and foot concerns. Its unique 20-in-1 formula is designed to provide comprehensive care, resulting in healthier nails and more beautiful feet. Here are the key benefits of using Rangii:</p>
<p><strong>Stronger nails:</strong> The ingredients of Rangii Toenail Fungus work within. Protects and strengthens nails. No more fragile nails!</p>
<p><strong>Nail growth:</strong> The serum promotes healthy nail growth for long and glamorous nails. Grow your nails.</p>
<p><strong>Better nails:</strong> The serum's moisturizing and nutrient-rich properties help revitalize nails.</p>
<p><strong>Nail repair:</strong> Nail fungus, discoloration and damage are gone. The serum's powerful ingredients address these issues, helping to keep your nails healthy.</p>
<p><strong>Hydrate nails and cuticles:</strong> The serum also hydrates the epidermis. It prevents dry skin and cuticle problems by moisturizing and softening.</p>
<p><strong>Simple to use:</strong> Applying the serum is easy. Apply the liquid to the nail and gently massage the area.</p>
<p><strong>Moisturize nail cuticles:</strong> Rangii believes that a dehydrated cuticle leaves nails dry and brittle. The liquid keeps them moist and strong, preventing infection.</p>
<p><strong>Helper Cells</strong>: <a href="https://rangii-toenail-fungus.bandcamp.com/track/rangii-clinically-porven-get-healthy-toes-beautiful-nails-toenail-fungus-remover-serum-spam-or-legit">Rangii</a> Toenail Fungus removes toxins and creates new nail cells. They help strengthen nails and prevent infections.</p>
<p><strong>Maintain blood flow:</strong> Infected fingernails and feet can interfere with blood circulation. Increased blood flow delivers vitamins and oxygen with Rangii Toenail Fungus. Blood circulation nourishes the nail and speeds up repair.</p>
<p><strong>Improves collagen production:</strong> Vitamins C and E in Rangii Toenail Fungus improve collagen. The serum stimulates collagen production, which helps strengthen nails.</p>
<h3 style="text-align: left;"><span style="font-family: georgia;"><strong>IMPORTANT: <span style="background-color: #ffe599;"><span style="color: red;"><a href="https://www.healthsupplement24x7.com/get-rangii">Shocking Truth About Rangii – This May Change Your Mind!</a></span></span></strong></span></h3>
<div class="separator" style="clear: both; text-align: center;"><a style="margin-left: 1em; margin-right: 1em;" href="https://www.healthsupplement24x7.com/get-rangii"><img src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjGF16k_OMNTEydHdlKIENq4ixtvJYwx1NLigQP-rcL5D3o9D_0LDNnqX4r38BSdWHT8HrFmwt7rglGgYzXU6MvgsqmQ5dymne1y1Kg1aoDqmjH19aZ-cHFeXHcAOkfKRE8X7YvjNCtzBSLUoTqkLOt0TjpU7-oynqf-XjeKVh9dYTsqXKVpcfAd79ydubY/w640-h334/Rangii%20Toenail%20Fungus%203.jpg" alt="" width="640" height="334" border="0" data-original-height="583" data-original-width="1119" /></a></div>
<h2 style="text-align: left;"><strong><a href="https://www.eventcreate.com/e/rangii">Rangii Toenail Fungus</a> Side Effects</strong></h2>
<p>No negative effects associated with <a href="https://gocrowdera.com/US/self/Rangii/rangiitoenail-51454">Rangii</a> were recorded. Its powerful and special formula combines ingredients that have undergone rigorous testing to improve skin and nail health without causing negative side effects. Always check the ingredient list to make sure there are no ingredients to which you could be allergic.</p>
<h3 style="text-align: left;"><strong style="font-family: georgia;">Read This: <a href="https://www.healthsupplement24x7.com/get-rangii"><span style="background-color: #ffe599; color: red;">"More Information From Knowledgeable Expertise of Health Labs Rangii"</span></a></strong></h3>
<h2 style="text-align: left;"><strong>How to Use <a href="https://www.podcasts.com/rangii-toenail-fungus">Rangii Toenail Fungus</a>?</strong></h2>
<p><a href="https://rangiitoenailfungus.hashnode.dev/rangii-clinically-porven-get-healthy-toes-beautiful-nails-toenail-fungus-remover-serumspam-or-legit">Rangii Toenail Fungus</a> comes in a bottle and an applicator brush, making it easy to use. Lift the applicator brush and apply the formula only on the affected area. The solution dries after a few seconds and starts working immediately.</p>
<p>Rangii Toenail Fungus is a formula for men and women of all ages struggling with nasty nail fungus and wanting to eliminate the problem. The manufacturer recommends using the nail formula daily to get the best results.</p>
<p>After using Rangii Toenail Fungus, you won't feel any irritation. It is friendly on the skin and does not cause any harmful side effects. However, you should consult your doctor before using Rangii Toenail Fungus if you are pregnant, breastfeeding, or have a chronic health condition.</p>
<p>If you have any skin condition, get clearance from your doctor before using the fungus fighter. Check the ingredients on the label to avoid allergic reactions. Use Rangii Toenail Fungus for a minimum of six months for permanent relief.</p>
<p>Expect significant results within a few weeks to one month of using <a href="https://www.yepdesk.com/rangii-usa-clinically-porven-get-healthy-toes-beautiful-nails-toenail-fungus-remover-serum">Rangii</a> Toenail Fungus. It is important to note that the results depend on your body's reaction and the severity of the nail fungus infection.</p>
<h3 style="text-align: left;"><span style="font-family: georgia;"><strong>READ ALSO: <span style="background-color: #ffe599;"><span style="color: red;"><a href="https://www.healthsupplement24x7.com/get-rangii">Does the Rangii Work For Everyone? Before you buy, read real customer reviews and testimonials!!</a></span></span></strong></span></h3>
<div class="separator" style="clear: both; text-align: center;"><a style="margin-left: 1em; margin-right: 1em;" href="https://www.healthsupplement24x7.com/get-rangii"><img src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEiI_XspCB32XSckI2b5ar-GFKV2rli7YwLnUwRb13RVPfMc_rMpDXpm53B6BJB4_8124biDke7Hb7fPHmFNv-55Tb1ppuNPN3pVCPoezMAvfi6MmxDBarjEFpk1GmUOGuCyDUGh6DcNCN9KkrGIG6kYLHt3CGU_42LUORWwDXYH9_Wylp9RWpq7ysFWp6fB/w640-h326/Rangii%20Toenail%20Fungus%205.jpg" alt="" width="640" height="326" border="0" data-original-height="789" data-original-width="1545" /></a></div>
<h2 style="text-align: left;"><strong>Where To Buy <a href="https://groups.google.com/g/rangii-toenail-fungus-reviews/c/zC9NHz0l_qk">Rangii Toenail Fungus</a>?</strong></h2>
<p>The official <a href="https://limitlessglucose1official.clubeo.com/calendar/2023/08/22/rangii-scientific-secret-nursing-formula-to-get-healthy-skin-nails-and-fungus-free-nails-work-or-hoax">Rangii</a> Toenail Fungus website has a convenient online store. The cost of the serum is divided into three separate categories and packages. Depending on your needs and budget, you can choose any of them.</p>
<p><strong>These are the Rangii costs which decline while getting more units simultaneously:</strong></p>
<p><strong>Basic -</strong> 1 Bottle Supply of Rangii USD 69/bottle + SMALL SHIPPING.<span style="color: red;"><br /></span></p>
<p><strong>Popular Pack -</strong> Buy 3 Get Bottle Supply of Rangii USD 49/bottle + SMALL SHIPPING + 2 FREE BOUNUSES.</p>
<p><strong>Best Value Pack - </strong>Buy 6 Bottle Supply of Rangii USD 39/bottle + FREE SHIPPING + 2 FREE BOUNUSES.</p>
<p style="text-align: left;"><a href="https://rangii.cgsociety.org/yiot/rangii-clinically-po">Rangii</a> Payments are made using 256-bit SSL technology to keep information safe and secure, and all orders arrive within a few business days of ordering.</p>
<h3 style="text-align: left;"><strong style="font-family: georgia;">Special Offer: <span style="background-color: #fff2cc; color: red;"><a href="https://www.healthsupplement24x7.com/get-rangii">Click Here To Get Heavy Discount Instantly!!</a></span></strong></h3>
<p><span style="font-family: times;"><span style="font-size: medium;"><span style="color: red;">Good News: Get additional discount on shipping when you checkout with Mastercard or Discover card!</span></span></span></p>
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<p style="text-align: left;"><span style="font-size: medium;"><a style="clear: left; float: left; margin-bottom: 1em; margin-left: 1em;" href="https://www.healthsupplement24x7.com/get-rangii"><img src="https://blogger.googleusercontent.com/img/a/AVvXsEgJqDXBj2s2sKgxhjLGKnDNPxD392fUjUkF8lQbqbuoFZwPHnPE27muXA18Hs1EzbsUHHsPlOR9Njx119fwMPFiCrLv9NlRRfEUdLPeIVlqZmqjexv1dJ0pMoSO6VUtSY89rewM_LiPyGpkGpNCHHdprDSvrWyt6MprtcceNFal6bdDPK_FyvLHnQzy-A" alt="" width="110" height="120" border="0" data-original-height="120" data-original-width="110" /></a><span style="font-family: helvetica;"><span style="font-size: small;"><strong><span style="color: red;">APPROVED!</span><br /></strong></span></span></span></p>
<p style="text-align: left;"><span style="font-family: helvetica;"><span style="font-size: small;">Limited supply available. We currently have product in stock and ready to ship within <span style="color: red;">24 hours</span>.</span></span></p>
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<p><span style="font-family: helvetica;"><span style="font-size: small;"><strong><span style="color: red;">EXPIRE SOON</span></strong></span></span></p>
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<p style="text-align: center;">By submitting, you affirm to have read and agreed to our <a href="https://www.healthsupplement24x7.com/get-rangii"><span style="color: red;">Terms & Conditions</span></a>.</p>
<p style="text-align: center;"><a href="https://www.healthsupplement24x7.com/get-rangii"><span style="font-size: medium;"><span style="background-color: #ffe599;"><span style="color: red;"><span style="font-family: georgia;"><strong>HUGE SAVINGS Get Your Rangii “Get Something OFF” Get 2+1 Offer Hurry Only For 1st User!!</strong></span></span></span></span></a></p>
<h2 style="text-align: left;"><strong>Is <a href="https://lookerstudio.google.com/reporting/d30136e6-7a42-45c3-b102-cbd5dc9b7aa7">Rangii Toenail Fungus</a> Legit? – Conclusion</strong></h2>
<p><a href="https://www.podcasts.com/rangii-toenail-fungus/episode/rangii-clinically-porven-get-healthy-toes-beautiful-nails-toenail-fungus-remover-serumspam-or-legit">Rangii</a> Toenail Fungus seems to be a legitimate toenail fungus remover. This serum is easy to apply and works almost instantly. The product is developed using all-natural and plant-based ingredients that have a long history of being used for their medical properties.</p>
<p>Overall, if you have brittle nails, poor nail and skin texture, toenail fungus, or any other foot and nail-related issue, Rangii Toenail Fungus can be a game changer. Also, make sure you combine it with the <a href="https://rangiitoenailfungus.contently.com/?public_only=true">Rangii</a> supplement to get the best results.</p>
<p class="ql-align-center" style="text-align: center;"><a href="https://www.healthsupplement24x7.com/get-rangii"><img src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgHYxn3NMPQqsmKG54OjkQESM8dw8D7zUXtssdLHaaWSYArzmNucZfEfKCOBsnUqZdp6i-enO0zDWtMGF2pKG2MifoTldIDExJOBDWxicPkSeox29VCmqX6Cz2feNaSfYBnC_BHUdfPT1qUGVgSNyn0NtyKxY-V-M-BDbo5jCOW4qSuxwu3TOTA3dSjIQ/s1600/Screenshot%20(1445).png" alt="" width="320" height="114" /></a></p>
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<p class="ql-align-center" style="text-align: center;"><span style="font-family: georgia;"><strong>© 2023 <a href="https://www.healthsupplement24x7.com/get-rangii">Rangii</a></strong><strong>. All Rights Reserved.</strong></span></p>
<p class="ql-align-center" style="text-align: left;"><span style="font-family: georgia;"><strong>Read More:</strong></span></p>
<p class="ql-align-center" style="text-align: left;"><span style="font-family: georgia;"><strong><a href="https://www.ourboox.com/books/rangii/">https://www.ourboox.com/books/rangii/</a></strong></span></p>
<p class="ql-align-center" style="text-align: left;"><span style="font-family: georgia;"><strong><a href="https://issuu.com/rangiitoenailfungus/docs/rangii">https://issuu.com/rangiitoenailfungus/docs/rangii</a></strong></span></p>
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rangiitoenailfungus/Rangii-Toenail-Fungus
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2023-08-23T08:58:36+00:00
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2023-08-23T08:59:01+00:00
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<h1 style="text-align: left;">Rangii</h1>
<p><span style="font-family: georgia;"><strong>Product Name - <a href="URL /></strong></span></p>
<p><span style="font-family: georgia;"><strong>Side Effects - No Side Effects (100% Natural)</strong></span></p>
<p><span style="font-family: georgia;"><strong>Main Benefits - Longer & Beautiful nails</strong></span></p>
<p><span style="font-family: georgia;"><strong>Category - Deep Nails Cleaner Liquid<br /></strong></span></p>
<p><span style="font-family: georgia;"><strong>Results - In 1-2 Months</strong></span></p>
<p><span style="font-family: georgia;"><strong>Availability - Online</strong></span></p>
<p><span style="font-family: georgia;"><strong>Customer Reviews - 4.9/5</strong></span></p>
<p><span style="font-family: georgia;"><strong>Price - Visit <a href="URL Website</a></strong></span></p>
<p><span style="font-family: georgia;"><strong><a href="URL/URL
<h3 style="text-align: center;"><span style="font-family: georgia;"><strong><span style="color: red;"><span style="background-color: #ffe599;"><a href="URL Huge Discount Now!!</a></span></span></strong></span></h3>
<h3 style="text-align: center;"><a href="URL style="font-family: georgia;"><span style="background-color: #fff2cc;"><span style="color: red;">Special Discount- As Low As On Rangii – Get Your Best Discount Online Hurry!!</span></span></span></strong></a></h3>
<div class="separator" style="clear: both; text-align: center;"><a style="margin-left: 1em; margin-right: 1em;" href="URL src="URL alt="" width="640" height="312" border="0" data-original-height="716" data-original-width="1468" /></a></div>
<p>The revolutionary 20-in-1 nail and pedicure product called Rangii Toenail Fungus or <a href="URL offers consumers extraordinary benefits. This product is a top choice for people with brittle nails and toenail fungus as it is designed to comprehensively support healthy nails and feet.</p>
<p>Clinical studies have shown that the all-natural ingredients of this advanced serum are extremely effective. These organic ingredients combine to provide exceptional and long-lasting benefits for feet and nails. Users only need a cotton ball to apply the serum on their nails and use is very simple.</p>
<p>The fact that <a href="URL Toenail Fungus is manufactured in an FDA approved facility using excellent manufacturing processes is one of its main advantages. This ensures the highest level of product safety, reliability and quality. Customers who have tried this product say they are very pleased with the results.</p>
<p>Rangii Toenail Fungus has benefits beyond improving the look of nails and feet. In addition, it contributes to their firmness, hydration and prevention of damage. Users can expect visible improvements in the condition and appearance of their feet and nails with continued use.</p>
<h2 style="text-align: center;"><a href="URL style="font-family: georgia;"><span style="background-color: #d9d2e9;"><span style="color: red;">SALE IS LIVE</span></span></span></strong></a></h2>
<h2 style="text-align: center;"><a href="URL style="font-family: georgia;"><span style="background-color: #ffe599;">Get <span style="color: red;">Rangii </span> “Now Available” Hurry Limited Time Offer Only For 1st User!!</span></span></strong></a></h2>
<h2 style="text-align: left;"><strong>What Is <a href="URL Toenail Fungus</a>?</strong></h2>
<p><a href="URL Toenail Fungus is a revolutionary 20-in-1 nail and feet-improving formula that offers exceptional benefits to users. The product is designed to provide comprehensive support to healthy nails and feet, and it is a top choice for individuals dealing with toenail fungus and brittle nails.</p>
<p>This advanced serum contains 100% natural ingredients that have been clinically proven to be highly effective. These natural ingredients work synergistically to provide superior and long-lasting benefits to the nails and feet. The serum is easy to apply, and users can simply use a cotton swab to apply it to their nails.</p>
<p>One of the key advantages of Rangii Toenail Fungus is that it is developed in an FDA-approved facility that follows good manufacturing practices. This ensures that the product is safe, reliable, and of the highest quality. Customers who have used this product have reported being satisfied with the results they have experienced.</p>
<p>The benefits of <a href="URL Toenail Fungus go beyond improving the appearance of nails and feet. It also helps to strengthen, nourish, and protect them from damage. With regular use, users can expect to see significant improvements in the health and appearance of their nails and feet.</p>
<h3 style="text-align: center;"><a href="URL style="background-color: #d9ead3;"><span style="color: red;"><span style="font-family: georgia;"><strong>LIMITED TIME OFFER</strong></span></span></span></a></h3>
<h3 style="text-align: center;"><a href="URL style="background-color: #ffe599;"><span style="color: red;"><span style="font-family: georgia;"><strong>Click Here to Order Rangii at Special Discounted Price</strong></span></span></span></a></h3>
<div class="separator" style="clear: both; text-align: center;"><a style="margin-left: 1em; margin-right: 1em;" href="URL src="URL alt="" width="492" height="369" border="0" data-original-height="1050" data-original-width="1400" /></a></div>
<h2 style="text-align: left;"><strong>How Does It <a href="URL Toenail Fungus</a> Work?</strong></h2>
<p><a href="URL Toenail Fungus</a> is specifically designed to combat toenail fungus and brittle nails. The key to its effectiveness lies in its ability to target the main cause of toenail fungus – T. Rubrum.</p>
<p>T. Rubrum is a type of fungus that is responsible for the majority of toenail fungus cases. It thrives in warm, moist environments such as shoes and socks and can spread quickly if left untreated. Rangii Toenail Fungus is designed to attack this fungus at its source by targeting and eliminating it from the nails.</p>
<p>The natural ingredients in <a href="URL Toenail Fungus work together to penetrate the nail bed and reach the fungus. These ingredients have antifungal properties that help destroy the fungus and promote healthy nail growth. By targeting the main cause of toenail fungus, Rangii Toenail Fungus is able to provide long-lasting relief to those suffering from this condition.</p>
<h3 style="text-align: left;"><strong style="font-family: georgia;">MUST SEE: <a href="URL style="background-color: #ffe599; color: red;">“Critical News Rangii Report – They Will Never Tell You This”</span></a></strong></h3>
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<h2 style="text-align: left;"><strong>The Potion of Potency: A Symphony of Natural and Effective Ingredients In <a href="URL /></strong></h2>
<p><a href="URL Serum is a powerhouse of natural and organic ingredients, each carefully selected for its ability to support healthy nails and beautiful feet. Here's a breakdown of the all-natural, powerful blend of ingredients:</p>
<p><strong>Witch Hazel, Scots Pine, and Horsetail Extract:</strong> These plant extracts have strong anti-inflammatory, antimicrobial, and astringent properties, ideal for fighting nail and foot infections and reducing swelling.</p>
<p><strong>Gotu Kola:</strong> This ancient herb is known for its wound-healing abilities and has been shown to improve blood circulation, promoting healthy nail and foot growth.</p>
<p><strong>Rosemary & Pelargonium Graveolens:</strong> These essential oils have antimicrobial and antifungal properties, helping to combat common nail and foot issues like athlete's foot and fungal nail infections.</p>
<p><strong>Glycerin:</strong> A natural humectant, glycerin helps to lock in moisture, preventing dryness and brittleness in nails and feet.</p>
<p><strong>Lemon Peel Extract & Aloe Vera:</strong> These natural ingredients are rich in antioxidants and have soothing properties, which can help reduce irritation and inflammation.</p>
<p><strong>Organic Green Tea & Hops:</strong> Packed with antioxidants, these ingredients help fight off free radicals and promote overall nail and foot health.</p>
<p><strong>Vitamin C and E:</strong> These essential vitamins promote collagen production, improve circulation, and protect against environmental damage, resulting in healthier nails and feet.</p>
<p><strong>Hyaluronic Acid:</strong> Known for its incredible moisturizing capabilities, it helps keep nails and feet hydrated and plump.</p>
<p><strong>Jojoba Seed Oil and Sage Leaf Extract:</strong> These natural oils provide nourishment and hydration, supporting the overall health of your nails and feet.</p>
<p><strong>MSM:</strong> Methylsulfonylmethane (MSM) is an organic sulfur compound that supports collagen production, contributing to the strength and resilience of nails and feet.</p>
<h3 style="text-align: left;"><a href="URL style="background-color: #ffe599;"><span style="color: red;"><span style="font-family: georgia;"><strong>To Learn More about Rangii Ingredients in Detail, Click Here to Head to Its Official Website</strong></span></span></span></a></h3>
<div class="separator" style="clear: both; text-align: center;"><a style="margin-left: 1em; margin-right: 1em;" href="URL src="URL alt="" width="640" height="342" border="0" data-original-height="749" data-original-width="1400" /></a></div>
<h2 style="text-align: left;"><strong><a href="URL Toenail Fungus</a> Real Benefits</strong></h2>
<p><a href="URL Toenail Fungus</a> Serum offers various advantages that cater to nail and foot concerns. Its unique 20-in-1 formula is designed to provide comprehensive care, resulting in healthier nails and more beautiful feet. Here are the key benefits of using Rangii:</p>
<p><strong>Stronger nails:</strong> The ingredients of Rangii Toenail Fungus work within. Protects and strengthens nails. No more fragile nails!</p>
<p><strong>Nail growth:</strong> The serum promotes healthy nail growth for long and glamorous nails. Grow your nails.</p>
<p><strong>Better nails:</strong> The serum's moisturizing and nutrient-rich properties help revitalize nails.</p>
<p><strong>Nail repair:</strong> Nail fungus, discoloration and damage are gone. The serum's powerful ingredients address these issues, helping to keep your nails healthy.</p>
<p><strong>Hydrate nails and cuticles:</strong> The serum also hydrates the epidermis. It prevents dry skin and cuticle problems by moisturizing and softening.</p>
<p><strong>Simple to use:</strong> Applying the serum is easy. Apply the liquid to the nail and gently massage the area.</p>
<p><strong>Moisturize nail cuticles:</strong> Rangii believes that a dehydrated cuticle leaves nails dry and brittle. The liquid keeps them moist and strong, preventing infection.</p>
<p><strong>Helper Cells</strong>: <a href="URL Toenail Fungus removes toxins and creates new nail cells. They help strengthen nails and prevent infections.</p>
<p><strong>Maintain blood flow:</strong> Infected fingernails and feet can interfere with blood circulation. Increased blood flow delivers vitamins and oxygen with Rangii Toenail Fungus. Blood circulation nourishes the nail and speeds up repair.</p>
<p><strong>Improves collagen production:</strong> Vitamins C and E in Rangii Toenail Fungus improve collagen. The serum stimulates collagen production, which helps strengthen nails.</p>
<h3 style="text-align: left;"><span style="font-family: georgia;"><strong>IMPORTANT: <span style="background-color: #ffe599;"><span style="color: red;"><a href="URL Truth About Rangii – This May Change Your Mind!</a></span></span></strong></span></h3>
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<h2 style="text-align: left;"><strong><a href="URL Toenail Fungus</a> Side Effects</strong></h2>
<p>No negative effects associated with <a href="URL were recorded. Its powerful and special formula combines ingredients that have undergone rigorous testing to improve skin and nail health without causing negative side effects. Always check the ingredient list to make sure there are no ingredients to which you could be allergic.</p>
<h3 style="text-align: left;"><strong style="font-family: georgia;">Read This: <a href="URL style="background-color: #ffe599; color: red;">"More Information From Knowledgeable Expertise of Health Labs Rangii"</span></a></strong></h3>
<h2 style="text-align: left;"><strong>How to Use <a href="URL Toenail Fungus</a>?</strong></h2>
<p><a href="URL Toenail Fungus</a> comes in a bottle and an applicator brush, making it easy to use. Lift the applicator brush and apply the formula only on the affected area. The solution dries after a few seconds and starts working immediately.</p>
<p>Rangii Toenail Fungus is a formula for men and women of all ages struggling with nasty nail fungus and wanting to eliminate the problem. The manufacturer recommends using the nail formula daily to get the best results.</p>
<p>After using Rangii Toenail Fungus, you won't feel any irritation. It is friendly on the skin and does not cause any harmful side effects. However, you should consult your doctor before using Rangii Toenail Fungus if you are pregnant, breastfeeding, or have a chronic health condition.</p>
<p>If you have any skin condition, get clearance from your doctor before using the fungus fighter. Check the ingredients on the label to avoid allergic reactions. Use Rangii Toenail Fungus for a minimum of six months for permanent relief.</p>
<p>Expect significant results within a few weeks to one month of using <a href="URL Toenail Fungus. It is important to note that the results depend on your body's reaction and the severity of the nail fungus infection.</p>
<h3 style="text-align: left;"><span style="font-family: georgia;"><strong>READ ALSO: <span style="background-color: #ffe599;"><span style="color: red;"><a href="URL the Rangii Work For Everyone? Before you buy, read real customer reviews and testimonials!!</a></span></span></strong></span></h3>
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<h2 style="text-align: left;"><strong>Where To Buy <a href="URL Toenail Fungus</a>?</strong></h2>
<p>The official <a href="URL Toenail Fungus website has a convenient online store. The cost of the serum is divided into three separate categories and packages. Depending on your needs and budget, you can choose any of them.</p>
<p><strong>These are the Rangii costs which decline while getting more units simultaneously:</strong></p>
<p><strong>Basic -</strong> 1 Bottle Supply of Rangii USD 69/bottle + SMALL SHIPPING.<span style="color: red;"><br /></span></p>
<p><strong>Popular Pack -</strong> Buy 3 Get Bottle Supply of Rangii USD 49/bottle + SMALL SHIPPING + 2 FREE BOUNUSES.</p>
<p><strong>Best Value Pack - </strong>Buy 6 Bottle Supply of Rangii USD 39/bottle + FREE SHIPPING + 2 FREE BOUNUSES.</p>
<p style="text-align: left;"><a href="URL Payments are made using 256-bit SSL technology to keep information safe and secure, and all orders arrive within a few business days of ordering.</p>
<h3 style="text-align: left;"><strong style="font-family: georgia;">Special Offer: <span style="background-color: #fff2cc; color: red;"><a href="URL Here To Get Heavy Discount Instantly!!</a></span></strong></h3>
<p><span style="font-family: times;"><span style="font-size: medium;"><span style="color: red;">Good News: Get additional discount on shipping when you checkout with Mastercard or Discover card!</span></span></span></p>
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<p style="text-align: left;"><span style="font-size: medium;"><a style="clear: left; float: left; margin-bottom: 1em; margin-left: 1em;" href="URL src="URL alt="" width="110" height="120" border="0" data-original-height="120" data-original-width="110" /></a><span style="font-family: helvetica;"><span style="font-size: small;"><strong><span style="color: red;">APPROVED!</span><br /></strong></span></span></span></p>
<p style="text-align: left;"><span style="font-family: helvetica;"><span style="font-size: small;">Limited supply available. We currently have product in stock and ready to ship within <span style="color: red;">24 hours</span>.</span></span></p>
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<p><span style="font-family: helvetica;"><span style="font-size: small;"><strong><span style="color: red;">EXPIRE SOON</span></strong></span></span></p>
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|
[] |
[
"TAGS\n#region-us \n"
] |
[
6
] |
[
"passage: TAGS\n#region-us \n"
] |
f9ddb2c37c629fbc423bfa869cdc091ad958ef67
|
# Dataset of washington (Kantai Collection)
This is the dataset of washington (Kantai Collection), containing 234 images and their tags.
The core tags of this character are `long_hair, grey_hair, ahoge, breasts, grey_eyes, large_breasts`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:-----------|:-----------------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 234 | 275.46 MiB | [Download](https://huggingface.co/datasets/CyberHarem/washington_kantaicollection/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 234 | 176.74 MiB | [Download](https://huggingface.co/datasets/CyberHarem/washington_kantaicollection/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 546 | 354.53 MiB | [Download](https://huggingface.co/datasets/CyberHarem/washington_kantaicollection/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 234 | 251.37 MiB | [Download](https://huggingface.co/datasets/CyberHarem/washington_kantaicollection/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 546 | 472.74 MiB | [Download](https://huggingface.co/datasets/CyberHarem/washington_kantaicollection/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/washington_kantaicollection',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 9 |  |  |  |  |  | 1girl, blue_hairband, official_alternate_costume, solo, blush, day, cowboy_shot, ocean, outdoors, blue_one-piece_swimsuit, blue_sky, cloud, hair_flower, sarong, looking_at_viewer, smile, casual_one-piece_swimsuit |
| 1 | 13 |  |  |  |  |  | 1girl, solo, blue_bikini, blue_hairband, simple_background, official_alternate_costume, white_background, looking_at_viewer, hair_flower, navel, blush, upper_body |
| 2 | 32 |  |  |  |  |  | 1girl, blue_necktie, sleeveless_shirt, solo, white_shirt, military_uniform, simple_background, headgear, looking_at_viewer, white_background, pleated_skirt, off_shoulder, bare_shoulders, black_pantyhose, white_skirt, cowboy_shot, closed_mouth |
| 3 | 16 |  |  |  |  |  | rabbit_ears, detached_collar, fake_animal_ears, playboy_bunny, 1girl, blue_necktie, simple_background, solo, strapless_leotard, white_background, looking_at_viewer, wrist_cuffs, black_pantyhose, cowboy_shot, cleavage, white_leotard, necktie_between_breasts, rabbit_tail, thighband_pantyhose |
| 4 | 13 |  |  |  |  |  | 1girl, solo, off-shoulder_sweater, blush, simple_background, white_background, long_sleeves, necklace, official_alternate_costume, looking_at_viewer, pink_skirt, pleated_skirt, white_pantyhose, cowboy_shot, smile |
| 5 | 5 |  |  |  |  |  | 1girl, cleavage, navel, official_alternate_costume, race_queen, solo, miniskirt, blue_choker, blue_eyes, blue_skirt, closed_mouth, holding_umbrella, midriff, simple_background, white_hair, black_skirt, blue_thighhighs, blush, cowboy_shot, crop_top, cropped_jacket, fingerless_gloves, full_body, hair_between_eyes, hand_on_hip, mismatched_legwear, multicolored_clothes, standing, two-tone_skirt, underboob, white_background, white_thighhighs |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | blue_hairband | official_alternate_costume | solo | blush | day | cowboy_shot | ocean | outdoors | blue_one-piece_swimsuit | blue_sky | cloud | hair_flower | sarong | looking_at_viewer | smile | casual_one-piece_swimsuit | blue_bikini | simple_background | white_background | navel | upper_body | blue_necktie | sleeveless_shirt | white_shirt | military_uniform | headgear | pleated_skirt | off_shoulder | bare_shoulders | black_pantyhose | white_skirt | closed_mouth | rabbit_ears | detached_collar | fake_animal_ears | playboy_bunny | strapless_leotard | wrist_cuffs | cleavage | white_leotard | necktie_between_breasts | rabbit_tail | thighband_pantyhose | off-shoulder_sweater | long_sleeves | necklace | pink_skirt | white_pantyhose | race_queen | miniskirt | blue_choker | blue_eyes | blue_skirt | holding_umbrella | midriff | white_hair | black_skirt | blue_thighhighs | crop_top | cropped_jacket | fingerless_gloves | full_body | hair_between_eyes | hand_on_hip | mismatched_legwear | multicolored_clothes | standing | two-tone_skirt | underboob | white_thighhighs |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:----------------|:-----------------------------|:-------|:--------|:------|:--------------|:--------|:-----------|:--------------------------|:-----------|:--------|:--------------|:---------|:--------------------|:--------|:----------------------------|:--------------|:--------------------|:-------------------|:--------|:-------------|:---------------|:-------------------|:--------------|:-------------------|:-----------|:----------------|:---------------|:-----------------|:------------------|:--------------|:---------------|:--------------|:------------------|:-------------------|:----------------|:--------------------|:--------------|:-----------|:----------------|:--------------------------|:--------------|:----------------------|:-----------------------|:---------------|:-----------|:-------------|:------------------|:-------------|:------------|:--------------|:------------|:-------------|:-------------------|:----------|:-------------|:--------------|:------------------|:-----------|:-----------------|:--------------------|:------------|:--------------------|:--------------|:---------------------|:-----------------------|:-----------|:-----------------|:------------|:-------------------|
| 0 | 9 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 1 | 13 |  |  |  |  |  | X | X | X | X | X | | | | | | | | X | | X | | | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 2 | 32 |  |  |  |  |  | X | | | X | | | X | | | | | | | | X | | | | X | X | | | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 3 | 16 |  |  |  |  |  | X | | | X | | | X | | | | | | | | X | | | | X | X | | | X | | | | | | | | X | | | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 4 | 13 |  |  |  |  |  | X | | X | X | X | | X | | | | | | | | X | X | | | X | X | | | | | | | | X | | | | | | | | | | | | | | | | | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | |
| 5 | 5 |  |  |  |  |  | X | | X | X | X | | X | | | | | | | | | | | | X | X | X | | | | | | | | | | | | X | | | | | | | X | | | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X |
|
CyberHarem/washington_kantaicollection
|
[
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] |
2023-08-23T09:07:34+00:00
|
{"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]}
|
2024-01-16T04:39:09+00:00
|
[] |
[] |
TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
|
Dataset of washington (Kantai Collection)
=========================================
This is the dataset of washington (Kantai Collection), containing 234 images and their tags.
The core tags of this character are 'long\_hair, grey\_hair, ahoge, breasts, grey\_eyes, large\_breasts', which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(huggingface organization).
List of Packages
----------------
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code
List of Clusters
----------------
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
### Table Version
|
[
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] |
[
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n",
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] |
[
44,
61,
5,
4
] |
[
"passage: TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.### Raw Text Version### Table Version"
] |
4f8b29bdbb02f97b334e8224582b7d2e2c4b25b4
|
# AutoTrain Dataset for project: img-classification
## Dataset Description
This dataset has been automatically processed by AutoTrain for project img-classification.
### Languages
The BCP-47 code for the dataset's language is unk.
## Dataset Structure
### Data Instances
A sample from this dataset looks as follows:
```json
[
{
"image": "<222x163 RGB PIL image>",
"target": 0
},
{
"image": "<222x163 RGB PIL image>",
"target": 3
}
]
```
### Dataset Fields
The dataset has the following fields (also called "features"):
```json
{
"image": "Image(decode=True, id=None)",
"target": "ClassLabel(names=['elf', 'goblin', 'knight', 'zombie'], id=None)"
}
```
### Dataset Splits
This dataset is split into a train and validation split. The split sizes are as follow:
| Split name | Num samples |
| ------------ | ------------------- |
| train | 1160 |
| valid | 291 |
|
neil-code/autotrain-data-img-classification
|
[
"task_categories:image-classification",
"region:us"
] |
2023-08-23T09:09:39+00:00
|
{"task_categories": ["image-classification"]}
|
2023-08-23T09:55:59+00:00
|
[] |
[] |
TAGS
#task_categories-image-classification #region-us
|
AutoTrain Dataset for project: img-classification
=================================================
Dataset Description
-------------------
This dataset has been automatically processed by AutoTrain for project img-classification.
### Languages
The BCP-47 code for the dataset's language is unk.
Dataset Structure
-----------------
### Data Instances
A sample from this dataset looks as follows:
### Dataset Fields
The dataset has the following fields (also called "features"):
### Dataset Splits
This dataset is split into a train and validation split. The split sizes are as follow:
|
[
"### Languages\n\n\nThe BCP-47 code for the dataset's language is unk.\n\n\nDataset Structure\n-----------------",
"### Data Instances\n\n\nA sample from this dataset looks as follows:",
"### Dataset Fields\n\n\nThe dataset has the following fields (also called \"features\"):",
"### Dataset Splits\n\n\nThis dataset is split into a train and validation split. The split sizes are as follow:"
] |
[
"TAGS\n#task_categories-image-classification #region-us \n",
"### Languages\n\n\nThe BCP-47 code for the dataset's language is unk.\n\n\nDataset Structure\n-----------------",
"### Data Instances\n\n\nA sample from this dataset looks as follows:",
"### Dataset Fields\n\n\nThe dataset has the following fields (also called \"features\"):",
"### Dataset Splits\n\n\nThis dataset is split into a train and validation split. The split sizes are as follow:"
] |
[
17,
27,
17,
23,
27
] |
[
"passage: TAGS\n#task_categories-image-classification #region-us \n### Languages\n\n\nThe BCP-47 code for the dataset's language is unk.\n\n\nDataset Structure\n-----------------### Data Instances\n\n\nA sample from this dataset looks as follows:### Dataset Fields\n\n\nThe dataset has the following fields (also called \"features\"):### Dataset Splits\n\n\nThis dataset is split into a train and validation split. The split sizes are as follow:"
] |
1d5779da8ce29d4a1aa74d3f87f176e1d402da3e
|
# Dataset of mikazuki (Kantai Collection)
This is the dataset of mikazuki (Kantai Collection), containing 336 images and their tags.
The core tags of this character are `long_hair, black_hair, ahoge, yellow_eyes, hair_between_eyes`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:-----------|:---------------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 336 | 236.44 MiB | [Download](https://huggingface.co/datasets/CyberHarem/mikazuki_kantaicollection/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 336 | 167.29 MiB | [Download](https://huggingface.co/datasets/CyberHarem/mikazuki_kantaicollection/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 736 | 345.20 MiB | [Download](https://huggingface.co/datasets/CyberHarem/mikazuki_kantaicollection/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 336 | 223.33 MiB | [Download](https://huggingface.co/datasets/CyberHarem/mikazuki_kantaicollection/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 736 | 434.18 MiB | [Download](https://huggingface.co/datasets/CyberHarem/mikazuki_kantaicollection/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/mikazuki_kantaicollection',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 12 |  |  |  |  |  | 1girl, black_serafuku, long_sleeves, looking_at_viewer, solo, skirt, blush, crescent, necktie, white_background, animal_ears, kemonomimi_mode, open_mouth, tail |
| 1 | 40 |  |  |  |  |  | 1girl, black_serafuku, solo, crescent_pin, looking_at_viewer, long_sleeves, white_necktie, simple_background, white_background, black_skirt, black_sailor_collar, blush, open_mouth, smile, pleated_skirt, white_neckerchief, twitter_username, black_shirt, one-hour_drawing_challenge |
| 2 | 12 |  |  |  |  |  | detached_collar, looking_at_viewer, playboy_bunny, rabbit_ears, wrist_cuffs, 1girl, solo, strapless_leotard, black_leotard, fake_animal_ears, alternate_costume, crescent, red_bowtie, small_breasts, open_mouth, black_pantyhose, cowboy_shot, flat_chest, rabbit_tail, white_background |
| 3 | 13 |  |  |  |  |  | 1girl, looking_at_viewer, solo, cowboy_shot, simple_background, collarbone, flat_chest, smile, blue_one-piece_swimsuit, blush, old_school_swimsuit, white_background, open_mouth, ass_visible_through_thighs, gradient_background, twitter_username, covered_navel, grey_background |
| 4 | 5 |  |  |  |  |  | 1girl, barefoot, blue_one-piece_swimsuit, blush, collarbone, competition_school_swimsuit, indoors, looking_at_viewer, poolside, rei_no_pool, sitting, solo, window, small_breasts, spread_legs, water, alternate_costume, covered_navel, smile, tile_floor, cameltoe, competition_swimsuit, feet, full_body, pool_ladder |
| 5 | 7 |  |  |  |  |  | 1girl, looking_at_viewer, simple_background, solo, white_background, black_dress, blush, enmaided, white_apron, frilled_apron, maid_apron, maid_headdress, puffy_sleeves, smile, black_footwear, bow, brown_eyes, cowboy_shot, dated, full_body, heart, shoes, short_sleeves |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | black_serafuku | long_sleeves | looking_at_viewer | solo | skirt | blush | crescent | necktie | white_background | animal_ears | kemonomimi_mode | open_mouth | tail | crescent_pin | white_necktie | simple_background | black_skirt | black_sailor_collar | smile | pleated_skirt | white_neckerchief | twitter_username | black_shirt | one-hour_drawing_challenge | detached_collar | playboy_bunny | rabbit_ears | wrist_cuffs | strapless_leotard | black_leotard | fake_animal_ears | alternate_costume | red_bowtie | small_breasts | black_pantyhose | cowboy_shot | flat_chest | rabbit_tail | collarbone | blue_one-piece_swimsuit | old_school_swimsuit | ass_visible_through_thighs | gradient_background | covered_navel | grey_background | barefoot | competition_school_swimsuit | indoors | poolside | rei_no_pool | sitting | window | spread_legs | water | tile_floor | cameltoe | competition_swimsuit | feet | full_body | pool_ladder | black_dress | enmaided | white_apron | frilled_apron | maid_apron | maid_headdress | puffy_sleeves | black_footwear | bow | brown_eyes | dated | heart | shoes | short_sleeves |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:-----------------|:---------------|:--------------------|:-------|:--------|:--------|:-----------|:----------|:-------------------|:--------------|:------------------|:-------------|:-------|:---------------|:----------------|:--------------------|:--------------|:----------------------|:--------|:----------------|:--------------------|:-------------------|:--------------|:-----------------------------|:------------------|:----------------|:--------------|:--------------|:--------------------|:----------------|:-------------------|:--------------------|:-------------|:----------------|:------------------|:--------------|:-------------|:--------------|:-------------|:--------------------------|:----------------------|:-----------------------------|:----------------------|:----------------|:------------------|:-----------|:------------------------------|:----------|:-----------|:--------------|:----------|:---------|:--------------|:--------|:-------------|:-----------|:-----------------------|:-------|:------------|:--------------|:--------------|:-----------|:--------------|:----------------|:-------------|:-----------------|:----------------|:-----------------|:------|:-------------|:--------|:--------|:--------|:----------------|
| 0 | 12 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 1 | 40 |  |  |  |  |  | X | X | X | X | X | | X | | | X | | | X | | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 2 | 12 |  |  |  |  |  | X | | | X | X | | | X | | X | | | X | | | | | | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 3 | 13 |  |  |  |  |  | X | | | X | X | | X | | | X | | | X | | | | X | | | X | | | X | | | | | | | | | | | | | | X | X | | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 4 | 5 |  |  |  |  |  | X | | | X | X | | X | | | | | | | | | | | | | X | | | | | | | | | | | | | X | | X | | | | | X | X | | | | X | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | |
| 5 | 7 |  |  |  |  |  | X | | | X | X | | X | | | X | | | | | | | X | | | X | | | | | | | | | | | | | | | | | X | | | | | | | | | | | | | | | | | | | | | | | X | | X | X | X | X | X | X | X | X | X | X | X | X | X | X |
|
CyberHarem/mikazuki_kantaicollection
|
[
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] |
2023-08-23T09:17:13+00:00
|
{"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]}
|
2024-01-16T08:56:19+00:00
|
[] |
[] |
TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
|
Dataset of mikazuki (Kantai Collection)
=======================================
This is the dataset of mikazuki (Kantai Collection), containing 336 images and their tags.
The core tags of this character are 'long\_hair, black\_hair, ahoge, yellow\_eyes, hair\_between\_eyes', which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(huggingface organization).
List of Packages
----------------
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code
List of Clusters
----------------
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
### Table Version
|
[
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] |
[
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n",
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] |
[
44,
61,
5,
4
] |
[
"passage: TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.### Raw Text Version### Table Version"
] |
1b20f2eabe5c94e1eb85b2ca7eec5a8ded14baf5
|
# Dataset Card for "annotated18k_training_dataset_90"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
fia24/annotated18k_training_dataset_90
|
[
"region:us"
] |
2023-08-23T09:18:46+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "test", "path": "data/test-*"}]}], "dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "translation", "struct": [{"name": "en", "dtype": "string"}, {"name": "fr", "dtype": "string"}]}], "splits": [{"name": "train", "num_bytes": 905284, "num_examples": 15273}, {"name": "test", "num_bytes": 101954, "num_examples": 1697}], "download_size": 538353, "dataset_size": 1007238}}
|
2023-08-23T09:18:59+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "annotated18k_training_dataset_90"
More Information needed
|
[
"# Dataset Card for \"annotated18k_training_dataset_90\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"annotated18k_training_dataset_90\"\n\nMore Information needed"
] |
[
6,
22
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"annotated18k_training_dataset_90\"\n\nMore Information needed"
] |
a12af8a56d4956e71f8483c8a0dfa52bf8353acf
|
# Dataset Card for Dataset Name
## Dataset Description
- **Homepage:**
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
This dataset card aims to be a base template for new datasets. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/datasetcard_template.md?plain=1).
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed]
|
roniwahyu/googleplay-bank-apps-indonesia
|
[
"region:us"
] |
2023-08-23T09:19:37+00:00
|
{}
|
2023-08-23T09:22:15+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for Dataset Name
## Dataset Description
- Homepage:
- Repository:
- Paper:
- Leaderboard:
- Point of Contact:
### Dataset Summary
This dataset card aims to be a base template for new datasets. It has been generated using this raw template.
### Supported Tasks and Leaderboards
### Languages
## Dataset Structure
### Data Instances
### Data Fields
### Data Splits
## Dataset Creation
### Curation Rationale
### Source Data
#### Initial Data Collection and Normalization
#### Who are the source language producers?
### Annotations
#### Annotation process
#### Who are the annotators?
### Personal and Sensitive Information
## Considerations for Using the Data
### Social Impact of Dataset
### Discussion of Biases
### Other Known Limitations
## Additional Information
### Dataset Curators
### Licensing Information
### Contributions
|
[
"# Dataset Card for Dataset Name",
"## Dataset Description\n\n- Homepage: \n- Repository: \n- Paper: \n- Leaderboard: \n- Point of Contact:",
"### Dataset Summary\n\nThis dataset card aims to be a base template for new datasets. It has been generated using this raw template.",
"### Supported Tasks and Leaderboards",
"### Languages",
"## Dataset Structure",
"### Data Instances",
"### Data Fields",
"### Data Splits",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?",
"### Annotations",
"#### Annotation process",
"#### Who are the annotators?",
"### Personal and Sensitive Information",
"## Considerations for Using the Data",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations",
"## Additional Information",
"### Dataset Curators",
"### Licensing Information",
"### Contributions"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for Dataset Name",
"## Dataset Description\n\n- Homepage: \n- Repository: \n- Paper: \n- Leaderboard: \n- Point of Contact:",
"### Dataset Summary\n\nThis dataset card aims to be a base template for new datasets. It has been generated using this raw template.",
"### Supported Tasks and Leaderboards",
"### Languages",
"## Dataset Structure",
"### Data Instances",
"### Data Fields",
"### Data Splits",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?",
"### Annotations",
"#### Annotation process",
"#### Who are the annotators?",
"### Personal and Sensitive Information",
"## Considerations for Using the Data",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations",
"## Additional Information",
"### Dataset Curators",
"### Licensing Information",
"### Contributions"
] |
[
6,
8,
24,
32,
10,
4,
6,
6,
5,
5,
5,
7,
4,
10,
10,
5,
5,
9,
8,
8,
7,
8,
7,
5,
6,
6,
5
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for Dataset Name## Dataset Description\n\n- Homepage: \n- Repository: \n- Paper: \n- Leaderboard: \n- Point of Contact:### Dataset Summary\n\nThis dataset card aims to be a base template for new datasets. It has been generated using this raw template.### Supported Tasks and Leaderboards### Languages## Dataset Structure### Data Instances### Data Fields### Data Splits## Dataset Creation### Curation Rationale### Source Data#### Initial Data Collection and Normalization#### Who are the source language producers?### Annotations#### Annotation process#### Who are the annotators?### Personal and Sensitive Information## Considerations for Using the Data### Social Impact of Dataset### Discussion of Biases### Other Known Limitations## Additional Information### Dataset Curators### Licensing Information### Contributions"
] |
46811b75e7bbef32a16e9f37ea0e53f5f48c1d0e
|
# Dataset of seaport_summer_hime/港湾夏姫/港湾夏姫 (Kantai Collection)
This is the dataset of seaport_summer_hime/港湾夏姫/港湾夏姫 (Kantai Collection), containing 41 images and their tags.
The core tags of this character are `breasts, long_hair, white_hair, hat, blue_eyes, hair_over_one_eye, large_breasts, sun_hat, white_skin, colored_skin, white_headwear, pale_skin`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:----------|:--------------------------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 41 | 49.96 MiB | [Download](https://huggingface.co/datasets/CyberHarem/seaport_summer_hime_kantaicollection/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 41 | 32.08 MiB | [Download](https://huggingface.co/datasets/CyberHarem/seaport_summer_hime_kantaicollection/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 98 | 66.53 MiB | [Download](https://huggingface.co/datasets/CyberHarem/seaport_summer_hime_kantaicollection/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 41 | 47.48 MiB | [Download](https://huggingface.co/datasets/CyberHarem/seaport_summer_hime_kantaicollection/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 98 | 88.81 MiB | [Download](https://huggingface.co/datasets/CyberHarem/seaport_summer_hime_kantaicollection/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/seaport_summer_hime_kantaicollection',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 6 |  |  |  |  |  | 1girl, abyssal_ship, black_gloves, cleavage, hat_flower, looking_at_viewer, solo, sundress, white_dress, bare_shoulders, sleeveless_dress, parted_lips |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | abyssal_ship | black_gloves | cleavage | hat_flower | looking_at_viewer | solo | sundress | white_dress | bare_shoulders | sleeveless_dress | parted_lips |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:---------------|:---------------|:-----------|:-------------|:--------------------|:-------|:-----------|:--------------|:-----------------|:-------------------|:--------------|
| 0 | 6 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X |
|
CyberHarem/seaport_summer_hime_kantaicollection
|
[
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] |
2023-08-23T09:35:10+00:00
|
{"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]}
|
2024-01-16T00:04:16+00:00
|
[] |
[] |
TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
|
Dataset of seaport\_summer\_hime/港湾夏姫/港湾夏姫 (Kantai Collection)
==============================================================
This is the dataset of seaport\_summer\_hime/港湾夏姫/港湾夏姫 (Kantai Collection), containing 41 images and their tags.
The core tags of this character are 'breasts, long\_hair, white\_hair, hat, blue\_eyes, hair\_over\_one\_eye, large\_breasts, sun\_hat, white\_skin, colored\_skin, white\_headwear, pale\_skin', which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(huggingface organization).
List of Packages
----------------
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code
List of Clusters
----------------
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
### Table Version
|
[
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] |
[
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n",
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] |
[
44,
61,
5,
4
] |
[
"passage: TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.### Raw Text Version### Table Version"
] |
877f4ff675f347c5891bdb3a800a4e5cc898ecf7
|
# Dataset Card for "products_desc_and_marktng_emails_dataset"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
ctiwary/products_desc_and_marktng_emails_dataset
|
[
"region:us"
] |
2023-08-23T09:51:07+00:00
|
{"dataset_info": {"features": [{"name": "product", "dtype": "string"}, {"name": "description", "dtype": "string"}, {"name": "marketing_email", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 18095, "num_examples": 10}], "download_size": 23309, "dataset_size": 18095}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-08-23T09:51:09+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "products_desc_and_marktng_emails_dataset"
More Information needed
|
[
"# Dataset Card for \"products_desc_and_marktng_emails_dataset\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"products_desc_and_marktng_emails_dataset\"\n\nMore Information needed"
] |
[
6,
26
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"products_desc_and_marktng_emails_dataset\"\n\nMore Information needed"
] |
55bc2f02b1a00063e5e9e8de410772e07cc12148
|
# Dataset of conte_di_cavour (Kantai Collection)
This is the dataset of conte_di_cavour (Kantai Collection), containing 362 images and their tags.
The core tags of this character are `long_hair, breasts, large_breasts, grey_hair, two_side_up, brown_eyes`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:-----------|:----------------------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 362 | 413.94 MiB | [Download](https://huggingface.co/datasets/CyberHarem/conte_di_cavour_kantaicollection/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 362 | 245.31 MiB | [Download](https://huggingface.co/datasets/CyberHarem/conte_di_cavour_kantaicollection/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 867 | 534.88 MiB | [Download](https://huggingface.co/datasets/CyberHarem/conte_di_cavour_kantaicollection/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 362 | 374.24 MiB | [Download](https://huggingface.co/datasets/CyberHarem/conte_di_cavour_kantaicollection/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 867 | 749.42 MiB | [Download](https://huggingface.co/datasets/CyberHarem/conte_di_cavour_kantaicollection/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/conte_di_cavour_kantaicollection',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:----------------------------------|:----------------------------------|:----------------------------------|:----------------------------------|:----------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 13 |  |  |  |  |  | 1girl, blush, fish_print, yukata, wide_sleeves, long_sleeves, obi, official_alternate_costume, solo, collarbone, print_kimono, cleavage, white_kimono, sitting, white_background, white_hair, open_mouth |
| 1 | 20 |  |  |  |  |  | 1girl, long_sleeves, white_kimono, miko, red_hakama, wide_sleeves, solo, blush, hakama_skirt, open_mouth, simple_background, cleavage, collarbone, official_alternate_costume, white_hair, white_background, holding, smile, twitter_username |
| 2 | 10 |  |  |  |  |  | 1girl, cleavage, solo, black_bikini, blush, simple_background, white_background, cowboy_shot, collarbone, navel, closed_mouth, necklace, purple_eyes, smile, white_hair, looking_at_viewer, open_mouth |
| 3 | 7 |  |  |  |  |  | 1girl, dated, one-hour_drawing_challenge, solo, simple_background, twitter_username, white_background, blush, cowboy_shot, upper_body, bikini, blue_one-piece_swimsuit, cleavage, collarbone, competition_swimsuit, looking_at_viewer, navel, open_mouth |
| 4 | 5 |  |  |  |  |  | 1girl, cleavage_cutout, frilled_dress, layered_dress, simple_background, solo, white_background, white_dress, white_gloves, corset, looking_at_viewer, two-tone_dress, smile, upper_body, one-hour_drawing_challenge, sleeveless_dress |
| 5 | 5 |  |  |  |  |  | 1girl, cleavage_cutout, corset, frilled_dress, grey_dress, layered_dress, simple_background, solo, two-tone_dress, white_background, white_dress, white_gloves, purple_eyes, short_sleeves, upper_body |
| 6 | 7 |  |  |  |  |  | 1girl, blush, cleavage_cutout, frilled_dress, layered_dress, long_sleeves, solo, white_dress, grey_dress, simple_background, two-tone_dress, looking_at_viewer, white_background, closed_mouth |
| 7 | 5 |  |  |  |  |  | 1girl, cleavage_cutout, frilled_dress, grey_dress, layered_dress, long_sleeves, solo, two-tone_dress, white_dress, corset, open_mouth, smile, blush, armpit_cutout |
| 8 | 5 |  |  |  |  |  | 1girl, blush, cleavage_cutout, frilled_dress, layered_dress, two-tone_dress, white_dress, white_gloves, open_mouth, solo, grey_dress, looking_at_viewer, short_sleeves, twitter_username, upper_body |
| 9 | 11 |  |  |  |  |  | 1girl, cleavage, day, open_mouth, outdoors, smile, solo, blue_sky, cloud, navel, looking_at_viewer, side-tie_bikini_bottom, blush, collarbone, cowboy_shot, ocean, black_bikini, necklace, purple_eyes |
| 10 | 7 |  |  |  |  |  | neckerchief, sailor_dress, sleeveless_dress, white_dress, white_sailor_collar, 1girl, blush, cosplay, solo, striped, simple_background, cowboy_shot, open_mouth, sideboob, white_background |
| 11 | 6 |  |  |  |  |  | 1girl, completely_nude, navel, nipples, solo, blush, collarbone, purple_eyes |
| 12 | 8 |  |  |  |  |  | 1girl, solo, long_sleeves, white_shirt, collared_shirt, cowboy_shot, pleated_skirt, one-hour_drawing_challenge, school_uniform, simple_background, white_background, black_skirt, blush, jacket, official_alternate_costume, open_mouth, smile |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | blush | fish_print | yukata | wide_sleeves | long_sleeves | obi | official_alternate_costume | solo | collarbone | print_kimono | cleavage | white_kimono | sitting | white_background | white_hair | open_mouth | miko | red_hakama | hakama_skirt | simple_background | holding | smile | twitter_username | black_bikini | cowboy_shot | navel | closed_mouth | necklace | purple_eyes | looking_at_viewer | dated | one-hour_drawing_challenge | upper_body | bikini | blue_one-piece_swimsuit | competition_swimsuit | cleavage_cutout | frilled_dress | layered_dress | white_dress | white_gloves | corset | two-tone_dress | sleeveless_dress | grey_dress | short_sleeves | armpit_cutout | day | outdoors | blue_sky | cloud | side-tie_bikini_bottom | ocean | neckerchief | sailor_dress | white_sailor_collar | cosplay | striped | sideboob | completely_nude | nipples | white_shirt | collared_shirt | pleated_skirt | school_uniform | black_skirt | jacket |
|----:|----------:|:----------------------------------|:----------------------------------|:----------------------------------|:----------------------------------|:----------------------------------|:--------|:--------|:-------------|:---------|:---------------|:---------------|:------|:-----------------------------|:-------|:-------------|:---------------|:-----------|:---------------|:----------|:-------------------|:-------------|:-------------|:-------|:-------------|:---------------|:--------------------|:----------|:--------|:-------------------|:---------------|:--------------|:--------|:---------------|:-----------|:--------------|:--------------------|:--------|:-----------------------------|:-------------|:---------|:--------------------------|:-----------------------|:------------------|:----------------|:----------------|:--------------|:---------------|:---------|:-----------------|:-------------------|:-------------|:----------------|:----------------|:------|:-----------|:-----------|:--------|:-------------------------|:--------|:--------------|:---------------|:----------------------|:----------|:----------|:-----------|:------------------|:----------|:--------------|:-----------------|:----------------|:-----------------|:--------------|:---------|
| 0 | 13 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 1 | 20 |  |  |  |  |  | X | X | | | X | X | | X | X | X | | X | X | | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 2 | 10 |  |  |  |  |  | X | X | | | | | | | X | X | | X | | | X | X | X | | | | X | | X | | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 3 | 7 |  |  |  |  |  | X | X | | | | | | | X | X | | X | | | X | | X | | | | X | | | X | | X | X | | | | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 4 | 5 |  |  |  |  |  | X | | | | | | | | X | | | | | | X | | | | | | X | | X | | | | | | | | X | | X | X | | | | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | |
| 5 | 5 |  |  |  |  |  | X | | | | | | | | X | | | | | | X | | | | | | X | | | | | | | | | X | | | | X | | | | X | X | X | X | X | X | X | | X | X | | | | | | | | | | | | | | | | | | | | | |
| 6 | 7 |  |  |  |  |  | X | X | | | | X | | | X | | | | | | X | | | | | | X | | | | | | | X | | | X | | | | | | | X | X | X | X | | | X | | X | | | | | | | | | | | | | | | | | | | | | | |
| 7 | 5 |  |  |  |  |  | X | X | | | | X | | | X | | | | | | | | X | | | | | | X | | | | | | | | | | | | | | | X | X | X | X | | X | X | | X | | X | | | | | | | | | | | | | | | | | | | | |
| 8 | 5 |  |  |  |  |  | X | X | | | | | | | X | | | | | | | | X | | | | | | | X | | | | | | | X | | | X | | | | X | X | X | X | X | | X | | X | X | | | | | | | | | | | | | | | | | | | | | |
| 9 | 11 |  |  |  |  |  | X | X | | | | | | | X | X | | X | | | | | X | | | | | | X | | X | X | X | | X | X | X | | | | | | | | | | | | | | | | | | X | X | X | X | X | X | | | | | | | | | | | | | | |
| 10 | 7 |  |  |  |  |  | X | X | | | | | | | X | | | | | | X | | X | | | | X | | | | | X | | | | | | | | | | | | | | | X | | | | X | | | | | | | | | | X | X | X | X | X | X | | | | | | | | |
| 11 | 6 |  |  |  |  |  | X | X | | | | | | | X | X | | | | | | | | | | | | | | | | | X | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | X | | | | | | |
| 12 | 8 |  |  |  |  |  | X | X | | | | X | | X | X | | | | | | X | | X | | | | X | | X | | | X | | | | | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | X | X | X | X | X |
|
CyberHarem/conte_di_cavour_kantaicollection
|
[
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] |
2023-08-23T09:54:56+00:00
|
{"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]}
|
2024-01-16T06:46:43+00:00
|
[] |
[] |
TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
|
Dataset of conte\_di\_cavour (Kantai Collection)
================================================
This is the dataset of conte\_di\_cavour (Kantai Collection), containing 362 images and their tags.
The core tags of this character are 'long\_hair, breasts, large\_breasts, grey\_hair, two\_side\_up, brown\_eyes', which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(huggingface organization).
List of Packages
----------------
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code
List of Clusters
----------------
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
### Table Version
|
[
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] |
[
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n",
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] |
[
44,
61,
5,
4
] |
[
"passage: TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.### Raw Text Version### Table Version"
] |
6adff61aac18312442b410e20140f05facf51648
|
# Dataset Card for Dataset Name
## Dataset Description
- **Homepage:** [BioCreative VII LitCovid Track](https://biocreative.bioinformatics.udel.edu/tasks/biocreative-vii/track-5/)
- **Paper:** [Multi-label classification for biomedical literature: an overview of the BioCreative VII LitCovid Track for COVID-19 literature topic annotations](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9428574/)
### Dataset Summary
Topic annotation in LitCovid is a multi-label document classification task that assigns one or more labels to each article. There are 7 topic labels used in LitCovid: Treatment, Diagnosis, Prevention, Mechanism, Transmission, Epidemic Forecasting, and Case Report. These topics have been demonstrated to be effective for information retrieval and have also been used in many downstream applications related to COVID-19.
## Dataset Structure
### Data Instances and Data Splits
- the training set contains 24,960 articles from LitCovid;
- the validation set contains 6,239 articles from LitCovid;
- the test set contains 2,500 articles from LitCovid;
### Data Fields
with the following fields retrieved from PubMed/LitCovid:
• pmid: PubMed Identifier
• journal: journal name
• title: article title
• abstract: article abstract
• keywords: author-provided keywords
• pub_type: article type, e.g., journal article
• authors: author names
• doi: Digital Object Identifier
• label: annotated topics in list format indicating absence or presence of labels in the order 'Treatment,Diagnosis,Prevention,Mechanism,Transmission,Epidemic Forecasting,Case Report'
• text: The text field is created as follows: '[Title]: ' + title + ' [Abstract]: ' + abstract + ' [Keywords]: ' + keywords
|
KushT/LitCovid_BioCreative
|
[
"task_categories:text-classification",
"size_categories:10K<n<100K",
"language:en",
"license:apache-2.0",
"region:us"
] |
2023-08-23T10:05:10+00:00
|
{"language": ["en"], "license": "apache-2.0", "size_categories": ["10K<n<100K"], "task_categories": ["text-classification"], "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "validation", "path": "data/validation-*"}, {"split": "test", "path": "data/test-*"}]}], "dataset_info": {"features": [{"name": "pmid", "dtype": "int64"}, {"name": "journal", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "abstract", "dtype": "string"}, {"name": "keywords", "dtype": "string"}, {"name": "pub_type", "dtype": "string"}, {"name": "authors", "dtype": "string"}, {"name": "doi", "dtype": "string"}, {"name": "label", "sequence": "int64"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 85014595, "num_examples": 24960}, {"name": "validation", "num_bytes": 9075648, "num_examples": 2500}, {"name": "test", "num_bytes": 21408810, "num_examples": 6239}], "download_size": 63244210, "dataset_size": 115499053}}
|
2023-08-23T10:19:09+00:00
|
[] |
[
"en"
] |
TAGS
#task_categories-text-classification #size_categories-10K<n<100K #language-English #license-apache-2.0 #region-us
|
# Dataset Card for Dataset Name
## Dataset Description
- Homepage: BioCreative VII LitCovid Track
- Paper: Multi-label classification for biomedical literature: an overview of the BioCreative VII LitCovid Track for COVID-19 literature topic annotations
### Dataset Summary
Topic annotation in LitCovid is a multi-label document classification task that assigns one or more labels to each article. There are 7 topic labels used in LitCovid: Treatment, Diagnosis, Prevention, Mechanism, Transmission, Epidemic Forecasting, and Case Report. These topics have been demonstrated to be effective for information retrieval and have also been used in many downstream applications related to COVID-19.
## Dataset Structure
### Data Instances and Data Splits
- the training set contains 24,960 articles from LitCovid;
- the validation set contains 6,239 articles from LitCovid;
- the test set contains 2,500 articles from LitCovid;
### Data Fields
with the following fields retrieved from PubMed/LitCovid:
• pmid: PubMed Identifier
• journal: journal name
• title: article title
• abstract: article abstract
• keywords: author-provided keywords
• pub_type: article type, e.g., journal article
• authors: author names
• doi: Digital Object Identifier
• label: annotated topics in list format indicating absence or presence of labels in the order 'Treatment,Diagnosis,Prevention,Mechanism,Transmission,Epidemic Forecasting,Case Report'
• text: The text field is created as follows: '[Title]: ' + title + ' [Abstract]: ' + abstract + ' [Keywords]: ' + keywords
|
[
"# Dataset Card for Dataset Name",
"## Dataset Description\n\n- Homepage: BioCreative VII LitCovid Track\n- Paper: Multi-label classification for biomedical literature: an overview of the BioCreative VII LitCovid Track for COVID-19 literature topic annotations",
"### Dataset Summary\n\nTopic annotation in LitCovid is a multi-label document classification task that assigns one or more labels to each article. There are 7 topic labels used in LitCovid: Treatment, Diagnosis, Prevention, Mechanism, Transmission, Epidemic Forecasting, and Case Report. These topics have been demonstrated to be effective for information retrieval and have also been used in many downstream applications related to COVID-19.",
"## Dataset Structure",
"### Data Instances and Data Splits\n\n- the training set contains 24,960 articles from LitCovid;\n- the validation set contains 6,239 articles from LitCovid;\n- the test set contains 2,500 articles from LitCovid;",
"### Data Fields\n\nwith the following fields retrieved from PubMed/LitCovid:\n• pmid: PubMed Identifier\n\n• journal: journal name\n\n• title: article title\n\n• abstract: article abstract\n\n• keywords: author-provided keywords\n\n• pub_type: article type, e.g., journal article\n\n• authors: author names\n\n• doi: Digital Object Identifier\n\n• label: annotated topics in list format indicating absence or presence of labels in the order 'Treatment,Diagnosis,Prevention,Mechanism,Transmission,Epidemic Forecasting,Case Report'\n\n• text: The text field is created as follows: '[Title]: ' + title + ' [Abstract]: ' + abstract + ' [Keywords]: ' + keywords"
] |
[
"TAGS\n#task_categories-text-classification #size_categories-10K<n<100K #language-English #license-apache-2.0 #region-us \n",
"# Dataset Card for Dataset Name",
"## Dataset Description\n\n- Homepage: BioCreative VII LitCovid Track\n- Paper: Multi-label classification for biomedical literature: an overview of the BioCreative VII LitCovid Track for COVID-19 literature topic annotations",
"### Dataset Summary\n\nTopic annotation in LitCovid is a multi-label document classification task that assigns one or more labels to each article. There are 7 topic labels used in LitCovid: Treatment, Diagnosis, Prevention, Mechanism, Transmission, Epidemic Forecasting, and Case Report. These topics have been demonstrated to be effective for information retrieval and have also been used in many downstream applications related to COVID-19.",
"## Dataset Structure",
"### Data Instances and Data Splits\n\n- the training set contains 24,960 articles from LitCovid;\n- the validation set contains 6,239 articles from LitCovid;\n- the test set contains 2,500 articles from LitCovid;",
"### Data Fields\n\nwith the following fields retrieved from PubMed/LitCovid:\n• pmid: PubMed Identifier\n\n• journal: journal name\n\n• title: article title\n\n• abstract: article abstract\n\n• keywords: author-provided keywords\n\n• pub_type: article type, e.g., journal article\n\n• authors: author names\n\n• doi: Digital Object Identifier\n\n• label: annotated topics in list format indicating absence or presence of labels in the order 'Treatment,Diagnosis,Prevention,Mechanism,Transmission,Epidemic Forecasting,Case Report'\n\n• text: The text field is created as follows: '[Title]: ' + title + ' [Abstract]: ' + abstract + ' [Keywords]: ' + keywords"
] |
[
41,
8,
49,
103,
6,
57,
180
] |
[
"passage: TAGS\n#task_categories-text-classification #size_categories-10K<n<100K #language-English #license-apache-2.0 #region-us \n# Dataset Card for Dataset Name## Dataset Description\n\n- Homepage: BioCreative VII LitCovid Track\n- Paper: Multi-label classification for biomedical literature: an overview of the BioCreative VII LitCovid Track for COVID-19 literature topic annotations### Dataset Summary\n\nTopic annotation in LitCovid is a multi-label document classification task that assigns one or more labels to each article. There are 7 topic labels used in LitCovid: Treatment, Diagnosis, Prevention, Mechanism, Transmission, Epidemic Forecasting, and Case Report. These topics have been demonstrated to be effective for information retrieval and have also been used in many downstream applications related to COVID-19.## Dataset Structure### Data Instances and Data Splits\n\n- the training set contains 24,960 articles from LitCovid;\n- the validation set contains 6,239 articles from LitCovid;\n- the test set contains 2,500 articles from LitCovid;### Data Fields\n\nwith the following fields retrieved from PubMed/LitCovid:\n• pmid: PubMed Identifier\n\n• journal: journal name\n\n• title: article title\n\n• abstract: article abstract\n\n• keywords: author-provided keywords\n\n• pub_type: article type, e.g., journal article\n\n• authors: author names\n\n• doi: Digital Object Identifier\n\n• label: annotated topics in list format indicating absence or presence of labels in the order 'Treatment,Diagnosis,Prevention,Mechanism,Transmission,Epidemic Forecasting,Case Report'\n\n• text: The text field is created as follows: '[Title]: ' + title + ' [Abstract]: ' + abstract + ' [Keywords]: ' + keywords"
] |
539e96be81d34f4856ee28f7c63749bb955016c2
|
# Dataset of south_dakota (Kantai Collection)
This is the dataset of south_dakota (Kantai Collection), containing 300 images and their tags.
The core tags of this character are `blue_hair, long_hair, multicolored_hair, red_hair, white_hair, breasts, large_breasts, bangs, headgear`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:-----------|:-------------------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 300 | 333.64 MiB | [Download](https://huggingface.co/datasets/CyberHarem/south_dakota_kantaicollection/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 300 | 203.10 MiB | [Download](https://huggingface.co/datasets/CyberHarem/south_dakota_kantaicollection/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 664 | 412.50 MiB | [Download](https://huggingface.co/datasets/CyberHarem/south_dakota_kantaicollection/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 300 | 298.72 MiB | [Download](https://huggingface.co/datasets/CyberHarem/south_dakota_kantaicollection/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 664 | 565.26 MiB | [Download](https://huggingface.co/datasets/CyberHarem/south_dakota_kantaicollection/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/south_dakota_kantaicollection',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 35 |  |  |  |  |  | 1girl, solo, cleavage, blue_bikini, sports_bikini, ponytail, navel, collarbone, visor_cap, smile, blush, cowboy_shot, hat, jacket, looking_at_viewer, star_(symbol) |
| 1 | 5 |  |  |  |  |  | black_bra, simple_background, star_(symbol), white_background, 1girl, black_panties, cowboy_shot, grey_eyes, looking_at_viewer, solo, underwear_only, ass, black_thighhighs, blush, twitter_username, alternate_costume, black_choker, cleavage, cropped_legs, frilled_bra, from_behind, garter_belt, lingerie, looking_back, navel, one-hour_drawing_challenge, ponytail, smile |
| 2 | 14 |  |  |  |  |  | 1girl, black_necktie, solo, star_(symbol), upper_body, crop_top, white_shirt, midriff, navel, simple_background, smile, blue_eyes, looking_at_viewer, white_background, open_jacket, black_gloves, elbow_gloves |
| 3 | 12 |  |  |  |  |  | 1girl, black_necktie, black_skirt, crop_top, midriff, navel, solo, star_(symbol), white_shirt, single_leg_pantyhose, uneven_legwear, dress_shirt, pencil_skirt, sleeveless, american_flag, holding, miniskirt, open_jacket, simple_background, white_background, smile, black_gloves, elbow_gloves, brown_jacket, closed_mouth, looking_at_viewer, single_thighhigh |
| 4 | 10 |  |  |  |  |  | detached_collar, fake_animal_ears, playboy_bunny, rabbit_ears, star_(symbol), black_necktie, simple_background, white_background, wrist_cuffs, 1girl, black_leotard, pantyhose, solo, strapless_leotard, smile, alternate_costume, looking_at_viewer, cowboy_shot, hand_on_hip, rabbit_tail |
| 5 | 24 |  |  |  |  |  | 1girl, yukata, obi, solo, floral_print, star_(symbol), blue_kimono, ponytail, print_kimono, official_alternate_costume, simple_background, candy_apple, holding, wide_sleeves, alternate_hairstyle, grey_eyes, long_sleeves, open_mouth, upper_body, cleavage, fox_mask |
| 6 | 9 |  |  |  |  |  | star_(symbol), simple_background, 1girl, competition_swimsuit, white_background, hands_on_hips, looking_at_viewer, solo_focus, alternate_costume, barefoot, blush, cowboy_shot, full_body, multiple_girls |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | solo | cleavage | blue_bikini | sports_bikini | ponytail | navel | collarbone | visor_cap | smile | blush | cowboy_shot | hat | jacket | looking_at_viewer | star_(symbol) | black_bra | simple_background | white_background | black_panties | grey_eyes | underwear_only | ass | black_thighhighs | twitter_username | alternate_costume | black_choker | cropped_legs | frilled_bra | from_behind | garter_belt | lingerie | looking_back | one-hour_drawing_challenge | black_necktie | upper_body | crop_top | white_shirt | midriff | blue_eyes | open_jacket | black_gloves | elbow_gloves | black_skirt | single_leg_pantyhose | uneven_legwear | dress_shirt | pencil_skirt | sleeveless | american_flag | holding | miniskirt | brown_jacket | closed_mouth | single_thighhigh | detached_collar | fake_animal_ears | playboy_bunny | rabbit_ears | wrist_cuffs | black_leotard | pantyhose | strapless_leotard | hand_on_hip | rabbit_tail | yukata | obi | floral_print | blue_kimono | print_kimono | official_alternate_costume | candy_apple | wide_sleeves | alternate_hairstyle | long_sleeves | open_mouth | fox_mask | competition_swimsuit | hands_on_hips | solo_focus | barefoot | full_body | multiple_girls |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:-------|:-----------|:--------------|:----------------|:-----------|:--------|:-------------|:------------|:--------|:--------|:--------------|:------|:---------|:--------------------|:----------------|:------------|:--------------------|:-------------------|:----------------|:------------|:-----------------|:------|:-------------------|:-------------------|:--------------------|:---------------|:---------------|:--------------|:--------------|:--------------|:-----------|:---------------|:-----------------------------|:----------------|:-------------|:-----------|:--------------|:----------|:------------|:--------------|:---------------|:---------------|:--------------|:-----------------------|:-----------------|:--------------|:---------------|:-------------|:----------------|:----------|:------------|:---------------|:---------------|:-------------------|:------------------|:-------------------|:----------------|:--------------|:--------------|:----------------|:------------|:--------------------|:--------------|:--------------|:---------|:------|:---------------|:--------------|:---------------|:-----------------------------|:--------------|:---------------|:----------------------|:---------------|:-------------|:-----------|:-----------------------|:----------------|:-------------|:-----------|:------------|:-----------------|
| 0 | 35 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 1 | 5 |  |  |  |  |  | X | X | X | | | X | X | | | X | X | X | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 2 | 14 |  |  |  |  |  | X | X | | | | | X | | | X | | | | | X | X | | X | X | | | | | | | | | | | | | | | | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 3 | 12 |  |  |  |  |  | X | X | | | | | X | | | X | | | | | X | X | | X | X | | | | | | | | | | | | | | | | X | | X | X | X | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 4 | 10 |  |  |  |  |  | X | X | | | | | | | | X | | X | | | X | X | | X | X | | | | | | | X | | | | | | | | | X | | | | | | | | | | | | | | | | | | | | | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | |
| 5 | 24 |  |  |  |  |  | X | X | X | | | X | | | | | | | | | | X | | X | | | X | | | | | | | | | | | | | | | X | | | | | | | | | | | | | | | X | | | | | | | | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | |
| 6 | 9 |  |  |  |  |  | X | | | | | | | | | | X | X | | | X | X | | X | X | | | | | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | X | X | X | X | X |
|
CyberHarem/south_dakota_kantaicollection
|
[
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] |
2023-08-23T10:35:36+00:00
|
{"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]}
|
2024-01-16T10:43:52+00:00
|
[] |
[] |
TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
|
Dataset of south\_dakota (Kantai Collection)
============================================
This is the dataset of south\_dakota (Kantai Collection), containing 300 images and their tags.
The core tags of this character are 'blue\_hair, long\_hair, multicolored\_hair, red\_hair, white\_hair, breasts, large\_breasts, bangs, headgear', which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(huggingface organization).
List of Packages
----------------
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code
List of Clusters
----------------
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
### Table Version
|
[
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] |
[
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n",
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] |
[
44,
61,
5,
4
] |
[
"passage: TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.### Raw Text Version### Table Version"
] |
e4b665757cd7df942351ab821d46cf8466608c39
|
# Dataset Card for "c4-en-validation-mini"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
Jackmin108/c4-en-validation-mini
|
[
"region:us"
] |
2023-08-23T10:40:22+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "validation", "path": "data/validation-*"}]}], "dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "timestamp", "dtype": "string"}, {"name": "url", "dtype": "string"}], "splits": [{"name": "validation", "num_bytes": 175483, "num_examples": 100}], "download_size": 116815, "dataset_size": 175483}}
|
2023-08-23T10:40:24+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "c4-en-validation-mini"
More Information needed
|
[
"# Dataset Card for \"c4-en-validation-mini\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"c4-en-validation-mini\"\n\nMore Information needed"
] |
[
6,
19
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"c4-en-validation-mini\"\n\nMore Information needed"
] |
6a801ba3b0ad6fb2434198f433242f961fa39dda
|
# Dataset Card for "llama-10k-annotations"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
loubnabnl/llama-10k-annotations
|
[
"region:us"
] |
2023-08-23T10:46:41+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "completion", "dtype": "string"}, {"name": "eval_prompt_header", "dtype": "string"}, {"name": "generation_config", "struct": [{"name": "do_sample", "dtype": "bool"}, {"name": "temperature", "dtype": "float64"}, {"name": "top_p", "dtype": "float64"}]}, {"name": "metadata", "struct": [{"name": "timestamp", "dtype": "string"}]}, {"name": "prompt", "dtype": "string"}, {"name": "review_model", "dtype": "string"}, {"name": "score", "dtype": "float64"}], "splits": [{"name": "train", "num_bytes": 51557354.2433, "num_examples": 9983}], "download_size": 14251796, "dataset_size": 51557354.2433}}
|
2023-08-23T10:46:43+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "llama-10k-annotations"
More Information needed
|
[
"# Dataset Card for \"llama-10k-annotations\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"llama-10k-annotations\"\n\nMore Information needed"
] |
[
6,
18
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"llama-10k-annotations\"\n\nMore Information needed"
] |
f5f1dd11c60de68cdbe2a7de65925617f40e080a
|
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<p>We have read hundreds of reviews and done extensive research on <a href="https://sites.google.com/view/rangi-toenail-fungus/home">Rangii Toenail Fungus</a> to give you one of the most accurate reviews available. With that said, let's begin our review. Continue reading to learn more about Rangii Toenail Fungus Skincare!</p>
<div class="separator" style="clear: both; text-align: center;"><a style="margin-left: 1em; margin-right: 1em;" href="https://www.healthsupplement24x7.com/get-rangii" target="_blank"><img src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEitIziNtWQpAKjNWDE451t4dEBgkE2PcLoKPwcIwrXusPUIhdUQ_xGMO12k8IQCB37nYcm46xP8wksSElWfEnHE6iZRkUBRb5NA4SKfPY_iKSS-gSz4RmZUSd3fYzLjKNpBP9DAuHK78neAJACQt_mypiZy89v6ERdhRyPg-1wuIOAhlEa9NR_PNa55ZAE/w640-h290/Rangii%20Toenail%20Fungus%202.png" alt="" width="640" height="290" border="0" data-original-height="702" data-original-width="1550" /></a></div>
<h2>Rangii Toenail Fungus Oil: What Is It?</h2>
<p>A natural oil supplement called <a href="https://devfolio.co/@RangiiToenail">Rangii Toenail Fungus</a> aids in preventing foot odor, dry skin, toenail fungus, and yellow, brittle nails. Its potent composition penetrates the skin and purges microorganisms from your body. A doctor-formulated mixture called Rangii Toenail Fungus for toenail fungus contains herbal extracts from natural plants.</p>
<p>Effective substances that deeply cleanse your skin of hazardous contaminants include clove bud oil and aloe vera gel extract.You can achieve dramatic results with this oil that will last a lifetime. Antioxidants and anti-inflammatories are also present, which soothe and calm sore nails and scars.</p>
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<h2>What Does the Rangii Toenail Fungus Oil Do for Toenail Fungus?</h2>
<p>With the aid of its all-natural ingredients, <a href="https://rangii-toenail.clubeo.com/calendar/2023/08/23/rangii-toenail-fungus-new-toenail-fungus-remover-all-you-need-to-know-about-rangii-offer?_ga=2.150821686.1658347636.1692764482-2009335326.1692764482">Rangii Toenail Fungus</a> Oil aids in the treatment of fungal diseases on the skin and nails. Nail and skin health have improved as a result of the Rangii Toenail Fungus components utilized in the product.</p>
<p>By removing its source of life, the oil affects the fungus under your fingernails or toenails. It targets them and eliminates them from the root, preventing any additional fungal development. Additionally, it eliminates moisture from your skin and nails to stop the spread of the prevalent skin and nail fungus.</p>
<p>It has long been available on the market and has assisted in the treatment of fungus infections. Numerous <a href="https://rangii-toenail.clubeo.com/page/rangii-toenail-fungus-1-clinical-toenail-fungus-remover-fda-approved-or-hoax.html">Rangii</a> Toenail Fungus testimonials on their official website provide concrete evidence to support the claims made by the product's creators.</p>
<h2>What Ingredients makeup Rangii Toenail Fungus Oil Drop?</h2>
<p>It's important to carefully review <a href="https://rangii-toenail.clubeo.com/page/rangii-toenail-fungus-1-inner-toenail-fungus-remover-formula-stops-the-funguss-mutating-and-growing.html">Rangii Toenail Fungus</a>' ingredients before deciding whether to purchase it for your damaged and brittle toenails.</p>
<p><strong>• Almond Oil:</strong> It offers significant advantages to your heart, skin, and hair. Vitamin E, which is an essential ingredient for keeping your skin supple and well-hydrated, is included in the oil. Using almond oil to reduce cellulitis and stretch marks is also very common. </p>
<p><strong>• Flaxseed Oil:</strong> It contains a lot of Omega 3 fatty acids, a miraculous nutrient that helps to support heart and brain health, reduce inflammation, and maintain healthy nails and teeth. For skin that is supple and well-hydrated, flaxseed oil is a fantastic serum. You can seem young and healthy thanks to its anti-aging qualities. After removing fungal growth, the oil boosts nails' resistance to infection and helps them grow stronger.</p>
<p><strong>• Essential Tea Tree Oil:</strong> It can aid in the speedy healing of wounds and the prevention of toenail fungus. Whether used alone or in conjunction with other components, the oil has had impressive results.</p>
<p><strong>• Aloe Vera:</strong> Antioxidants like polyphenols found in aloe vera help prevent the formation of germs and fungi. Additionally, it has antibacterial, antifungal, and antiseptic qualities. By having a calming effect, the extracts also aid with skin inflammation and itching.</p>
<p><strong>• Lemongrass Oil:</strong> lt is an excellent anti-inflammatory because of its antioxidant, anti-fungal, and antibacterial qualities. It oil is an excellent treatment for brittle nails, toenail fungus, and skin infections due to its antioxidant, antifungal, and antibacterial qualities. </p>
<p><strong>• Clove Bud Oil:</strong> It helps to eliminate fungi and kill bacteria due to antibacterial characteristics. Clove oil penetrates and kills the bacteria at the source. Additionally, it helps with other skin issues like chronic itching and teeth erosion.</p>
<p><strong>• Manuka:</strong> Its anti-inflammatory qualities aid in reducing pain and inflammation. It is a perfect ingredient in <a href="https://www.eventcreate.com/e/rangii-toenail-fungus">Rangii Toenail Fungus</a> because it also has antimicrobial qualities. Due to its high concentration of MGO and hydrogen peroxide, manuka honey possesses antibacterial characteristics. </p>
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<div class="separator" style="clear: both; text-align: center;"><a style="margin-left: 1em; margin-right: 1em;" href="https://www.healthsupplement24x7.com/get-rangii" target="_blank"><img src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjjGrgntm3-oqfh6J-0qF7uAh-5Ew0LRdBEwZ61X60GK_bzKWXpNv_rYIoJSM2701vWiZqaIydlPrGKHwtiy0fveBhGOzpWxRbWNPjSQ-PS0_S3J7iO_QLj16cGsVBI8Ue6zakP-FldcxKhMGrhAd2RVoVYMfv5ZIk_Z-NUynWLLk_4peRGTvnzx86KkLc/w640-h342/Rangii%20Toenail%20Fungus%204.png" alt="" width="640" height="342" border="0" data-original-height="749" data-original-width="1400" /></a></div>
<h2>Rangii Toenail Fungus Oil – Benefits, Precautions</h2>
<p>Some advantages of the <a href="https://www.sympla.com.br/evento/rangii-toenail-fungus-2023-new-toenail-fungus-remover-is-rangii-right-choice/2131904">Rangii Toenail Fungus</a> nail health support formula include the following:</p>
<p>►► Your nail and skin immunity is improved by the nail health support product.<br /> ►► The mixture encourages your nails to grow again in a healthy way.<br /> ►► Provides your nails a white hue and gets rid of the yellow stains on them.<br /> ►► Stops the fungus's mutating and growing.<br /> ►► Keeps your cuticles and nails well-hydrated and moisturized.<br /> ►► It is also beneficial in maintaining healthy nails and enhancing general nail and skin health.<br /> ►► Because of its high nutritional value, the nail improvement solution can help revitalize your entire body.<br /> ►► This nail health mixture also aids in the fight against numerous hazardous bacteria, detoxifying your skin as well as the overall body.</p>
<p><a href="https://www.townscript.com/e/rangii-toenail-fungus-new-2023-does-it-work-or-just-scam-321411">Rangii Toenail Fungus</a> is appropriate for everyone over the age of 18 and offers its consumers a variety of health advantages. It is not recommended to use by a pregnant, lactating, or nursing woman.</p>
<h2>Rangii Toenail Fungus Side Effects – Is It Safe?</h2>
<p>Rangii Toenail Fungus only uses natural products that have no negative side effects. It is manufactured in an FDA-approved facility using strict and accurate GMP standards. This product contains no stimulants or hazardous toxins.</p>
<p>The solution is safe for everyone to use, according to the manufacturer, and will not itch or irritate your cuticles or nails when applied to them. This nail fungus eliminator's components are likewise safe for human usage; there is no risk of side effects from using this solution.</p>
<h2>How to Apply Rangii Toenail Fungus Oil? What Thinks Should Be Remember While Applying It?</h2>
<p>The oil supplement <a href="https://rangii-update.clubeo.com/calendar/2023/08/23/rangii-toenail-fungus-dr-warning-is-rangii-worth-buying-what-do-customers-say">Rangii Toenail Fungus</a> is a simple-to-use product. It must be applied four times each day (twice in the morning and twice in the afternoon). You may apply the oil to your affected area effectively with the help of the contained brush applicator that is included with it. To get the best benefits, shake the supplement well before use and use it every day.</p>
<p>Here are a few additional steps you may take to prevent toenail fungus on your feet in addition to using the solution.</p>
<p>►► Avoid barefooted walking in public areas.<br /> ►► Never swap socks, sheets, or shoes with someone who has an infected toenail.<br /> ►► Avoid sweaty feet by keeping your feet dry.<br /> ►► Healthy feet and skin depend on hygiene, so wash them properly each day</p>
<h2 style="text-align: center;"><a href="https://www.healthsupplement24x7.com/get-rangii" target="_blank"><span style="background-color: red; color: #f1c232;">(THE BIG BILLION DAYS SALE) - THE MOST SELLING "RANGII TOENAIL FUNGUS" IS HERE ORDER NOW</span></a></h2>
<div class="separator" style="clear: both; text-align: center;"><a style="margin-left: 1em; margin-right: 1em;" href="https://www.healthsupplement24x7.com/get-rangii" target="_blank"><img src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEilNX5zHBzsDXHZp3KuJKLSQqqqzr6k5WIh7oz_SM7mWe-3btgTOZxh1jgz_o1Rq5YPVd-9pIXUfhHXHU63duiHWw2eC3eCZrZfGQPZUd6c77MOjPECoQXC9GljNDnT7aBIIRu31YeRSNzBd8_fDe3_QtEztwBlpXf4afSecqKIl2Q0bftW7oKF8X-HovM/w640-h462/Screenshot%20(1229).png" alt="" width="640" height="462" border="0" data-original-height="731" data-original-width="1011" /></a></div>
<h2>What’s the Price of Rangii Toenail Fungus Oil?</h2>
<p><a href="https://sketchfab.com/3d-models/rangii-toenail-fungus-new-2023-reviews-f9ec56326c874ea49d70e13930663e6a">Rangii Toenail Fungus</a> come in bottles that are 15 ml/0.5 oz in size, which is enough for one month's worth of use. Currently, Rangii Toenail Fungus comes in three separate packages, with the following pricing structure:</p>
<p>o One bottle of <a href="https://www.ivoox.com/rangii-toenail-fungus-new-2023-does-it-work-audios-mp3_rf_114763403_1.html">Rangii</a> Toenail Fungus Each bottle Costs $69 plus free shipping.<br /> o Three bottles of Rangii Toenail Fungus Each bottle costs $49 plus free shipping.<br /> o 180-day supply: For six months' Each bottle costs $39 plus free shipping.</p>
<h2>Rangii Toenail Fungus Reviews: Conclusive Ending</h2>
<p><a href="https://sites.google.com/view/rangii/home">Rangii Toenail Fungus</a> is a potent remedy that could help you permanently get rid of toenail fungus. The mixture contains premium ingredients that are said to possess qualities that can aid in the removal of fungus from your entire body. Every element in Rangii Toenail Fungus was selected after careful consideration of the ingredient's health-related attributes.</p>
<p>The formula for <a href="https://pdfhost.io/v/A5DB1UWHb_Rangii_Toenail_Fungus_Helps_To_Rejuvenates_And_Revitalize_Toe_Skin_And_Nail_Health">Rangii Toenail Fungus</a> was designed by the company behind it to be simple to use and apply to your cuticles and nails. You are advised to use <a href="https://rangiitoenailfungus.bandcamp.com/track/rangii-toenail-fungus-new-2023-does-it-work-or-just-scam">Rangii Toenail Fungus</a> four times every day, twice in the morning and twice in the afternoon, to get the most results from the formula, according to the official website. You can benefit from Rangii Toenail Fungus' anti-fungal oil in this circumstance.</p>
<h3>READ MORE ON OFFICIAL WEBSITE:</h3>
<p><a href="https://rangii-toenail.clubeo.com/calendar/2023/08/23/rangii-toenail-fungus-new-toenail-fungus-remover-all-you-need-to-know-about-rangii-offer?_ga=2.150821686.1658347636.1692764482-2009335326.1692764482">https://rangii-toenail.clubeo.com/calendar/2023/08/23/rangii-toenail-fungus-new-toenail-fungus-remover-all-you-need-to-know-about-rangii-offer</a></p>
<p><a href="https://www.ivoox.com/rangii-toenail-fungus-new-2023-does-it-work-audios-mp3_rf_114763403_1.html">https://www.ivoox.com/rangii-toenail-fungus-new-2023-does-it-work-audios-mp3_rf_114763403_1.html</a></p>
<p><a href="https://pdfhost.io/v/vw6Jc~oiy_Peoples_Keto_Gummies_New_Keto_ACV_Gummies_All_You_Need_To_Know_About_Peoples_Keto_Gummies_Offer">https://pdfhost.io/v/vw6Jc~oiy_Peoples_Keto_Gummies_New_Keto_ACV_Gummies_All_You_Need_To_Know_About_Peoples_Keto_Gummies_Offer</a></p>
<p><a href="https://sketchfab.com/3d-models/rangii-toenail-fungus-new-2023-reviews-f9ec56326c874ea49d70e13930663e6a">https://sketchfab.com/3d-models/rangii-toenail-fungus-new-2023-reviews-f9ec56326c874ea49d70e13930663e6a</a></p>
<p><a href="https://sites.google.com/view/rangiitoenailfungus/home">https://sites.google.com/view/rangiitoenailfungus/home</a></p>
<p><a href="https://rangiitoenailfungus.bandcamp.com/track/rangii-toenail-fungus-new-2023-does-it-work-or-just-scam">https://rangiitoenailfungus.bandcamp.com/track/rangii-toenail-fungus-new-2023-does-it-work-or-just-scam</a></p>
<p><a href="https://www.townscript.com/e/rangii-toenail-fungus-new-2023-does-it-work-or-just-scam-321411">https://www.townscript.com/e/rangii-toenail-fungus-new-2023-does-it-work-or-just-scam-321411</a></p>
<p><a href="https://rangii-toenail.clubeo.com/page/rangii-toenail-fungus-1-clinical-toenail-fungus-remover-fda-approved-or-hoax.html">https://rangii-toenail.clubeo.com/page/rangii-toenail-fungus-1-clinical-toenail-fungus-remover-fda-approved-or-hoax.html</a></p>
<p><a href="https://devfolio.co/@RangiiToenail">https://devfolio.co/@RangiiToenail</a></p>
<p><a href="https://www.eventcreate.com/e/rangii-toenail-fungus">https://www.eventcreate.com/e/rangii-toenail-fungus</a></p>
<p><a href="https://sites.google.com/view/rangii/home">https://sites.google.com/view/rangii/home</a></p>
<p><a href="https://rangii-update.clubeo.com/calendar/2023/08/23/rangii-toenail-fungus-dr-warning-is-rangii-worth-buying-what-do-customers-say">https://rangii-update.clubeo.com/calendar/2023/08/23/rangii-toenail-fungus-dr-warning-is-rangii-worth-buying-what-do-customers-say</a></p>
<p><a href="https://sites.google.com/view/rangi-toenail-fungus/home">https://sites.google.com/view/rangi-toenail-fungus/home</a></p>
<p><a href="https://www.sympla.com.br/evento/rangii-toenail-fungus-2023-new-toenail-fungus-remover-is-rangii-right-choice/2131904">https://www.sympla.com.br/evento/rangii-toenail-fungus-2023-new-toenail-fungus-remover-is-rangii-right-choice/2131904</a></p>
|
RangiiReviews/Rangii-Toenail-Fungus
|
[
"region:us"
] |
2023-08-23T10:59:58+00:00
|
{}
|
2023-08-23T11:05:45+00:00
|
[] |
[] |
TAGS
#region-us
|
<h2 style="text-align: center;"><a href="URL style="color: #ff0000;">Rangii Toenail Fungus Oil Toenail Fungus – Shocking Customer Compliant – In This Review!</span></a></h2>
<p><strong>Rangii Toenail Fungus (NEW 2023!) Reviews:</strong> So, after exhaustively searching for a solution, we came across <a href="URL Toenail Fungus</a>, a natural toenail support oil. It is one such product that is adored by many people all over the world due to its effectiveness and efficiency. When it comes to treating toenail infections, one medicine can turn the tide.</p>
<p style="background-color: white; box-sizing: border-box; color: black; font-family: 'Times New Roman'; font-size: medium; font-style: normal; font-variant-caps: normal; font-variant-ligatures: normal; font-weight: 400; letter-spacing: normal; margin: 0px 0px 10px; padding: 0px; text-align: left; text-decoration-color: initial; text-decoration-style: initial; text-decoration-thickness: initial; text-indent: 0px; text-transform: none; white-space: normal; word-spacing: 0px;"><strong style="box-sizing: border-box; font-style: normal; font-weight: bold;"> <span style="box-sizing: border-box; color: #993300;">Product Name -</span> <span style="box-sizing: border-box; color: red;">{Rangii} (Rangii Toenail Fungus)</span><br style="box-sizing: border-box;" /> <span style="box-sizing: border-box; color: green;">Benefits - Rangii Stops the fungus's mutating and growing!</span><br style="box-sizing: border-box;" /> <span style="box-sizing: border-box; color: olive;">Category -</span></strong><strong style="box-sizing: border-box; font-style: normal; font-weight: bold;"> Toenail Fungus Oil </strong><strong style="box-sizing: border-box; font-style: normal; font-weight: bold;"><br style="box-sizing: border-box;" /> <span style="box-sizing: border-box; color: purple;">Availability –</span> Online<br style="box-sizing: border-box;" /> <span style="box-sizing: border-box; color: navy;">Rating: -</span> <span style="box-sizing: border-box; color: red;">5.0/5.0</span> ⭐⭐⭐⭐⭐</strong></p>
<h2 style="background-color: white; box-sizing: border-box; color: black; font-family: Roboto, Helvetica, Arial, sans-serif; font-size: 1.5em; font-style: normal; font-variant-caps: normal; font-variant-ligatures: normal; font-weight: bold; letter-spacing: normal; line-height: 1.1; margin: 10px 0px; padding: 0px; text-align: start; text-decoration-color: initial; text-decoration-style: initial; text-decoration-thickness: initial; text-indent: 0px; text-transform: none; white-space: normal; word-spacing: 0px;"><a href="URL target="_blank"><span style="background-color: red; box-sizing: border-box;"><strong style="box-sizing: border-box; font-style: normal; font-weight: bold;"><span style="box-sizing: border-box; color: #ffcc00;">Click Here To Visit – “OFFICIAL WEBSITE”</span></strong></span></a></h2>
<h2 style="background-color: white; box-sizing: border-box; color: black; font-family: Roboto, Helvetica, Arial, sans-serif; font-size: 1.5em; font-style: normal; font-variant-caps: normal; font-variant-ligatures: normal; font-weight: bold; letter-spacing: normal; line-height: 1.1; margin: 10px 0px; padding: 0px; text-align: start; text-decoration-color: initial; text-decoration-style: initial; text-decoration-thickness: initial; text-indent: 0px; text-transform: none; white-space: normal; word-spacing: 0px;"><a href="URL target="_blank"><span style="background-color: red; box-sizing: border-box;"><strong style="box-sizing: border-box; font-style: normal; font-weight: bold;"><span style="box-sizing: border-box; color: #ffcc00;">Click Here To Visit – “OFFICIAL WEBSITE”</span></strong></span></a></h2>
<h2 style="background-color: white; box-sizing: border-box; color: black; font-family: Roboto, Helvetica, Arial, sans-serif; font-size: 1.5em; font-style: normal; font-variant-caps: normal; font-variant-ligatures: normal; font-weight: bold; letter-spacing: normal; line-height: 1.1; margin: 10px 0px; padding: 0px; text-align: start; text-decoration-color: initial; text-decoration-style: initial; text-decoration-thickness: initial; text-indent: 0px; text-transform: none; white-space: normal; word-spacing: 0px;"><a href="URL target="_blank"><span style="background-color: red; box-sizing: border-box;"><strong style="box-sizing: border-box; font-style: normal; font-weight: bold;"><span style="box-sizing: border-box; color: #ffcc00;">Click Here To Visit – “OFFICIAL WEBSITE”</span></strong></span></a></h2>
<p>We have read hundreds of reviews and done extensive research on <a href="URL Toenail Fungus</a> to give you one of the most accurate reviews available. With that said, let's begin our review. Continue reading to learn more about Rangii Toenail Fungus Skincare!</p>
<div class="separator" style="clear: both; text-align: center;"><a style="margin-left: 1em; margin-right: 1em;" href="URL target="_blank"><img src="URL alt="" width="640" height="290" border="0" data-original-height="702" data-original-width="1550" /></a></div>
<h2>Rangii Toenail Fungus Oil: What Is It?</h2>
<p>A natural oil supplement called <a href="URL Toenail Fungus</a> aids in preventing foot odor, dry skin, toenail fungus, and yellow, brittle nails. Its potent composition penetrates the skin and purges microorganisms from your body. A doctor-formulated mixture called Rangii Toenail Fungus for toenail fungus contains herbal extracts from natural plants.</p>
<p>Effective substances that deeply cleanse your skin of hazardous contaminants include clove bud oil and aloe vera gel extract.You can achieve dramatic results with this oil that will last a lifetime. Antioxidants and anti-inflammatories are also present, which soothe and calm sore nails and scars.</p>
<h2 style="text-align: center;"><a href="URL target="_blank"><span style="background-color: red; color: #f1c232;">(THE BIG BILLION DAYS SALE) - THE MOST SELLING "RANGII TOENAIL FUNGUS" IS HERE ORDER NOW</span></a></h2>
<h2>What Does the Rangii Toenail Fungus Oil Do for Toenail Fungus?</h2>
<p>With the aid of its all-natural ingredients, <a href="URL Toenail Fungus</a> Oil aids in the treatment of fungal diseases on the skin and nails. Nail and skin health have improved as a result of the Rangii Toenail Fungus components utilized in the product.</p>
<p>By removing its source of life, the oil affects the fungus under your fingernails or toenails. It targets them and eliminates them from the root, preventing any additional fungal development. Additionally, it eliminates moisture from your skin and nails to stop the spread of the prevalent skin and nail fungus.</p>
<p>It has long been available on the market and has assisted in the treatment of fungus infections. Numerous <a href="URL Toenail Fungus testimonials on their official website provide concrete evidence to support the claims made by the product's creators.</p>
<h2>What Ingredients makeup Rangii Toenail Fungus Oil Drop?</h2>
<p>It's important to carefully review <a href="URL Toenail Fungus</a>' ingredients before deciding whether to purchase it for your damaged and brittle toenails.</p>
<p><strong>• Almond Oil:</strong> It offers significant advantages to your heart, skin, and hair. Vitamin E, which is an essential ingredient for keeping your skin supple and well-hydrated, is included in the oil. Using almond oil to reduce cellulitis and stretch marks is also very common. </p>
<p><strong>• Flaxseed Oil:</strong> It contains a lot of Omega 3 fatty acids, a miraculous nutrient that helps to support heart and brain health, reduce inflammation, and maintain healthy nails and teeth. For skin that is supple and well-hydrated, flaxseed oil is a fantastic serum. You can seem young and healthy thanks to its anti-aging qualities. After removing fungal growth, the oil boosts nails' resistance to infection and helps them grow stronger.</p>
<p><strong>• Essential Tea Tree Oil:</strong> It can aid in the speedy healing of wounds and the prevention of toenail fungus. Whether used alone or in conjunction with other components, the oil has had impressive results.</p>
<p><strong>• Aloe Vera:</strong> Antioxidants like polyphenols found in aloe vera help prevent the formation of germs and fungi. Additionally, it has antibacterial, antifungal, and antiseptic qualities. By having a calming effect, the extracts also aid with skin inflammation and itching.</p>
<p><strong>• Lemongrass Oil:</strong> lt is an excellent anti-inflammatory because of its antioxidant, anti-fungal, and antibacterial qualities. It oil is an excellent treatment for brittle nails, toenail fungus, and skin infections due to its antioxidant, antifungal, and antibacterial qualities. </p>
<p><strong>• Clove Bud Oil:</strong> It helps to eliminate fungi and kill bacteria due to antibacterial characteristics. Clove oil penetrates and kills the bacteria at the source. Additionally, it helps with other skin issues like chronic itching and teeth erosion.</p>
<p><strong>• Manuka:</strong> Its anti-inflammatory qualities aid in reducing pain and inflammation. It is a perfect ingredient in <a href="URL Toenail Fungus</a> because it also has antimicrobial qualities. Due to its high concentration of MGO and hydrogen peroxide, manuka honey possesses antibacterial characteristics. </p>
<h2 style="text-align: center;"><a href="URL target="_blank"><span style="background-color: red; color: #f1c232;">(THE BIG BILLION DAYS SALE) - THE MOST SELLING "RANGII TOENAIL FUNGUS" IS HERE ORDER NOW</span></a></h2>
<div class="separator" style="clear: both; text-align: center;"><a style="margin-left: 1em; margin-right: 1em;" href="URL target="_blank"><img src="URL alt="" width="640" height="342" border="0" data-original-height="749" data-original-width="1400" /></a></div>
<h2>Rangii Toenail Fungus Oil – Benefits, Precautions</h2>
<p>Some advantages of the <a href="URL Toenail Fungus</a> nail health support formula include the following:</p>
<p>►► Your nail and skin immunity is improved by the nail health support product.<br /> ►► The mixture encourages your nails to grow again in a healthy way.<br /> ►► Provides your nails a white hue and gets rid of the yellow stains on them.<br /> ►► Stops the fungus's mutating and growing.<br /> ►► Keeps your cuticles and nails well-hydrated and moisturized.<br /> ►► It is also beneficial in maintaining healthy nails and enhancing general nail and skin health.<br /> ►► Because of its high nutritional value, the nail improvement solution can help revitalize your entire body.<br /> ►► This nail health mixture also aids in the fight against numerous hazardous bacteria, detoxifying your skin as well as the overall body.</p>
<p><a href="URL Toenail Fungus</a> is appropriate for everyone over the age of 18 and offers its consumers a variety of health advantages. It is not recommended to use by a pregnant, lactating, or nursing woman.</p>
<h2>Rangii Toenail Fungus Side Effects – Is It Safe?</h2>
<p>Rangii Toenail Fungus only uses natural products that have no negative side effects. It is manufactured in an FDA-approved facility using strict and accurate GMP standards. This product contains no stimulants or hazardous toxins.</p>
<p>The solution is safe for everyone to use, according to the manufacturer, and will not itch or irritate your cuticles or nails when applied to them. This nail fungus eliminator's components are likewise safe for human usage; there is no risk of side effects from using this solution.</p>
<h2>How to Apply Rangii Toenail Fungus Oil? What Thinks Should Be Remember While Applying It?</h2>
<p>The oil supplement <a href="URL Toenail Fungus</a> is a simple-to-use product. It must be applied four times each day (twice in the morning and twice in the afternoon). You may apply the oil to your affected area effectively with the help of the contained brush applicator that is included with it. To get the best benefits, shake the supplement well before use and use it every day.</p>
<p>Here are a few additional steps you may take to prevent toenail fungus on your feet in addition to using the solution.</p>
<p>►► Avoid barefooted walking in public areas.<br /> ►► Never swap socks, sheets, or shoes with someone who has an infected toenail.<br /> ►► Avoid sweaty feet by keeping your feet dry.<br /> ►► Healthy feet and skin depend on hygiene, so wash them properly each day</p>
<h2 style="text-align: center;"><a href="URL target="_blank"><span style="background-color: red; color: #f1c232;">(THE BIG BILLION DAYS SALE) - THE MOST SELLING "RANGII TOENAIL FUNGUS" IS HERE ORDER NOW</span></a></h2>
<div class="separator" style="clear: both; text-align: center;"><a style="margin-left: 1em; margin-right: 1em;" href="URL target="_blank"><img src="URL alt="" width="640" height="462" border="0" data-original-height="731" data-original-width="1011" /></a></div>
<h2>What’s the Price of Rangii Toenail Fungus Oil?</h2>
<p><a href="URL Toenail Fungus</a> come in bottles that are 15 ml/0.5 oz in size, which is enough for one month's worth of use. Currently, Rangii Toenail Fungus comes in three separate packages, with the following pricing structure:</p>
<p>o One bottle of <a href="URL Toenail Fungus Each bottle Costs $69 plus free shipping.<br /> o Three bottles of Rangii Toenail Fungus Each bottle costs $49 plus free shipping.<br /> o 180-day supply: For six months' Each bottle costs $39 plus free shipping.</p>
<h2>Rangii Toenail Fungus Reviews: Conclusive Ending</h2>
<p><a href="URL Toenail Fungus</a> is a potent remedy that could help you permanently get rid of toenail fungus. The mixture contains premium ingredients that are said to possess qualities that can aid in the removal of fungus from your entire body. Every element in Rangii Toenail Fungus was selected after careful consideration of the ingredient's health-related attributes.</p>
<p>The formula for <a href="URL Toenail Fungus</a> was designed by the company behind it to be simple to use and apply to your cuticles and nails. You are advised to use <a href="URL Toenail Fungus</a> four times every day, twice in the morning and twice in the afternoon, to get the most results from the formula, according to the official website. You can benefit from Rangii Toenail Fungus' anti-fungal oil in this circumstance.</p>
<h3>READ MORE ON OFFICIAL WEBSITE:</h3>
<p><a href="URL/URL
<p><a href="URL/URL
<p><a href="URL/URL
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<p><a href="URL/URL
|
[] |
[
"TAGS\n#region-us \n"
] |
[
6
] |
[
"passage: TAGS\n#region-us \n"
] |
1d6a59238fc26bdfb15cd5beb586fa52c336345c
|
## Dataset Description
- **Scottish grant scheme descriptions**
### Dataset Summary
This dataset represents raw text that has been converted into vector space. The text data has been extracted from PDF files that describe existing
grant schemes in Scotland for woodland, riverwood, and hedge preservation and creation.
### Languages
English
|
afish3/all_grants
|
[
"task_categories:question-answering",
"language:en",
"finance",
"region:us"
] |
2023-08-23T11:06:29+00:00
|
{"language": ["en"], "task_categories": ["question-answering"], "tags": ["finance"]}
|
2023-08-23T13:39:47+00:00
|
[] |
[
"en"
] |
TAGS
#task_categories-question-answering #language-English #finance #region-us
|
## Dataset Description
- Scottish grant scheme descriptions
### Dataset Summary
This dataset represents raw text that has been converted into vector space. The text data has been extracted from PDF files that describe existing
grant schemes in Scotland for woodland, riverwood, and hedge preservation and creation.
### Languages
English
|
[
"## Dataset Description\n\n- Scottish grant scheme descriptions",
"### Dataset Summary\n\nThis dataset represents raw text that has been converted into vector space. The text data has been extracted from PDF files that describe existing\ngrant schemes in Scotland for woodland, riverwood, and hedge preservation and creation.",
"### Languages\n\nEnglish"
] |
[
"TAGS\n#task_categories-question-answering #language-English #finance #region-us \n",
"## Dataset Description\n\n- Scottish grant scheme descriptions",
"### Dataset Summary\n\nThis dataset represents raw text that has been converted into vector space. The text data has been extracted from PDF files that describe existing\ngrant schemes in Scotland for woodland, riverwood, and hedge preservation and creation.",
"### Languages\n\nEnglish"
] |
[
25,
11,
56,
5
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[
"passage: TAGS\n#task_categories-question-answering #language-English #finance #region-us \n## Dataset Description\n\n- Scottish grant scheme descriptions### Dataset Summary\n\nThis dataset represents raw text that has been converted into vector space. The text data has been extracted from PDF files that describe existing\ngrant schemes in Scotland for woodland, riverwood, and hedge preservation and creation.### Languages\n\nEnglish"
] |
9da38e58981164be65bc60409c381971dd859ad2
|
# Dataset of i_13/伊13 (Kantai Collection)
This is the dataset of i_13/伊13 (Kantai Collection), containing 357 images and their tags.
The core tags of this character are `short_hair, black_hair, hair_between_eyes, brown_eyes, asymmetrical_hair, hat, headphones, framed_breasts, breasts`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:-----------|:-----------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 357 | 300.91 MiB | [Download](https://huggingface.co/datasets/CyberHarem/i_13_kantaicollection/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 357 | 207.05 MiB | [Download](https://huggingface.co/datasets/CyberHarem/i_13_kantaicollection/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 827 | 440.45 MiB | [Download](https://huggingface.co/datasets/CyberHarem/i_13_kantaicollection/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 357 | 277.69 MiB | [Download](https://huggingface.co/datasets/CyberHarem/i_13_kantaicollection/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 827 | 556.43 MiB | [Download](https://huggingface.co/datasets/CyberHarem/i_13_kantaicollection/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/i_13_kantaicollection',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 25 |  |  |  |  |  | 1girl, black_one-piece_swimsuit, school_swimsuit, solo, breast_cutout, white_sailor_collar, looking_at_viewer, simple_background, brown_neckerchief, cowboy_shot, white_background, one-hour_drawing_challenge, twitter_username |
| 1 | 8 |  |  |  |  |  | 1girl, looking_at_viewer, one-piece_swimsuit, partially_fingerless_gloves, sailor_collar, school_swimsuit, single_glove, solo, cowboy_shot, open_mouth, twitter_username, blush |
| 2 | 5 |  |  |  |  |  | 1girl, looking_at_viewer, sailor_collar, school_swimsuit, single_glove, solo, black_one-piece_swimsuit, neckerchief, partially_fingerless_gloves, character_name, sitting, open_mouth, smile |
| 3 | 7 |  |  |  |  |  | 1girl, looking_at_viewer, one-piece_swimsuit, sailor_collar, school_swimsuit, solo, white_background, simple_background, open_mouth, blush, smile |
| 4 | 11 |  |  |  |  |  | one-piece_swimsuit, sailor_collar, school_swimsuit, 2girls, looking_at_viewer, smile, blush, solo_focus, partially_fingerless_gloves, open_mouth, single_glove, holding_hands, out_of_frame |
| 5 | 10 |  |  |  |  |  | 1girl, alternate_costume, black_skirt, white_shirt, open_mouth, short_sleeves, smile, solo, white_background, black_necktie, collared_shirt, simple_background, blush, cowboy_shot, looking_at_viewer, pencil_skirt, dated, pleated_skirt |
| 6 | 5 |  |  |  |  |  | 1girl, black_leotard, detached_collar, fake_animal_ears, looking_at_viewer, playboy_bunny, rabbit_ears, strapless_leotard, wrist_cuffs, simple_background, solo, alternate_costume, grey_background, small_breasts, adapted_costume, black_one-piece_swimsuit, black_pantyhose, boots, brown_neckerchief, cowboy_shot, dated, headphones_around_neck, medium_breasts, rabbit_tail, red_bowtie, red_footwear, sitting, smile, toeless_footwear, white_background |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | black_one-piece_swimsuit | school_swimsuit | solo | breast_cutout | white_sailor_collar | looking_at_viewer | simple_background | brown_neckerchief | cowboy_shot | white_background | one-hour_drawing_challenge | twitter_username | one-piece_swimsuit | partially_fingerless_gloves | sailor_collar | single_glove | open_mouth | blush | neckerchief | character_name | sitting | smile | 2girls | solo_focus | holding_hands | out_of_frame | alternate_costume | black_skirt | white_shirt | short_sleeves | black_necktie | collared_shirt | pencil_skirt | dated | pleated_skirt | black_leotard | detached_collar | fake_animal_ears | playboy_bunny | rabbit_ears | strapless_leotard | wrist_cuffs | grey_background | small_breasts | adapted_costume | black_pantyhose | boots | headphones_around_neck | medium_breasts | rabbit_tail | red_bowtie | red_footwear | toeless_footwear |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:---------------------------|:------------------|:-------|:----------------|:----------------------|:--------------------|:--------------------|:--------------------|:--------------|:-------------------|:-----------------------------|:-------------------|:---------------------|:------------------------------|:----------------|:---------------|:-------------|:--------|:--------------|:-----------------|:----------|:--------|:---------|:-------------|:----------------|:---------------|:--------------------|:--------------|:--------------|:----------------|:----------------|:-----------------|:---------------|:--------|:----------------|:----------------|:------------------|:-------------------|:----------------|:--------------|:--------------------|:--------------|:------------------|:----------------|:------------------|:------------------|:--------|:-------------------------|:-----------------|:--------------|:-------------|:---------------|:-------------------|
| 0 | 25 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 1 | 8 |  |  |  |  |  | X | | X | X | | | X | | | X | | | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 2 | 5 |  |  |  |  |  | X | X | X | X | | | X | | | | | | | | X | X | X | X | | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 3 | 7 |  |  |  |  |  | X | | X | X | | | X | X | | | X | | | X | | X | | X | X | | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 4 | 11 |  |  |  |  |  | | | X | | | | X | | | | | | | X | X | X | X | X | X | | | | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 5 | 10 |  |  |  |  |  | X | | | X | | | X | X | | X | X | | | | | | | X | X | | | | X | | | | | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | |
| 6 | 5 |  |  |  |  |  | X | X | | X | | | X | X | X | X | X | | | | | | | | | | | X | X | | | | | X | | | | | | | X | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X |
|
CyberHarem/i_13_kantaicollection
|
[
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] |
2023-08-23T11:07:07+00:00
|
{"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]}
|
2024-01-15T22:29:31+00:00
|
[] |
[] |
TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
|
Dataset of i\_13/伊13 (Kantai Collection)
========================================
This is the dataset of i\_13/伊13 (Kantai Collection), containing 357 images and their tags.
The core tags of this character are 'short\_hair, black\_hair, hair\_between\_eyes, brown\_eyes, asymmetrical\_hair, hat, headphones, framed\_breasts, breasts', which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(huggingface organization).
List of Packages
----------------
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code
List of Clusters
----------------
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
### Table Version
|
[
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] |
[
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n",
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] |
[
44,
61,
5,
4
] |
[
"passage: TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.### Raw Text Version### Table Version"
] |
de47673b76c89feb709e9ef0293a1b1840e8bd75
|
# Dataset Card for "yt_thumbnail_dataset"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
vargr/yt_thumbnail_dataset
|
[
"region:us"
] |
2023-08-23T11:08:14+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "test", "path": "data/test-*"}]}], "dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "label", "dtype": "int64"}, {"name": "title", "dtype": "string"}, {"name": "videoId", "dtype": "string"}, {"name": "channelId", "dtype": "string"}, {"name": "subscribers", "dtype": "float64"}, {"name": "isVerified", "dtype": "bool"}, {"name": "keywords", "dtype": "string"}, {"name": "country", "dtype": "string"}, {"name": "description", "dtype": "string"}, {"name": "views", "dtype": "int64"}, {"name": "published", "dtype": "timestamp[us]"}, {"name": "length", "dtype": "string"}, {"name": "url", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 3917528866.3737946, "num_examples": 28276}, {"name": "test", "num_bytes": 1010554492.3202056, "num_examples": 7070}], "download_size": 5006700814, "dataset_size": 4928083358.694}}
|
2023-08-23T11:18:10+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "yt_thumbnail_dataset"
More Information needed
|
[
"# Dataset Card for \"yt_thumbnail_dataset\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"yt_thumbnail_dataset\"\n\nMore Information needed"
] |
[
6,
19
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"yt_thumbnail_dataset\"\n\nMore Information needed"
] |
d1573bb82042f1bb9a8e17a7ded7ad5bcbfa9546
|
# Dataset Card for "codeparrot-ds-mapped_ids"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
lengoctuong/codeparrot-ds-mapped_ids
|
[
"region:us"
] |
2023-08-23T11:13:53+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "valid", "path": "data/valid-*"}]}], "dataset_info": {"features": [{"name": "input_ids", "sequence": "int32"}], "splits": [{"name": "train", "num_bytes": 8618263476, "num_examples": 16702061}, {"name": "valid", "num_bytes": 48072624, "num_examples": 93164}], "download_size": 3804670316, "dataset_size": 8666336100}}
|
2023-08-23T11:20:07+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "codeparrot-ds-mapped_ids"
More Information needed
|
[
"# Dataset Card for \"codeparrot-ds-mapped_ids\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"codeparrot-ds-mapped_ids\"\n\nMore Information needed"
] |
[
6,
20
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"codeparrot-ds-mapped_ids\"\n\nMore Information needed"
] |
1a61de3d5bfe211da29f1d58105f21667df8942b
|
<p><h1>🐋 The OpenOrca Dataset Norwegian! 🐋</h1></p>
This is a subset of 15000 rows of the OpenOrca dataset, translated into Norwegian.
Translation is done with Amazon Translate, and is provided by [Ruter](https://ruter.no) as an artifact from Ruter AI Lab.
## Dataset structure
The dataset is structured in the following way:
```json
{
"instruction": "Norwegian instruction",
"input": "Norwegian input",
"output": "Norwegian output",
"instruction_en": "English instruction",
"input_en": "English input",
"output_en": "English output",
}
```
## Dataset creation
Please refer the original [OpenOrca modelcard](https://huggingface.co/datasets/Open-Orca/OpenOrca) for more information on how the dataset was created.
## License
The dataset is licensed under the MIT license.
<br><br>
<p><h1>🐋 OpenOrca Datasett på Norsk! 🐋</h1></p>
Dette er et utvalg på 15000 rader fra OpenOrca datasettet, oversatt til norsk.
Oversettelsen er gjort med Amazon Translate, og er levert av [Ruter](https://ruter.no) som et produkt fra Ruter AI Lab.
## Datasettstruktur
Datasettet er strukturert på følgende måte:
```json
{
"instruction": "Instruksjon på norsk",
"input": "Inndata på norsk",
"output": "Utdata på norsk",
"instruction_en": "Instruksjon på engelsk",
"input_en": "Engelsk inndata",
"output_en": "Engelsk utdata",
}
```
## Opprettelse av datasett
Vennligst se den originale [OpenOrca modelkortet](https://huggingface.co/datasets/Open-Orca/OpenOrca) for mer informasjon om hvordan datasettet ble opprettet.
## Lisens
Datasettet er lisensiert under MIT-lisensen.
|
RuterNorway/OpenOrcaNo-15k
|
[
"task_categories:conversational",
"task_categories:text-classification",
"task_categories:token-classification",
"task_categories:table-question-answering",
"task_categories:question-answering",
"task_categories:zero-shot-classification",
"task_categories:summarization",
"task_categories:feature-extraction",
"task_categories:text-generation",
"task_categories:text2text-generation",
"size_categories:10k<n<20k",
"language:no",
"license:mit",
"region:us"
] |
2023-08-23T11:25:07+00:00
|
{"language": [false], "license": "mit", "size_categories": ["10k<n<20k"], "task_categories": ["conversational", "text-classification", "token-classification", "table-question-answering", "question-answering", "zero-shot-classification", "summarization", "feature-extraction", "text-generation", "text2text-generation"], "pretty_name": "OpenOrcaNO"}
|
2023-10-11T05:06:31+00:00
|
[] |
[
"no"
] |
TAGS
#task_categories-conversational #task_categories-text-classification #task_categories-token-classification #task_categories-table-question-answering #task_categories-question-answering #task_categories-zero-shot-classification #task_categories-summarization #task_categories-feature-extraction #task_categories-text-generation #task_categories-text2text-generation #size_categories-10k<n<20k #language-Norwegian #license-mit #region-us
|
<p><h1> The OpenOrca Dataset Norwegian! </h1></p>
This is a subset of 15000 rows of the OpenOrca dataset, translated into Norwegian.
Translation is done with Amazon Translate, and is provided by Ruter as an artifact from Ruter AI Lab.
## Dataset structure
The dataset is structured in the following way:
## Dataset creation
Please refer the original OpenOrca modelcard for more information on how the dataset was created.
## License
The dataset is licensed under the MIT license.
<br><br>
<p><h1> OpenOrca Datasett på Norsk! </h1></p>
Dette er et utvalg på 15000 rader fra OpenOrca datasettet, oversatt til norsk.
Oversettelsen er gjort med Amazon Translate, og er levert av Ruter som et produkt fra Ruter AI Lab.
## Datasettstruktur
Datasettet er strukturert på følgende måte:
## Opprettelse av datasett
Vennligst se den originale OpenOrca modelkortet for mer informasjon om hvordan datasettet ble opprettet.
## Lisens
Datasettet er lisensiert under MIT-lisensen.
|
[
"## Dataset structure\nThe dataset is structured in the following way:",
"## Dataset creation\nPlease refer the original OpenOrca modelcard for more information on how the dataset was created.",
"## License\nThe dataset is licensed under the MIT license.\n\n\n<br><br>\n<p><h1> OpenOrca Datasett på Norsk! </h1></p>\n\nDette er et utvalg på 15000 rader fra OpenOrca datasettet, oversatt til norsk.\n\nOversettelsen er gjort med Amazon Translate, og er levert av Ruter som et produkt fra Ruter AI Lab.",
"## Datasettstruktur\nDatasettet er strukturert på følgende måte:",
"## Opprettelse av datasett\n\nVennligst se den originale OpenOrca modelkortet for mer informasjon om hvordan datasettet ble opprettet.",
"## Lisens\n\nDatasettet er lisensiert under MIT-lisensen."
] |
[
"TAGS\n#task_categories-conversational #task_categories-text-classification #task_categories-token-classification #task_categories-table-question-answering #task_categories-question-answering #task_categories-zero-shot-classification #task_categories-summarization #task_categories-feature-extraction #task_categories-text-generation #task_categories-text2text-generation #size_categories-10k<n<20k #language-Norwegian #license-mit #region-us \n",
"## Dataset structure\nThe dataset is structured in the following way:",
"## Dataset creation\nPlease refer the original OpenOrca modelcard for more information on how the dataset was created.",
"## License\nThe dataset is licensed under the MIT license.\n\n\n<br><br>\n<p><h1> OpenOrca Datasett på Norsk! </h1></p>\n\nDette er et utvalg på 15000 rader fra OpenOrca datasettet, oversatt til norsk.\n\nOversettelsen er gjort med Amazon Translate, og er levert av Ruter som et produkt fra Ruter AI Lab.",
"## Datasettstruktur\nDatasettet er strukturert på følgende måte:",
"## Opprettelse av datasett\n\nVennligst se den originale OpenOrca modelkortet for mer informasjon om hvordan datasettet ble opprettet.",
"## Lisens\n\nDatasettet er lisensiert under MIT-lisensen."
] |
[
147,
15,
24,
86,
14,
27,
16
] |
[
"passage: TAGS\n#task_categories-conversational #task_categories-text-classification #task_categories-token-classification #task_categories-table-question-answering #task_categories-question-answering #task_categories-zero-shot-classification #task_categories-summarization #task_categories-feature-extraction #task_categories-text-generation #task_categories-text2text-generation #size_categories-10k<n<20k #language-Norwegian #license-mit #region-us \n## Dataset structure\nThe dataset is structured in the following way:## Dataset creation\nPlease refer the original OpenOrca modelcard for more information on how the dataset was created.## License\nThe dataset is licensed under the MIT license.\n\n\n<br><br>\n<p><h1> OpenOrca Datasett på Norsk! </h1></p>\n\nDette er et utvalg på 15000 rader fra OpenOrca datasettet, oversatt til norsk.\n\nOversettelsen er gjort med Amazon Translate, og er levert av Ruter som et produkt fra Ruter AI Lab.## Datasettstruktur\nDatasettet er strukturert på følgende måte:## Opprettelse av datasett\n\nVennligst se den originale OpenOrca modelkortet for mer informasjon om hvordan datasettet ble opprettet.## Lisens\n\nDatasettet er lisensiert under MIT-lisensen."
] |
9c6b2d46c3d1fdd0b1c15669c7f316e6f2ec0ced
|
# Dataset of ta_class/戦艦タ級 (Kantai Collection)
This is the dataset of ta_class/戦艦タ級 (Kantai Collection), containing 91 images and their tags.
The core tags of this character are `long_hair, grey_hair, pale_skin, breasts, white_hair, yellow_eyes, large_breasts`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:-----------|:---------------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 91 | 84.10 MiB | [Download](https://huggingface.co/datasets/CyberHarem/ta_class_kantaicollection/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 91 | 55.66 MiB | [Download](https://huggingface.co/datasets/CyberHarem/ta_class_kantaicollection/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 164 | 97.74 MiB | [Download](https://huggingface.co/datasets/CyberHarem/ta_class_kantaicollection/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 91 | 76.26 MiB | [Download](https://huggingface.co/datasets/CyberHarem/ta_class_kantaicollection/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 164 | 128.08 MiB | [Download](https://huggingface.co/datasets/CyberHarem/ta_class_kantaicollection/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/ta_class_kantaicollection',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 49 |  |  |  |  |  | abyssal_ship, 1girl, serafuku, solo, smile, black_panties, looking_at_viewer, navel, thighhighs, green_eyes, cape, glowing_eyes, no_pants |
| 1 | 7 |  |  |  |  |  | 1girl, abyssal_ship, neckerchief, sailor_shirt, serafuku, solo, hair_between_eyes, looking_at_viewer, shiny, short_sleeves, upper_body, blush, blue_sailor_collar, medium_breasts, open_mouth, simple_background, white_background |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | abyssal_ship | 1girl | serafuku | solo | smile | black_panties | looking_at_viewer | navel | thighhighs | green_eyes | cape | glowing_eyes | no_pants | neckerchief | sailor_shirt | hair_between_eyes | shiny | short_sleeves | upper_body | blush | blue_sailor_collar | medium_breasts | open_mouth | simple_background | white_background |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:---------------|:--------|:-----------|:-------|:--------|:----------------|:--------------------|:--------|:-------------|:-------------|:-------|:---------------|:-----------|:--------------|:---------------|:--------------------|:--------|:----------------|:-------------|:--------|:---------------------|:-----------------|:-------------|:--------------------|:-------------------|
| 0 | 49 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | |
| 1 | 7 |  |  |  |  |  | X | X | X | X | | | X | | | | | | | X | X | X | X | X | X | X | X | X | X | X | X |
|
CyberHarem/ta_class_kantaicollection
|
[
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] |
2023-08-23T11:33:31+00:00
|
{"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]}
|
2024-01-15T23:07:21+00:00
|
[] |
[] |
TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
|
Dataset of ta\_class/戦艦タ級 (Kantai Collection)
=============================================
This is the dataset of ta\_class/戦艦タ級 (Kantai Collection), containing 91 images and their tags.
The core tags of this character are 'long\_hair, grey\_hair, pale\_skin, breasts, white\_hair, yellow\_eyes, large\_breasts', which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(huggingface organization).
List of Packages
----------------
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code
List of Clusters
----------------
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
### Table Version
|
[
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] |
[
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n",
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] |
[
44,
61,
5,
4
] |
[
"passage: TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.### Raw Text Version### Table Version"
] |
d81a5e633521e76f8761e4656cf67065d3a6179e
|
## Dataset Description
This dataset was created in an effort to create a machine translation model for English-to-Kinyarwanda translation and vice-versa in a tourism-geared context.
- **Repository:**[link](https://github.com/Digital-Umuganda/twb_nllb_project_tourism_education) to the GitHub repository containing the code for training the model on this data, and the code for the collection of the monolingual data.
- **Data Format:** TSV
- **Data Source:** web scraping, manual annotation
- **Model:** huggingface [model link](mbazaNLP/Nllb_finetuned_tourism_en_kin).
### Data Instances
```
25375 49363 21210 Bird watching is best in June, so save your money on that during the other months, birds ar everywhere anyway if you are observant and patient. Kureba inyoni ni byiza cyane muri Kamena, bityo rero ujye uzigama amafaranga yawe mu gihe cy'amezi yindi, inyoni ziba hose uko byagenda kose niba witonze kandi wihanganye. 2023-05-15 18:08:54 19.0 1 3 tourism trip_advisor 125-195
```
### Data Fields
- id
- source_id
- source
- phrase
- timestamp
- user_id
- validation_state
- validation_score
- domain
- source_files
- str_ranges
### Data Splits
- **Training Data:** 25374
- **Validation Data:** 2508
- **Test Data:** 1086
## Data Preprocessing
- **Data Splitting:** To create a test set; all data sources are equally represented in terms of the number of sentences contributed to the test dataset. In terms of sentence length, the test set distribution is similar to the sentence length distribution of the whole dataset. After picking the test set, from the remaining data the train and validation data are split using sklearn's [train_test_split](https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.train_test_split.html).
## Data Collection
- **Data Collection Process:** The monolingual source sentences were obtained through web-scraping of several websites, and contain both Kinyarwanda and English sentences.
- **Data Sources:**
- Trip_advisor reviews on hotels and tourist attractions in Rwanda.
- Inyamibwa historical data.
- Igihe tourism news.
- Tourism scenarios dialogue generated by GPT-3.5.
- Booking.com Rwandan hotel reviews.
- Rwanda's wiki_travel page.
## Dataset Creation
After collecting the monolingual dataset, human translators were employed to produce translations for the collected sentences. To ensure quality, each sentence was translated more than once, and each generated translation was assigned **validation_score** that was used to pick the best translation. The test dataset was further revised to remove or correct sentences with faulty translations.
|
mbazaNLP/NMT_Tourism_parallel_data_en_kin
|
[
"task_categories:translation",
"size_categories:10K<n<100K",
"language:en",
"language:rw",
"license:cc-by-2.0",
"region:us"
] |
2023-08-23T11:34:04+00:00
|
{"language": ["en", "rw"], "license": "cc-by-2.0", "size_categories": ["10K<n<100K"], "task_categories": ["translation"]}
|
2023-09-11T12:22:11+00:00
|
[] |
[
"en",
"rw"
] |
TAGS
#task_categories-translation #size_categories-10K<n<100K #language-English #language-Kinyarwanda #license-cc-by-2.0 #region-us
|
## Dataset Description
This dataset was created in an effort to create a machine translation model for English-to-Kinyarwanda translation and vice-versa in a tourism-geared context.
- Repository:link to the GitHub repository containing the code for training the model on this data, and the code for the collection of the monolingual data.
- Data Format: TSV
- Data Source: web scraping, manual annotation
- Model: huggingface model link.
### Data Instances
### Data Fields
- id
- source_id
- source
- phrase
- timestamp
- user_id
- validation_state
- validation_score
- domain
- source_files
- str_ranges
### Data Splits
- Training Data: 25374
- Validation Data: 2508
- Test Data: 1086
## Data Preprocessing
- Data Splitting: To create a test set; all data sources are equally represented in terms of the number of sentences contributed to the test dataset. In terms of sentence length, the test set distribution is similar to the sentence length distribution of the whole dataset. After picking the test set, from the remaining data the train and validation data are split using sklearn's train_test_split.
## Data Collection
- Data Collection Process: The monolingual source sentences were obtained through web-scraping of several websites, and contain both Kinyarwanda and English sentences.
- Data Sources:
- Trip_advisor reviews on hotels and tourist attractions in Rwanda.
- Inyamibwa historical data.
- Igihe tourism news.
- Tourism scenarios dialogue generated by GPT-3.5.
- URL Rwandan hotel reviews.
- Rwanda's wiki_travel page.
## Dataset Creation
After collecting the monolingual dataset, human translators were employed to produce translations for the collected sentences. To ensure quality, each sentence was translated more than once, and each generated translation was assigned validation_score that was used to pick the best translation. The test dataset was further revised to remove or correct sentences with faulty translations.
|
[
"## Dataset Description\n\nThis dataset was created in an effort to create a machine translation model for English-to-Kinyarwanda translation and vice-versa in a tourism-geared context.\n- Repository:link to the GitHub repository containing the code for training the model on this data, and the code for the collection of the monolingual data.\n- Data Format: TSV\n- Data Source: web scraping, manual annotation\n- Model: huggingface model link.",
"### Data Instances",
"### Data Fields\n\n- id\n- source_id\n- source\n- phrase\n- timestamp\n- user_id\n- validation_state\n- validation_score\n- domain\n- source_files\n- str_ranges",
"### Data Splits\n\n- Training Data: 25374\n- Validation Data: 2508\n- Test Data: 1086",
"## Data Preprocessing\n\n- Data Splitting: To create a test set; all data sources are equally represented in terms of the number of sentences contributed to the test dataset. In terms of sentence length, the test set distribution is similar to the sentence length distribution of the whole dataset. After picking the test set, from the remaining data the train and validation data are split using sklearn's train_test_split.",
"## Data Collection\n\n- Data Collection Process: The monolingual source sentences were obtained through web-scraping of several websites, and contain both Kinyarwanda and English sentences. \n\n- Data Sources:\n - Trip_advisor reviews on hotels and tourist attractions in Rwanda.\n - Inyamibwa historical data.\n - Igihe tourism news.\n - Tourism scenarios dialogue generated by GPT-3.5.\n - URL Rwandan hotel reviews.\n - Rwanda's wiki_travel page.",
"## Dataset Creation\n\nAfter collecting the monolingual dataset, human translators were employed to produce translations for the collected sentences. To ensure quality, each sentence was translated more than once, and each generated translation was assigned validation_score that was used to pick the best translation. The test dataset was further revised to remove or correct sentences with faulty translations."
] |
[
"TAGS\n#task_categories-translation #size_categories-10K<n<100K #language-English #language-Kinyarwanda #license-cc-by-2.0 #region-us \n",
"## Dataset Description\n\nThis dataset was created in an effort to create a machine translation model for English-to-Kinyarwanda translation and vice-versa in a tourism-geared context.\n- Repository:link to the GitHub repository containing the code for training the model on this data, and the code for the collection of the monolingual data.\n- Data Format: TSV\n- Data Source: web scraping, manual annotation\n- Model: huggingface model link.",
"### Data Instances",
"### Data Fields\n\n- id\n- source_id\n- source\n- phrase\n- timestamp\n- user_id\n- validation_state\n- validation_score\n- domain\n- source_files\n- str_ranges",
"### Data Splits\n\n- Training Data: 25374\n- Validation Data: 2508\n- Test Data: 1086",
"## Data Preprocessing\n\n- Data Splitting: To create a test set; all data sources are equally represented in terms of the number of sentences contributed to the test dataset. In terms of sentence length, the test set distribution is similar to the sentence length distribution of the whole dataset. After picking the test set, from the remaining data the train and validation data are split using sklearn's train_test_split.",
"## Data Collection\n\n- Data Collection Process: The monolingual source sentences were obtained through web-scraping of several websites, and contain both Kinyarwanda and English sentences. \n\n- Data Sources:\n - Trip_advisor reviews on hotels and tourist attractions in Rwanda.\n - Inyamibwa historical data.\n - Igihe tourism news.\n - Tourism scenarios dialogue generated by GPT-3.5.\n - URL Rwandan hotel reviews.\n - Rwanda's wiki_travel page.",
"## Dataset Creation\n\nAfter collecting the monolingual dataset, human translators were employed to produce translations for the collected sentences. To ensure quality, each sentence was translated more than once, and each generated translation was assigned validation_score that was used to pick the best translation. The test dataset was further revised to remove or correct sentences with faulty translations."
] |
[
47,
107,
6,
46,
25,
96,
105,
89
] |
[
"passage: TAGS\n#task_categories-translation #size_categories-10K<n<100K #language-English #language-Kinyarwanda #license-cc-by-2.0 #region-us \n## Dataset Description\n\nThis dataset was created in an effort to create a machine translation model for English-to-Kinyarwanda translation and vice-versa in a tourism-geared context.\n- Repository:link to the GitHub repository containing the code for training the model on this data, and the code for the collection of the monolingual data.\n- Data Format: TSV\n- Data Source: web scraping, manual annotation\n- Model: huggingface model link.### Data Instances### Data Fields\n\n- id\n- source_id\n- source\n- phrase\n- timestamp\n- user_id\n- validation_state\n- validation_score\n- domain\n- source_files\n- str_ranges### Data Splits\n\n- Training Data: 25374\n- Validation Data: 2508\n- Test Data: 1086## Data Preprocessing\n\n- Data Splitting: To create a test set; all data sources are equally represented in terms of the number of sentences contributed to the test dataset. In terms of sentence length, the test set distribution is similar to the sentence length distribution of the whole dataset. After picking the test set, from the remaining data the train and validation data are split using sklearn's train_test_split.## Data Collection\n\n- Data Collection Process: The monolingual source sentences were obtained through web-scraping of several websites, and contain both Kinyarwanda and English sentences. \n\n- Data Sources:\n - Trip_advisor reviews on hotels and tourist attractions in Rwanda.\n - Inyamibwa historical data.\n - Igihe tourism news.\n - Tourism scenarios dialogue generated by GPT-3.5.\n - URL Rwandan hotel reviews.\n - Rwanda's wiki_travel page."
] |
98710f5a4903ae4921d2c63473378a1fd3254359
|
# Dataset of scamp (Kantai Collection)
This is the dataset of scamp (Kantai Collection), containing 166 images and their tags.
The core tags of this character are `long_hair, side_ponytail, hair_ornament, star_hair_ornament, hat, grey_hair, garrison_cap, aqua_headwear, hair_ribbon, ribbon, black_ribbon, grey_eyes, breasts, small_breasts, brown_eyes, hair_between_eyes, headgear`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:-----------|:------------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 166 | 222.60 MiB | [Download](https://huggingface.co/datasets/CyberHarem/scamp_kantaicollection/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 166 | 118.98 MiB | [Download](https://huggingface.co/datasets/CyberHarem/scamp_kantaicollection/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 433 | 279.86 MiB | [Download](https://huggingface.co/datasets/CyberHarem/scamp_kantaicollection/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 166 | 194.58 MiB | [Download](https://huggingface.co/datasets/CyberHarem/scamp_kantaicollection/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 433 | 396.62 MiB | [Download](https://huggingface.co/datasets/CyberHarem/scamp_kantaicollection/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/scamp_kantaicollection',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 5 |  |  |  |  |  | 1girl, black_one-piece_swimsuit, competition_swimsuit, highleg_swimsuit, solo, star_(symbol), white_gloves, looking_at_viewer, short_shorts, tongue_out, white_shorts, cowboy_shot, sitting |
| 1 | 15 |  |  |  |  |  | 1girl, black_one-piece_swimsuit, competition_swimsuit, highleg_swimsuit, solo, star_(symbol), white_gloves, white_shorts, short_shorts, cowboy_shot |
| 2 | 8 |  |  |  |  |  | 1girl, black_one-piece_swimsuit, competition_swimsuit, highleg_swimsuit, short_shorts, simple_background, solo, star_(symbol), white_background, white_gloves, white_shorts, cowboy_shot, twitter_username, collarbone, dated, blush, covered_navel, one-hour_drawing_challenge |
| 3 | 6 |  |  |  |  |  | 1girl, black_one-piece_swimsuit, competition_swimsuit, highleg_swimsuit, short_shorts, simple_background, solo, star_(symbol), white_background, white_gloves, white_shorts, holding_candy, blush, cowboy_shot, collarbone, smile |
| 4 | 7 |  |  |  |  |  | 1girl, black_one-piece_swimsuit, competition_swimsuit, cowboy_shot, highleg_swimsuit, holding_candy, short_shorts, solo, star_(symbol), white_gloves, white_shorts, lollipop, tongue_out, character_name |
| 5 | 8 |  |  |  |  |  | 1girl, black_one-piece_swimsuit, competition_swimsuit, highleg_swimsuit, solo, star_(symbol), white_gloves, white_background, simple_background, collarbone, open_mouth, upper_body |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | black_one-piece_swimsuit | competition_swimsuit | highleg_swimsuit | solo | star_(symbol) | white_gloves | looking_at_viewer | short_shorts | tongue_out | white_shorts | cowboy_shot | sitting | simple_background | white_background | twitter_username | collarbone | dated | blush | covered_navel | one-hour_drawing_challenge | holding_candy | smile | lollipop | character_name | open_mouth | upper_body |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:---------------------------|:-----------------------|:-------------------|:-------|:----------------|:---------------|:--------------------|:---------------|:-------------|:---------------|:--------------|:----------|:--------------------|:-------------------|:-------------------|:-------------|:--------|:--------|:----------------|:-----------------------------|:----------------|:--------|:-----------|:-----------------|:-------------|:-------------|
| 0 | 5 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | |
| 1 | 15 |  |  |  |  |  | X | X | X | X | X | X | X | | X | | X | X | | | | | | | | | | | | | | | |
| 2 | 8 |  |  |  |  |  | X | X | X | X | X | X | X | | X | | X | X | | X | X | X | X | X | X | X | X | | | | | | |
| 3 | 6 |  |  |  |  |  | X | X | X | X | X | X | X | | X | | X | X | | X | X | | X | | X | | | X | X | | | | |
| 4 | 7 |  |  |  |  |  | X | X | X | X | X | X | X | | X | X | X | X | | | | | | | | | | X | | X | X | | |
| 5 | 8 |  |  |  |  |  | X | X | X | X | X | X | X | | | | | | | X | X | | X | | | | | | | | | X | X |
|
CyberHarem/scamp_kantaicollection
|
[
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] |
2023-08-23T11:35:21+00:00
|
{"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]}
|
2024-01-16T02:26:37+00:00
|
[] |
[] |
TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
|
Dataset of scamp (Kantai Collection)
====================================
This is the dataset of scamp (Kantai Collection), containing 166 images and their tags.
The core tags of this character are 'long\_hair, side\_ponytail, hair\_ornament, star\_hair\_ornament, hat, grey\_hair, garrison\_cap, aqua\_headwear, hair\_ribbon, ribbon, black\_ribbon, grey\_eyes, breasts, small\_breasts, brown\_eyes, hair\_between\_eyes, headgear', which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(huggingface organization).
List of Packages
----------------
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code
List of Clusters
----------------
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
### Table Version
|
[
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] |
[
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n",
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] |
[
44,
61,
5,
4
] |
[
"passage: TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.### Raw Text Version### Table Version"
] |
932b5122f77e3566b90806e0eb1b4c1e746213d5
|
<p><h1>🦙 Alpaca Translate Norwegian 🦙</h1></p>
This dataset is based on [Fleurs](https://huggingface.co/datasets/google/fleurs) from Google. We matched the English sentences with Norwegian sentences and formatted it to an Alpaca-style dataset.
## Dataset Structure
```json
{
"instruction": "Oversett teksten fra engelsk til norsk",
"input": "English string",
"output": "Norwegian string"
}
```
This dataset was created by [Ruter](https://ruter.no) during Ruter's AI Lab effort to fine-tune LLaMA-2 models for Norwegian.
## License
Following the original dataset from Google, this dataset is released under the [Creative Commons Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0/) license.
<p><h1>🦙 Alpaca Translate Norsk 🦙</h1></p>
Dette datasettet er basert på [Fleurs](https://huggingface.co/datasets/google/fleurs) utgitt av Google. Vi har sammenstilt de engelske setningene med norske setninger og formatert det til et Alpaca-stil datasett.
## Datasettstruktur
```json
{
"instruction": "Oversett teksten fra engelsk til norsk",
"input": "English string",
"output": "Norwegian string"
}
```
Datasettet ble laget av [Ruter](https://ruter.no) AI Lab under arbeidet med å finjustere LLaMA-2-modeller for norsk.
## License
Vi følger det originale datasettet fra Google sin lisens, som er utgitt under en [Creative Commons Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0/).
|
RuterNorway/Fleurs-Alpaca-EN-NO
|
[
"task_categories:translation",
"size_categories:1k<n<5k",
"language:no",
"language:en",
"license:cc-by-4.0",
"region:us"
] |
2023-08-23T11:42:35+00:00
|
{"language": [false, "en"], "license": "cc-by-4.0", "size_categories": ["1k<n<5k"], "task_categories": ["translation"], "pretty_name": "Fleurs-Alpaca-EN-NO"}
|
2023-08-23T11:43:59+00:00
|
[] |
[
"no",
"en"
] |
TAGS
#task_categories-translation #size_categories-1k<n<5k #language-Norwegian #language-English #license-cc-by-4.0 #region-us
|
<p><h1> Alpaca Translate Norwegian </h1></p>
This dataset is based on Fleurs from Google. We matched the English sentences with Norwegian sentences and formatted it to an Alpaca-style dataset.
## Dataset Structure
This dataset was created by Ruter during Ruter's AI Lab effort to fine-tune LLaMA-2 models for Norwegian.
## License
Following the original dataset from Google, this dataset is released under the Creative Commons Attribution 4.0 International license.
<p><h1> Alpaca Translate Norsk </h1></p>
Dette datasettet er basert på Fleurs utgitt av Google. Vi har sammenstilt de engelske setningene med norske setninger og formatert det til et Alpaca-stil datasett.
## Datasettstruktur
Datasettet ble laget av Ruter AI Lab under arbeidet med å finjustere LLaMA-2-modeller for norsk.
## License
Vi følger det originale datasettet fra Google sin lisens, som er utgitt under en Creative Commons Attribution 4.0 International.
|
[
"## Dataset Structure\n\n\nThis dataset was created by Ruter during Ruter's AI Lab effort to fine-tune LLaMA-2 models for Norwegian.",
"## License\nFollowing the original dataset from Google, this dataset is released under the Creative Commons Attribution 4.0 International license.\n\n\n<p><h1> Alpaca Translate Norsk </h1></p>\n\nDette datasettet er basert på Fleurs utgitt av Google. Vi har sammenstilt de engelske setningene med norske setninger og formatert det til et Alpaca-stil datasett.",
"## Datasettstruktur\n\n\nDatasettet ble laget av Ruter AI Lab under arbeidet med å finjustere LLaMA-2-modeller for norsk.",
"## License\nVi følger det originale datasettet fra Google sin lisens, som er utgitt under en Creative Commons Attribution 4.0 International."
] |
[
"TAGS\n#task_categories-translation #size_categories-1k<n<5k #language-Norwegian #language-English #license-cc-by-4.0 #region-us \n",
"## Dataset Structure\n\n\nThis dataset was created by Ruter during Ruter's AI Lab effort to fine-tune LLaMA-2 models for Norwegian.",
"## License\nFollowing the original dataset from Google, this dataset is released under the Creative Commons Attribution 4.0 International license.\n\n\n<p><h1> Alpaca Translate Norsk </h1></p>\n\nDette datasettet er basert på Fleurs utgitt av Google. Vi har sammenstilt de engelske setningene med norske setninger og formatert det til et Alpaca-stil datasett.",
"## Datasettstruktur\n\n\nDatasettet ble laget av Ruter AI Lab under arbeidet med å finjustere LLaMA-2-modeller for norsk.",
"## License\nVi følger det originale datasettet fra Google sin lisens, som er utgitt under en Creative Commons Attribution 4.0 International."
] |
[
46,
34,
85,
30,
26
] |
[
"passage: TAGS\n#task_categories-translation #size_categories-1k<n<5k #language-Norwegian #language-English #license-cc-by-4.0 #region-us \n## Dataset Structure\n\n\nThis dataset was created by Ruter during Ruter's AI Lab effort to fine-tune LLaMA-2 models for Norwegian.## License\nFollowing the original dataset from Google, this dataset is released under the Creative Commons Attribution 4.0 International license.\n\n\n<p><h1> Alpaca Translate Norsk </h1></p>\n\nDette datasettet er basert på Fleurs utgitt av Google. Vi har sammenstilt de engelske setningene med norske setninger og formatert det til et Alpaca-stil datasett.## Datasettstruktur\n\n\nDatasettet ble laget av Ruter AI Lab under arbeidet med å finjustere LLaMA-2-modeller for norsk.## License\nVi følger det originale datasettet fra Google sin lisens, som er utgitt under en Creative Commons Attribution 4.0 International."
] |
c41089f7a97ab520c969bda57434062add3b00f8
|
## Dataset Description
This dataset was created to develop a machine translation model for bidirectional translation between Kinyarwanda and English for education-based sentences, in particular for the [Atingi](https://www.atingi.org/) learning platform.
- **Repository:**[link](https://github.com/Digital-Umuganda/twb_nllb_project_tourism_education) to the GitHub repository containing the code for training the model on this data, and the code for the collection of the monolingual data.
- **Data Format:** TSV
- **Model:** huggingface [model link](mbazaNLP/Nllb_finetuned_education_en_kin).
### Dataset Summary
### Data Instances
```
118347 103384 And their ideas was that the teachers just didn't care and had no time for them. Kandi igitekerezo cyabo nuko abarimu batabitayeho gusa kandi ntibabone umwanya. 2023-06-25 09:40:28 223 1 3 education coursera 72-93
```
### Data Fields
- id
- source_id
- source
- phrase
- timestamp
- user_id
- validation_state
- validation_score
- domain
- source_files
- str_ranges
### Data Splits
- **Training Data:** 58251
- **Validation Data:** 2456
- **Test Data:** 1060
## Data Preprocessing
- **Data Splitting:** To create a test set; all data sources are equally represented in terms of the number of sentences contributed to the test dataset. In terms of sentence length, the test set distribution is similar to the sentence length distribution of the whole dataset. After picking the test set, from the remaining data the train and validation data are split using sklearn's [train_test_split](https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.train_test_split.html).
## Data Collection
- **Data Collection Process:** The monolingual source sentences were obtained through web-scraping of several websites containing English sentences.
- **Data Sources:**
- Coursera
- Atingi
- Wikipedia
## Dataset Creation
After collecting the monolingual dataset, human translators were employed to produce translations for the collected sentences. To ensure quality, each sentence was translated more than once, and each generated translation was assigned **validation_score** that was used to pick the best translation. The test dataset was further revised to remove or correct sentences with faulty translations.
|
mbazaNLP/NMT_Education_parallel_data_en_kin
|
[
"task_categories:translation",
"size_categories:10K<n<100K",
"language:en",
"language:rw",
"license:cc-by-2.0",
"region:us"
] |
2023-08-23T11:46:02+00:00
|
{"language": ["en", "rw"], "license": "cc-by-2.0", "size_categories": ["10K<n<100K"], "task_categories": ["translation"]}
|
2023-09-11T12:23:44+00:00
|
[] |
[
"en",
"rw"
] |
TAGS
#task_categories-translation #size_categories-10K<n<100K #language-English #language-Kinyarwanda #license-cc-by-2.0 #region-us
|
## Dataset Description
This dataset was created to develop a machine translation model for bidirectional translation between Kinyarwanda and English for education-based sentences, in particular for the Atingi learning platform.
- Repository:link to the GitHub repository containing the code for training the model on this data, and the code for the collection of the monolingual data.
- Data Format: TSV
- Model: huggingface model link.
### Dataset Summary
### Data Instances
### Data Fields
- id
- source_id
- source
- phrase
- timestamp
- user_id
- validation_state
- validation_score
- domain
- source_files
- str_ranges
### Data Splits
- Training Data: 58251
- Validation Data: 2456
- Test Data: 1060
## Data Preprocessing
- Data Splitting: To create a test set; all data sources are equally represented in terms of the number of sentences contributed to the test dataset. In terms of sentence length, the test set distribution is similar to the sentence length distribution of the whole dataset. After picking the test set, from the remaining data the train and validation data are split using sklearn's train_test_split.
## Data Collection
- Data Collection Process: The monolingual source sentences were obtained through web-scraping of several websites containing English sentences.
- Data Sources:
- Coursera
- Atingi
- Wikipedia
## Dataset Creation
After collecting the monolingual dataset, human translators were employed to produce translations for the collected sentences. To ensure quality, each sentence was translated more than once, and each generated translation was assigned validation_score that was used to pick the best translation. The test dataset was further revised to remove or correct sentences with faulty translations.
|
[
"## Dataset Description\nThis dataset was created to develop a machine translation model for bidirectional translation between Kinyarwanda and English for education-based sentences, in particular for the Atingi learning platform.\n- Repository:link to the GitHub repository containing the code for training the model on this data, and the code for the collection of the monolingual data.\n- Data Format: TSV\n- Model: huggingface model link.",
"### Dataset Summary",
"### Data Instances",
"### Data Fields\n\n- id\n- source_id\n- source\n- phrase\n- timestamp\n- user_id\n- validation_state\n- validation_score\n- domain\n- source_files\n- str_ranges",
"### Data Splits\n\n- Training Data: 58251\n- Validation Data: 2456\n- Test Data: 1060",
"## Data Preprocessing\n\n- Data Splitting: To create a test set; all data sources are equally represented in terms of the number of sentences contributed to the test dataset. In terms of sentence length, the test set distribution is similar to the sentence length distribution of the whole dataset. After picking the test set, from the remaining data the train and validation data are split using sklearn's train_test_split.",
"## Data Collection\n\n- Data Collection Process: The monolingual source sentences were obtained through web-scraping of several websites containing English sentences. \n\n- Data Sources:\n - Coursera\n - Atingi\n - Wikipedia",
"## Dataset Creation\n\nAfter collecting the monolingual dataset, human translators were employed to produce translations for the collected sentences. To ensure quality, each sentence was translated more than once, and each generated translation was assigned validation_score that was used to pick the best translation. The test dataset was further revised to remove or correct sentences with faulty translations."
] |
[
"TAGS\n#task_categories-translation #size_categories-10K<n<100K #language-English #language-Kinyarwanda #license-cc-by-2.0 #region-us \n",
"## Dataset Description\nThis dataset was created to develop a machine translation model for bidirectional translation between Kinyarwanda and English for education-based sentences, in particular for the Atingi learning platform.\n- Repository:link to the GitHub repository containing the code for training the model on this data, and the code for the collection of the monolingual data.\n- Data Format: TSV\n- Model: huggingface model link.",
"### Dataset Summary",
"### Data Instances",
"### Data Fields\n\n- id\n- source_id\n- source\n- phrase\n- timestamp\n- user_id\n- validation_state\n- validation_score\n- domain\n- source_files\n- str_ranges",
"### Data Splits\n\n- Training Data: 58251\n- Validation Data: 2456\n- Test Data: 1060",
"## Data Preprocessing\n\n- Data Splitting: To create a test set; all data sources are equally represented in terms of the number of sentences contributed to the test dataset. In terms of sentence length, the test set distribution is similar to the sentence length distribution of the whole dataset. After picking the test set, from the remaining data the train and validation data are split using sklearn's train_test_split.",
"## Data Collection\n\n- Data Collection Process: The monolingual source sentences were obtained through web-scraping of several websites containing English sentences. \n\n- Data Sources:\n - Coursera\n - Atingi\n - Wikipedia",
"## Dataset Creation\n\nAfter collecting the monolingual dataset, human translators were employed to produce translations for the collected sentences. To ensure quality, each sentence was translated more than once, and each generated translation was assigned validation_score that was used to pick the best translation. The test dataset was further revised to remove or correct sentences with faulty translations."
] |
[
47,
97,
6,
6,
46,
26,
96,
46,
89
] |
[
"passage: TAGS\n#task_categories-translation #size_categories-10K<n<100K #language-English #language-Kinyarwanda #license-cc-by-2.0 #region-us \n## Dataset Description\nThis dataset was created to develop a machine translation model for bidirectional translation between Kinyarwanda and English for education-based sentences, in particular for the Atingi learning platform.\n- Repository:link to the GitHub repository containing the code for training the model on this data, and the code for the collection of the monolingual data.\n- Data Format: TSV\n- Model: huggingface model link.### Dataset Summary### Data Instances### Data Fields\n\n- id\n- source_id\n- source\n- phrase\n- timestamp\n- user_id\n- validation_state\n- validation_score\n- domain\n- source_files\n- str_ranges### Data Splits\n\n- Training Data: 58251\n- Validation Data: 2456\n- Test Data: 1060## Data Preprocessing\n\n- Data Splitting: To create a test set; all data sources are equally represented in terms of the number of sentences contributed to the test dataset. In terms of sentence length, the test set distribution is similar to the sentence length distribution of the whole dataset. After picking the test set, from the remaining data the train and validation data are split using sklearn's train_test_split.## Data Collection\n\n- Data Collection Process: The monolingual source sentences were obtained through web-scraping of several websites containing English sentences. \n\n- Data Sources:\n - Coursera\n - Atingi\n - Wikipedia## Dataset Creation\n\nAfter collecting the monolingual dataset, human translators were employed to produce translations for the collected sentences. To ensure quality, each sentence was translated more than once, and each generated translation was assigned validation_score that was used to pick the best translation. The test dataset was further revised to remove or correct sentences with faulty translations."
] |
7f05a300d37fbd1427753efc0f5633215800ebe9
|
# Dataset Card for "pubmed_nonbiomedical"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
zxvix/pubmed_nonbiomedical
|
[
"region:us"
] |
2023-08-23T11:49:42+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "test", "path": "data/test-*"}]}], "dataset_info": {"features": [{"name": "MedlineCitation", "struct": [{"name": "PMID", "dtype": "int32"}, {"name": "DateCompleted", "struct": [{"name": "Year", "dtype": "int32"}, {"name": "Month", "dtype": "int32"}, {"name": "Day", "dtype": "int32"}]}, {"name": "NumberOfReferences", "dtype": "int32"}, {"name": "DateRevised", "struct": [{"name": "Year", "dtype": "int32"}, {"name": "Month", "dtype": "int32"}, {"name": "Day", "dtype": "int32"}]}, {"name": "Article", "struct": [{"name": "Abstract", "struct": [{"name": "AbstractText", "dtype": "string"}]}, {"name": "ArticleTitle", "dtype": "string"}, {"name": "AuthorList", "struct": [{"name": "Author", "sequence": [{"name": "LastName", "dtype": "string"}, {"name": "ForeName", "dtype": "string"}, {"name": "Initials", "dtype": "string"}, {"name": "CollectiveName", "dtype": "string"}]}]}, {"name": "Language", "dtype": "string"}, {"name": "GrantList", "struct": [{"name": "Grant", "sequence": [{"name": "GrantID", "dtype": "string"}, {"name": "Agency", "dtype": "string"}, {"name": "Country", "dtype": "string"}]}]}, {"name": "PublicationTypeList", "struct": [{"name": "PublicationType", "sequence": "string"}]}]}, {"name": "MedlineJournalInfo", "struct": [{"name": "Country", "dtype": "string"}]}, {"name": "ChemicalList", "struct": [{"name": "Chemical", "sequence": [{"name": "RegistryNumber", "dtype": "string"}, {"name": "NameOfSubstance", "dtype": "string"}]}]}, {"name": "CitationSubset", "dtype": "string"}, {"name": "MeshHeadingList", "struct": [{"name": "MeshHeading", "sequence": [{"name": "DescriptorName", "dtype": "string"}, {"name": "QualifierName", "dtype": "string"}]}]}]}, {"name": "PubmedData", "struct": [{"name": "ArticleIdList", "sequence": [{"name": "ArticleId", "sequence": "string"}]}, {"name": "PublicationStatus", "dtype": "string"}, {"name": "History", "struct": [{"name": "PubMedPubDate", "sequence": [{"name": "Year", "dtype": "int32"}, {"name": "Month", "dtype": "int32"}, {"name": "Day", "dtype": "int32"}]}]}, {"name": "ReferenceList", "sequence": [{"name": "Citation", "dtype": "string"}, {"name": "CitationId", "dtype": "int32"}]}]}, {"name": "text", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "original_text", "dtype": "string"}], "splits": [{"name": "test", "num_bytes": 4015623.996, "num_examples": 996}], "download_size": 2191434, "dataset_size": 4015623.996}}
|
2023-08-25T02:25:34+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "pubmed_nonbiomedical"
More Information needed
|
[
"# Dataset Card for \"pubmed_nonbiomedical\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"pubmed_nonbiomedical\"\n\nMore Information needed"
] |
[
6,
17
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"pubmed_nonbiomedical\"\n\nMore Information needed"
] |
f4c9942fab85cc57bc235aedc6913f7882e9ac20
|
# Dataset Card for "CtoD_CS_ForFineTune"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
vincenttttt/CtoD_CS_ForFineTune
|
[
"region:us"
] |
2023-08-23T11:56:25+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "question", "dtype": "string"}, {"name": "answer", "dtype": "string"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 10897, "num_examples": 27}], "download_size": 6403, "dataset_size": 10897}}
|
2023-08-23T11:56:27+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "CtoD_CS_ForFineTune"
More Information needed
|
[
"# Dataset Card for \"CtoD_CS_ForFineTune\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"CtoD_CS_ForFineTune\"\n\nMore Information needed"
] |
[
6,
21
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"CtoD_CS_ForFineTune\"\n\nMore Information needed"
] |
ab8565f73820d05e2ff27538d9bc0c8f35feb8fc
|
# Dataset Card for "SEED"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
HuggingFaceM4/SEED
|
[
"region:us"
] |
2023-08-23T12:00:14+00:00
|
{"configs": [{"config_name": "Instance_Attributes", "data_files": [{"split": "test", "path": "Instance_Attributes/test-*"}]}, {"config_name": "Instance_Identity", "data_files": [{"split": "test", "path": "Instance_Identity/test-*"}]}, {"config_name": "Instance_Interaction", "data_files": [{"split": "test", "path": "Instance_Interaction/test-*"}]}, {"config_name": "Instance_Location", "data_files": [{"split": "test", "path": "Instance_Location/test-*"}]}, {"config_name": "Instances_Counting", "data_files": [{"split": "test", "path": "Instances_Counting/test-*"}]}, {"config_name": "Scene_Understanding", "data_files": [{"split": "test", "path": "Scene_Understanding/test-*"}]}, {"config_name": "Spatial_Relation", "data_files": [{"split": "test", "path": "Spatial_Relation/test-*"}]}, {"config_name": "Text_Understanding", "data_files": [{"split": "test", "path": "Text_Understanding/test-*"}]}, {"config_name": "Visual_Reasoning", "data_files": [{"split": "test", "path": "Visual_Reasoning/test-*"}]}, {"config_name": "default", "data_files": [{"split": "test", "path": "data/test-*"}]}], "dataset_info": [{"config_name": "Instance_Attributes", "features": [{"name": "answer", "dtype": {"class_label": {"names": {"0": "A", "1": "B", "2": "C", "3": "D"}}}}, {"name": "choice_a", "dtype": "string"}, {"name": "choice_b", "dtype": "string"}, {"name": "choice_c", "dtype": "string"}, {"name": "choice_d", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "question_type_id", "dtype": "string"}, {"name": "image", "dtype": "image"}], "splits": [{"name": "test", "num_bytes": 1334222748.4732733, "num_examples": 4649}], "download_size": 0, "dataset_size": 1334222748.4732733}, {"config_name": "Instance_Identity", "features": [{"name": "answer", "dtype": {"class_label": {"names": {"0": "A", "1": "B", "2": "C", "3": "D"}}}}, {"name": "choice_a", "dtype": "string"}, {"name": "choice_b", "dtype": "string"}, {"name": "choice_c", "dtype": "string"}, {"name": "choice_d", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "question_type_id", "dtype": "string"}, {"name": "image", "dtype": "image"}], "splits": [{"name": "test", "num_bytes": 584470534.4340912, "num_examples": 1831}], "download_size": 0, "dataset_size": 584470534.4340912}, {"config_name": "Instance_Interaction", "features": [{"name": "answer", "dtype": {"class_label": {"names": {"0": "A", "1": "B", "2": "C", "3": "D"}}}}, {"name": "choice_a", "dtype": "string"}, {"name": "choice_b", "dtype": "string"}, {"name": "choice_c", "dtype": "string"}, {"name": "choice_d", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "question_type_id", "dtype": "string"}, {"name": "image", "dtype": "image"}], "splits": [{"name": "test", "num_bytes": 30580182.345886324, "num_examples": 97}], "download_size": 29830492, "dataset_size": 30580182.345886324}, {"config_name": "Instance_Location", "features": [{"name": "answer", "dtype": {"class_label": {"names": {"0": "A", "1": "B", "2": "C", "3": "D"}}}}, {"name": "choice_a", "dtype": "string"}, {"name": "choice_b", "dtype": "string"}, {"name": "choice_c", "dtype": "string"}, {"name": "choice_d", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "question_type_id", "dtype": "string"}, {"name": "image", "dtype": "image"}], "splits": [{"name": "test", "num_bytes": 309244446.6420291, "num_examples": 978}], "download_size": 0, "dataset_size": 309244446.6420291}, {"config_name": "Instances_Counting", "features": [{"name": "answer", "dtype": {"class_label": {"names": {"0": "A", "1": "B", "2": "C", "3": "D"}}}}, {"name": "choice_a", "dtype": "string"}, {"name": "choice_b", "dtype": "string"}, {"name": "choice_c", "dtype": "string"}, {"name": "choice_d", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "question_type_id", "dtype": "string"}, {"name": "image", "dtype": "image"}], "splits": [{"name": "test", "num_bytes": 659598672.0028641, "num_examples": 2447}], "download_size": 712591981, "dataset_size": 659598672.0028641}, {"config_name": "Scene_Understanding", "features": [{"name": "answer", "dtype": {"class_label": {"names": {"0": "A", "1": "B", "2": "C", "3": "D"}}}}, {"name": "choice_a", "dtype": "string"}, {"name": "choice_b", "dtype": "string"}, {"name": "choice_c", "dtype": "string"}, {"name": "choice_d", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "question_type_id", "dtype": "string"}, {"name": "image", "dtype": "image"}], "splits": [{"name": "test", "num_bytes": 967763011.0467318, "num_examples": 3158}], "download_size": 960725386, "dataset_size": 967763011.0467318}, {"config_name": "Spatial_Relation", "features": [{"name": "answer", "dtype": {"class_label": {"names": {"0": "A", "1": "B", "2": "C", "3": "D"}}}}, {"name": "choice_a", "dtype": "string"}, {"name": "choice_b", "dtype": "string"}, {"name": "choice_c", "dtype": "string"}, {"name": "choice_d", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "question_type_id", "dtype": "string"}, {"name": "image", "dtype": "image"}], "splits": [{"name": "test", "num_bytes": 197810012.16749808, "num_examples": 657}], "download_size": 185916519, "dataset_size": 197810012.16749808}, {"config_name": "Text_Understanding", "features": [{"name": "answer", "dtype": {"class_label": {"names": {"0": "A", "1": "B", "2": "C", "3": "D"}}}}, {"name": "choice_a", "dtype": "string"}, {"name": "choice_b", "dtype": "string"}, {"name": "choice_c", "dtype": "string"}, {"name": "choice_d", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "question_type_id", "dtype": "string"}, {"name": "image", "dtype": "image"}], "splits": [{"name": "test", "num_bytes": 16869944.571137495, "num_examples": 85}], "download_size": 15415331, "dataset_size": 16869944.571137495}, {"config_name": "Visual_Reasoning", "features": [{"name": "answer", "dtype": {"class_label": {"names": {"0": "A", "1": "B", "2": "C", "3": "D"}}}}, {"name": "choice_a", "dtype": "string"}, {"name": "choice_b", "dtype": "string"}, {"name": "choice_c", "dtype": "string"}, {"name": "choice_d", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "question_type_id", "dtype": "string"}, {"name": "image", "dtype": "image"}], "splits": [{"name": "test", "num_bytes": 114655703.95348836, "num_examples": 331}], "download_size": 111131917, "dataset_size": 114655703.95348836}, {"config_name": "default", "features": [{"name": "answer", "dtype": {"class_label": {"names": {"0": "A", "1": "B", "2": "C", "3": "D"}}}}, {"name": "choice_a", "dtype": "string"}, {"name": "choice_b", "dtype": "string"}, {"name": "choice_c", "dtype": "string"}, {"name": "choice_d", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "question_type_id", "dtype": "string"}, {"name": "image", "dtype": "image"}], "splits": [{"name": "test", "num_bytes": 3877231682.444, "num_examples": 14233}], "download_size": 4251234968, "dataset_size": 3877231682.444}]}
|
2023-08-23T12:32:09+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "SEED"
More Information needed
|
[
"# Dataset Card for \"SEED\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"SEED\"\n\nMore Information needed"
] |
[
6,
12
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"SEED\"\n\nMore Information needed"
] |
702041ce3d96f2c3f7dab47010b86391a56ea7f5
|
# Dataset of hayashio (Kantai Collection)
This is the dataset of hayashio (Kantai Collection), containing 180 images and their tags.
The core tags of this character are `black_hair, long_hair, brown_eyes, mole, mole_under_eye, blue_ribbon, ribbon, neck_ribbon`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:-----------|:---------------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 180 | 166.13 MiB | [Download](https://huggingface.co/datasets/CyberHarem/hayashio_kantaicollection/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 180 | 107.58 MiB | [Download](https://huggingface.co/datasets/CyberHarem/hayashio_kantaicollection/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 418 | 224.35 MiB | [Download](https://huggingface.co/datasets/CyberHarem/hayashio_kantaicollection/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 180 | 153.54 MiB | [Download](https://huggingface.co/datasets/CyberHarem/hayashio_kantaicollection/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 418 | 298.80 MiB | [Download](https://huggingface.co/datasets/CyberHarem/hayashio_kantaicollection/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/hayashio_kantaicollection',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 16 |  |  |  |  |  | 1girl, black_vest, short_sleeves, solo, white_shirt, black_skirt, pleated_skirt, school_uniform, simple_background, white_background, white_gloves, looking_at_viewer, cowboy_shot, smile, blush, collared_shirt, red_eyes |
| 1 | 10 |  |  |  |  |  | 1girl, black_skirt, black_vest, kneehighs, pleated_skirt, school_uniform, short_sleeves, white_shirt, brown_footwear, loafers, white_gloves, black_socks, full_body, solo, red_eyes, smile, cannon, collared_shirt, simple_background, standing, machinery, turret, white_background |
| 2 | 7 |  |  |  |  |  | 1girl, black_vest, looking_at_viewer, solo, upper_body, white_shirt, short_sleeves, orange_eyes, blush, grin, school_uniform, dress_shirt |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | black_vest | short_sleeves | solo | white_shirt | black_skirt | pleated_skirt | school_uniform | simple_background | white_background | white_gloves | looking_at_viewer | cowboy_shot | smile | blush | collared_shirt | red_eyes | kneehighs | brown_footwear | loafers | black_socks | full_body | cannon | standing | machinery | turret | upper_body | orange_eyes | grin | dress_shirt |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:-------------|:----------------|:-------|:--------------|:--------------|:----------------|:-----------------|:--------------------|:-------------------|:---------------|:--------------------|:--------------|:--------|:--------|:-----------------|:-----------|:------------|:-----------------|:----------|:--------------|:------------|:---------|:-----------|:------------|:---------|:-------------|:--------------|:-------|:--------------|
| 0 | 16 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | |
| 1 | 10 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | | | X | | X | X | X | X | X | X | X | X | X | X | X | | | | |
| 2 | 7 |  |  |  |  |  | X | X | X | X | X | | | X | | | | X | | | X | | | | | | | | | | | | X | X | X | X |
|
CyberHarem/hayashio_kantaicollection
|
[
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] |
2023-08-23T12:10:00+00:00
|
{"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]}
|
2024-01-16T10:36:44+00:00
|
[] |
[] |
TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
|
Dataset of hayashio (Kantai Collection)
=======================================
This is the dataset of hayashio (Kantai Collection), containing 180 images and their tags.
The core tags of this character are 'black\_hair, long\_hair, brown\_eyes, mole, mole\_under\_eye, blue\_ribbon, ribbon, neck\_ribbon', which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(huggingface organization).
List of Packages
----------------
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code
List of Clusters
----------------
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
### Table Version
|
[
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] |
[
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n",
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] |
[
44,
61,
5,
4
] |
[
"passage: TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.### Raw Text Version### Table Version"
] |
218708a5e99f098ba710cad71f18832b86b223a6
|
# moss-003-sft-data
本数据集可用于中文多轮对话指令微调，包含110万中英文多轮对话数据。该数据集来自[MOSS项目](https://github.com/OpenLMLab/MOSS#%E6%95%B0%E6%8D%AE) 中的moss-003-sft-data数据集。
fork from [[YeungNLP/moss-003-sft-data]](https://huggingface.co/datasets/YeungNLP/moss-003-sft-data)
在原数据集的基础上，我们去除了冗余信息，仅提取出有效的对话信息，并且调整数据格式，以便在训练中更加灵活地组织数据格式。更多详细信息，可参考MOSS项目介绍。
格式从`YeungNLP/moss-003-sft-data`的多轮转为:
```json
[
{
"instruction": "听起来很不错。人工智能可能在哪些方面面临挑战呢？",
"input": "",
"output": "人工智能面临的挑战包括数据隐私、安全和道德方面的问题，以及影响就业机会的自动化等问题。",
"history": [
["你好，你能帮我解答一个问题吗？", "当然，请问有什么问题？"],
["我想了解人工智能的未来发展方向，你有什么想法吗？", "人工智能在未来的发展方向可能包括更强大的机器学习算法，更先进的自然语言处理技术，以及更加智能的机器人。"]
]
}
]
```
|
ticoAg/moss-003-sft-data
|
[
"region:us"
] |
2023-08-23T12:10:49+00:00
|
{}
|
2023-08-23T12:45:36+00:00
|
[] |
[] |
TAGS
#region-us
|
# moss-003-sft-data
本数据集可用于中文多轮对话指令微调，包含110万中英文多轮对话数据。该数据集来自MOSS项目 中的moss-003-sft-data数据集。
fork from [[YeungNLP/moss-003-sft-data]](URL
在原数据集的基础上，我们去除了冗余信息，仅提取出有效的对话信息，并且调整数据格式，以便在训练中更加灵活地组织数据格式。更多详细信息，可参考MOSS项目介绍。
格式从'YeungNLP/moss-003-sft-data'的多轮转为:
|
[
"# moss-003-sft-data\n本数据集可用于中文多轮对话指令微调，包含110万中英文多轮对话数据。该数据集来自MOSS项目 中的moss-003-sft-data数据集。\n\nfork from [[YeungNLP/moss-003-sft-data]](URL\n\n在原数据集的基础上，我们去除了冗余信息，仅提取出有效的对话信息，并且调整数据格式，以便在训练中更加灵活地组织数据格式。更多详细信息，可参考MOSS项目介绍。\n\n格式从'YeungNLP/moss-003-sft-data'的多轮转为:"
] |
[
"TAGS\n#region-us \n",
"# moss-003-sft-data\n本数据集可用于中文多轮对话指令微调，包含110万中英文多轮对话数据。该数据集来自MOSS项目 中的moss-003-sft-data数据集。\n\nfork from [[YeungNLP/moss-003-sft-data]](URL\n\n在原数据集的基础上，我们去除了冗余信息，仅提取出有效的对话信息，并且调整数据格式，以便在训练中更加灵活地组织数据格式。更多详细信息，可参考MOSS项目介绍。\n\n格式从'YeungNLP/moss-003-sft-data'的多轮转为:"
] |
[
6,
148
] |
[
"passage: TAGS\n#region-us \n# moss-003-sft-data\n本数据集可用于中文多轮对话指令微调，包含110万中英文多轮对话数据。该数据集来自MOSS项目 中的moss-003-sft-data数据集。\n\nfork from [[YeungNLP/moss-003-sft-data]](URL\n\n在原数据集的基础上，我们去除了冗余信息，仅提取出有效的对话信息，并且调整数据格式，以便在训练中更加灵活地组织数据格式。更多详细信息，可参考MOSS项目介绍。\n\n格式从'YeungNLP/moss-003-sft-data'的多轮转为:"
] |
f7d7804b1f72732a0676925b7e9751290628c70b
|
# Dataset Card for "inversion-mutation"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
djaekim/inversion-mutation
|
[
"region:us"
] |
2023-08-23T12:14:08+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "type", "dtype": "string"}, {"name": "dataset", "dtype": "string"}, {"name": "input", "dtype": "string"}, {"name": "instruction", "dtype": "string"}, {"name": "output", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 8482395, "num_examples": 1965}], "download_size": 2794490, "dataset_size": 8482395}}
|
2023-08-23T12:14:14+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "inversion-mutation"
More Information needed
|
[
"# Dataset Card for \"inversion-mutation\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"inversion-mutation\"\n\nMore Information needed"
] |
[
6,
15
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"inversion-mutation\"\n\nMore Information needed"
] |
37f579d7a7330a7d6ff3ee9af7b139a40a4cc952
|
# Dataset of makinami (Kantai Collection)
This is the dataset of makinami (Kantai Collection), containing 99 images and their tags.
The core tags of this character are `black_hair, long_hair, double_bun, hair_bun, multicolored_hair, ahoge, green_hair, hair_ornament, hairclip, ribbon, grey_eyes, neck_ribbon, blue_ribbon`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:-----------|:---------------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 99 | 93.29 MiB | [Download](https://huggingface.co/datasets/CyberHarem/makinami_kantaicollection/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 99 | 59.01 MiB | [Download](https://huggingface.co/datasets/CyberHarem/makinami_kantaicollection/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 225 | 125.68 MiB | [Download](https://huggingface.co/datasets/CyberHarem/makinami_kantaicollection/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 99 | 84.18 MiB | [Download](https://huggingface.co/datasets/CyberHarem/makinami_kantaicollection/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 225 | 168.41 MiB | [Download](https://huggingface.co/datasets/CyberHarem/makinami_kantaicollection/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/makinami_kantaicollection',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 10 |  |  |  |  |  | 1girl, forehead, grey_pantyhose, halterneck, pleated_dress, purple_dress, simple_background, solo, white_shirt, long_sleeves, school_uniform, white_background, cowboy_shot, open_mouth, smile |
| 1 | 6 |  |  |  |  |  | 1girl, blush, forehead, purple_dress, school_uniform, solo, upper_body, white_shirt, halterneck, long_sleeves, looking_at_viewer, simple_background, smile, white_background |
| 2 | 5 |  |  |  |  |  | 1girl, red_bra, red_panties, solo, simple_background, underwear_only, blush, forehead, looking_at_viewer, navel, small_breasts, collarbone, white_background |
| 3 | 9 |  |  |  |  |  | 1girl, forehead, fur_trim, red_headwear, santa_hat, solo, fur-trimmed_headwear, santa_costume, dress, holding_food, skirt, black_thighhighs, official_alternate_costume, simple_background, christmas, long_sleeves, open_mouth, pom_pom_(clothes), white_background |
| 4 | 8 |  |  |  |  |  | detached_collar, playboy_bunny, 1girl, fake_animal_ears, forehead, rabbit_ears, wrist_cuffs, simple_background, solo, purple_leotard, strapless_leotard, white_background, grey_pantyhose, small_breasts, adapted_costume, bowtie, covered_navel, fishnets, rabbit_tail |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | forehead | grey_pantyhose | halterneck | pleated_dress | purple_dress | simple_background | solo | white_shirt | long_sleeves | school_uniform | white_background | cowboy_shot | open_mouth | smile | blush | upper_body | looking_at_viewer | red_bra | red_panties | underwear_only | navel | small_breasts | collarbone | fur_trim | red_headwear | santa_hat | fur-trimmed_headwear | santa_costume | dress | holding_food | skirt | black_thighhighs | official_alternate_costume | christmas | pom_pom_(clothes) | detached_collar | playboy_bunny | fake_animal_ears | rabbit_ears | wrist_cuffs | purple_leotard | strapless_leotard | adapted_costume | bowtie | covered_navel | fishnets | rabbit_tail |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:-----------|:-----------------|:-------------|:----------------|:---------------|:--------------------|:-------|:--------------|:---------------|:-----------------|:-------------------|:--------------|:-------------|:--------|:--------|:-------------|:--------------------|:----------|:--------------|:-----------------|:--------|:----------------|:-------------|:-----------|:---------------|:------------|:-----------------------|:----------------|:--------|:---------------|:--------|:-------------------|:-----------------------------|:------------|:--------------------|:------------------|:----------------|:-------------------|:--------------|:--------------|:-----------------|:--------------------|:------------------|:---------|:----------------|:-----------|:--------------|
| 0 | 10 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 1 | 6 |  |  |  |  |  | X | X | | X | | X | X | X | X | X | X | X | | | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 2 | 5 |  |  |  |  |  | X | X | | | | | X | X | | | | X | | | | X | | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | |
| 3 | 9 |  |  |  |  |  | X | X | | | | | X | X | | X | | X | | X | | | | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | |
| 4 | 8 |  |  |  |  |  | X | X | X | | | | X | X | | | | X | | | | | | | | | | | X | | | | | | | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | X |
|
CyberHarem/makinami_kantaicollection
|
[
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] |
2023-08-23T12:28:58+00:00
|
{"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]}
|
2024-01-16T10:25:23+00:00
|
[] |
[] |
TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
|
Dataset of makinami (Kantai Collection)
=======================================
This is the dataset of makinami (Kantai Collection), containing 99 images and their tags.
The core tags of this character are 'black\_hair, long\_hair, double\_bun, hair\_bun, multicolored\_hair, ahoge, green\_hair, hair\_ornament, hairclip, ribbon, grey\_eyes, neck\_ribbon, blue\_ribbon', which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(huggingface organization).
List of Packages
----------------
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code
List of Clusters
----------------
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
### Table Version
|
[
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] |
[
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n",
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] |
[
44,
61,
5,
4
] |
[
"passage: TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.### Raw Text Version### Table Version"
] |
f043f846cccdf7fa421cd31bf71bba7a9c2c740f
|
# Dataset Card for "directv-zocalos_1.0fps_15-08-2023_17-08-2023"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
Seenka/directv-zocalos_1.0fps_15-08-2023_17-08-2023
|
[
"region:us"
] |
2023-08-23T12:30:39+00:00
|
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "image_filename", "dtype": "string"}, {"name": "frame_number", "dtype": "int64"}, {"name": "is_L_shape", "dtype": "bool"}, {"name": "horizontal_check", "dtype": "bool"}, {"name": "vertical_check", "dtype": "bool"}, {"name": "black_image", "dtype": "bool"}, {"name": "horizontal_xmin", "dtype": "int64"}, {"name": "horizontal_xmax", "dtype": "int64"}, {"name": "horizontal_ymin", "dtype": "int64"}, {"name": "horizontal_ymax", "dtype": "int64"}, {"name": "vertical_xmin", "dtype": "int64"}, {"name": "vertical_xmax", "dtype": "int64"}, {"name": "vertical_ymin", "dtype": "int64"}, {"name": "vertical_ymax", "dtype": "int64"}, {"name": "cropped_image_horizontal", "dtype": "image"}, {"name": "cropped_image_vertical", "dtype": "null"}, {"name": "width", "dtype": "int64"}, {"name": "height", "dtype": "int64"}, {"name": "embedding_horizontal", "sequence": "float32"}], "splits": [{"name": "train", "num_bytes": 976987.0, "num_examples": 5}], "download_size": 960063, "dataset_size": 976987.0}}
|
2023-08-23T12:30:43+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "directv-zocalos_1.0fps_15-08-2023_17-08-2023"
More Information needed
|
[
"# Dataset Card for \"directv-zocalos_1.0fps_15-08-2023_17-08-2023\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"directv-zocalos_1.0fps_15-08-2023_17-08-2023\"\n\nMore Information needed"
] |
[
6,
29
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"directv-zocalos_1.0fps_15-08-2023_17-08-2023\"\n\nMore Information needed"
] |
d09ba7cb03b68946f0fcbe3ff2b68da109001cff
|
# Dataset of ariake (Kantai Collection)
This is the dataset of ariake (Kantai Collection), containing 126 images and their tags.
The core tags of this character are `long_hair, purple_eyes, purple_hair, black_hair, hair_over_one_eye, hat, low_ponytail, black_headwear, breasts, gradient_hair, beret, bangs, multicolored_hair`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:-----------|:-------------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 126 | 127.60 MiB | [Download](https://huggingface.co/datasets/CyberHarem/ariake_kantaicollection/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 126 | 76.64 MiB | [Download](https://huggingface.co/datasets/CyberHarem/ariake_kantaicollection/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 293 | 158.88 MiB | [Download](https://huggingface.co/datasets/CyberHarem/ariake_kantaicollection/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 126 | 113.33 MiB | [Download](https://huggingface.co/datasets/CyberHarem/ariake_kantaicollection/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 293 | 220.04 MiB | [Download](https://huggingface.co/datasets/CyberHarem/ariake_kantaicollection/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/ariake_kantaicollection',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 9 |  |  |  |  |  | 1girl, blue_sky, cloud, highleg_bikini, solo, brown_shorts, day, eyewear_on_head, outdoors, red_necktie, sunglasses, between_breasts, cowboy_shot, looking_at_viewer, highleg_swimsuit, medium_breasts, large_breasts, navel, short_shorts, beach, black_shorts, ocean, purple_bikini |
| 1 | 34 |  |  |  |  |  | 1girl, collared_shirt, grey_skirt, pleated_skirt, red_necktie, solo, white_shirt, black_jacket, long_sleeves, black_gloves, fingerless_gloves, blazer, looking_at_viewer, smile, simple_background, white_background, cowboy_shot |
| 2 | 5 |  |  |  |  |  | 1girl, black_jacket, collared_shirt, long_sleeves, looking_at_viewer, red_necktie, simple_background, solo, white_background, white_shirt, black_gloves, fingerless_gloves, upper_body, ahoge, one-hour_drawing_challenge, blazer, twitter_username |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | blue_sky | cloud | highleg_bikini | solo | brown_shorts | day | eyewear_on_head | outdoors | red_necktie | sunglasses | between_breasts | cowboy_shot | looking_at_viewer | highleg_swimsuit | medium_breasts | large_breasts | navel | short_shorts | beach | black_shorts | ocean | purple_bikini | collared_shirt | grey_skirt | pleated_skirt | white_shirt | black_jacket | long_sleeves | black_gloves | fingerless_gloves | blazer | smile | simple_background | white_background | upper_body | ahoge | one-hour_drawing_challenge | twitter_username |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:-----------|:--------|:-----------------|:-------|:---------------|:------|:------------------|:-----------|:--------------|:-------------|:------------------|:--------------|:--------------------|:-------------------|:-----------------|:----------------|:--------|:---------------|:--------|:---------------|:--------|:----------------|:-----------------|:-------------|:----------------|:--------------|:---------------|:---------------|:---------------|:--------------------|:---------|:--------|:--------------------|:-------------------|:-------------|:--------|:-----------------------------|:-------------------|
| 0 | 9 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | |
| 1 | 34 |  |  |  |  |  | X | | | | X | | | | | X | | | X | X | | | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | | | | |
| 2 | 5 |  |  |  |  |  | X | | | | X | | | | | X | | | | X | | | | | | | | | | X | | | X | X | X | X | X | X | | X | X | X | X | X | X |
|
CyberHarem/ariake_kantaicollection
|
[
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] |
2023-08-23T12:54:12+00:00
|
{"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]}
|
2024-01-16T07:49:45+00:00
|
[] |
[] |
TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
|
Dataset of ariake (Kantai Collection)
=====================================
This is the dataset of ariake (Kantai Collection), containing 126 images and their tags.
The core tags of this character are 'long\_hair, purple\_eyes, purple\_hair, black\_hair, hair\_over\_one\_eye, hat, low\_ponytail, black\_headwear, breasts, gradient\_hair, beret, bangs, multicolored\_hair', which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(huggingface organization).
List of Packages
----------------
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code
List of Clusters
----------------
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
### Table Version
|
[
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] |
[
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n",
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] |
[
44,
61,
5,
4
] |
[
"passage: TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.### Raw Text Version### Table Version"
] |
e862640d973a1a773ba87d427dfaa0b4ddb469d6
|
# Dataset Card for "viquad-qag"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
namngo/viquad-qag
|
[
"region:us"
] |
2023-08-23T12:57:07+00:00
|
{"dataset_info": {"features": [{"name": "paragraph", "dtype": "string"}, {"name": "questions", "sequence": "string"}, {"name": "answers", "sequence": "string"}, {"name": "questions_answers", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 323168, "num_examples": 138}, {"name": "validation", "num_bytes": 42356, "num_examples": 18}, {"name": "test", "num_bytes": 42356, "num_examples": 18}], "download_size": 264361, "dataset_size": 407880}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "validation", "path": "data/validation-*"}, {"split": "test", "path": "data/test-*"}]}]}
|
2023-08-23T12:57:13+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "viquad-qag"
More Information needed
|
[
"# Dataset Card for \"viquad-qag\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"viquad-qag\"\n\nMore Information needed"
] |
[
6,
15
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"viquad-qag\"\n\nMore Information needed"
] |
143ddd985d74eedacb313583802467e31481da4e
|
将sharegpt数据集 转为符合chatgpt fine-tuning格式
|
yhfgyyf/sharegpt-dataset-for-chatgpt-fine-tuning
|
[
"license:apache-2.0",
"region:us"
] |
2023-08-23T13:02:02+00:00
|
{"license": "apache-2.0"}
|
2023-08-23T13:07:03+00:00
|
[] |
[] |
TAGS
#license-apache-2.0 #region-us
|
将sharegpt数据集 转为符合chatgpt fine-tuning格式
|
[] |
[
"TAGS\n#license-apache-2.0 #region-us \n"
] |
[
14
] |
[
"passage: TAGS\n#license-apache-2.0 #region-us \n"
] |
6f0787ded10ffbe8360b6ea7e4768720654b9c81
|
# Dataset Card for "rick"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
dmitrijsk/rick
|
[
"region:us"
] |
2023-08-23T13:02:59+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "validation", "path": "data/validation-*"}, {"split": "test", "path": "data/test-*"}]}], "dataset_info": {"features": [{"name": "context_name", "dtype": "string"}, {"name": "context_name_line", "dtype": "string"}, {"name": "line", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 592370.4808013355, "num_examples": 499}, {"name": "validation", "num_bytes": 59355.75959933222, "num_examples": 50}, {"name": "test", "num_bytes": 59355.75959933222, "num_examples": 50}], "download_size": 417787, "dataset_size": 711082.0}}
|
2023-08-25T13:00:19+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "rick"
More Information needed
|
[
"# Dataset Card for \"rick\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"rick\"\n\nMore Information needed"
] |
[
6,
11
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"rick\"\n\nMore Information needed"
] |
99a07699b09dc9eefa8bc33d1eca7a01ff01c859
|
# Dataset Card for "HWD_Standard_Dataset"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
Baiheng/HWD_Standard_Dataset
|
[
"region:us"
] |
2023-08-23T13:04:03+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "labels", "sequence": "string"}], "splits": [{"name": "train", "num_bytes": 124407555.14, "num_examples": 62706}], "download_size": 173792970, "dataset_size": 124407555.14}}
|
2023-08-23T13:05:16+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "HWD_Standard_Dataset"
More Information needed
|
[
"# Dataset Card for \"HWD_Standard_Dataset\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"HWD_Standard_Dataset\"\n\nMore Information needed"
] |
[
6,
17
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"HWD_Standard_Dataset\"\n\nMore Information needed"
] |
6c650d6a2bd717f917432d46d53b23796aa40c89
|
# Dataset Card for "oa_dolly_15k_fi"
Taken from https://github.com/TurkuNLP/dolly-fi
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
Villekom/oa_dolly_15k_fi
|
[
"region:us"
] |
2023-08-23T13:15:04+00:00
|
{"dataset_info": {"features": [{"name": "INSTRUCTION", "dtype": "string"}, {"name": "RESPONSE", "dtype": "string"}, {"name": "SOURCE", "dtype": "string"}, {"name": "METADATA", "struct": [{"name": "CATEGORY", "dtype": "string"}, {"name": "CONTEXT", "dtype": "string"}]}], "splits": [{"name": "train", "num_bytes": 13654728, "num_examples": 15015}], "download_size": 8698896, "dataset_size": 13654728}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-11-28T10:52:22+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "oa_dolly_15k_fi"
Taken from URL
More Information needed
|
[
"# Dataset Card for \"oa_dolly_15k_fi\"\n\nTaken from URL\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"oa_dolly_15k_fi\"\n\nTaken from URL\n\nMore Information needed"
] |
[
6,
24
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"oa_dolly_15k_fi\"\n\nTaken from URL\n\nMore Information needed"
] |
9a287419fbfd81e924b6abae052344148fa7c4cf
|
# AutoTrain Dataset for project: test-summarization
## Dataset Description
This dataset has been automatically processed by AutoTrain for project test-summarization.
### Languages
The BCP-47 code for the dataset's language is en.
## Dataset Structure
### Data Instances
A sample from this dataset looks as follows:
```json
[
{
"feat_id": "train_1087",
"text": "#Person1#: Hello sir, how can I help you?\n#Person2#: Yes, I need this prescription please.\n#Person1#: Let's see. Okay, so 50 mg of Prozac, would you prefer this in capsule or tablet?\n#Person2#: Capsules are fine.\n#Person1#: Okay, you should take 1 capsule 3 times a day. Be sure not to take it on an empty stomach, and also, don't ever mix it with alcohol!\n#Person2#: Yes, I know. It's not the first time I'm taking this! Don't worry, I won't overdose!\n#Person1#: Okay, anything else I can get you?\n#Person2#: Oh, yes, I almost forgot! Can I also get some eye drops and um, some condoms?\n#Person1#: Sure. Darn condoms aren't registered in our system.\n#Person2#: Oh, well that's okay, I'll get some later, thanks. . . Really it's no problem.\n#Person1#: Just hang on there a sec. Can I get a price check on ' Fun Times Ribbed Condoms ' please!",
"target": "#Person1# gives #Person2# #Person2#'s prescription. #Person2# also wants some eye drops and some condoms but is told that darn condoms are registered in their system.",
"feat_topic": "the pharmacy"
},
{
"feat_id": "train_1193",
"text": "#Person1#: Don't be too sad. If you really think that you have no feeling with him, then, in my opinion, getting divorced maybe is the best way to solve the problem. \n#Person2#: I know clearly at the bottom of my heart. I just can't set my mind at rest because of the child. She's little. She cannot understand us and accept such truth. \n#Person1#: Yeah, child is the matter. Don't tell Jenny the truth, only tell her the white lie. When she grows up, you find the suitable opportunity to tell her. \n#Person2#: I see. OK. ",
"target": "#Person1# suggests #Person2# get divorced if #Person2# has no feeling with a man and tell their daughter the white lie.",
"feat_topic": "divorce"
}
]
```
### Dataset Fields
The dataset has the following fields (also called "features"):
```json
{
"feat_id": "Value(dtype='string', id=None)",
"text": "Value(dtype='string', id=None)",
"target": "Value(dtype='string', id=None)",
"feat_topic": "Value(dtype='string', id=None)"
}
```
### Dataset Splits
This dataset is split into a train and validation split. The split sizes are as follow:
| Split name | Num samples |
| ------------ | ------------------- |
| train | 1599 |
| valid | 400 |
|
neil-code/autotrain-data-test-summarization
|
[
"task_categories:summarization",
"language:en",
"region:us"
] |
2023-08-23T13:15:58+00:00
|
{"language": ["en"], "task_categories": ["summarization"]}
|
2023-08-24T03:20:55+00:00
|
[] |
[
"en"
] |
TAGS
#task_categories-summarization #language-English #region-us
|
AutoTrain Dataset for project: test-summarization
=================================================
Dataset Description
-------------------
This dataset has been automatically processed by AutoTrain for project test-summarization.
### Languages
The BCP-47 code for the dataset's language is en.
Dataset Structure
-----------------
### Data Instances
A sample from this dataset looks as follows:
### Dataset Fields
The dataset has the following fields (also called "features"):
### Dataset Splits
This dataset is split into a train and validation split. The split sizes are as follow:
|
[
"### Languages\n\n\nThe BCP-47 code for the dataset's language is en.\n\n\nDataset Structure\n-----------------",
"### Data Instances\n\n\nA sample from this dataset looks as follows:",
"### Dataset Fields\n\n\nThe dataset has the following fields (also called \"features\"):",
"### Dataset Splits\n\n\nThis dataset is split into a train and validation split. The split sizes are as follow:"
] |
[
"TAGS\n#task_categories-summarization #language-English #region-us \n",
"### Languages\n\n\nThe BCP-47 code for the dataset's language is en.\n\n\nDataset Structure\n-----------------",
"### Data Instances\n\n\nA sample from this dataset looks as follows:",
"### Dataset Fields\n\n\nThe dataset has the following fields (also called \"features\"):",
"### Dataset Splits\n\n\nThis dataset is split into a train and validation split. The split sizes are as follow:"
] |
[
20,
26,
17,
23,
27
] |
[
"passage: TAGS\n#task_categories-summarization #language-English #region-us \n### Languages\n\n\nThe BCP-47 code for the dataset's language is en.\n\n\nDataset Structure\n-----------------### Data Instances\n\n\nA sample from this dataset looks as follows:### Dataset Fields\n\n\nThe dataset has the following fields (also called \"features\"):### Dataset Splits\n\n\nThis dataset is split into a train and validation split. The split sizes are as follow:"
] |
ad42db5eb1efe889d57e2b31bb4a5350bd0349d3
|
# Dataset Card for "directv-zocalos_1.0fps_03-08-2023_05-08-2023"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
Seenka/directv-zocalos_1.0fps_03-08-2023_05-08-2023
|
[
"region:us"
] |
2023-08-23T13:21:22+00:00
|
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "image_filename", "dtype": "string"}, {"name": "frame_time", "dtype": "int64"}, {"name": "video_storage_path", "dtype": "string"}, {"name": "zocalo_id", "dtype": "string"}, {"name": "frame_number", "dtype": "int64"}, {"name": "is_L_shape", "dtype": "bool"}, {"name": "horizontal_check", "dtype": "bool"}, {"name": "vertical_check", "dtype": "bool"}, {"name": "black_image", "dtype": "bool"}, {"name": "horizontal_xmin", "dtype": "int64"}, {"name": "horizontal_xmax", "dtype": "int64"}, {"name": "horizontal_ymin", "dtype": "int64"}, {"name": "horizontal_ymax", "dtype": "int64"}, {"name": "vertical_xmin", "dtype": "int64"}, {"name": "vertical_xmax", "dtype": "int64"}, {"name": "vertical_ymin", "dtype": "int64"}, {"name": "vertical_ymax", "dtype": "int64"}, {"name": "cropped_image_horizontal", "dtype": "image"}, {"name": "cropped_image_vertical", "dtype": "null"}, {"name": "width", "dtype": "int64"}, {"name": "height", "dtype": "int64"}, {"name": "embedding_horizontal", "sequence": "float32"}], "splits": [{"name": "train", "num_bytes": 5576518.0, "num_examples": 26}], "download_size": 3227023, "dataset_size": 5576518.0}}
|
2023-08-23T13:50:06+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "directv-zocalos_1.0fps_03-08-2023_05-08-2023"
More Information needed
|
[
"# Dataset Card for \"directv-zocalos_1.0fps_03-08-2023_05-08-2023\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"directv-zocalos_1.0fps_03-08-2023_05-08-2023\"\n\nMore Information needed"
] |
[
6,
29
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"directv-zocalos_1.0fps_03-08-2023_05-08-2023\"\n\nMore Information needed"
] |
fac665b1bdf07931031a56d00be95fc20f6c3229
|
# Dataset of oboro/朧 (Kantai Collection)
This is the dataset of oboro/朧 (Kantai Collection), containing 500 images and their tags.
The core tags of this character are `short_hair, brown_hair, bandaid_on_face, brown_eyes`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:-----------|:------------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 500 | 404.22 MiB | [Download](https://huggingface.co/datasets/CyberHarem/oboro_kantaicollection/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 500 | 274.00 MiB | [Download](https://huggingface.co/datasets/CyberHarem/oboro_kantaicollection/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 1003 | 546.17 MiB | [Download](https://huggingface.co/datasets/CyberHarem/oboro_kantaicollection/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 500 | 369.79 MiB | [Download](https://huggingface.co/datasets/CyberHarem/oboro_kantaicollection/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 1003 | 710.61 MiB | [Download](https://huggingface.co/datasets/CyberHarem/oboro_kantaicollection/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/oboro_kantaicollection',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:----------------------------------|:----------------------------------|:----------------------------------|:----------------------------------|:----------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 26 |  |  |  |  |  | 1girl, bandaid, solo, alternate_costume, crab, green_coat, green_jacket, blue_hairband, simple_background, striped_shirt, looking_at_viewer, open_mouth, white_background, ahoge, white_shirt, blue_skirt, smile, wristwatch |
| 1 | 9 |  |  |  |  |  | 1girl, bandaid, blue_skirt, pleated_skirt, serafuku, simple_background, white_background, solo, blue_sailor_collar, looking_at_viewer, cowboy_shot, crab, smile |
| 2 | 5 |  |  |  |  |  | bandaid, open_mouth, purple_hair, serafuku, blue_sailor_collar, blue_skirt, hair_bell, hair_flower, jingle_bell, pleated_skirt, short_sleeves, side_ponytail, 2girls, 3girls, blush, crab, solo_focus, :d, ahoge, cherry_blossoms, cowboy_shot, elbow_pads, holding_bottle, light_brown_hair, looking_at_viewer, very_long_hair, white_background |
| 3 | 12 |  |  |  |  |  | 1girl, solo, ahoge, looking_at_viewer, blush, plaid_scarf, smile, blue_skirt, brown_scarf, gift_box, green_coat, long_sleeves, pleated_skirt, crab, green_jacket, holding_gift, open_mouth, bandaid_on_hand, simple_background, upper_body, valentine |
| 4 | 6 |  |  |  |  |  | 1girl, bandaid, cowboy_shot, looking_at_viewer, official_alternate_costume, smile, solo, choker, open_mouth, shirt, yellow_shorts, double-breasted, fang, short_sleeves |
| 5 | 20 |  |  |  |  |  | 1girl, bandaid, solo, looking_at_viewer, collarbone, blush, open_mouth, simple_background, navel, white_background, medium_breasts, crab, cleavage, green_bikini, hair_between_eyes |
| 6 | 12 |  |  |  |  |  | bandaid, 1girl, green_kimono, obi, solo, yukata, crab, hair_ornament, looking_at_viewer, smile, blush, open_mouth, upper_body |
| 7 | 10 |  |  |  |  |  | 1girl, bandaid, solo, medium_breasts, blush, cleavage, crab, green_bra, panties, upper_body, looking_at_viewer, navel, simple_background, underwear_only, yellow_bra |
| 8 | 6 |  |  |  |  |  | 1girl, bandaid, fur_trim, solo, black_pantyhose, long_sleeves, looking_at_viewer, red_dress, red_skirt, cowboy_shot, crab, official_alternate_costume, open_mouth, santa_costume, simple_background, smile, white_background |
| 9 | 8 |  |  |  |  |  | beer_mug, happi, holding, 1girl, bandaid, solo, tray, apron, hairclip, smile, cowboy_shot, looking_at_viewer, fang, official_alternate_costume, open_mouth, pantyhose, simple_background, white_background |
| 10 | 11 |  |  |  |  |  | alternate_costume, bandaid, strapless_leotard, 1girl, detached_collar, medium_breasts, playboy_bunny, wrist_cuffs, rabbit_ears, rabbit_tail, solo, cowboy_shot, crab, looking_at_viewer, brown_pantyhose, green_bowtie, green_leotard, twitter_username, black_leotard, cleavage, fake_animal_ears, sitting |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | bandaid | solo | alternate_costume | crab | green_coat | green_jacket | blue_hairband | simple_background | striped_shirt | looking_at_viewer | open_mouth | white_background | ahoge | white_shirt | blue_skirt | smile | wristwatch | pleated_skirt | serafuku | blue_sailor_collar | cowboy_shot | purple_hair | hair_bell | hair_flower | jingle_bell | short_sleeves | side_ponytail | 2girls | 3girls | blush | solo_focus | :d | cherry_blossoms | elbow_pads | holding_bottle | light_brown_hair | very_long_hair | plaid_scarf | brown_scarf | gift_box | long_sleeves | holding_gift | bandaid_on_hand | upper_body | valentine | official_alternate_costume | choker | shirt | yellow_shorts | double-breasted | fang | collarbone | navel | medium_breasts | cleavage | green_bikini | hair_between_eyes | green_kimono | obi | yukata | hair_ornament | green_bra | panties | underwear_only | yellow_bra | fur_trim | black_pantyhose | red_dress | red_skirt | santa_costume | beer_mug | happi | holding | tray | apron | hairclip | pantyhose | strapless_leotard | detached_collar | playboy_bunny | wrist_cuffs | rabbit_ears | rabbit_tail | brown_pantyhose | green_bowtie | green_leotard | twitter_username | black_leotard | fake_animal_ears | sitting |
|----:|----------:|:----------------------------------|:----------------------------------|:----------------------------------|:----------------------------------|:----------------------------------|:--------|:----------|:-------|:--------------------|:-------|:-------------|:---------------|:----------------|:--------------------|:----------------|:--------------------|:-------------|:-------------------|:--------|:--------------|:-------------|:--------|:-------------|:----------------|:-----------|:---------------------|:--------------|:--------------|:------------|:--------------|:--------------|:----------------|:----------------|:---------|:---------|:--------|:-------------|:-----|:------------------|:-------------|:-----------------|:-------------------|:-----------------|:--------------|:--------------|:-----------|:---------------|:---------------|:------------------|:-------------|:------------|:-----------------------------|:---------|:--------|:----------------|:------------------|:-------|:-------------|:--------|:-----------------|:-----------|:---------------|:--------------------|:---------------|:------|:---------|:----------------|:------------|:----------|:-----------------|:-------------|:-----------|:------------------|:------------|:------------|:----------------|:-----------|:--------|:----------|:-------|:--------|:-----------|:------------|:--------------------|:------------------|:----------------|:--------------|:--------------|:--------------|:------------------|:---------------|:----------------|:-------------------|:----------------|:-------------------|:----------|
| 0 | 26 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 1 | 9 |  |  |  |  |  | X | X | X | | X | | | | X | | X | | X | | | X | X | | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 2 | 5 |  |  |  |  |  | | X | | | X | | | | | | X | X | X | X | | X | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 3 | 12 |  |  |  |  |  | X | | X | | X | X | X | | X | | X | X | | X | | X | X | | X | | | | | | | | | | | | X | | | | | | | | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 4 | 6 |  |  |  |  |  | X | X | X | | | | | | | | X | X | | | | | X | | | | | X | | | | | X | | | | | | | | | | | | | | | | | | | | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 5 | 20 |  |  |  |  |  | X | X | X | | X | | | | X | | X | X | X | | | | | | | | | | | | | | | | | | X | | | | | | | | | | | | | | | | | | | | | | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 6 | 12 |  |  |  |  |  | X | X | X | | X | | | | | | X | X | | | | | X | | | | | | | | | | | | | | X | | | | | | | | | | | | | | X | | | | | | | | | | | | | | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 7 | 10 |  |  |  |  |  | X | X | X | | X | | | | X | | X | | | | | | | | | | | | | | | | | | | | X | | | | | | | | | | | | | | X | | | | | | | | | X | X | X | | | | | | | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | |
| 8 | 6 |  |  |  |  |  | X | X | X | | X | | | | X | | X | X | X | | | | X | | | | | X | | | | | | | | | | | | | | | | | | | | X | | | | | X | | | | | | | | | | | | | | | | | | | | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | |
| 9 | 8 |  |  |  |  |  | X | X | X | | | | | | X | | X | X | X | | | | X | | | | | X | | | | | | | | | | | | | | | | | | | | | | | | | X | | | | | X | | | | | | | | | | | | | | | | | | | | X | X | X | X | X | X | X | | | | | | | | | | | | | |
| 10 | 11 |  |  |  |  |  | X | X | X | X | X | | | | | | X | | | | | | | | | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | X | | | | | | | | | | | | | | | | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | X |
|
CyberHarem/oboro_kantaicollection
|
[
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] |
2023-08-23T13:21:23+00:00
|
{"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]}
|
2024-01-15T06:38:11+00:00
|
[] |
[] |
TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
|
Dataset of oboro/朧 (Kantai Collection)
======================================
This is the dataset of oboro/朧 (Kantai Collection), containing 500 images and their tags.
The core tags of this character are 'short\_hair, brown\_hair, bandaid\_on\_face, brown\_eyes', which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(huggingface organization).
List of Packages
----------------
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code
List of Clusters
----------------
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
### Table Version
|
[
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] |
[
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n",
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] |
[
44,
61,
5,
4
] |
[
"passage: TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.### Raw Text Version### Table Version"
] |
b883db871992be4f48c4b446a45385c040aabb04
|
# Dataset Card for "omni3d_6"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
ZiAngGu/omni3d_6
|
[
"region:us"
] |
2023-08-23T13:24:22+00:00
|
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "conditioning_image", "dtype": "image"}, {"name": "text", "dtype": "string"}, {"name": "label", "sequence": "string"}, {"name": "box2d_pro", "sequence": {"sequence": {"sequence": "int64"}}}], "splits": [{"name": "train", "num_bytes": 24149199949.632, "num_examples": 213792}], "download_size": 23239060092, "dataset_size": 24149199949.632}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-08-23T17:49:11+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "omni3d_6"
More Information needed
|
[
"# Dataset Card for \"omni3d_6\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"omni3d_6\"\n\nMore Information needed"
] |
[
6,
16
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"omni3d_6\"\n\nMore Information needed"
] |
d807fcd4b4b6d6d566f328cbadf43ebc22a3d5a5
|
Using these files requires that you have already agreed to the Natural Scenes Dataset's Terms and Conditions: https://cvnlab.slite.page/p/IB6BSeW_7o/Terms-and-Conditions
Webdatasets only contain behavioral information, .tar filename numbering correspondings to the scanning session of the subject.
(Always use "new_test" instead of "test" in the wds folders, "test" refers to using the old NSD data from before they released the full set of scanning sessions.)
behavior numpy files correspond to the following order, in relation to the variables listed here: https://cvnlab.slite.page/p/fRv4lz5V2F/Untitled
"-1" values were used in place of NaN.
behavior = {
- "cocoidx": int(behav.iloc[jj]['73KID'])-1, #0
- "subject": subject, #1
- "session": int(behav.iloc[jj]['SESSION']), #2
- "run": int(behav.iloc[jj]['RUN']), #3
- "trial": int(behav.iloc[jj]['TRIAL']), #4
- "global_trial": int(i * (tar + 1)), #5
- "time": int(behav.iloc[jj]['TIME']), #6
- "isold": int(behav.iloc[jj]['ISOLD']), #7
- "iscorrect": iscorrect, #8
- "rt": rt, # 0 = no RT #9
- "changemind": changemind, #10
- "isoldcurrent": isoldcurrent, #11
- "iscorrectcurrent": iscorrectcurrent, #12
- "total1": total1, #13
- "total2": total2, #14
- "button": button, #15
- "shared1000": is_shared1000, #16
}
|
pscotti/mindeyev2
|
[
"region:us"
] |
2023-08-23T13:31:23+00:00
|
{}
|
2024-02-12T17:11:05+00:00
|
[] |
[] |
TAGS
#region-us
|
Using these files requires that you have already agreed to the Natural Scenes Dataset's Terms and Conditions: URL
Webdatasets only contain behavioral information, .tar filename numbering correspondings to the scanning session of the subject.
(Always use "new_test" instead of "test" in the wds folders, "test" refers to using the old NSD data from before they released the full set of scanning sessions.)
behavior numpy files correspond to the following order, in relation to the variables listed here: URL
"-1" values were used in place of NaN.
behavior = {
- "cocoidx": int(URL[jj]['73KID'])-1, #0
- "subject": subject, #1
- "session": int(URL[jj]['SESSION']), #2
- "run": int(URL[jj]['RUN']), #3
- "trial": int(URL[jj]['TRIAL']), #4
- "global_trial": int(i * (tar + 1)), #5
- "time": int(URL[jj]['TIME']), #6
- "isold": int(URL[jj]['ISOLD']), #7
- "iscorrect": iscorrect, #8
- "rt": rt, # 0 = no RT #9
- "changemind": changemind, #10
- "isoldcurrent": isoldcurrent, #11
- "iscorrectcurrent": iscorrectcurrent, #12
- "total1": total1, #13
- "total2": total2, #14
- "button": button, #15
- "shared1000": is_shared1000, #16
}
|
[
"# 0 = no RT #9\n- \"changemind\": changemind, #10\n- \"isoldcurrent\": isoldcurrent, #11\n- \"iscorrectcurrent\": iscorrectcurrent, #12\n- \"total1\": total1, #13\n- \"total2\": total2, #14\n- \"button\": button, #15\n- \"shared1000\": is_shared1000, #16\n}"
] |
[
"TAGS\n#region-us \n",
"# 0 = no RT #9\n- \"changemind\": changemind, #10\n- \"isoldcurrent\": isoldcurrent, #11\n- \"iscorrectcurrent\": iscorrectcurrent, #12\n- \"total1\": total1, #13\n- \"total2\": total2, #14\n- \"button\": button, #15\n- \"shared1000\": is_shared1000, #16\n}"
] |
[
6,
96
] |
[
"passage: TAGS\n#region-us \n# 0 = no RT #9\n- \"changemind\": changemind, #10\n- \"isoldcurrent\": isoldcurrent, #11\n- \"iscorrectcurrent\": iscorrectcurrent, #12\n- \"total1\": total1, #13\n- \"total2\": total2, #14\n- \"button\": button, #15\n- \"shared1000\": is_shared1000, #16\n}"
] |
79e76a99d400ad738405d5d76dd2f41f7362708c
|
# Dataset Card for "meta-shepherd-human-data"
Original Dataset: https://github.com/facebookresearch/Shepherd
## Example
```bash
### Question: Where on the planet would you expect a bald eagle to live?
Here are the options:
Option 1: colorado
Option 2: outside
Option 3: protection
Option 4: zoo exhibit
Option 5: world
Please choose the correct option and justify your choice:
### Answer: Bald eagles are found throughout most of North America, from Alaska and Canada south to Mexico. They are also found in some parts of Europe and Asia. In the United States, bald eagles are usually seen near large bodies of water where they hunt for fish. They build their nests high up in tall trees near rivers, lakes, marshes, and coastal areas. Because of hunting and loss of habitat, bald eagle populations declined dramatically during the twentieth century. Conservation efforts have helped increase their numbers, and they were removed from the U.S. Endangered Species List in 2007.
### Feedback: The answer adds extra information, last 2 sentences are unnecessary. It does not state which options specifically are where bald eagles might live, like outside, Colorado, Zoo Exhibit, and World.
```
|
philschmid/meta-shepherd-human-data
|
[
"license:cc-by-nc-4.0",
"region:us"
] |
2023-08-23T13:36:13+00:00
|
{"license": "cc-by-nc-4.0", "dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "dataset", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "answer", "dtype": "string"}, {"name": "feedback", "dtype": "string"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 2588873, "num_examples": 1317}], "download_size": 1319368, "dataset_size": 2588873}}
|
2023-08-23T13:38:43+00:00
|
[] |
[] |
TAGS
#license-cc-by-nc-4.0 #region-us
|
# Dataset Card for "meta-shepherd-human-data"
Original Dataset: URL
## Example
|
[
"# Dataset Card for \"meta-shepherd-human-data\"\n\nOriginal Dataset: URL",
"## Example"
] |
[
"TAGS\n#license-cc-by-nc-4.0 #region-us \n",
"# Dataset Card for \"meta-shepherd-human-data\"\n\nOriginal Dataset: URL",
"## Example"
] |
[
17,
21,
3
] |
[
"passage: TAGS\n#license-cc-by-nc-4.0 #region-us \n# Dataset Card for \"meta-shepherd-human-data\"\n\nOriginal Dataset: URL## Example"
] |
8f5a4460e0d336e013059dec1ccce52361b047c2
|
# Dataset Card for "viquad-qg"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
namngo/viquad-qg
|
[
"region:us"
] |
2023-08-23T13:47:14+00:00
|
{"dataset_info": {"features": [{"name": "question", "dtype": "string"}, {"name": "paragraph", "dtype": "string"}, {"name": "answer", "dtype": "string"}, {"name": "sentence", "dtype": "string"}, {"name": "paragraph_sentence", "dtype": "string"}, {"name": "paragraph_answer", "dtype": "string"}, {"name": "sentence_answer", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 72612959, "num_examples": 18432}, {"name": "validation", "num_bytes": 8472773, "num_examples": 2250}, {"name": "test", "num_bytes": 8472773, "num_examples": 2250}], "download_size": 18598577, "dataset_size": 89558505}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "validation", "path": "data/validation-*"}, {"split": "test", "path": "data/test-*"}]}]}
|
2023-08-23T13:47:21+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "viquad-qg"
More Information needed
|
[
"# Dataset Card for \"viquad-qg\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"viquad-qg\"\n\nMore Information needed"
] |
[
6,
15
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"viquad-qg\"\n\nMore Information needed"
] |
39d691b2e4780f744425cd50dbd9ac33a8c7c08b
|
# Dataset Card for "gpt4-1k-annotations"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
loubnabnl/gpt4-1k-annotations
|
[
"region:us"
] |
2023-08-23T13:47:37+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "completion", "dtype": "string"}, {"name": "eval_prompt_header", "dtype": "string"}, {"name": "generation_config", "struct": [{"name": "do_sample", "dtype": "bool"}, {"name": "temperature", "dtype": "float64"}, {"name": "top_p", "dtype": "float64"}]}, {"name": "metadata", "struct": [{"name": "timestamp", "dtype": "string"}]}, {"name": "prompt", "dtype": "string"}, {"name": "review_model", "dtype": "string"}, {"name": "score", "dtype": "float64"}, {"name": "code_file", "dtype": "string"}, {"name": "size", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 7384714, "num_examples": 1000}], "download_size": 2350749, "dataset_size": 7384714}}
|
2023-08-23T13:47:39+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "gpt4-1k-annotations"
More Information needed
|
[
"# Dataset Card for \"gpt4-1k-annotations\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"gpt4-1k-annotations\"\n\nMore Information needed"
] |
[
6,
18
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"gpt4-1k-annotations\"\n\nMore Information needed"
] |
e0e40ad561efbe6ea047bf3f5eab25ab5db01725
|
# Dataset Card for "image_generation_prompts_SDXL"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
Falah/image_generation_prompts_SDXL
|
[
"region:us"
] |
2023-08-23T13:48:08+00:00
|
{"dataset_info": {"features": [{"name": "prompts", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 305708411, "num_examples": 1000000}], "download_size": 42523277, "dataset_size": 305708411}}
|
2023-08-23T13:48:17+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "image_generation_prompts_SDXL"
More Information needed
|
[
"# Dataset Card for \"image_generation_prompts_SDXL\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"image_generation_prompts_SDXL\"\n\nMore Information needed"
] |
[
6,
21
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"image_generation_prompts_SDXL\"\n\nMore Information needed"
] |
777e40f852540deba3a15fd7128d5f2d13d5db5a
|
# Dataset Card for "bigquery-swift-unfiltered-no-duplicate"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
drewparo/bigquery-swift-filtered-no-duplicate
|
[
"region:us"
] |
2023-08-23T13:56:50+00:00
|
{"dataset_info": {"features": [{"name": "repo_name", "dtype": "string"}, {"name": "ref", "dtype": "string"}, {"name": "path", "dtype": "string"}, {"name": "license", "dtype": "string"}, {"name": "copies", "dtype": "string"}, {"name": "content", "dtype": "string"}, {"name": "hash", "dtype": "string"}, {"name": "line_mean", "dtype": "float64"}, {"name": "line_max", "dtype": "int64"}, {"name": "alpha_frac", "dtype": "float64"}, {"name": "autogenerated", "dtype": "bool"}, {"name": "config_or_test", "dtype": "bool"}, {"name": "has_no_keywords", "dtype": "bool"}, {"name": "has_few_assignments", "dtype": "bool"}], "splits": [{"name": "train", "num_bytes": 1366718500.8048851, "num_examples": 308891}], "download_size": 576460240, "dataset_size": 1366718500.8048851}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-08-23T13:59:01+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "bigquery-swift-unfiltered-no-duplicate"
More Information needed
|
[
"# Dataset Card for \"bigquery-swift-unfiltered-no-duplicate\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"bigquery-swift-unfiltered-no-duplicate\"\n\nMore Information needed"
] |
[
6,
27
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"bigquery-swift-unfiltered-no-duplicate\"\n\nMore Information needed"
] |
60eabb226d040136334954eee0dc5a2ccf863def
|
# Dataset Card for "directv-zocalos_1.0fps_03-07-2023_05-07-2023"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
Seenka/directv-zocalos_1.0fps_03-07-2023_05-07-2023
|
[
"region:us"
] |
2023-08-23T14:10:14+00:00
|
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "image_filename", "dtype": "string"}, {"name": "frame_time", "dtype": "time64[us]"}, {"name": "video_storage_path", "dtype": "string"}, {"name": "zocalo_id", "dtype": "string"}, {"name": "frame_number", "dtype": "int64"}, {"name": "is_L_shape", "dtype": "bool"}, {"name": "horizontal_check", "dtype": "bool"}, {"name": "vertical_check", "dtype": "bool"}, {"name": "black_image", "dtype": "bool"}, {"name": "horizontal_xmin", "dtype": "int64"}, {"name": "horizontal_xmax", "dtype": "int64"}, {"name": "horizontal_ymin", "dtype": "int64"}, {"name": "horizontal_ymax", "dtype": "int64"}, {"name": "vertical_xmin", "dtype": "int64"}, {"name": "vertical_xmax", "dtype": "int64"}, {"name": "vertical_ymin", "dtype": "int64"}, {"name": "vertical_ymax", "dtype": "int64"}, {"name": "cropped_image_horizontal", "dtype": "image"}, {"name": "cropped_image_vertical", "dtype": "null"}, {"name": "width", "dtype": "int64"}, {"name": "height", "dtype": "int64"}, {"name": "embedding_horizontal", "sequence": "float32"}], "splits": [{"name": "train", "num_bytes": 6073069.0, "num_examples": 10}], "download_size": 6023337, "dataset_size": 6073069.0}}
|
2023-08-23T14:10:24+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "directv-zocalos_1.0fps_03-07-2023_05-07-2023"
More Information needed
|
[
"# Dataset Card for \"directv-zocalos_1.0fps_03-07-2023_05-07-2023\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"directv-zocalos_1.0fps_03-07-2023_05-07-2023\"\n\nMore Information needed"
] |
[
6,
29
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"directv-zocalos_1.0fps_03-07-2023_05-07-2023\"\n\nMore Information needed"
] |
8b4b55c7c836532667f37efa643678e74b5968b0
|
# Dataset Card for "Dataset_Ins_Test"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
mischel/Dataset_Ins_Test
|
[
"region:us"
] |
2023-08-23T14:13:09+00:00
|
{"dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 507941, "num_examples": 1661}], "download_size": 139651, "dataset_size": 507941}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-08-28T10:27:19+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "Dataset_Ins_Test"
More Information needed
|
[
"# Dataset Card for \"Dataset_Ins_Test\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"Dataset_Ins_Test\"\n\nMore Information needed"
] |
[
6,
17
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"Dataset_Ins_Test\"\n\nMore Information needed"
] |
b180c9eb20fad416a473d6e712c02c80ed981aac
|
# Dataset Card for "tokenized_bert_context_len_256"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
yardeny/tokenized_bert_context_len_256
|
[
"region:us"
] |
2023-08-23T14:17:35+00:00
|
{"dataset_info": {"features": [{"name": "input_ids", "sequence": "int32"}, {"name": "token_type_ids", "sequence": "int8"}, {"name": "attention_mask", "sequence": "int8"}], "splits": [{"name": "train", "num_bytes": 15879303402, "num_examples": 80462898}], "download_size": 5357270136, "dataset_size": 15879303402}}
|
2023-08-23T17:59:50+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "tokenized_bert_context_len_256"
More Information needed
|
[
"# Dataset Card for \"tokenized_bert_context_len_256\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"tokenized_bert_context_len_256\"\n\nMore Information needed"
] |
[
6,
22
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"tokenized_bert_context_len_256\"\n\nMore Information needed"
] |
90412d502ad47ea691f5bc9dbf63956cbeea3785
|
# Dataset Card for "processed_bert_context_len_256"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
yardeny/processed_bert_context_len_256
|
[
"region:us"
] |
2023-08-23T14:17:39+00:00
|
{"dataset_info": {"features": [{"name": "input_ids", "sequence": "int32"}, {"name": "token_type_ids", "sequence": "int8"}, {"name": "attention_mask", "sequence": "int8"}], "splits": [{"name": "train", "num_bytes": 14968483524.0, "num_examples": 9669563}], "download_size": 5205237526, "dataset_size": 14968483524.0}}
|
2023-08-23T19:22:34+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "processed_bert_context_len_256"
More Information needed
|
[
"# Dataset Card for \"processed_bert_context_len_256\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"processed_bert_context_len_256\"\n\nMore Information needed"
] |
[
6,
21
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"processed_bert_context_len_256\"\n\nMore Information needed"
] |
16c6d09df7a875fd37586fe64f544cacbe935411
|
# Dataset Card for Dataset Name
## Dataset Description
- **Homepage:**
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
This dataset card aims to be a base template for new datasets. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/datasetcard_template.md?plain=1).
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed]
|
ayesh22/Text_Gen_GPT
|
[
"task_categories:table-question-answering",
"size_categories:n<1K",
"language:en",
"license:afl-3.0",
"biology",
"region:us"
] |
2023-08-23T14:20:05+00:00
|
{"language": ["en"], "license": "afl-3.0", "size_categories": ["n<1K"], "task_categories": ["table-question-answering"], "pretty_name": "little insight", "tags": ["biology"]}
|
2023-08-23T14:27:17+00:00
|
[] |
[
"en"
] |
TAGS
#task_categories-table-question-answering #size_categories-n<1K #language-English #license-afl-3.0 #biology #region-us
|
# Dataset Card for Dataset Name
## Dataset Description
- Homepage:
- Repository:
- Paper:
- Leaderboard:
- Point of Contact:
### Dataset Summary
This dataset card aims to be a base template for new datasets. It has been generated using this raw template.
### Supported Tasks and Leaderboards
### Languages
## Dataset Structure
### Data Instances
### Data Fields
### Data Splits
## Dataset Creation
### Curation Rationale
### Source Data
#### Initial Data Collection and Normalization
#### Who are the source language producers?
### Annotations
#### Annotation process
#### Who are the annotators?
### Personal and Sensitive Information
## Considerations for Using the Data
### Social Impact of Dataset
### Discussion of Biases
### Other Known Limitations
## Additional Information
### Dataset Curators
### Licensing Information
### Contributions
|
[
"# Dataset Card for Dataset Name",
"## Dataset Description\n\n- Homepage: \n- Repository: \n- Paper: \n- Leaderboard: \n- Point of Contact:",
"### Dataset Summary\n\nThis dataset card aims to be a base template for new datasets. It has been generated using this raw template.",
"### Supported Tasks and Leaderboards",
"### Languages",
"## Dataset Structure",
"### Data Instances",
"### Data Fields",
"### Data Splits",
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"## Considerations for Using the Data",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations",
"## Additional Information",
"### Dataset Curators",
"### Licensing Information",
"### Contributions"
] |
[
"TAGS\n#task_categories-table-question-answering #size_categories-n<1K #language-English #license-afl-3.0 #biology #region-us \n",
"# Dataset Card for Dataset Name",
"## Dataset Description\n\n- Homepage: \n- Repository: \n- Paper: \n- Leaderboard: \n- Point of Contact:",
"### Dataset Summary\n\nThis dataset card aims to be a base template for new datasets. It has been generated using this raw template.",
"### Supported Tasks and Leaderboards",
"### Languages",
"## Dataset Structure",
"### Data Instances",
"### Data Fields",
"### Data Splits",
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"### Personal and Sensitive Information",
"## Considerations for Using the Data",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations",
"## Additional Information",
"### Dataset Curators",
"### Licensing Information",
"### Contributions"
] |
[
45,
8,
24,
32,
10,
4,
6,
6,
5,
5,
5,
7,
4,
10,
10,
5,
5,
9,
8,
8,
7,
8,
7,
5,
6,
6,
5
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[
"passage: TAGS\n#task_categories-table-question-answering #size_categories-n<1K #language-English #license-afl-3.0 #biology #region-us \n# Dataset Card for Dataset Name## Dataset Description\n\n- Homepage: \n- Repository: \n- Paper: \n- Leaderboard: \n- Point of Contact:### Dataset Summary\n\nThis dataset card aims to be a base template for new datasets. It has been generated using this raw template.### Supported Tasks and Leaderboards### Languages## Dataset Structure### Data Instances### Data Fields### Data Splits## Dataset Creation### Curation Rationale### Source Data#### Initial Data Collection and Normalization#### Who are the source language producers?### Annotations#### Annotation process#### Who are the annotators?### Personal and Sensitive Information## Considerations for Using the Data### Social Impact of Dataset### Discussion of Biases### Other Known Limitations## Additional Information### Dataset Curators### Licensing Information### Contributions"
] |
36577818194dc76e08a24c53ace5416d554c3b34
|
# Dataset Card for Evaluation run of openlm-research/open_llama_7b_v2
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/openlm-research/open_llama_7b_v2
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** [email protected]
### Dataset Summary
Dataset automatically created during the evaluation run of model [openlm-research/open_llama_7b_v2](https://huggingface.co/openlm-research/open_llama_7b_v2) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 122 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 5 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_openlm-research__open_llama_7b_v2",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-15T02:55:02.194930](https://huggingface.co/datasets/open-llm-leaderboard/details_openlm-research__open_llama_7b_v2/blob/main/results_2023-10-15T02-55-02.194930.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"em": 0.001153523489932886,
"em_stderr": 0.0003476179896857104,
"f1": 0.05493078859060421,
"f1_stderr": 0.0013198629767466948,
"acc": 0.36431985198420513,
"acc_stderr": 0.009003933368912346
},
"harness|drop|3": {
"em": 0.001153523489932886,
"em_stderr": 0.0003476179896857104,
"f1": 0.05493078859060421,
"f1_stderr": 0.0013198629767466948
},
"harness|gsm8k|5": {
"acc": 0.034874905231235785,
"acc_stderr": 0.0050534807650222295
},
"harness|winogrande|5": {
"acc": 0.6937647987371744,
"acc_stderr": 0.012954385972802462
}
}
```
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed]
|
open-llm-leaderboard/details_openlm-research__open_llama_7b_v2
|
[
"region:us"
] |
2023-08-23T14:23:05+00:00
|
{"pretty_name": "Evaluation run of openlm-research/open_llama_7b_v2", "dataset_summary": "Dataset automatically created during the evaluation run of model [openlm-research/open_llama_7b_v2](https://huggingface.co/openlm-research/open_llama_7b_v2) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 122 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 5 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\nTo load the details from a run, you can for instance do the following:\n```python\nfrom datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_openlm-research__open_llama_7b_v2\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-10-15T02:55:02.194930](https://huggingface.co/datasets/open-llm-leaderboard/details_openlm-research__open_llama_7b_v2/blob/main/results_2023-10-15T02-55-02.194930.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):\n\n```python\n{\n \"all\": {\n \"em\": 0.001153523489932886,\n \"em_stderr\": 0.0003476179896857104,\n \"f1\": 0.05493078859060421,\n \"f1_stderr\": 0.0013198629767466948,\n \"acc\": 0.36431985198420513,\n \"acc_stderr\": 0.009003933368912346\n },\n \"harness|drop|3\": {\n \"em\": 0.001153523489932886,\n \"em_stderr\": 0.0003476179896857104,\n \"f1\": 0.05493078859060421,\n \"f1_stderr\": 0.0013198629767466948\n },\n \"harness|gsm8k|5\": {\n \"acc\": 0.034874905231235785,\n \"acc_stderr\": 0.0050534807650222295\n },\n \"harness|winogrande|5\": {\n \"acc\": 0.6937647987371744,\n \"acc_stderr\": 0.012954385972802462\n }\n}\n```", "repo_url": "https://huggingface.co/openlm-research/open_llama_7b_v2", "leaderboard_url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard", "point_of_contact": "[email protected]", "configs": [{"config_name": "harness_arc_challenge_25", "data_files": [{"split": 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|
2023-10-15T01:55:10+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for Evaluation run of openlm-research/open_llama_7b_v2
## Dataset Description
- Homepage:
- Repository: URL
- Paper:
- Leaderboard: URL
- Point of Contact: clementine@URL
### Dataset Summary
Dataset automatically created during the evaluation run of model openlm-research/open_llama_7b_v2 on the Open LLM Leaderboard.
The dataset is composed of 122 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 5 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).
To load the details from a run, you can for instance do the following:
## Latest results
These are the latest results from run 2023-10-15T02:55:02.194930(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
### Supported Tasks and Leaderboards
### Languages
## Dataset Structure
### Data Instances
### Data Fields
### Data Splits
## Dataset Creation
### Curation Rationale
### Source Data
#### Initial Data Collection and Normalization
#### Who are the source language producers?
### Annotations
#### Annotation process
#### Who are the annotators?
### Personal and Sensitive Information
## Considerations for Using the Data
### Social Impact of Dataset
### Discussion of Biases
### Other Known Limitations
## Additional Information
### Dataset Curators
### Licensing Information
### Contributions
|
[
"# Dataset Card for Evaluation run of openlm-research/open_llama_7b_v2",
"## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL",
"### Dataset Summary\n\nDataset automatically created during the evaluation run of model openlm-research/open_llama_7b_v2 on the Open LLM Leaderboard.\n\nThe dataset is composed of 122 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 5 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2023-10-15T02:55:02.194930(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"### Supported Tasks and Leaderboards",
"### Languages",
"## Dataset Structure",
"### Data Instances",
"### Data Fields",
"### Data Splits",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?",
"### Annotations",
"#### Annotation process",
"#### Who are the annotators?",
"### Personal and Sensitive Information",
"## Considerations for Using the Data",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations",
"## Additional Information",
"### Dataset Curators",
"### Licensing Information",
"### Contributions"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for Evaluation run of openlm-research/open_llama_7b_v2",
"## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL",
"### Dataset Summary\n\nDataset automatically created during the evaluation run of model openlm-research/open_llama_7b_v2 on the Open LLM Leaderboard.\n\nThe dataset is composed of 122 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 5 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2023-10-15T02:55:02.194930(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"### Supported Tasks and Leaderboards",
"### Languages",
"## Dataset Structure",
"### Data Instances",
"### Data Fields",
"### Data Splits",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?",
"### Annotations",
"#### Annotation process",
"#### Who are the annotators?",
"### Personal and Sensitive Information",
"## Considerations for Using the Data",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations",
"## Additional Information",
"### Dataset Curators",
"### Licensing Information",
"### Contributions"
] |
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[
"passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of openlm-research/open_llama_7b_v2## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL### Dataset Summary\n\nDataset automatically created during the evaluation run of model openlm-research/open_llama_7b_v2 on the Open LLM Leaderboard.\n\nThe dataset is composed of 122 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 5 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2023-10-15T02:55:02.194930(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):### Supported Tasks and Leaderboards### Languages## Dataset Structure### Data Instances### Data Fields### Data Splits## Dataset Creation### Curation Rationale### Source Data#### Initial Data Collection and Normalization#### Who are the source language producers?### Annotations#### Annotation process#### Who are the annotators?### Personal and Sensitive Information## Considerations for Using the Data### Social Impact of Dataset### Discussion of Biases### Other Known Limitations## Additional Information### Dataset Curators### Licensing Information### Contributions"
] |
720b7572b1b1b44eec11f49e5c28a2f7430a6a0b
|
This is a filtered ConvoEvol for sub4000 tokens on LLAMA-2 encoder
There is 4.7k turns of human/assistant conversations in this dataset. Unfiltered for refusals (future work)
|
NobodyExistsOnTheInternet/sub4000ctx
|
[
"license:mit",
"region:us"
] |
2023-08-23T14:40:43+00:00
|
{"license": "mit"}
|
2023-08-28T04:45:52+00:00
|
[] |
[] |
TAGS
#license-mit #region-us
|
This is a filtered ConvoEvol for sub4000 tokens on LLAMA-2 encoder
There is 4.7k turns of human/assistant conversations in this dataset. Unfiltered for refusals (future work)
|
[] |
[
"TAGS\n#license-mit #region-us \n"
] |
[
11
] |
[
"passage: TAGS\n#license-mit #region-us \n"
] |
3c7ef9d31a6b743c7b1a083b5ac5e176ec99c1af
|
# Dataset Card for Dataset Name
## Dataset Description
- **Homepage:**
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
This dataset card aims to be a base template for new datasets. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/datasetcard_template.md?plain=1).
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed]
|
aspringer207/PublicDomainMaps
|
[
"task_categories:token-classification",
"task_categories:text-classification",
"size_categories:n<1K",
"language:en",
"license:openrail",
"art",
"region:us"
] |
2023-08-23T14:43:39+00:00
|
{"language": ["en"], "license": "openrail", "size_categories": ["n<1K"], "task_categories": ["token-classification", "text-classification"], "pretty_name": "PDMAPS", "tags": ["art"]}
|
2023-08-23T22:28:23+00:00
|
[] |
[
"en"
] |
TAGS
#task_categories-token-classification #task_categories-text-classification #size_categories-n<1K #language-English #license-openrail #art #region-us
|
# Dataset Card for Dataset Name
## Dataset Description
- Homepage:
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- Paper:
- Leaderboard:
- Point of Contact:
### Dataset Summary
This dataset card aims to be a base template for new datasets. It has been generated using this raw template.
### Supported Tasks and Leaderboards
### Languages
## Dataset Structure
### Data Instances
### Data Fields
### Data Splits
## Dataset Creation
### Curation Rationale
### Source Data
#### Initial Data Collection and Normalization
#### Who are the source language producers?
### Annotations
#### Annotation process
#### Who are the annotators?
### Personal and Sensitive Information
## Considerations for Using the Data
### Social Impact of Dataset
### Discussion of Biases
### Other Known Limitations
## Additional Information
### Dataset Curators
### Licensing Information
### Contributions
|
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"passage: TAGS\n#task_categories-token-classification #task_categories-text-classification #size_categories-n<1K #language-English #license-openrail #art #region-us \n# Dataset Card for Dataset Name## Dataset Description\n\n- Homepage: \n- Repository: \n- Paper: \n- Leaderboard: \n- Point of Contact:### Dataset Summary\n\nThis dataset card aims to be a base template for new datasets. It has been generated using this raw template.### Supported Tasks and Leaderboards### Languages## Dataset Structure### Data Instances### Data Fields### Data Splits## Dataset Creation### Curation Rationale### Source Data#### Initial Data Collection and Normalization#### Who are the source language producers?### Annotations#### Annotation process#### Who are the annotators?### Personal and Sensitive Information## Considerations for Using the Data### Social Impact of Dataset### Discussion of Biases### Other Known Limitations## Additional Information### Dataset Curators### Licensing Information### Contributions"
] |
a006a40054ba54d3e977916ffebdb0a9ff916721
|
# Dataset Card for Dataset Name
## Dataset Description
- **Homepage:**
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
This dataset card aims to be a base template for new datasets. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/datasetcard_template.md?plain=1).
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed]
|
Siyoun/exercise
|
[
"region:us"
] |
2023-08-23T14:45:31+00:00
|
{}
|
2023-08-24T12:03:04+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for Dataset Name
## Dataset Description
- Homepage:
- Repository:
- Paper:
- Leaderboard:
- Point of Contact:
### Dataset Summary
This dataset card aims to be a base template for new datasets. It has been generated using this raw template.
### Supported Tasks and Leaderboards
### Languages
## Dataset Structure
### Data Instances
### Data Fields
### Data Splits
## Dataset Creation
### Curation Rationale
### Source Data
#### Initial Data Collection and Normalization
#### Who are the source language producers?
### Annotations
#### Annotation process
#### Who are the annotators?
### Personal and Sensitive Information
## Considerations for Using the Data
### Social Impact of Dataset
### Discussion of Biases
### Other Known Limitations
## Additional Information
### Dataset Curators
### Licensing Information
### Contributions
|
[
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] |
8c197b204ccdd856078899f5f3324da9901ff8dc
|
This repository holds archives (zip files) of main versions of ShapeNetCore, a subset of [ShapeNet](https://shapenet.org).
ShapeNetCore is a densely annotated subset of ShapeNet covering 55 common object categories with ~51,300 unique 3D models. Each model in ShapeNetCore are linked to an appropriate synset in [WordNet 3.0](https://wordnet.princeton.edu/).
Please see [DATA.md](DATA.md) for details about the data.
If you use ShapeNet data, you agree to abide by the [ShapeNet terms of use](https://shapenet.org/terms). You are only allowed to redistribute the data to your research associates and colleagues provided that they first agree to be bound by these terms and conditions.
If you use this data, please cite the main ShapeNet technical report.
```
@techreport{shapenet2015,
title = {{ShapeNet: An Information-Rich 3D Model Repository}},
author = {Chang, Angel X. and Funkhouser, Thomas and Guibas, Leonidas and Hanrahan, Pat and Huang, Qixing and Li, Zimo and Savarese, Silvio and Savva, Manolis and Song, Shuran and Su, Hao and Xiao, Jianxiong and Yi, Li and Yu, Fisher},
number = {arXiv:1512.03012 [cs.GR]},
institution = {Stanford University --- Princeton University --- Toyota Technological Institute at Chicago},
year = {2015}
}
```
For more information, please contact us at [email protected] and indicate ShapeNetCore v2 in the title of your email.
|
ShapeNet/ShapeNetCore-archive
|
[
"language:en",
"license:other",
"3D shapes",
"region:us"
] |
2023-08-23T14:55:19+00:00
|
{"language": ["en"], "license": "other", "pretty_name": "ShapeNetCore", "tags": ["3D shapes"], "extra_gated_heading": "Acknowledge license to accept the repository", "extra_gated_prompt": "To request access to this ShapeNet repo, you will need to provide your **full name** (please provide both your first and last name), the name of your **advisor or the principal investigator (PI)** of your lab (in the PI/Advisor) fields, and the **school or company** that you are affiliated with (the **Affiliation** field). After requesting access to this ShapeNet repo, you will be considered for access approval. \n\nAfter access approval, you (the \"Researcher\") receive permission to use the ShapeNet database (the \"Database\") at Princeton University and Stanford University. In exchange for being able to join the ShapeNet community and receive such permission, Researcher hereby agrees to the following terms and conditions: Researcher shall use the Database only for non-commercial research and educational purposes. Princeton University and Stanford University make no representations or warranties regarding the Database, including but not limited to warranties of non-infringement or fitness for a particular purpose. Researcher accepts full responsibility for his or her use of the Database and shall defend and indemnify Princeton University and Stanford University, including their employees, Trustees, officers and agents, against any and all claims arising from Researcher's use of the Database, including but not limited to Researcher's use of any copies of copyrighted 3D models that he or she may create from the Database. Researcher may provide research associates and colleagues with access to the Database provided that they first agree to be bound by these terms and conditions. Princeton University and Stanford University reserve the right to terminate Researcher's access to the Database at any time. If Researcher is employed by a for-profit, commercial entity, Researcher's employer shall also be bound by these terms and conditions, and Researcher hereby represents that he or she is fully authorized to enter into this agreement on behalf of such employer. The law of the State of New Jersey shall apply to all disputes under this agreement.\n\nFor access to the data, please fill in your **full name** (both first and last name), the name of your **advisor or principal investigator (PI)**, and the name of the **school or company** you are affliated with. Please actually fill out the fields (DO NOT put the word \"Advisor\" for PI/Advisor and the word \"School\" for \"Affiliation\", please specify the name of your advisor and the name of your school).", "extra_gated_fields": {"Name": "text", "PI/Advisor": "text", "Affiliation": "text", "Purpose": "text", "Country": "text", "I agree to use this dataset for non-commercial use ONLY": "checkbox"}}
|
2023-09-20T14:05:16+00:00
|
[] |
[
"en"
] |
TAGS
#language-English #license-other #3D shapes #region-us
|
This repository holds archives (zip files) of main versions of ShapeNetCore, a subset of ShapeNet.
ShapeNetCore is a densely annotated subset of ShapeNet covering 55 common object categories with ~51,300 unique 3D models. Each model in ShapeNetCore are linked to an appropriate synset in WordNet 3.0.
Please see URL for details about the data.
If you use ShapeNet data, you agree to abide by the ShapeNet terms of use. You are only allowed to redistribute the data to your research associates and colleagues provided that they first agree to be bound by these terms and conditions.
If you use this data, please cite the main ShapeNet technical report.
For more information, please contact us at shapenetwebmaster@URL and indicate ShapeNetCore v2 in the title of your email.
|
[] |
[
"TAGS\n#language-English #license-other #3D shapes #region-us \n"
] |
[
19
] |
[
"passage: TAGS\n#language-English #license-other #3D shapes #region-us \n"
] |
b57dd1397753c1f98e5c2bea534a55fe346a8406
|
# Dataset of sagiri (Kantai Collection)
This is the dataset of sagiri (Kantai Collection), containing 388 images and their tags.
The core tags of this character are `grey_hair, long_hair, bangs, purple_eyes, hairband, swept_bangs, asymmetrical_bangs, breasts`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:-----------|:-------------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 388 | 361.79 MiB | [Download](https://huggingface.co/datasets/CyberHarem/sagiri_kantaicollection/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 388 | 234.38 MiB | [Download](https://huggingface.co/datasets/CyberHarem/sagiri_kantaicollection/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 864 | 480.09 MiB | [Download](https://huggingface.co/datasets/CyberHarem/sagiri_kantaicollection/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 388 | 332.49 MiB | [Download](https://huggingface.co/datasets/CyberHarem/sagiri_kantaicollection/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 864 | 631.69 MiB | [Download](https://huggingface.co/datasets/CyberHarem/sagiri_kantaicollection/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/sagiri_kantaicollection',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 21 |  |  |  |  |  | 1girl, casual_one-piece_swimsuit, hair_flower, looking_at_viewer, official_alternate_costume, solo, white_one-piece_swimsuit, earrings, frilled_swimsuit, cowboy_shot, highleg_swimsuit, shawl, covered_navel, small_breasts, white_choker |
| 1 | 13 |  |  |  |  |  | 1girl, solo, long_sleeves, simple_background, smile, open_mouth, white_background, blush, looking_at_viewer, official_alternate_costume, holding, twitter_username, white_dress |
| 2 | 13 |  |  |  |  |  | 1girl, solo, white_shirt, looking_at_viewer, simple_background, black_choker, black_hairband, suspender_skirt, plaid_skirt, white_background, bag, blush, boots, official_alternate_costume, open_mouth, smile, twitter_username, umbrella, upper_body |
| 3 | 9 |  |  |  |  |  | 1girl, alternate_costume, solo, looking_at_viewer, purple_shirt, white_skirt, blouse, smile, long_sleeves, simple_background, bag, long_skirt, white_background |
| 4 | 12 |  |  |  |  |  | 1girl, grey_sailor_collar, grey_skirt, pleated_skirt, serafuku, short_sleeves, solo, looking_at_viewer, simple_background, smile, bow, grey_ribbon, blue_hairband, purple_hairband, white_background, cowboy_shot, twitter_username |
| 5 | 10 |  |  |  |  |  | yukata, 1girl, obi, solo, open_mouth, smile, looking_at_viewer, simple_background, alternate_costume, alternate_hairstyle, white_background, floral_print, hair_ornament, wide_sleeves |
| 6 | 5 |  |  |  |  |  | 1girl, detached_collar, fake_animal_ears, looking_at_viewer, playboy_bunny, rabbit_ears, simple_background, solo, strapless_leotard, white_background, wrist_cuffs, alternate_costume, blush, open_mouth, small_breasts, bowtie, cowboy_shot, white_leotard, ass_visible_through_thighs, bare_shoulders, blue_bow, covered_navel, pantyhose, rabbit_tail |
| 7 | 7 |  |  |  |  |  | 1girl, bar_censor, blush, hetero, penis, 1boy, solo_focus, open_mouth, sex, vaginal, blue_hairband, bra, cum, nipples, nude, small_breasts, tears |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | casual_one-piece_swimsuit | hair_flower | looking_at_viewer | official_alternate_costume | solo | white_one-piece_swimsuit | earrings | frilled_swimsuit | cowboy_shot | highleg_swimsuit | shawl | covered_navel | small_breasts | white_choker | long_sleeves | simple_background | smile | open_mouth | white_background | blush | holding | twitter_username | white_dress | white_shirt | black_choker | black_hairband | suspender_skirt | plaid_skirt | bag | boots | umbrella | upper_body | alternate_costume | purple_shirt | white_skirt | blouse | long_skirt | grey_sailor_collar | grey_skirt | pleated_skirt | serafuku | short_sleeves | bow | grey_ribbon | blue_hairband | purple_hairband | yukata | obi | alternate_hairstyle | floral_print | hair_ornament | wide_sleeves | detached_collar | fake_animal_ears | playboy_bunny | rabbit_ears | strapless_leotard | wrist_cuffs | bowtie | white_leotard | ass_visible_through_thighs | bare_shoulders | blue_bow | pantyhose | rabbit_tail | bar_censor | hetero | penis | 1boy | solo_focus | sex | vaginal | bra | cum | nipples | nude | tears |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:----------------------------|:--------------|:--------------------|:-----------------------------|:-------|:---------------------------|:-----------|:-------------------|:--------------|:-------------------|:--------|:----------------|:----------------|:---------------|:---------------|:--------------------|:--------|:-------------|:-------------------|:--------|:----------|:-------------------|:--------------|:--------------|:---------------|:-----------------|:------------------|:--------------|:------|:--------|:-----------|:-------------|:--------------------|:---------------|:--------------|:---------|:-------------|:---------------------|:-------------|:----------------|:-----------|:----------------|:------|:--------------|:----------------|:------------------|:---------|:------|:----------------------|:---------------|:----------------|:---------------|:------------------|:-------------------|:----------------|:--------------|:--------------------|:--------------|:---------|:----------------|:-----------------------------|:-----------------|:-----------|:------------|:--------------|:-------------|:---------|:--------|:-------|:-------------|:------|:----------|:------|:------|:----------|:-------|:--------|
| 0 | 21 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 1 | 13 |  |  |  |  |  | X | | | X | X | X | | | | | | | | | | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 2 | 13 |  |  |  |  |  | X | | | X | X | X | | | | | | | | | | | X | X | X | X | X | | X | | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 3 | 9 |  |  |  |  |  | X | | | X | | X | | | | | | | | | | X | X | X | | X | | | | | | | | | | X | | | | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 4 | 12 |  |  |  |  |  | X | | | X | | X | | | | X | | | | | | | X | X | | X | | | X | | | | | | | | | | | | | | | | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 5 | 10 |  |  |  |  |  | X | | | X | | X | | | | | | | | | | | X | X | X | X | | | | | | | | | | | | | | X | | | | | | | | | | | | | | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | |
| 6 | 5 |  |  |  |  |  | X | | | X | | X | | | | X | | | X | X | | | X | | X | X | X | | | | | | | | | | | | | X | | | | | | | | | | | | | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | |
| 7 | 7 |  |  |  |  |  | X | | | | | | | | | | | | | X | | | | | X | | X | | | | | | | | | | | | | | | | | | | | | | | | | X | | | | | | | | | | | | | | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | X |
|
CyberHarem/sagiri_kantaicollection
|
[
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] |
2023-08-23T15:06:02+00:00
|
{"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]}
|
2024-01-16T07:06:18+00:00
|
[] |
[] |
TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
|
Dataset of sagiri (Kantai Collection)
=====================================
This is the dataset of sagiri (Kantai Collection), containing 388 images and their tags.
The core tags of this character are 'grey\_hair, long\_hair, bangs, purple\_eyes, hairband, swept\_bangs, asymmetrical\_bangs, breasts', which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(huggingface organization).
List of Packages
----------------
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code
List of Clusters
----------------
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
### Table Version
|
[
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] |
[
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n",
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] |
[
44,
61,
5,
4
] |
[
"passage: TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.### Raw Text Version### Table Version"
] |
931267b6ea453f53cfee1bf7a0d875cdb9a34aef
|
# Dataset Card for "logits-korpus_malti-512"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
amitness/logits-korpus_malti-512
|
[
"region:us"
] |
2023-08-23T15:09:26+00:00
|
{"dataset_info": {"features": [{"name": "input_ids", "sequence": "int32"}, {"name": "token_type_ids", "sequence": "int8"}, {"name": "attention_mask", "sequence": "int8"}, {"name": "labels", "sequence": "int64"}, {"name": "teacher_logits", "sequence": {"sequence": "float64"}}, {"name": "teacher_indices", "sequence": {"sequence": "int64"}}, {"name": "teacher_mask_indices", "sequence": "int64"}], "splits": [{"name": "train", "num_bytes": 13752300816, "num_examples": 892332}], "download_size": 4804356040, "dataset_size": 13752300816}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-08-23T21:34:22+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "logits-korpus_malti-512"
More Information needed
|
[
"# Dataset Card for \"logits-korpus_malti-512\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"logits-korpus_malti-512\"\n\nMore Information needed"
] |
[
6,
20
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"logits-korpus_malti-512\"\n\nMore Information needed"
] |
a5b6cc5f121147330d39df0137ca9c83a85632dd
|
# Dataset of junyou/隼鷹 (Kantai Collection)
This is the dataset of junyou/隼鷹 (Kantai Collection), containing 442 images and their tags.
The core tags of this character are `purple_hair, long_hair, spiked_hair, purple_eyes, breasts, large_breasts`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:-----------|:-------------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 442 | 380.79 MiB | [Download](https://huggingface.co/datasets/CyberHarem/junyou_kantaicollection/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 442 | 269.48 MiB | [Download](https://huggingface.co/datasets/CyberHarem/junyou_kantaicollection/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 914 | 516.33 MiB | [Download](https://huggingface.co/datasets/CyberHarem/junyou_kantaicollection/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 442 | 357.74 MiB | [Download](https://huggingface.co/datasets/CyberHarem/junyou_kantaicollection/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 914 | 652.11 MiB | [Download](https://huggingface.co/datasets/CyberHarem/junyou_kantaicollection/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/junyou_kantaicollection',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 7 |  |  |  |  |  | 1girl, blouse, dress_shirt, hakama_pants, magatama, onmyouji, red_hakama, shikigami, smile, solo, hakama_skirt, red_pants, looking_at_viewer, scroll, simple_background, full_body, open_mouth, airplane, white_background |
| 1 | 5 |  |  |  |  |  | 1girl, blouse, dress_shirt, hakama_pants, red_hakama, red_pants, smile, solo, blush, open_mouth, vest, magatama_earrings, onmyouji, scroll, shikigami, airplane, fire, looking_at_viewer |
| 2 | 6 |  |  |  |  |  | 1girl, blush, dress_shirt, drunk, smile, solo, hakama_pants, magatama, sake_bottle, sitting, open_mouth, red_hakama, red_pants, vest, blouse, looking_at_viewer |
| 3 | 5 |  |  |  |  |  | 1girl, magatama, smile, solo, japanese_clothes, onmyouji, scroll, shikigami, dress_shirt, looking_at_viewer, open_mouth |
| 4 | 9 |  |  |  |  |  | 1girl, looking_at_viewer, magatama, solo, shirt, blush, grin, upper_body |
| 5 | 11 |  |  |  |  |  | 1girl, blush, solo, nude, partially_submerged, looking_at_viewer, water, onsen, smile, open_mouth, sake, bathing, steam, tokkuri, cleavage, choko_(cup), nipples, sakazuki |
| 6 | 5 |  |  |  |  |  | 2girls, blush, brown_hair, open_mouth, smile, magatama, cup, dress_shirt, ^_^ |
| 7 | 12 |  |  |  |  |  | playboy_bunny, 1girl, detached_collar, fake_animal_ears, rabbit_ears, solo, wrist_cuffs, cleavage, pantyhose, blush, leotard, looking_at_viewer, simple_background, bowtie, magatama, rabbit_tail, fishnets, grin |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | blouse | dress_shirt | hakama_pants | magatama | onmyouji | red_hakama | shikigami | smile | solo | hakama_skirt | red_pants | looking_at_viewer | scroll | simple_background | full_body | open_mouth | airplane | white_background | blush | vest | magatama_earrings | fire | drunk | sake_bottle | sitting | japanese_clothes | shirt | grin | upper_body | nude | partially_submerged | water | onsen | sake | bathing | steam | tokkuri | cleavage | choko_(cup) | nipples | sakazuki | 2girls | brown_hair | cup | ^_^ | playboy_bunny | detached_collar | fake_animal_ears | rabbit_ears | wrist_cuffs | pantyhose | leotard | bowtie | rabbit_tail | fishnets |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:---------|:--------------|:---------------|:-----------|:-----------|:-------------|:------------|:--------|:-------|:---------------|:------------|:--------------------|:---------|:--------------------|:------------|:-------------|:-----------|:-------------------|:--------|:-------|:--------------------|:-------|:--------|:--------------|:----------|:-------------------|:--------|:-------|:-------------|:-------|:----------------------|:--------|:--------|:-------|:----------|:--------|:----------|:-----------|:--------------|:----------|:-----------|:---------|:-------------|:------|:------|:----------------|:------------------|:-------------------|:--------------|:--------------|:------------|:----------|:---------|:--------------|:-----------|
| 0 | 7 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 1 | 5 |  |  |  |  |  | X | X | X | X | | X | X | X | X | X | | X | X | X | | | X | X | | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 2 | 6 |  |  |  |  |  | X | X | X | X | X | | X | | X | X | | X | X | | | | X | | | X | X | | | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 3 | 5 |  |  |  |  |  | X | | X | | X | X | | X | X | X | | | X | X | | | X | | | | | | | | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 4 | 9 |  |  |  |  |  | X | | | | X | | | | | X | | | X | | | | | | | X | | | | | | | | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 5 | 11 |  |  |  |  |  | X | | | | | | | | X | X | | | X | | | | X | | | X | | | | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | |
| 6 | 5 |  |  |  |  |  | | | X | | X | | | | X | | | | | | | | X | | | X | | | | | | | | | | | | | | | | | | | | | | | X | X | X | X | | | | | | | | | | |
| 7 | 12 |  |  |  |  |  | X | | | | X | | | | | X | | | X | | X | | | | | X | | | | | | | | | X | | | | | | | | | | X | | | | | | | | X | X | X | X | X | X | X | X | X | X |
|
CyberHarem/junyou_kantaicollection
|
[
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] |
2023-08-23T16:08:19+00:00
|
{"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]}
|
2024-01-15T10:16:51+00:00
|
[] |
[] |
TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
|
Dataset of junyou/隼鷹 (Kantai Collection)
========================================
This is the dataset of junyou/隼鷹 (Kantai Collection), containing 442 images and their tags.
The core tags of this character are 'purple\_hair, long\_hair, spiked\_hair, purple\_eyes, breasts, large\_breasts', which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(huggingface organization).
List of Packages
----------------
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code
List of Clusters
----------------
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
### Table Version
|
[
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] |
[
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n",
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] |
[
44,
61,
5,
4
] |
[
"passage: TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.### Raw Text Version### Table Version"
] |
776122e170781aeef522ad7c512698e120581c9a
|
# FactChecks.br
## Dataset Description
- **Homepage:**
- **Repository:** [github.com/fake-news-UFG/FactChecks.br](github.com/fake-news-UFG/FactChecks.br)
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
Collection of Portuguese Fact-Checking Benchmarks.
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
The dataset is in Portuguese.
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
If you use "FactChecks.br Dataset", please include a cite:
```bibtex
@misc{FactChecksbr,
author = {R. S. Gomes, Juliana},
title = {FactChecks.br},
url = {https://github.com/fake-news-UFG/FactChecks.br},
doi = { 10.57967/hf/1016 },
}
```
### Contributions
Thanks to [@ju-resplande](https://github.com/ju-resplande) for adding this dataset.
|
fake-news-UFG/FactChecksbr
|
[
"task_categories:text-classification",
"size_categories:10K<n<100K",
"language:pt",
"license:mit",
"doi:10.57967/hf/1016",
"region:us"
] |
2023-08-23T16:15:02+00:00
|
{"language": ["pt"], "license": "mit", "size_categories": ["10K<n<100K"], "task_categories": ["text-classification"], "pretty_name": "FactChecks.br"}
|
2023-08-24T16:40:04+00:00
|
[] |
[
"pt"
] |
TAGS
#task_categories-text-classification #size_categories-10K<n<100K #language-Portuguese #license-mit #doi-10.57967/hf/1016 #region-us
|
# URL
## Dataset Description
- Homepage:
- Repository: URL
- Paper:
- Leaderboard:
- Point of Contact:
### Dataset Summary
Collection of Portuguese Fact-Checking Benchmarks.
### Supported Tasks and Leaderboards
### Languages
The dataset is in Portuguese.
## Dataset Structure
### Data Instances
### Data Fields
### Data Splits
## Dataset Creation
### Curation Rationale
### Source Data
#### Initial Data Collection and Normalization
#### Who are the source language producers?
### Annotations
#### Annotation process
#### Who are the annotators?
### Personal and Sensitive Information
## Considerations for Using the Data
### Social Impact of Dataset
### Discussion of Biases
### Other Known Limitations
## Additional Information
### Dataset Curators
### Licensing Information
If you use "URL Dataset", please include a cite:
### Contributions
Thanks to @ju-resplande for adding this dataset.
|
[
"# URL",
"## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: \n- Point of Contact:",
"### Dataset Summary\n\nCollection of Portuguese Fact-Checking Benchmarks.",
"### Supported Tasks and Leaderboards",
"### Languages\n\nThe dataset is in Portuguese.",
"## Dataset Structure",
"### Data Instances",
"### Data Fields",
"### Data Splits",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?",
"### Annotations",
"#### Annotation process",
"#### Who are the annotators?",
"### Personal and Sensitive Information",
"## Considerations for Using the Data",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations",
"## Additional Information",
"### Dataset Curators",
"### Licensing Information\n\n\n\n\n\nIf you use \"URL Dataset\", please include a cite:",
"### Contributions\n\nThanks to @ju-resplande for adding this dataset."
] |
[
"TAGS\n#task_categories-text-classification #size_categories-10K<n<100K #language-Portuguese #license-mit #doi-10.57967/hf/1016 #region-us \n",
"# URL",
"## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: \n- Point of Contact:",
"### Dataset Summary\n\nCollection of Portuguese Fact-Checking Benchmarks.",
"### Supported Tasks and Leaderboards",
"### Languages\n\nThe dataset is in Portuguese.",
"## Dataset Structure",
"### Data Instances",
"### Data Fields",
"### Data Splits",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?",
"### Annotations",
"#### Annotation process",
"#### Who are the annotators?",
"### Personal and Sensitive Information",
"## Considerations for Using the Data",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations",
"## Additional Information",
"### Dataset Curators",
"### Licensing Information\n\n\n\n\n\nIf you use \"URL Dataset\", please include a cite:",
"### Contributions\n\nThanks to @ju-resplande for adding this dataset."
] |
[
52,
2,
25,
19,
10,
13,
6,
6,
5,
5,
5,
7,
4,
10,
10,
5,
5,
9,
8,
8,
7,
8,
7,
5,
6,
19,
19
] |
[
"passage: TAGS\n#task_categories-text-classification #size_categories-10K<n<100K #language-Portuguese #license-mit #doi-10.57967/hf/1016 #region-us \n# URL## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: \n- Point of Contact:### Dataset Summary\n\nCollection of Portuguese Fact-Checking Benchmarks.### Supported Tasks and Leaderboards### Languages\n\nThe dataset is in Portuguese.## Dataset Structure### Data Instances### Data Fields### Data Splits## Dataset Creation### Curation Rationale### Source Data#### Initial Data Collection and Normalization#### Who are the source language producers?### Annotations#### Annotation process#### Who are the annotators?### Personal and Sensitive Information## Considerations for Using the Data### Social Impact of Dataset### Discussion of Biases### Other Known Limitations## Additional Information### Dataset Curators### Licensing Information\n\n\n\n\n\nIf you use \"URL Dataset\", please include a cite:### Contributions\n\nThanks to @ju-resplande for adding this dataset."
] |
497f6acf342f41c06aa45e27b0d809d4adcecbbe
|
# Dataset Card for HiFiTTS
Hi-Fi Multi-Speaker English TTS Dataset (Hi-Fi TTS) is based on LibriVox's public domain audio books and Gutenberg Project texts.
|
MikhailT/hifi-tts-light
|
[
"language:en",
"license:cc-by-4.0",
"region:us"
] |
2023-08-23T16:20:18+00:00
|
{"language": ["en"], "license": ["cc-by-4.0"], "pretty_name": "HiFiTTS", "configs": [{"config_name": "clean", "version": "1.0.0", "data_files": [{"split": "train", "path": "data/train.clean*.parquet"}, {"split": "test", "path": "data/test.clean*.parquet"}, {"split": "dev", "path": "data/dev.clean*.parquet"}]}, {"config_name": "other", "version": "1.0.0", "data_files": [{"split": "train", "path": "data/train.other*.parquet"}, {"split": "test", "path": "data/test.other*.parquet"}, {"split": "dev", "path": "data/dev.other*.parquet"}]}, {"config_name": "all", "version": "1.0.0", "data_files": [{"split": "train.clean", "path": "data/train.clean*.parquet"}, {"split": "train.other", "path": "data/train.other*.parquet"}, {"split": "test.clean", "path": "data/test.clean*.parquet"}, {"split": "test.other", "path": "data/test.other*.parquet"}, {"split": "dev.clean", "path": "data/dev.clean*.parquet"}, {"split": "dev.other", "path": "data/dev.other*.parquet"}]}], "dataset_info": [{"config_name": "clean", "features": [{"name": "speaker", "dtype": "string"}, {"name": "file", "dtype": "string"}, {"name": "duration", "dtype": "float32"}, {"name": "text", "dtype": "string"}, {"name": "text_no_preprocessing", "dtype": "string"}, {"name": "text_normalized", "dtype": "string"}, {"name": "audio", "dtype": {"audio": {"sampling_rate": 44100}}}], "splits": [{"name": "train", "num_bytes": 1158544, "num_examples": 9}, {"name": "dev", "num_bytes": 904913, "num_examples": 9}, {"name": "test", "num_bytes": 800999, "num_examples": 9}], "download_size": 0, "dataset_size": 2864456}, {"config_name": "other", "features": [{"name": "speaker", "dtype": "string"}, {"name": "file", "dtype": "string"}, {"name": "duration", "dtype": "float32"}, {"name": "text", "dtype": "string"}, {"name": "text_no_preprocessing", "dtype": "string"}, {"name": "text_normalized", "dtype": "string"}, {"name": "audio", "dtype": {"audio": {"sampling_rate": 44100}}}], "splits": [{"name": "train", "num_bytes": 3632881, "num_examples": 21}, {"name": "dev", "num_bytes": 3255234, "num_examples": 18}, {"name": "test", "num_bytes": 3180854, "num_examples": 18}], "download_size": 0, "dataset_size": 10068969}, {"config_name": "all", "features": [{"name": "speaker", "dtype": "string"}, {"name": "file", "dtype": "string"}, {"name": "duration", "dtype": "float32"}, {"name": "text", "dtype": "string"}, {"name": "text_no_preprocessing", "dtype": "string"}, {"name": "text_normalized", "dtype": "string"}, {"name": "audio", "dtype": {"audio": {"sampling_rate": 44100}}}], "splits": [{"name": "train.clean", "num_bytes": 1158544, "num_examples": 9}, {"name": "train.other", "num_bytes": 3632881, "num_examples": 21}, {"name": "dev.clean", "num_bytes": 904913, "num_examples": 9}, {"name": "dev.other", "num_bytes": 3255234, "num_examples": 18}, {"name": "test.clean", "num_bytes": 800999, "num_examples": 9}, {"name": "test.other", "num_bytes": 3180854, "num_examples": 18}], "download_size": 0, "dataset_size": 12933425}], "description": "Hi-Fi Multi-Speaker English TTS Dataset (Hi-Fi TTS) is based on LibriVox's public domain audio books and Gutenberg Project texts.", "homepage": "http://www.openslr.org/109", "citation": "@article{bakhturina2021hi,\n title={{Hi-Fi Multi-Speaker English TTS Dataset}},\n author={Bakhturina, Evelina and Lavrukhin, Vitaly and Ginsburg, Boris and Zhang, Yang},\n journal={arXiv preprint arXiv:2104.01497},\n year={2021}\n}\n"}
|
2023-08-24T12:24:33+00:00
|
[] |
[
"en"
] |
TAGS
#language-English #license-cc-by-4.0 #region-us
|
# Dataset Card for HiFiTTS
Hi-Fi Multi-Speaker English TTS Dataset (Hi-Fi TTS) is based on LibriVox's public domain audio books and Gutenberg Project texts.
|
[
"# Dataset Card for HiFiTTS\n\nHi-Fi Multi-Speaker English TTS Dataset (Hi-Fi TTS) is based on LibriVox's public domain audio books and Gutenberg Project texts."
] |
[
"TAGS\n#language-English #license-cc-by-4.0 #region-us \n",
"# Dataset Card for HiFiTTS\n\nHi-Fi Multi-Speaker English TTS Dataset (Hi-Fi TTS) is based on LibriVox's public domain audio books and Gutenberg Project texts."
] |
[
19,
47
] |
[
"passage: TAGS\n#language-English #license-cc-by-4.0 #region-us \n# Dataset Card for HiFiTTS\n\nHi-Fi Multi-Speaker English TTS Dataset (Hi-Fi TTS) is based on LibriVox's public domain audio books and Gutenberg Project texts."
] |
2cfcbfcc5db2c6a6c55adf65dc81a6f95744c526
|
# Dataset Card for Evaluation run of facebook/opt-66b
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/facebook/opt-66b
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** [email protected]
### Dataset Summary
Dataset automatically created during the evaluation run of model [facebook/opt-66b](https://huggingface.co/facebook/opt-66b) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 122 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 5 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_facebook__opt-66b",
"harness_gsm8k_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-12-03T00:30:57.404111](https://huggingface.co/datasets/open-llm-leaderboard/details_facebook__opt-66b/blob/main/results_2023-12-03T00-30-57.404111.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"acc": 0.016679302501895376,
"acc_stderr": 0.0035275958887224556
},
"harness|gsm8k|5": {
"acc": 0.016679302501895376,
"acc_stderr": 0.0035275958887224556
}
}
```
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed]
|
open-llm-leaderboard/details_facebook__opt-66b
|
[
"region:us"
] |
2023-08-23T17:08:15+00:00
|
{"pretty_name": "Evaluation run of facebook/opt-66b", "dataset_summary": "Dataset automatically created during the evaluation run of model [facebook/opt-66b](https://huggingface.co/facebook/opt-66b) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 122 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 5 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\nTo load the details from a run, you can for instance do the following:\n```python\nfrom datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_facebook__opt-66b\",\n\t\"harness_gsm8k_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-12-03T00:30:57.404111](https://huggingface.co/datasets/open-llm-leaderboard/details_facebook__opt-66b/blob/main/results_2023-12-03T00-30-57.404111.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):\n\n```python\n{\n \"all\": {\n \"acc\": 0.016679302501895376,\n \"acc_stderr\": 0.0035275958887224556\n },\n \"harness|gsm8k|5\": {\n \"acc\": 0.016679302501895376,\n \"acc_stderr\": 0.0035275958887224556\n }\n}\n```", "repo_url": "https://huggingface.co/facebook/opt-66b", "leaderboard_url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard", "point_of_contact": "[email protected]", "configs": [{"config_name": "harness_arc_challenge_25", "data_files": [{"split": "2023_08_23T18_07_59.118983", "path": ["**/details_harness|arc:challenge|25_2023-08-23T18:07:59.118983.parquet"]}, {"split": "2023_08_24T00_29_23.220857", "path": ["**/details_harness|arc:challenge|25_2023-08-24T00:29:23.220857.parquet"]}, {"split": "latest", "path": ["**/details_harness|arc:challenge|25_2023-08-24T00:29:23.220857.parquet"]}]}, {"config_name": "harness_drop_3", "data_files": [{"split": "2023_09_09T17_37_15.988083", "path": ["**/details_harness|drop|3_2023-09-09T17-37-15.988083.parquet"]}, {"split": "latest", "path": ["**/details_harness|drop|3_2023-09-09T17-37-15.988083.parquet"]}]}, {"config_name": "harness_gsm8k_5", "data_files": [{"split": "2023_09_09T17_37_15.988083", "path": ["**/details_harness|gsm8k|5_2023-09-09T17-37-15.988083.parquet"]}, {"split": "2023_12_03T00_30_57.404111", "path": ["**/details_harness|gsm8k|5_2023-12-03T00-30-57.404111.parquet"]}, {"split": "latest", "path": ["**/details_harness|gsm8k|5_2023-12-03T00-30-57.404111.parquet"]}]}, {"config_name": "harness_hellaswag_10", "data_files": [{"split": "2023_08_23T18_07_59.118983", "path": ["**/details_harness|hellaswag|10_2023-08-23T18:07:59.118983.parquet"]}, {"split": "2023_08_24T00_29_23.220857", "path": ["**/details_harness|hellaswag|10_2023-08-24T00:29:23.220857.parquet"]}, {"split": "latest", "path": ["**/details_harness|hellaswag|10_2023-08-24T00:29:23.220857.parquet"]}]}, {"config_name": "harness_hendrycksTest_5", "data_files": 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2023-12-03T00:31:05+00:00
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TAGS
#region-us
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# Dataset Card for Evaluation run of facebook/opt-66b
## Dataset Description
- Homepage:
- Repository: URL
- Paper:
- Leaderboard: URL
- Point of Contact: clementine@URL
### Dataset Summary
Dataset automatically created during the evaluation run of model facebook/opt-66b on the Open LLM Leaderboard.
The dataset is composed of 122 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 5 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).
To load the details from a run, you can for instance do the following:
## Latest results
These are the latest results from run 2023-12-03T00:30:57.404111(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
### Supported Tasks and Leaderboards
### Languages
## Dataset Structure
### Data Instances
### Data Fields
### Data Splits
## Dataset Creation
### Curation Rationale
### Source Data
#### Initial Data Collection and Normalization
#### Who are the source language producers?
### Annotations
#### Annotation process
#### Who are the annotators?
### Personal and Sensitive Information
## Considerations for Using the Data
### Social Impact of Dataset
### Discussion of Biases
### Other Known Limitations
## Additional Information
### Dataset Curators
### Licensing Information
### Contributions
|
[
"# Dataset Card for Evaluation run of facebook/opt-66b",
"## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL",
"### Dataset Summary\n\nDataset automatically created during the evaluation run of model facebook/opt-66b on the Open LLM Leaderboard.\n\nThe dataset is composed of 122 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 5 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2023-12-03T00:30:57.404111(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"### Supported Tasks and Leaderboards",
"### Languages",
"## Dataset Structure",
"### Data Instances",
"### Data Fields",
"### Data Splits",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?",
"### Annotations",
"#### Annotation process",
"#### Who are the annotators?",
"### Personal and Sensitive Information",
"## Considerations for Using the Data",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations",
"## Additional Information",
"### Dataset Curators",
"### Licensing Information",
"### Contributions"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for Evaluation run of facebook/opt-66b",
"## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL",
"### Dataset Summary\n\nDataset automatically created during the evaluation run of model facebook/opt-66b on the Open LLM Leaderboard.\n\nThe dataset is composed of 122 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 5 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2023-12-03T00:30:57.404111(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"### Supported Tasks and Leaderboards",
"### Languages",
"## Dataset Structure",
"### Data Instances",
"### Data Fields",
"### Data Splits",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?",
"### Annotations",
"#### Annotation process",
"#### Who are the annotators?",
"### Personal and Sensitive Information",
"## Considerations for Using the Data",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations",
"## Additional Information",
"### Dataset Curators",
"### Licensing Information",
"### Contributions"
] |
[
6,
16,
31,
165,
68,
10,
4,
6,
6,
5,
5,
5,
7,
4,
10,
10,
5,
5,
9,
8,
8,
7,
8,
7,
5,
6,
6,
5
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of facebook/opt-66b## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL### Dataset Summary\n\nDataset automatically created during the evaluation run of model facebook/opt-66b on the Open LLM Leaderboard.\n\nThe dataset is composed of 122 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 5 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2023-12-03T00:30:57.404111(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):### Supported Tasks and Leaderboards### Languages## Dataset Structure### Data Instances### Data Fields### Data Splits## Dataset Creation### Curation Rationale### Source Data#### Initial Data Collection and Normalization#### Who are the source language producers?### Annotations#### Annotation process#### Who are the annotators?### Personal and Sensitive Information## Considerations for Using the Data### Social Impact of Dataset### Discussion of Biases### Other Known Limitations## Additional Information### Dataset Curators### Licensing Information### Contributions"
] |
5b17445865d287efcc4bdf43a790bbcb39579fea
|
# Dataset of fujinami/藤波/藤波 (Kantai Collection)
This is the dataset of fujinami/藤波/藤波 (Kantai Collection), containing 278 images and their tags.
The core tags of this character are `ahoge, long_hair, ribbon, side_ponytail, hair_ribbon, yellow_eyes, bangs, white_ribbon, black_hair, bow, purple_hair, asymmetrical_bangs`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:-----------|:---------------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 278 | 247.18 MiB | [Download](https://huggingface.co/datasets/CyberHarem/fujinami_kantaicollection/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 278 | 169.41 MiB | [Download](https://huggingface.co/datasets/CyberHarem/fujinami_kantaicollection/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 601 | 340.78 MiB | [Download](https://huggingface.co/datasets/CyberHarem/fujinami_kantaicollection/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 278 | 228.59 MiB | [Download](https://huggingface.co/datasets/CyberHarem/fujinami_kantaicollection/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 601 | 438.21 MiB | [Download](https://huggingface.co/datasets/CyberHarem/fujinami_kantaicollection/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/fujinami_kantaicollection',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:----------------------------------|:----------------------------------|:----------------------------------|:----------------------------------|:----------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 6 |  |  |  |  |  | 1girl, alternate_costume, looking_at_viewer, simple_background, solo, white_background, black_shorts, smile, clothes_writing, full_body, standing, white_shirt, closed_mouth, navel, short_sleeves, small_breasts, sportswear, t-shirt |
| 1 | 25 |  |  |  |  |  | 1girl, bowtie, school_uniform, solo, white_shirt, long_sleeves, sleeveless_dress, purple_dress, looking_at_viewer, simple_background, white_background, smile, upper_body, blue_bow |
| 2 | 16 |  |  |  |  |  | 1girl, looking_at_viewer, school_uniform, sleeveless_dress, solo, white_shirt, cowboy_shot, grey_pantyhose, long_sleeves, purple_dress, aqua_bowtie, smile, simple_background, pleated_dress, blue_bowtie, white_background, blush |
| 3 | 5 |  |  |  |  |  | 1girl, bowtie, grey_pantyhose, grin, lace-up_boots, long_sleeves, school_uniform, sleeveless_dress, solo, white_shirt, full_body, machinery, rigging, smokestack, torpedo_tubes, turret, looking_at_viewer, purple_dress, standing, white_background, blush, cannon, character_name, mast, pleated_dress, seamed_legwear, simple_background |
| 4 | 5 |  |  |  |  |  | 1girl, bowtie, full_body, grey_pantyhose, lace-up_boots, long_sleeves, school_uniform, sleeveless_dress, solo, standing, white_shirt, looking_at_viewer, simple_background, purple_dress, smile, pleated_dress, white_background |
| 5 | 15 |  |  |  |  |  | 1girl, solo, alternate_costume, long_sleeves, simple_background, bespectacled, blue_jacket, looking_at_viewer, open_clothes, smile, collarbone, white_background, open_mouth, shoulder_bag, yellow_dress, handbag, mary_janes |
| 6 | 13 |  |  |  |  |  | 1girl, solo, small_breasts, cowboy_shot, looking_at_viewer, navel, bikini, one-piece_swimsuit, smile |
| 7 | 5 |  |  |  |  |  | 1girl, blue_sky, cloud, day, looking_at_viewer, solo, outdoors, small_breasts, smile, ass, cowboy_shot, ocean, standing, from_behind, horizon, navel, open_mouth, side-tie_bikini_bottom |
| 8 | 9 |  |  |  |  |  | 1girl, small_breasts, solo, striped_bra, striped_panties, grey_panties, looking_at_viewer, blush, navel, grey_bra, simple_background, underwear_only, collarbone, cowboy_shot, open_mouth, white_background |
| 9 | 10 |  |  |  |  |  | 1girl, alternate_costume, obi, solo, yukata, floral_print, food, holding, looking_at_viewer, pink_kimono, simple_background, white_background, full_body, mouth_hold, print_kimono, smile, wide_sleeves |
| 10 | 6 |  |  |  |  |  | detached_collar, fake_animal_ears, playboy_bunny, purple_leotard, rabbit_ears, wrist_cuffs, 1girl, rabbit_tail, simple_background, solo, strapless_leotard, aqua_bowtie, blue_bow, fishnet_pantyhose, full_body, smile, thighband_pantyhose, white_background, adapted_costume, grey_pantyhose, looking_at_viewer, small_breasts |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | alternate_costume | looking_at_viewer | simple_background | solo | white_background | black_shorts | smile | clothes_writing | full_body | standing | white_shirt | closed_mouth | navel | short_sleeves | small_breasts | sportswear | t-shirt | bowtie | school_uniform | long_sleeves | sleeveless_dress | purple_dress | upper_body | blue_bow | cowboy_shot | grey_pantyhose | aqua_bowtie | pleated_dress | blue_bowtie | blush | grin | lace-up_boots | machinery | rigging | smokestack | torpedo_tubes | turret | cannon | character_name | mast | seamed_legwear | bespectacled | blue_jacket | open_clothes | collarbone | open_mouth | shoulder_bag | yellow_dress | handbag | mary_janes | bikini | one-piece_swimsuit | blue_sky | cloud | day | outdoors | ass | ocean | from_behind | horizon | side-tie_bikini_bottom | striped_bra | striped_panties | grey_panties | grey_bra | underwear_only | obi | yukata | floral_print | food | holding | pink_kimono | mouth_hold | print_kimono | wide_sleeves | detached_collar | fake_animal_ears | playboy_bunny | purple_leotard | rabbit_ears | wrist_cuffs | rabbit_tail | strapless_leotard | fishnet_pantyhose | thighband_pantyhose | adapted_costume |
|----:|----------:|:----------------------------------|:----------------------------------|:----------------------------------|:----------------------------------|:----------------------------------|:--------|:--------------------|:--------------------|:--------------------|:-------|:-------------------|:---------------|:--------|:------------------|:------------|:-----------|:--------------|:---------------|:--------|:----------------|:----------------|:-------------|:----------|:---------|:-----------------|:---------------|:-------------------|:---------------|:-------------|:-----------|:--------------|:-----------------|:--------------|:----------------|:--------------|:--------|:-------|:----------------|:------------|:----------|:-------------|:----------------|:---------|:---------|:-----------------|:-------|:-----------------|:---------------|:--------------|:---------------|:-------------|:-------------|:---------------|:---------------|:----------|:-------------|:---------|:---------------------|:-----------|:--------|:------|:-----------|:------|:--------|:--------------|:----------|:-------------------------|:--------------|:------------------|:---------------|:-----------|:-----------------|:------|:---------|:---------------|:-------|:----------|:--------------|:-------------|:---------------|:---------------|:------------------|:-------------------|:----------------|:-----------------|:--------------|:--------------|:--------------|:--------------------|:--------------------|:----------------------|:------------------|
| 0 | 6 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 1 | 25 |  |  |  |  |  | X | | X | X | X | X | | X | | | | X | | | | | | | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 2 | 16 |  |  |  |  |  | X | | X | X | X | X | | X | | | | X | | | | | | | | X | X | X | X | | | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 3 | 5 |  |  |  |  |  | X | | X | X | X | X | | | | X | X | X | | | | | | | X | X | X | X | X | | | | X | | X | | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 4 | 5 |  |  |  |  |  | X | | X | X | X | X | | X | | X | X | X | | | | | | | X | X | X | X | X | | | | X | | X | | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 5 | 15 |  |  |  |  |  | X | X | X | X | X | X | | X | | | | | | | | | | | | | X | | | | | | | | | | | | | | | | | | | | | | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 6 | 13 |  |  |  |  |  | X | | X | | X | | | X | | | | | | X | | X | | | | | | | | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 7 | 5 |  |  |  |  |  | X | | X | | X | | | X | | | X | | | X | | X | | | | | | | | | | X | | | | | | | | | | | | | | | | | | | | | X | | | | | | | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | |
| 8 | 9 |  |  |  |  |  | X | | X | X | X | X | | | | | | | | X | | X | | | | | | | | | | X | | | | | X | | | | | | | | | | | | | | | X | X | | | | | | | | | | | | | | | | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | |
| 9 | 10 |  |  |  |  |  | X | X | X | X | X | X | | X | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | X | X | X | X | X | X | X | X | | | | | | | | | | | |
| 10 | 6 |  |  |  |  |  | X | | X | X | X | X | | X | | X | | | | | | X | | | | | | | | | X | | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | X | X | X | X | X | X | X | X | X | X |
|
CyberHarem/fujinami_kantaicollection
|
[
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] |
2023-08-23T17:08:22+00:00
|
{"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]}
|
2024-01-15T22:28:52+00:00
|
[] |
[] |
TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
|
Dataset of fujinami/藤波/藤波 (Kantai Collection)
=============================================
This is the dataset of fujinami/藤波/藤波 (Kantai Collection), containing 278 images and their tags.
The core tags of this character are 'ahoge, long\_hair, ribbon, side\_ponytail, hair\_ribbon, yellow\_eyes, bangs, white\_ribbon, black\_hair, bow, purple\_hair, asymmetrical\_bangs', which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(huggingface organization).
List of Packages
----------------
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code
List of Clusters
----------------
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
### Table Version
|
[
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] |
[
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n",
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] |
[
44,
61,
5,
4
] |
[
"passage: TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.### Raw Text Version### Table Version"
] |
8aa7625fbf268ec5092818ad944f1f101764a553
|
# Dataset Card for "logits-italian-512"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
amitness/logits-italian-512
|
[
"region:us"
] |
2023-08-23T17:17:21+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "input_ids", "sequence": "int32"}, {"name": "token_type_ids", "sequence": "int8"}, {"name": "attention_mask", "sequence": "int8"}, {"name": "labels", "sequence": "int64"}, {"name": "teacher_logits", "sequence": {"sequence": "float64"}}, {"name": "teacher_indices", "sequence": {"sequence": "int64"}}, {"name": "teacher_mask_indices", "sequence": "int64"}], "splits": [{"name": "train", "num_bytes": 37372996972, "num_examples": 2055197}], "download_size": 13720127033, "dataset_size": 37372996972}}
|
2023-09-21T21:01:23+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "logits-italian-512"
More Information needed
|
[
"# Dataset Card for \"logits-italian-512\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"logits-italian-512\"\n\nMore Information needed"
] |
[
6,
17
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"logits-italian-512\"\n\nMore Information needed"
] |
8a3a9b4c48621e2586c0c4795fdc7aa2e00990be
|
# Dataset Card for "mathy-phase2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
approach0/mathy-phase2
|
[
"region:us"
] |
2023-08-23T18:02:05+00:00
|
{"dataset_info": {"features": [{"name": "problem", "dtype": "string"}, {"name": "query", "dtype": "string"}, {"name": "prompt", "dtype": "string"}, {"name": "solution", "dtype": "string"}, {"name": "ground_truth", "dtype": "null"}, {"name": "judge_buffer", "dtype": "null"}, {"name": "manual_query", "dtype": "null"}, {"name": "manual_rating", "dtype": "int64"}, {"name": "args", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 470590.71186440677, "num_examples": 114}, {"name": "test", "num_bytes": 260063.28813559323, "num_examples": 63}], "download_size": 0, "dataset_size": 730654.0}}
|
2023-08-23T23:25:38+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "mathy-phase2"
More Information needed
|
[
"# Dataset Card for \"mathy-phase2\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"mathy-phase2\"\n\nMore Information needed"
] |
[
6,
15
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"mathy-phase2\"\n\nMore Information needed"
] |
fa887ac3b93be69a92faf0c44114d0759331cc9b
|
# Dataset of tanikaze/谷風 (Kantai Collection)
This is the dataset of tanikaze/谷風 (Kantai Collection), containing 261 images and their tags.
The core tags of this character are `short_hair, hairband, black_hair, brown_eyes, brown_hair, white_hairband`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:-----------|:---------------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 261 | 171.87 MiB | [Download](https://huggingface.co/datasets/CyberHarem/tanikaze_kantaicollection/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 261 | 129.56 MiB | [Download](https://huggingface.co/datasets/CyberHarem/tanikaze_kantaicollection/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 508 | 242.29 MiB | [Download](https://huggingface.co/datasets/CyberHarem/tanikaze_kantaicollection/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 261 | 163.22 MiB | [Download](https://huggingface.co/datasets/CyberHarem/tanikaze_kantaicollection/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 508 | 291.25 MiB | [Download](https://huggingface.co/datasets/CyberHarem/tanikaze_kantaicollection/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/tanikaze_kantaicollection',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 11 |  |  |  |  |  | 1girl, pleated_skirt, serafuku, short_sleeves, solo, white_gloves, white_thighhighs, yellow_neckerchief, grey_skirt, smile, blue_sailor_collar, full_body, looking_at_viewer, white_background, simple_background, standing, open_mouth, white_shirt |
| 1 | 21 |  |  |  |  |  | 1girl, pleated_skirt, serafuku, solo, neckerchief, white_gloves, white_thighhighs, looking_at_viewer, open_mouth, short_sleeves, white_background, :d, machinery, sitting |
| 2 | 12 |  |  |  |  |  | 1girl, pleated_skirt, serafuku, short_sleeves, solo, white_thighhighs, green_panties, simple_background, white_shirt, blouse, blue_sailor_collar, grey_skirt, white_background, yellow_neckerchief, blush, sitting, white_gloves, looking_at_viewer, small_breasts, spread_legs |
| 3 | 5 |  |  |  |  |  | 1girl, blush, looking_at_viewer, navel, serafuku, solo, white_thighhighs, torn_skirt, torn_thighhighs, white_background, white_gloves, sitting, small_breasts, torn_shirt, open_mouth, simple_background, tears, tongue_out, white_panties |
| 4 | 5 |  |  |  |  |  | 1girl, serafuku, skirt, solo, tears, torn_thighhighs, white_thighhighs, blush, looking_at_viewer, open_mouth, smile, navel, sitting, white_gloves |
| 5 | 5 |  |  |  |  |  | 1girl, blush, hetero, nipples, 1boy, closed_eyes, nude, open_mouth, thighhighs, bar_censor, cum_in_pussy, navel, small_breasts, solo_focus, vaginal, cowgirl_position, girl_on_top, hair_between_eyes, heart, penis, sex_from_behind, simple_background, sweat, tears, white_background, white_gloves |
| 6 | 5 |  |  |  |  |  | 1girl, playboy_bunny, rabbit_ears, solo, strapless_leotard, wrist_cuffs, detached_collar, fake_animal_ears, simple_background, white_background, black_leotard, black_pantyhose, looking_at_viewer, rabbit_tail, small_breasts, alternate_costume, fishnet_pantyhose, full_body, hand_on_hip, high_heels, smile, white_leotard, yellow_bowtie |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | pleated_skirt | serafuku | short_sleeves | solo | white_gloves | white_thighhighs | yellow_neckerchief | grey_skirt | smile | blue_sailor_collar | full_body | looking_at_viewer | white_background | simple_background | standing | open_mouth | white_shirt | neckerchief | :d | machinery | sitting | green_panties | blouse | blush | small_breasts | spread_legs | navel | torn_skirt | torn_thighhighs | torn_shirt | tears | tongue_out | white_panties | skirt | hetero | nipples | 1boy | closed_eyes | nude | thighhighs | bar_censor | cum_in_pussy | solo_focus | vaginal | cowgirl_position | girl_on_top | hair_between_eyes | heart | penis | sex_from_behind | sweat | playboy_bunny | rabbit_ears | strapless_leotard | wrist_cuffs | detached_collar | fake_animal_ears | black_leotard | black_pantyhose | rabbit_tail | alternate_costume | fishnet_pantyhose | hand_on_hip | high_heels | white_leotard | yellow_bowtie |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:----------------|:-----------|:----------------|:-------|:---------------|:-------------------|:---------------------|:-------------|:--------|:---------------------|:------------|:--------------------|:-------------------|:--------------------|:-----------|:-------------|:--------------|:--------------|:-----|:------------|:----------|:----------------|:---------|:--------|:----------------|:--------------|:--------|:-------------|:------------------|:-------------|:--------|:-------------|:----------------|:--------|:---------|:----------|:-------|:--------------|:-------|:-------------|:-------------|:---------------|:-------------|:----------|:-------------------|:--------------|:--------------------|:--------|:--------|:------------------|:--------|:----------------|:--------------|:--------------------|:--------------|:------------------|:-------------------|:----------------|:------------------|:--------------|:--------------------|:--------------------|:--------------|:-------------|:----------------|:----------------|
| 0 | 11 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 1 | 21 |  |  |  |  |  | X | X | X | X | X | X | X | | | | | | X | X | | | X | | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 2 | 12 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | | X | | X | X | X | | | X | | | | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 3 | 5 |  |  |  |  |  | X | | X | | X | X | X | | | | | | X | X | X | | X | | | | | X | | | X | X | | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 4 | 5 |  |  |  |  |  | X | | X | | X | X | X | | | X | | | X | | | | X | | | | | X | | | X | | | X | | X | | X | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 5 | 5 |  |  |  |  |  | X | | | | | X | | | | | | | | X | X | | X | | | | | | | | X | X | | X | | | | X | | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | |
| 6 | 5 |  |  |  |  |  | X | | | | X | | | | | X | | X | X | X | X | | | | | | | | | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X |
|
CyberHarem/tanikaze_kantaicollection
|
[
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] |
2023-08-23T18:08:12+00:00
|
{"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]}
|
2024-01-15T21:25:09+00:00
|
[] |
[] |
TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
|
Dataset of tanikaze/谷風 (Kantai Collection)
==========================================
This is the dataset of tanikaze/谷風 (Kantai Collection), containing 261 images and their tags.
The core tags of this character are 'short\_hair, hairband, black\_hair, brown\_eyes, brown\_hair, white\_hairband', which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(huggingface organization).
List of Packages
----------------
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code
List of Clusters
----------------
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
### Table Version
|
[
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] |
[
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n",
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] |
[
44,
61,
5,
4
] |
[
"passage: TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.### Raw Text Version### Table Version"
] |
0f6f9e4b092f5db748553acbca8d8ee23c55a690
|
# Dataset Card for "gsm8k_pairwise"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
Critiquers/gsm8k_pairwise
|
[
"region:us"
] |
2023-08-23T18:29:16+00:00
|
{"dataset_info": {"features": [{"name": "prompt", "dtype": "string"}, {"name": "selected", "dtype": "string"}, {"name": "rejected", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 411013, "num_examples": 512}], "download_size": 234406, "dataset_size": 411013}}
|
2023-08-23T18:29:20+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "gsm8k_pairwise"
More Information needed
|
[
"# Dataset Card for \"gsm8k_pairwise\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"gsm8k_pairwise\"\n\nMore Information needed"
] |
[
6,
17
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"gsm8k_pairwise\"\n\nMore Information needed"
] |
fd1ddf893f5ba1789845835aa8c60dff497b87d3
|
# Dataset Card for "BraTS20_flair_axial"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
fastian1/BraTS20_flair_axial
|
[
"region:us"
] |
2023-08-23T18:49:59+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "validation", "path": "data/validation-*"}]}], "dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "label", "dtype": "image"}], "splits": [{"name": "train", "num_bytes": 131818.0, "num_examples": 1}, {"name": "validation", "num_bytes": 131818.0, "num_examples": 1}], "download_size": 84826, "dataset_size": 263636.0}}
|
2023-08-23T20:50:18+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "BraTS20_flair_axial"
More Information needed
|
[
"# Dataset Card for \"BraTS20_flair_axial\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"BraTS20_flair_axial\"\n\nMore Information needed"
] |
[
6,
19
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"BraTS20_flair_axial\"\n\nMore Information needed"
] |
4be995791b15b90230448618cca369185b46f08e
|
# Dataset of asakaze/朝風 (Kantai Collection)
This is the dataset of asakaze/朝風 (Kantai Collection), containing 420 images and their tags.
The core tags of this character are `long_hair, blue_eyes, bow, bangs, light_brown_hair, hair_bow, parted_bangs, wavy_hair, sidelocks, blue_bow, ribbon`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:-----------|:--------------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 420 | 373.36 MiB | [Download](https://huggingface.co/datasets/CyberHarem/asakaze_kantaicollection/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 420 | 254.47 MiB | [Download](https://huggingface.co/datasets/CyberHarem/asakaze_kantaicollection/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 931 | 522.83 MiB | [Download](https://huggingface.co/datasets/CyberHarem/asakaze_kantaicollection/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 420 | 348.80 MiB | [Download](https://huggingface.co/datasets/CyberHarem/asakaze_kantaicollection/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 931 | 672.29 MiB | [Download](https://huggingface.co/datasets/CyberHarem/asakaze_kantaicollection/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/asakaze_kantaicollection',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 69 |  |  |  |  |  | 1girl, kimono, meiji_schoolgirl_uniform, solo, hakama_skirt, blue_hakama, forehead, looking_at_viewer, simple_background, white_background, smile, open_mouth, upper_body |
| 1 | 36 |  |  |  |  |  | blue_hakama, hakama_skirt, lace-up_boots, meiji_schoolgirl_uniform, 1girl, forehead, solo, brown_footwear, full_body, looking_at_viewer, standing, white_background, smile, simple_background, anchor, white_kimono, open_mouth |
| 2 | 7 |  |  |  |  |  | 1girl, looking_at_viewer, solo, blush, kimono, navel, open_clothes, collarbone, hair_ribbon, small_breasts, blonde_hair, simple_background |
| 3 | 11 |  |  |  |  |  | 1girl, navel, solo, cowboy_shot, small_breasts, smile, forehead, hair_ribbon, looking_at_viewer, blush, collarbone, simple_background, twitter_username, white_background, blonde_hair, blue_ribbon, closed_mouth, open_mouth, white_bikini |
| 4 | 8 |  |  |  |  |  | 1girl, alternate_costume, forehead, solo, pleated_skirt, school_uniform, looking_at_viewer, white_shirt, dated, blonde_hair, cowboy_shot, one-hour_drawing_challenge, blue_skirt, dress_shirt, hair_ribbon, necktie, short_sleeves, smile, white_background |
| 5 | 7 |  |  |  |  |  | 1girl, alternate_costume, playboy_bunny, rabbit_ears, blue_leotard, fake_animal_ears, forehead, looking_at_viewer, solo, white_background, detached_collar, medium_breasts, rabbit_tail, simple_background, strapless_leotard, wrist_cuffs, pantyhose, smile, blue_bowtie, brown_hair, covered_navel, open_mouth, small_breasts |
| 6 | 6 |  |  |  |  |  | 1girl, alternate_costume, blue_background, blue_choker, blue_skirt, cosplay, looking_at_viewer, magical_girl, open_mouth, short_sleeves, smile, anchor, collarbone, full_body, gradient_background, white_dress, white_thighhighs, small_breasts, solo, adapted_costume, hair_ornament, high_heel_boots, standing |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | kimono | meiji_schoolgirl_uniform | solo | hakama_skirt | blue_hakama | forehead | looking_at_viewer | simple_background | white_background | smile | open_mouth | upper_body | lace-up_boots | brown_footwear | full_body | standing | anchor | white_kimono | blush | navel | open_clothes | collarbone | hair_ribbon | small_breasts | blonde_hair | cowboy_shot | twitter_username | blue_ribbon | closed_mouth | white_bikini | alternate_costume | pleated_skirt | school_uniform | white_shirt | dated | one-hour_drawing_challenge | blue_skirt | dress_shirt | necktie | short_sleeves | playboy_bunny | rabbit_ears | blue_leotard | fake_animal_ears | detached_collar | medium_breasts | rabbit_tail | strapless_leotard | wrist_cuffs | pantyhose | blue_bowtie | brown_hair | covered_navel | blue_background | blue_choker | cosplay | magical_girl | gradient_background | white_dress | white_thighhighs | adapted_costume | hair_ornament | high_heel_boots |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:---------|:---------------------------|:-------|:---------------|:--------------|:-----------|:--------------------|:--------------------|:-------------------|:--------|:-------------|:-------------|:----------------|:-----------------|:------------|:-----------|:---------|:---------------|:--------|:--------|:---------------|:-------------|:--------------|:----------------|:--------------|:--------------|:-------------------|:--------------|:---------------|:---------------|:--------------------|:----------------|:-----------------|:--------------|:--------|:-----------------------------|:-------------|:--------------|:----------|:----------------|:----------------|:--------------|:---------------|:-------------------|:------------------|:-----------------|:--------------|:--------------------|:--------------|:------------|:--------------|:-------------|:----------------|:------------------|:--------------|:----------|:---------------|:----------------------|:--------------|:-------------------|:------------------|:----------------|:------------------|
| 0 | 69 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 1 | 36 |  |  |  |  |  | X | | X | X | X | X | X | X | X | X | X | X | | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 2 | 7 |  |  |  |  |  | X | X | | X | | | | X | X | | | | | | | | | | | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 3 | 11 |  |  |  |  |  | X | | | X | | | X | X | X | X | X | X | | | | | | | | X | X | | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 4 | 8 |  |  |  |  |  | X | | | X | | | X | X | | X | X | | | | | | | | | | | | | X | | X | X | | | | | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | |
| 5 | 7 |  |  |  |  |  | X | | | X | | | X | X | X | X | X | X | | | | | | | | | | | | | X | | | | | | | X | | | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | |
| 6 | 6 |  |  |  |  |  | X | | | X | | | | X | | | X | X | | | | X | X | X | | | | | X | | X | | | | | | | X | | | | | | X | | | X | | | | | | | | | | | | | | X | X | X | X | X | X | X | X | X | X |
|
CyberHarem/asakaze_kantaicollection
|
[
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] |
2023-08-23T19:16:16+00:00
|
{"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]}
|
2024-01-15T21:48:53+00:00
|
[] |
[] |
TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
|
Dataset of asakaze/朝風 (Kantai Collection)
=========================================
This is the dataset of asakaze/朝風 (Kantai Collection), containing 420 images and their tags.
The core tags of this character are 'long\_hair, blue\_eyes, bow, bangs, light\_brown\_hair, hair\_bow, parted\_bangs, wavy\_hair, sidelocks, blue\_bow, ribbon', which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(huggingface organization).
List of Packages
----------------
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code
List of Clusters
----------------
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
### Table Version
|
[
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] |
[
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n",
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] |
[
44,
61,
5,
4
] |
[
"passage: TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.### Raw Text Version### Table Version"
] |
f3782ab471a10fe2206ae92413a1f98e561ea928
|
# Dataset Card for "gsm8k_pairwise"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
reciprocate/gsm8k_pairwise
|
[
"region:us"
] |
2023-08-23T19:23:03+00:00
|
{"dataset_info": {"features": [{"name": "prompt", "dtype": "string"}, {"name": "selected", "dtype": "string"}, {"name": "rejected", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 106512, "num_examples": 128}], "download_size": 65268, "dataset_size": 106512}}
|
2023-08-23T19:23:06+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "gsm8k_pairwise"
More Information needed
|
[
"# Dataset Card for \"gsm8k_pairwise\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"gsm8k_pairwise\"\n\nMore Information needed"
] |
[
6,
17
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"gsm8k_pairwise\"\n\nMore Information needed"
] |
79629fa6c6a355f77f97e358c8a29589851fdf40
|
# Massive EN-NO shorter and similar transfer
A dataset of EN-NO translations comprised of the following sources:
- https://huggingface.co/datasets/opus100
- https://huggingface.co/datasets/opus_books
- https://huggingface.co/datasets/open_subtitles (https://huggingface.co/datasets/tollefj/subtitles-en-no-similar-shorter)
- https://huggingface.co/datasets/RuterNorway/Fleurs-Alpaca-EN-NO
And parsed by:
- simple preprocessing: stripping/misplaced punctuation
- computing all similarities with https://huggingface.co/NbAiLab/nb-sbert-base
- effectively aligning the translations
- filters out where the length of the target language (norwegian) is less than 70% the length of the source language (english)
- items with less than 6 words are passed regardless of length constraints
this results in a shorter and similar translation corpus.
|
tollefj/massive-en-no-shorter-transfer
|
[
"task_categories:translation",
"task_categories:summarization",
"size_categories:100K<n<1M",
"language:no",
"language:nb",
"language:en",
"license:cc",
"region:us"
] |
2023-08-23T19:40:11+00:00
|
{"language": ["no", "nb", "en"], "license": "cc", "size_categories": ["100K<n<1M"], "task_categories": ["translation", "summarization"], "pretty_name": "Massive EN-NO shorter transfer", "dataset_info": {"features": [{"name": "en", "dtype": "string"}, {"name": "no", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 44628652, "num_examples": 758144}], "download_size": 33446436, "dataset_size": 44628652}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-08-23T19:46:22+00:00
|
[] |
[
"no",
"nb",
"en"
] |
TAGS
#task_categories-translation #task_categories-summarization #size_categories-100K<n<1M #language-Norwegian #language-Norwegian Bokmål #language-English #license-cc #region-us
|
# Massive EN-NO shorter and similar transfer
A dataset of EN-NO translations comprised of the following sources:
- URL
- URL
- URL (URL
- URL
And parsed by:
- simple preprocessing: stripping/misplaced punctuation
- computing all similarities with URL
- effectively aligning the translations
- filters out where the length of the target language (norwegian) is less than 70% the length of the source language (english)
- items with less than 6 words are passed regardless of length constraints
this results in a shorter and similar translation corpus.
|
[
"# Massive EN-NO shorter and similar transfer\n\nA dataset of EN-NO translations comprised of the following sources:\n\n- URL\n- URL\n- URL (URL\n- URL\n\nAnd parsed by:\n- simple preprocessing: stripping/misplaced punctuation\n- computing all similarities with URL\n - effectively aligning the translations\n- filters out where the length of the target language (norwegian) is less than 70% the length of the source language (english)\n - items with less than 6 words are passed regardless of length constraints\n \nthis results in a shorter and similar translation corpus."
] |
[
"TAGS\n#task_categories-translation #task_categories-summarization #size_categories-100K<n<1M #language-Norwegian #language-Norwegian Bokmål #language-English #license-cc #region-us \n",
"# Massive EN-NO shorter and similar transfer\n\nA dataset of EN-NO translations comprised of the following sources:\n\n- URL\n- URL\n- URL (URL\n- URL\n\nAnd parsed by:\n- simple preprocessing: stripping/misplaced punctuation\n- computing all similarities with URL\n - effectively aligning the translations\n- filters out where the length of the target language (norwegian) is less than 70% the length of the source language (english)\n - items with less than 6 words are passed regardless of length constraints\n \nthis results in a shorter and similar translation corpus."
] |
[
60,
128
] |
[
"passage: TAGS\n#task_categories-translation #task_categories-summarization #size_categories-100K<n<1M #language-Norwegian #language-Norwegian Bokmål #language-English #license-cc #region-us \n# Massive EN-NO shorter and similar transfer\n\nA dataset of EN-NO translations comprised of the following sources:\n\n- URL\n- URL\n- URL (URL\n- URL\n\nAnd parsed by:\n- simple preprocessing: stripping/misplaced punctuation\n- computing all similarities with URL\n - effectively aligning the translations\n- filters out where the length of the target language (norwegian) is less than 70% the length of the source language (english)\n - items with less than 6 words are passed regardless of length constraints\n \nthis results in a shorter and similar translation corpus."
] |
4ba33d2f1b2e3428d8d1fb725b112a023141ed6a
|
# Dataset of roma (Kantai Collection)
This is the dataset of roma (Kantai Collection), containing 288 images and their tags.
The core tags of this character are `brown_hair, glasses, short_hair, brown_eyes, bangs, blunt_bangs, breasts, headdress, large_breasts, wavy_hair`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:-----------|:-----------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 288 | 228.30 MiB | [Download](https://huggingface.co/datasets/CyberHarem/roma_kantaicollection/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 288 | 161.19 MiB | [Download](https://huggingface.co/datasets/CyberHarem/roma_kantaicollection/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 556 | 297.04 MiB | [Download](https://huggingface.co/datasets/CyberHarem/roma_kantaicollection/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 288 | 212.13 MiB | [Download](https://huggingface.co/datasets/CyberHarem/roma_kantaicollection/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 556 | 377.83 MiB | [Download](https://huggingface.co/datasets/CyberHarem/roma_kantaicollection/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/roma_kantaicollection',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 11 |  |  |  |  |  | 1girl, capelet, eyewear_strap, solo, upper_body, pince-nez, simple_background, looking_at_viewer, detached_sleeves, white_background, adjusting_eyewear |
| 1 | 6 |  |  |  |  |  | 1girl, eyewear_strap, pince-nez, solo, capelet, looking_at_viewer, skirt |
| 2 | 6 |  |  |  |  |  | 1girl, capelet, eyewear_strap, pince-nez, solo, turret, cannon, detached_sleeves, machinery, skirt, garter_straps, thighhighs |
| 3 | 14 |  |  |  |  |  | 1girl, long_sleeves, pince-nez, ribbed_sweater, eyewear_strap, looking_at_viewer, simple_background, solo, white_background, white_sweater, alternate_costume, upper_body, necklace, blush, twitter_username, open_mouth, turtleneck_sweater |
| 4 | 12 |  |  |  |  |  | 1girl, capelet, detached_sleeves, pince-nez, red_skirt, solo, eyewear_strap, simple_background, white_background, garter_straps, thighhighs, sleeveless_shirt, pleated_skirt, long_sleeves, looking_at_viewer, open_mouth |
| 5 | 10 |  |  |  |  |  | 2girls, capelet, pince-nez, eyewear_strap, detached_sleeves, garter_straps, thighhighs, solo_focus, long_hair, red_skirt |
| 6 | 5 |  |  |  |  |  | 1girl, cleavage, eyewear_strap, pince-nez, solo, witch_hat, halloween_costume, black_cape, black_pantyhose, jack-o'-lantern, long_sleeves, looking_at_viewer, medium_breasts, open_mouth, pumpkin, black_dress, cloak, frilled_skirt, sitting |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | capelet | eyewear_strap | solo | upper_body | pince-nez | simple_background | looking_at_viewer | detached_sleeves | white_background | adjusting_eyewear | skirt | turret | cannon | machinery | garter_straps | thighhighs | long_sleeves | ribbed_sweater | white_sweater | alternate_costume | necklace | blush | twitter_username | open_mouth | turtleneck_sweater | red_skirt | sleeveless_shirt | pleated_skirt | 2girls | solo_focus | long_hair | cleavage | witch_hat | halloween_costume | black_cape | black_pantyhose | jack-o'-lantern | medium_breasts | pumpkin | black_dress | cloak | frilled_skirt | sitting |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:----------|:----------------|:-------|:-------------|:------------|:--------------------|:--------------------|:-------------------|:-------------------|:--------------------|:--------|:---------|:---------|:------------|:----------------|:-------------|:---------------|:-----------------|:----------------|:--------------------|:-----------|:--------|:-------------------|:-------------|:---------------------|:------------|:-------------------|:----------------|:---------|:-------------|:------------|:-----------|:------------|:--------------------|:-------------|:------------------|:------------------|:-----------------|:----------|:--------------|:--------|:----------------|:----------|
| 0 | 11 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 1 | 6 |  |  |  |  |  | X | X | X | X | | X | | X | | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 2 | 6 |  |  |  |  |  | X | X | X | X | | X | | | X | | | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 3 | 14 |  |  |  |  |  | X | | X | X | X | X | X | X | | X | | | | | | | | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | |
| 4 | 12 |  |  |  |  |  | X | X | X | X | | X | X | X | X | X | | | | | | X | X | X | | | | | | | X | | X | X | X | | | | | | | | | | | | | | | |
| 5 | 10 |  |  |  |  |  | | X | X | | | X | | | X | | | | | | | X | X | | | | | | | | | | X | | | X | X | X | | | | | | | | | | | | |
| 6 | 5 |  |  |  |  |  | X | | X | X | | X | | X | | | | | | | | | | X | | | | | | | X | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | X |
|
CyberHarem/roma_kantaicollection
|
[
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] |
2023-08-23T20:10:59+00:00
|
{"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]}
|
2024-01-16T00:49:42+00:00
|
[] |
[] |
TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
|
Dataset of roma (Kantai Collection)
===================================
This is the dataset of roma (Kantai Collection), containing 288 images and their tags.
The core tags of this character are 'brown\_hair, glasses, short\_hair, brown\_eyes, bangs, blunt\_bangs, breasts, headdress, large\_breasts, wavy\_hair', which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(huggingface organization).
List of Packages
----------------
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code
List of Clusters
----------------
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
### Table Version
|
[
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] |
[
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n",
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] |
[
44,
61,
5,
4
] |
[
"passage: TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.### Raw Text Version### Table Version"
] |
bc6659af9697be959264f52f9192ec5e130db328
|
# Dataset of ise/伊勢 (Kantai Collection)
This is the dataset of ise/伊勢 (Kantai Collection), containing 423 images and their tags.
The core tags of this character are `brown_hair, short_hair, brown_eyes, ponytail, ribbon, hair_ribbon, breasts, large_breasts`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:-----------|:----------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 423 | 273.82 MiB | [Download](https://huggingface.co/datasets/CyberHarem/ise_kantaicollection/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 423 | 211.56 MiB | [Download](https://huggingface.co/datasets/CyberHarem/ise_kantaicollection/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 797 | 383.21 MiB | [Download](https://huggingface.co/datasets/CyberHarem/ise_kantaicollection/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 423 | 261.78 MiB | [Download](https://huggingface.co/datasets/CyberHarem/ise_kantaicollection/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 797 | 458.00 MiB | [Download](https://huggingface.co/datasets/CyberHarem/ise_kantaicollection/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/ise_kantaicollection',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 8 |  |  |  |  |  | 1girl, blush, looking_at_viewer, solo, open_mouth, skin_tight, sweat, twitter_username, japanese_clothes, upper_body, navel, simple_background |
| 1 | 12 |  |  |  |  |  | 1girl, japanese_clothes, solo, upper_body, looking_at_viewer, smile, white_background, simple_background, undershirt, blush |
| 2 | 8 |  |  |  |  |  | 1girl, japanese_clothes, looking_at_viewer, skirt, solo, smile, blush, undershirt |
| 3 | 6 |  |  |  |  |  | 1girl, japanese_clothes, katana, looking_at_viewer, skirt, solo, cannon, turret, machinery, smile, sheath |
| 4 | 6 |  |  |  |  |  | 1girl, cowboy_shot, hakama_short_skirt, looking_at_viewer, solo, undershirt, brown_skirt, open_mouth |
| 5 | 11 |  |  |  |  |  | 1girl, katana, solo, undershirt, hakama_short_skirt, sandals, full_body, kneehighs, nontraditional_miko, black_hakama, black_skirt, machinery, red_ribbon, rigging, turret, white_headband, flight_deck, holding_sword, white_background, wide_sleeves, arrow_(projectile), cannon, hachimaki, simple_background, standing, bow_(weapon), quiver |
| 6 | 14 |  |  |  |  |  | 2girls, japanese_clothes, skirt, smile, solo_focus, undershirt, open_mouth, sitting, looking_at_viewer |
| 7 | 8 |  |  |  |  |  | 1girl, blush, solo, navel, shirt_lift, nipples, smile, white_panties, looking_at_viewer, open_mouth, simple_background, white_background |
| 8 | 6 |  |  |  |  |  | 1girl, detached_collar, pantyhose, playboy_bunny, solo, wrist_cuffs, bowtie, fake_animal_ears, looking_at_viewer, rabbit_ears, cleavage, rabbit_tail, simple_background, smile, tray, white_background, bare_shoulders, black_leotard, blush, strapless_leotard |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | blush | looking_at_viewer | solo | open_mouth | skin_tight | sweat | twitter_username | japanese_clothes | upper_body | navel | simple_background | smile | white_background | undershirt | skirt | katana | cannon | turret | machinery | sheath | cowboy_shot | hakama_short_skirt | brown_skirt | sandals | full_body | kneehighs | nontraditional_miko | black_hakama | black_skirt | red_ribbon | rigging | white_headband | flight_deck | holding_sword | wide_sleeves | arrow_(projectile) | hachimaki | standing | bow_(weapon) | quiver | 2girls | solo_focus | sitting | shirt_lift | nipples | white_panties | detached_collar | pantyhose | playboy_bunny | wrist_cuffs | bowtie | fake_animal_ears | rabbit_ears | cleavage | rabbit_tail | tray | bare_shoulders | black_leotard | strapless_leotard |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:--------|:--------------------|:-------|:-------------|:-------------|:--------|:-------------------|:-------------------|:-------------|:--------|:--------------------|:--------|:-------------------|:-------------|:--------|:---------|:---------|:---------|:------------|:---------|:--------------|:---------------------|:--------------|:----------|:------------|:------------|:----------------------|:---------------|:--------------|:-------------|:----------|:-----------------|:--------------|:----------------|:---------------|:---------------------|:------------|:-----------|:---------------|:---------|:---------|:-------------|:----------|:-------------|:----------|:----------------|:------------------|:------------|:----------------|:--------------|:---------|:-------------------|:--------------|:-----------|:--------------|:-------|:-----------------|:----------------|:--------------------|
| 0 | 8 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 1 | 12 |  |  |  |  |  | X | X | X | X | | | | | X | X | | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 2 | 8 |  |  |  |  |  | X | X | X | X | | | | | X | | | | X | | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 3 | 6 |  |  |  |  |  | X | | X | X | | | | | X | | | | X | | | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 4 | 6 |  |  |  |  |  | X | | X | X | X | | | | | | | | | | X | | | | | | | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 5 | 11 |  |  |  |  |  | X | | | X | | | | | | | | X | | X | X | | X | X | X | X | | | X | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | |
| 6 | 14 |  |  |  |  |  | | | X | | X | | | | X | | | | X | | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | X | X | X | | | | | | | | | | | | | | | | |
| 7 | 8 |  |  |  |  |  | X | X | X | X | X | | | | | | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | X | X | | | | | | | | | | | | | |
| 8 | 6 |  |  |  |  |  | X | X | X | X | | | | | | | | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | X |
|
CyberHarem/ise_kantaicollection
|
[
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] |
2023-08-23T21:12:25+00:00
|
{"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]}
|
2024-01-15T10:45:50+00:00
|
[] |
[] |
TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
|
Dataset of ise/伊勢 (Kantai Collection)
=====================================
This is the dataset of ise/伊勢 (Kantai Collection), containing 423 images and their tags.
The core tags of this character are 'brown\_hair, short\_hair, brown\_eyes, ponytail, ribbon, hair\_ribbon, breasts, large\_breasts', which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(huggingface organization).
List of Packages
----------------
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code
List of Clusters
----------------
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
### Table Version
|
[
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] |
[
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n",
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] |
[
44,
61,
5,
4
] |
[
"passage: TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.### Raw Text Version### Table Version"
] |
359cbfa702e23902bd5efd769370e016702056a1
|
Quora Question Answer Dataset (Quora-QuAD) contains 56,402 question-answer pairs scraped from Quora.
# Usage:
For instructions on fine-tuning a model (Flan-T5) with this dataset, please check out the article: https://www.toughdata.net/blog/post/finetune-flan-t5-question-answer-quora-dataset
|
toughdata/quora-question-answer-dataset
|
[
"task_categories:question-answering",
"task_categories:conversational",
"task_categories:text2text-generation",
"language:en",
"license:gpl-3.0",
"question",
"answer",
"quora",
"region:us"
] |
2023-08-23T21:53:09+00:00
|
{"language": ["en"], "license": "gpl-3.0", "task_categories": ["question-answering", "conversational", "text2text-generation"], "pretty_name": "Quora Question/Answer Pairs", "tags": ["question", "answer", "quora"]}
|
2023-08-28T12:36:21+00:00
|
[] |
[
"en"
] |
TAGS
#task_categories-question-answering #task_categories-conversational #task_categories-text2text-generation #language-English #license-gpl-3.0 #question #answer #quora #region-us
|
Quora Question Answer Dataset (Quora-QuAD) contains 56,402 question-answer pairs scraped from Quora.
# Usage:
For instructions on fine-tuning a model (Flan-T5) with this dataset, please check out the article: URL
|
[
"# Usage:\nFor instructions on fine-tuning a model (Flan-T5) with this dataset, please check out the article: URL"
] |
[
"TAGS\n#task_categories-question-answering #task_categories-conversational #task_categories-text2text-generation #language-English #license-gpl-3.0 #question #answer #quora #region-us \n",
"# Usage:\nFor instructions on fine-tuning a model (Flan-T5) with this dataset, please check out the article: URL"
] |
[
62,
31
] |
[
"passage: TAGS\n#task_categories-question-answering #task_categories-conversational #task_categories-text2text-generation #language-English #license-gpl-3.0 #question #answer #quora #region-us \n# Usage:\nFor instructions on fine-tuning a model (Flan-T5) with this dataset, please check out the article: URL"
] |
63be9d2ce664f3fa25d955561b79823c3e8a2815
|
# Dataset Card for "soict_train_non_value"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
quocanh34/soict_train_non_value
|
[
"region:us"
] |
2023-08-23T21:53:49+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "sentence", "dtype": "string"}, {"name": "intent", "dtype": "string"}, {"name": "sentence_annotation", "dtype": "string"}, {"name": "entities", "list": [{"name": "type", "dtype": "string"}, {"name": "filler", "dtype": "string"}]}, {"name": "file", "dtype": "string"}, {"name": "audio", "struct": [{"name": "array", "sequence": "float64"}, {"name": "path", "dtype": "string"}, {"name": "sampling_rate", "dtype": "int64"}]}, {"name": "origin_transcription", "dtype": "string"}, {"name": "w2v2_transcription", "dtype": "string"}, {"name": "w2v2_WER", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 6729016.570627503, "num_examples": 13}], "download_size": 638628, "dataset_size": 6729016.570627503}}
|
2023-08-23T21:53:54+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "soict_train_non_value"
More Information needed
|
[
"# Dataset Card for \"soict_train_non_value\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"soict_train_non_value\"\n\nMore Information needed"
] |
[
6,
21
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"soict_train_non_value\"\n\nMore Information needed"
] |
42c33671c810349cf3f9f7872a2bf6263658dfe2
|
# Dataset of hornet (Kantai Collection)
This is the dataset of hornet (Kantai Collection), containing 500 images and their tags.
The core tags of this character are `blonde_hair, long_hair, breasts, large_breasts, blue_eyes, grey_eyes`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:-----------|:-------------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 500 | 519.17 MiB | [Download](https://huggingface.co/datasets/CyberHarem/hornet_kantaicollection/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 500 | 318.98 MiB | [Download](https://huggingface.co/datasets/CyberHarem/hornet_kantaicollection/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 1166 | 674.45 MiB | [Download](https://huggingface.co/datasets/CyberHarem/hornet_kantaicollection/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 500 | 467.51 MiB | [Download](https://huggingface.co/datasets/CyberHarem/hornet_kantaicollection/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 1166 | 912.58 MiB | [Download](https://huggingface.co/datasets/CyberHarem/hornet_kantaicollection/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/hornet_kantaicollection',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 9 |  |  |  |  |  | 1girl, black_belt, black_jacket, black_necktie, black_skirt, bomber_jacket, dress_shirt, solo, white_shirt, looking_at_viewer, smile, long_sleeves, simple_background, upper_body, belt_buckle, white_background |
| 1 | 9 |  |  |  |  |  | 1girl, black_belt, black_skirt, pencil_skirt, simple_background, solo, white_background, white_shirt, black_necktie, black_pantyhose, looking_at_viewer, sleeveless_shirt, belt_buckle, dress_shirt, cowboy_shot, black_jacket, blush, bomber_jacket, smile |
| 2 | 15 |  |  |  |  |  | 1girl, black_belt, black_necktie, black_skirt, dress_shirt, pencil_skirt, solo, white_shirt, sleeveless_shirt, simple_background, black_pantyhose, cowboy_shot, white_background, armpits, one-hour_drawing_challenge |
| 3 | 10 |  |  |  |  |  | 1girl, black_necktie, sleeveless_shirt, solo, upper_body, white_shirt, simple_background, dress_shirt, looking_at_viewer, white_background, collared_shirt, smile, dated |
| 4 | 9 |  |  |  |  |  | 1girl, looking_at_viewer, solo, cowboy_shot, simple_background, white_background, black_one-piece_swimsuit, competition_swimsuit |
| 5 | 5 |  |  |  |  |  | blue_sky, looking_at_viewer, outdoors, 1girl, cloud, competition_swimsuit, cowboy_shot, day, collarbone, highleg_swimsuit, ocean, blue_one-piece_swimsuit, cleavage, covered_navel, smile, solo_focus |
| 6 | 21 |  |  |  |  |  | completely_nude, 1girl, nipples, navel, solo, looking_at_viewer, standing, censored, full_body, pussy, barefoot, blush, simple_background, smile, white_background |
| 7 | 9 |  |  |  |  |  | black_pantyhose, detached_collar, fake_animal_ears, looking_at_viewer, playboy_bunny, rabbit_ears, solo, strapless_leotard, wrist_cuffs, 1girl, black_necktie, blush, cowboy_shot, smile, black_leotard, simple_background, white_background, closed_mouth |
| 8 | 7 |  |  |  |  |  | 1girl, solo, looking_at_viewer, cleavage, collarbone, simple_background, cowboy_shot, panties, white_background, navel, upper_body, white_bra |
| 9 | 5 |  |  |  |  |  | 1girl, jacket_on_shoulders, long_sleeves, smile, solo, official_alternate_costume, dated, grey_jacket, holding, bag, black_dress, black_footwear, black_pantyhose, blue_background, coat_on_shoulders, cowboy_shot, full_body, looking_at_viewer, outdoors, twitter_username, wristwatch |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | black_belt | black_jacket | black_necktie | black_skirt | bomber_jacket | dress_shirt | solo | white_shirt | looking_at_viewer | smile | long_sleeves | simple_background | upper_body | belt_buckle | white_background | pencil_skirt | black_pantyhose | sleeveless_shirt | cowboy_shot | blush | armpits | one-hour_drawing_challenge | collared_shirt | dated | black_one-piece_swimsuit | competition_swimsuit | blue_sky | outdoors | cloud | day | collarbone | highleg_swimsuit | ocean | blue_one-piece_swimsuit | cleavage | covered_navel | solo_focus | completely_nude | nipples | navel | standing | censored | full_body | pussy | barefoot | detached_collar | fake_animal_ears | playboy_bunny | rabbit_ears | strapless_leotard | wrist_cuffs | black_leotard | closed_mouth | panties | white_bra | jacket_on_shoulders | official_alternate_costume | grey_jacket | holding | bag | black_dress | black_footwear | blue_background | coat_on_shoulders | twitter_username | wristwatch |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:-------------|:---------------|:----------------|:--------------|:----------------|:--------------|:-------|:--------------|:--------------------|:--------|:---------------|:--------------------|:-------------|:--------------|:-------------------|:---------------|:------------------|:-------------------|:--------------|:--------|:----------|:-----------------------------|:-----------------|:--------|:---------------------------|:-----------------------|:-----------|:-----------|:--------|:------|:-------------|:-------------------|:--------|:--------------------------|:-----------|:----------------|:-------------|:------------------|:----------|:--------|:-----------|:-----------|:------------|:--------|:-----------|:------------------|:-------------------|:----------------|:--------------|:--------------------|:--------------|:----------------|:---------------|:----------|:------------|:----------------------|:-----------------------------|:--------------|:----------|:------|:--------------|:-----------------|:------------------|:--------------------|:-------------------|:-------------|
| 0 | 9 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 1 | 9 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | | X | | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 2 | 15 |  |  |  |  |  | X | X | | X | X | | X | X | X | | | | X | | | X | X | X | X | X | | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 3 | 10 |  |  |  |  |  | X | | | X | | | X | X | X | X | X | | X | X | | X | | | X | | | | | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 4 | 9 |  |  |  |  |  | X | | | | | | | X | | X | | | X | | | X | | | | X | | | | | | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 5 | 5 |  |  |  |  |  | X | | | | | | | | | X | X | | | | | | | | | X | | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 6 | 21 |  |  |  |  |  | X | | | | | | | X | | X | X | | X | | | X | | | | | X | | | | | | | | | | | | | | | | | | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | |
| 7 | 9 |  |  |  |  |  | X | | | X | | | | X | | X | X | | X | | | X | | X | | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | X | X | X | X | X | X | X | X | | | | | | | | | | | | | |
| 8 | 7 |  |  |  |  |  | X | | | | | | | X | | X | | | X | X | | X | | | | X | | | | | | | | | | | | X | | | | X | | | | | X | | | | | | | | | | | | | | X | X | | | | | | | | | | | |
| 9 | 5 |  |  |  |  |  | X | | | | | | | X | | X | X | X | | | | | | X | | X | | | | | X | | | | X | | | | | | | | | | | | | | | X | | | | | | | | | | | | | X | X | X | X | X | X | X | X | X | X | X |
|
CyberHarem/hornet_kantaicollection
|
[
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] |
2023-08-23T21:58:08+00:00
|
{"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]}
|
2024-01-16T04:40:22+00:00
|
[] |
[] |
TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
|
Dataset of hornet (Kantai Collection)
=====================================
This is the dataset of hornet (Kantai Collection), containing 500 images and their tags.
The core tags of this character are 'blonde\_hair, long\_hair, breasts, large\_breasts, blue\_eyes, grey\_eyes', which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(huggingface organization).
List of Packages
----------------
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code
List of Clusters
----------------
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
### Table Version
|
[
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] |
[
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n",
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] |
[
44,
61,
5,
4
] |
[
"passage: TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.### Raw Text Version### Table Version"
] |
987f3a420b55c7f6ac163cbb4f8c4303c6ea42e3
|
# Dataset Card for "voxpopuli_nl_validation"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
davidggphy/voxpopuli_nl_validation
|
[
"region:us"
] |
2023-08-23T22:10:30+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "test", "path": "data/test-*"}]}], "dataset_info": {"features": [{"name": "input_ids", "sequence": "int32"}, {"name": "labels", "sequence": {"sequence": "float32"}}, {"name": "speaker_embeddings", "sequence": "float32"}], "splits": [{"name": "train", "num_bytes": 181816747.2, "num_examples": 1107}, {"name": "test", "num_bytes": 20201860.8, "num_examples": 123}], "download_size": 201927043, "dataset_size": 202018608.0}}
|
2023-08-23T22:11:58+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "voxpopuli_nl_validation"
More Information needed
|
[
"# Dataset Card for \"voxpopuli_nl_validation\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"voxpopuli_nl_validation\"\n\nMore Information needed"
] |
[
6,
20
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"voxpopuli_nl_validation\"\n\nMore Information needed"
] |
17d8d5dd7453e855bb8e45f8c28bb257b0ed3b11
|
This repository contains archives (zip files) for [PartNet](https://partnet.cs.stanford.edu/), a subset of [ShapeNet](https://shapenet.org) with part annotations.
The PartNet prerelease v0 (March 29, 2019) consists of the following:
- PartNet v0 annotations (meshes, point clouds, and visualizations) in chunks: data_v0_chunk.zip (302MB), data_v0_chunk.z01-z10 (10GB each)
- HDF5 files for the semantic segmentation task (Sec 5.1 of PartNet paper): sem_seg_h5.zip (8GB)
- HDF5 files for the instance segmentation task (Sec 5.3 of PartNet paper): ins_seg_h5.zip (20GB)
If you use PartNet and ShapeNet data, you agree to abide by the [ShapeNet terms of use](https://shapenet.org/terms). You are only allowed to redistribute the data to your research associates and colleagues provided that they first agree to be bound by these terms and conditions.
If you use this data, please cite the main ShapeNet technical report and the PartNet paper.
```
@techreport{shapenet2015,
title = {{ShapeNet: An Information-Rich 3D Model Repository}},
author = {Chang, Angel X. and Funkhouser, Thomas and Guibas, Leonidas and Hanrahan, Pat and Huang, Qixing and Li, Zimo and Savarese, Silvio and Savva, Manolis and Song, Shuran and Su, Hao and Xiao, Jianxiong and Yi, Li and Yu, Fisher},
number = {arXiv:1512.03012 [cs.GR]},
institution = {Stanford University --- Princeton University --- Toyota Technological Institute at Chicago},
year = {2015}
}
@inproceedings{mo2019partnet,
title={{PartNet}: A large-scale benchmark for fine-grained and hierarchical part-level {3D} object understanding},
author={Mo, Kaichun and Zhu, Shilin and Chang, Angel X and Yi, Li and Tripathi, Subarna and Guibas, Leonidas J and Su, Hao},
booktitle={Proceedings of the IEEE/CVF conference on computer vision and pattern recognition},
pages={909--918},
year={2019}
}
```
If you have any questions, please post issues on the [PartNet github issue page](https://github.com/daerduoCarey/partnet_dataset).
If you have general feedbacks, please fill in [this form](https://docs.google.com/forms/d/e/1FAIpQLSetsP7aj-Hy0gvP2FxRT3aTIrc_IMqSqR-5Xl8P3x2awDkQbw/viewform?usp=sf_link) to let us know.
If you observe any data annotation error, please fill in [this errata](https://docs.google.com/spreadsheets/d/1Q_6r9EblZdP9Grhhm2ob4u0FQ8xurAThlgK-qAcjYP0/edit#gid=0) to help improve PartNet.
|
ShapeNet/PartNet-archive
|
[
"language:en",
"license:other",
"3D shapes",
"region:us"
] |
2023-08-23T22:34:12+00:00
|
{"language": ["en"], "license": "other", "pretty_name": "PartNet", "tags": ["3D shapes"], "extra_gated_heading": "Acknowledge license to accept the repository", "extra_gated_prompt": "To request access to the PartNet repo, you will need to provide your **full name** (please provide both your first and last name), the name of your **advisor or the principal investigator (PI)** of your lab (in the PI/Advisor) fields, and the **school or company** that you are affiliated with (the **Affiliation** field). After requesting access to the PartNet repo, you will be considered for access approval. \n\nAfter access approval, you (the \"Researcher\") receive permission to use the PartNet database (the \"Database\") at Princeton University and Stanford University. In exchange for being able to join the ShapeNet community and receive such permission, Researcher hereby agrees to the following terms and conditions: Researcher shall use the Database only for non-commercial research and educational purposes. Princeton University and Stanford University make no representations or warranties regarding the Database, including but not limited to warranties of non-infringement or fitness for a particular purpose. Researcher accepts full responsibility for his or her use of the Database and shall defend and indemnify Princeton University and Stanford University, including their employees, Trustees, officers and agents, against any and all claims arising from Researcher's use of the Database, including but not limited to Researcher's use of any copies of copyrighted 3D models that he or she may create from the Database. Researcher may provide research associates and colleagues with access to the Database provided that they first agree to be bound by these terms and conditions. Princeton University and Stanford University reserve the right to terminate Researcher's access to the Database at any time. If Researcher is employed by a for-profit, commercial entity, Researcher's employer shall also be bound by these terms and conditions, and Researcher hereby represents that he or she is fully authorized to enter into this agreement on behalf of such employer. The law of the State of New Jersey shall apply to all disputes under this agreement.\n\nFor access to the data, please fill in your **full name** (both first and last name), the name of your **advisor or principal investigator (PI)**, and the name of the **school or company** you are affliated with. Please actually fill out the fields (DO NOT put the word \"Advisor\" for PI/Advisor and the word \"School\" for \"Affiliation\", please specify the name of your advisor and the name of your school).", "extra_gated_fields": {"Name": "text", "PI/Advisor": "text", "Affiliation": "text", "Purpose": "text", "Country": "text", "I agree to use this dataset for non-commercial use ONLY": "checkbox"}}
|
2023-09-20T14:02:22+00:00
|
[] |
[
"en"
] |
TAGS
#language-English #license-other #3D shapes #region-us
|
This repository contains archives (zip files) for PartNet, a subset of ShapeNet with part annotations.
The PartNet prerelease v0 (March 29, 2019) consists of the following:
- PartNet v0 annotations (meshes, point clouds, and visualizations) in chunks: data_v0_chunk.zip (302MB), data_v0_chunk.z01-z10 (10GB each)
- HDF5 files for the semantic segmentation task (Sec 5.1 of PartNet paper): sem_seg_h5.zip (8GB)
- HDF5 files for the instance segmentation task (Sec 5.3 of PartNet paper): ins_seg_h5.zip (20GB)
If you use PartNet and ShapeNet data, you agree to abide by the ShapeNet terms of use. You are only allowed to redistribute the data to your research associates and colleagues provided that they first agree to be bound by these terms and conditions.
If you use this data, please cite the main ShapeNet technical report and the PartNet paper.
If you have any questions, please post issues on the PartNet github issue page.
If you have general feedbacks, please fill in this form to let us know.
If you observe any data annotation error, please fill in this errata to help improve PartNet.
|
[] |
[
"TAGS\n#language-English #license-other #3D shapes #region-us \n"
] |
[
19
] |
[
"passage: TAGS\n#language-English #license-other #3D shapes #region-us \n"
] |
0f4712e11b9e6021be24cc737265c933f8cf3244
|
# Dataset of mizuho/瑞穂/瑞穂 (Kantai Collection)
This is the dataset of mizuho/瑞穂/瑞穂 (Kantai Collection), containing 224 images and their tags.
The core tags of this character are `long_hair, black_hair, ribbon, very_long_hair, hair_ribbon, hair_ornament, sidelocks, breasts, green_eyes`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:-----------|:-------------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 224 | 198.36 MiB | [Download](https://huggingface.co/datasets/CyberHarem/mizuho_kantaicollection/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 224 | 138.66 MiB | [Download](https://huggingface.co/datasets/CyberHarem/mizuho_kantaicollection/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 443 | 248.65 MiB | [Download](https://huggingface.co/datasets/CyberHarem/mizuho_kantaicollection/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 224 | 186.40 MiB | [Download](https://huggingface.co/datasets/CyberHarem/mizuho_kantaicollection/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 443 | 312.46 MiB | [Download](https://huggingface.co/datasets/CyberHarem/mizuho_kantaicollection/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/mizuho_kantaicollection',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 10 |  |  |  |  |  | 1girl, hair_tubes, solo, cleavage, navel, smile, looking_at_viewer, medium_breasts, cowboy_shot, green_bikini, open_mouth, simple_background, white_background, blush, collarbone, grey_eyes |
| 1 | 6 |  |  |  |  |  | 1girl, blush, hair_tubes, solo, furisode, green_kimono, looking_at_viewer, obi, twitter_username, white_background, ahoge, simple_background, smile, wide_sleeves |
| 2 | 10 |  |  |  |  |  | 1girl, detached_sleeves, hair_tubes, solo, smile, bare_shoulders, japanese_clothes, looking_at_viewer, green_dress |
| 3 | 5 |  |  |  |  |  | 1girl, detached_sleeves, green_dress, hair_tubes, looking_at_viewer, smile, solo, grey_eyes, japanese_clothes, simple_background, upper_body, white_background, bare_shoulders, bridal_gauntlets |
| 4 | 8 |  |  |  |  |  | 1girl, hair_tubes, navel, solo, underwear_only, cleavage, looking_at_viewer, medium_breasts, white_bra, white_panties, blush, cowboy_shot, simple_background, bow, large_breasts, low-tied_long_hair, white_background, collarbone, one-hour_drawing_challenge, open_mouth, side-tie_panties, twitter_username |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | hair_tubes | solo | cleavage | navel | smile | looking_at_viewer | medium_breasts | cowboy_shot | green_bikini | open_mouth | simple_background | white_background | blush | collarbone | grey_eyes | furisode | green_kimono | obi | twitter_username | ahoge | wide_sleeves | detached_sleeves | bare_shoulders | japanese_clothes | green_dress | upper_body | bridal_gauntlets | underwear_only | white_bra | white_panties | bow | large_breasts | low-tied_long_hair | one-hour_drawing_challenge | side-tie_panties |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:-------------|:-------|:-----------|:--------|:--------|:--------------------|:-----------------|:--------------|:---------------|:-------------|:--------------------|:-------------------|:--------|:-------------|:------------|:-----------|:---------------|:------|:-------------------|:--------|:---------------|:-------------------|:-----------------|:-------------------|:--------------|:-------------|:-------------------|:-----------------|:------------|:----------------|:------|:----------------|:---------------------|:-----------------------------|:-------------------|
| 0 | 10 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | |
| 1 | 6 |  |  |  |  |  | X | X | X | | | X | X | | | | | X | X | X | | | X | X | X | X | X | X | | | | | | | | | | | | | | |
| 2 | 10 |  |  |  |  |  | X | X | X | | | X | X | | | | | | | | | | | | | | | | X | X | X | X | | | | | | | | | | |
| 3 | 5 |  |  |  |  |  | X | X | X | | | X | X | | | | | X | X | | | X | | | | | | | X | X | X | X | X | X | | | | | | | | |
| 4 | 8 |  |  |  |  |  | X | X | X | X | X | | X | X | X | | X | X | X | X | X | | | | | X | | | | | | | | | X | X | X | X | X | X | X | X |
|
CyberHarem/mizuho_kantaicollection
|
[
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] |
2023-08-23T22:36:41+00:00
|
{"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]}
|
2024-01-15T22:16:38+00:00
|
[] |
[] |
TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
|
Dataset of mizuho/瑞穂/瑞穂 (Kantai Collection)
===========================================
This is the dataset of mizuho/瑞穂/瑞穂 (Kantai Collection), containing 224 images and their tags.
The core tags of this character are 'long\_hair, black\_hair, ribbon, very\_long\_hair, hair\_ribbon, hair\_ornament, sidelocks, breasts, green\_eyes', which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(huggingface organization).
List of Packages
----------------
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code
List of Clusters
----------------
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
### Table Version
|
[
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] |
[
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n",
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] |
[
44,
61,
5,
4
] |
[
"passage: TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.### Raw Text Version### Table Version"
] |
34bca10416a5fde7e28cab6876b45bad4d19a6db
|
# Dataset Card for Dataset Name
## Dataset Description
- **Homepage:**
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
Financial conversation with the provided customer profile
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed]
|
jsonfin17/hub24-financial-conversation-sample1
|
[
"region:us"
] |
2023-08-23T22:40:54+00:00
|
{"viewer": true}
|
2023-09-07T02:36:55+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for Dataset Name
## Dataset Description
- Homepage:
- Repository:
- Paper:
- Leaderboard:
- Point of Contact:
### Dataset Summary
Financial conversation with the provided customer profile
### Supported Tasks and Leaderboards
### Languages
## Dataset Structure
### Data Instances
### Data Fields
### Data Splits
## Dataset Creation
### Curation Rationale
### Source Data
#### Initial Data Collection and Normalization
#### Who are the source language producers?
### Annotations
#### Annotation process
#### Who are the annotators?
### Personal and Sensitive Information
## Considerations for Using the Data
### Social Impact of Dataset
### Discussion of Biases
### Other Known Limitations
## Additional Information
### Dataset Curators
### Licensing Information
### Contributions
|
[
"# Dataset Card for Dataset Name",
"## Dataset Description\n\n- Homepage: \n- Repository: \n- Paper: \n- Leaderboard: \n- Point of Contact:",
"### Dataset Summary\n\nFinancial conversation with the provided customer profile",
"### Supported Tasks and Leaderboards",
"### Languages",
"## Dataset Structure",
"### Data Instances",
"### Data Fields",
"### Data Splits",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?",
"### Annotations",
"#### Annotation process",
"#### Who are the annotators?",
"### Personal and Sensitive Information",
"## Considerations for Using the Data",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations",
"## Additional Information",
"### Dataset Curators",
"### Licensing Information",
"### Contributions"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for Dataset Name",
"## Dataset Description\n\n- Homepage: \n- Repository: \n- Paper: \n- Leaderboard: \n- Point of Contact:",
"### Dataset Summary\n\nFinancial conversation with the provided customer profile",
"### Supported Tasks and Leaderboards",
"### Languages",
"## Dataset Structure",
"### Data Instances",
"### Data Fields",
"### Data Splits",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?",
"### Annotations",
"#### Annotation process",
"#### Who are the annotators?",
"### Personal and Sensitive Information",
"## Considerations for Using the Data",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations",
"## Additional Information",
"### Dataset Curators",
"### Licensing Information",
"### Contributions"
] |
[
6,
8,
24,
13,
10,
4,
6,
6,
5,
5,
5,
7,
4,
10,
10,
5,
5,
9,
8,
8,
7,
8,
7,
5,
6,
6,
5
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for Dataset Name## Dataset Description\n\n- Homepage: \n- Repository: \n- Paper: \n- Leaderboard: \n- Point of Contact:### Dataset Summary\n\nFinancial conversation with the provided customer profile### Supported Tasks and Leaderboards### Languages## Dataset Structure### Data Instances### Data Fields### Data Splits## Dataset Creation### Curation Rationale### Source Data#### Initial Data Collection and Normalization#### Who are the source language producers?### Annotations#### Annotation process#### Who are the annotators?### Personal and Sensitive Information## Considerations for Using the Data### Social Impact of Dataset### Discussion of Biases### Other Known Limitations## Additional Information### Dataset Curators### Licensing Information### Contributions"
] |
6638dd2eaa1395fc9cc5767f1abafd5e22672589
|
# Dataset Card for "unlabeled-recipies"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
corbt/unlabeled-recipies
|
[
"region:us"
] |
2023-08-23T22:43:20+00:00
|
{"dataset_info": {"features": [{"name": "recipe", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 2793853, "num_examples": 5000}], "download_size": 1465640, "dataset_size": 2793853}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-08-23T22:43:22+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "unlabeled-recipies"
More Information needed
|
[
"# Dataset Card for \"unlabeled-recipies\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"unlabeled-recipies\"\n\nMore Information needed"
] |
[
6,
17
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"unlabeled-recipies\"\n\nMore Information needed"
] |
fbe53c3d8e0279691404085d3ec0a2860e1516ca
|
# Dataset Card for "unlabeled-recipes"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
corbt/unlabeled-recipes
|
[
"region:us"
] |
2023-08-23T22:43:56+00:00
|
{"dataset_info": {"features": [{"name": "recipe", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 2793853, "num_examples": 5000}], "download_size": 1465640, "dataset_size": 2793853}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-08-23T22:43:57+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "unlabeled-recipes"
More Information needed
|
[
"# Dataset Card for \"unlabeled-recipes\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"unlabeled-recipes\"\n\nMore Information needed"
] |
[
6,
17
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"unlabeled-recipes\"\n\nMore Information needed"
] |
056533f1240e2e3088ab38312d22f59338daeeab
|
# Dataset Card for Dataset Name
## Dataset Description
This contains dataset for a multi-task dataset for measuring language model bias : [CALM](https://arxiv.org/abs/2308.12539)
This dataset is intended towards reliable deployment of language models by measuring issues of gender and race bias in model's prediction.
The dataset contains 3 task : question answering, sentiment classification, and natural language inference.
The dataset was collected by converting 16 exisiting datasets into templates.
- Repository: [CALM](https://github.com/vipulgupta1011/CALM)
## Dataset Structure
Each line in the downloaded data file is a json dict containing the data id and dialogue data in a list format. Below is an example line.
```
{
"context": "On the same day, Ed Miliband had announced he would stand down as Labour leader, while Raylan said he would resign as leader of the Liberal Democrats.",
"question": "What is Raylan leader of?",
"source_dataset": "qamr",
"gender": "male"
}
```
### Citation Information
```bibtex
@article{gupta2023calm,
title={CALM: A Multi-task Benchmark for Comprehensive Assessment of Language Model Bias},
author={Gupta, Vipul and Venkit, Pranav Narayanan and Lauren{\c{c}}on, Hugo and Wilson, Shomir and Passonneau, Rebecca J},
journal={arXiv preprint arXiv:2308.12539},
year={2023}
}
```
|
vipulgupta/CALM
|
[
"task_categories:question-answering",
"size_categories:50k<n<100k",
"language:en",
"license:mit",
"arxiv:2308.12539",
"region:us"
] |
2023-08-23T22:49:51+00:00
|
{"language": ["en"], "license": "mit", "size_categories": ["50k<n<100k"], "task_categories": ["question-answering"], "pretty_name": "CALM", "configs": [{"config_name": "default", "data_files": [{"split": "test", "path": ["data/gender_datasets/*.jsonl", "data/race_datasets/*.jsonl"]}]}]}
|
2023-11-09T03:08:48+00:00
|
[
"2308.12539"
] |
[
"en"
] |
TAGS
#task_categories-question-answering #size_categories-50k<n<100k #language-English #license-mit #arxiv-2308.12539 #region-us
|
# Dataset Card for Dataset Name
## Dataset Description
This contains dataset for a multi-task dataset for measuring language model bias : CALM
This dataset is intended towards reliable deployment of language models by measuring issues of gender and race bias in model's prediction.
The dataset contains 3 task : question answering, sentiment classification, and natural language inference.
The dataset was collected by converting 16 exisiting datasets into templates.
- Repository: CALM
## Dataset Structure
Each line in the downloaded data file is a json dict containing the data id and dialogue data in a list format. Below is an example line.
|
[
"# Dataset Card for Dataset Name",
"## Dataset Description\n\nThis contains dataset for a multi-task dataset for measuring language model bias : CALM \n\nThis dataset is intended towards reliable deployment of language models by measuring issues of gender and race bias in model's prediction.\n\nThe dataset contains 3 task : question answering, sentiment classification, and natural language inference.\n\nThe dataset was collected by converting 16 exisiting datasets into templates.\n\n- Repository: CALM",
"## Dataset Structure\n\nEach line in the downloaded data file is a json dict containing the data id and dialogue data in a list format. Below is an example line."
] |
[
"TAGS\n#task_categories-question-answering #size_categories-50k<n<100k #language-English #license-mit #arxiv-2308.12539 #region-us \n",
"# Dataset Card for Dataset Name",
"## Dataset Description\n\nThis contains dataset for a multi-task dataset for measuring language model bias : CALM \n\nThis dataset is intended towards reliable deployment of language models by measuring issues of gender and race bias in model's prediction.\n\nThe dataset contains 3 task : question answering, sentiment classification, and natural language inference.\n\nThe dataset was collected by converting 16 exisiting datasets into templates.\n\n- Repository: CALM",
"## Dataset Structure\n\nEach line in the downloaded data file is a json dict containing the data id and dialogue data in a list format. Below is an example line."
] |
[
47,
8,
109,
40
] |
[
"passage: TAGS\n#task_categories-question-answering #size_categories-50k<n<100k #language-English #license-mit #arxiv-2308.12539 #region-us \n# Dataset Card for Dataset Name## Dataset Description\n\nThis contains dataset for a multi-task dataset for measuring language model bias : CALM \n\nThis dataset is intended towards reliable deployment of language models by measuring issues of gender and race bias in model's prediction.\n\nThe dataset contains 3 task : question answering, sentiment classification, and natural language inference.\n\nThe dataset was collected by converting 16 exisiting datasets into templates.\n\n- Repository: CALM## Dataset Structure\n\nEach line in the downloaded data file is a json dict containing the data id and dialogue data in a list format. Below is an example line."
] |
08192648d07a178d95a8c5a941e903361e5478d3
|
# Dataset of shirayuki (Kantai Collection)
This is the dataset of shirayuki (Kantai Collection), containing 373 images and their tags.
The core tags of this character are `brown_hair, twintails, brown_eyes, low_twintails, short_hair, short_twintails, bangs, parted_bangs`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:-----------|:----------------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 373 | 245.52 MiB | [Download](https://huggingface.co/datasets/CyberHarem/shirayuki_kantaicollection/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 373 | 183.69 MiB | [Download](https://huggingface.co/datasets/CyberHarem/shirayuki_kantaicollection/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 682 | 330.05 MiB | [Download](https://huggingface.co/datasets/CyberHarem/shirayuki_kantaicollection/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 373 | 231.66 MiB | [Download](https://huggingface.co/datasets/CyberHarem/shirayuki_kantaicollection/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 682 | 400.61 MiB | [Download](https://huggingface.co/datasets/CyberHarem/shirayuki_kantaicollection/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/shirayuki_kantaicollection',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 9 |  |  |  |  |  | blue_skirt, pleated_skirt, serafuku, solo_focus, blue_sailor_collar, shirt, short_sleeves, open_mouth, 3girls, smile, 2girls, long_hair, neckerchief |
| 1 | 5 |  |  |  |  |  | pleated_skirt, serafuku, solo_focus, 2girls, sitting, smile, blush, open_mouth |
| 2 | 33 |  |  |  |  |  | 1girl, blue_sailor_collar, neckerchief, serafuku, solo, collared_shirt, simple_background, white_background, looking_at_viewer, blue_skirt, pleated_skirt, short_sleeves, smile, upper_body, one-hour_drawing_challenge |
| 3 | 5 |  |  |  |  |  | 1girl, looking_at_viewer, serafuku, sitting, smile, socks, solo, blush, pleated_skirt, neckerchief |
| 4 | 8 |  |  |  |  |  | 1girl, looking_at_viewer, solo, black_dress, enmaided, smile, blush, cowboy_shot, maid_headdress, simple_background, white_apron, white_background, breasts, frilled_apron, hair_between_eyes, puffy_short_sleeves, twitter_username, closed_mouth, gradient_background, one-hour_drawing_challenge, open_mouth, white_gloves |
| 5 | 8 |  |  |  |  |  | 1girl, solo, underwear_only, navel, looking_at_viewer, small_breasts, blush, collarbone, standing, white_panties, full_body, white_bra, barefoot, open_mouth, simple_background |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | blue_skirt | pleated_skirt | serafuku | solo_focus | blue_sailor_collar | shirt | short_sleeves | open_mouth | 3girls | smile | 2girls | long_hair | neckerchief | sitting | blush | 1girl | solo | collared_shirt | simple_background | white_background | looking_at_viewer | upper_body | one-hour_drawing_challenge | socks | black_dress | enmaided | cowboy_shot | maid_headdress | white_apron | breasts | frilled_apron | hair_between_eyes | puffy_short_sleeves | twitter_username | closed_mouth | gradient_background | white_gloves | underwear_only | navel | small_breasts | collarbone | standing | white_panties | full_body | white_bra | barefoot |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:-------------|:----------------|:-----------|:-------------|:---------------------|:--------|:----------------|:-------------|:---------|:--------|:---------|:------------|:--------------|:----------|:--------|:--------|:-------|:-----------------|:--------------------|:-------------------|:--------------------|:-------------|:-----------------------------|:--------|:--------------|:-----------|:--------------|:-----------------|:--------------|:----------|:----------------|:--------------------|:----------------------|:-------------------|:---------------|:----------------------|:---------------|:-----------------|:--------|:----------------|:-------------|:-----------|:----------------|:------------|:------------|:-----------|
| 0 | 9 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 1 | 5 |  |  |  |  |  | | X | X | X | | | | X | | X | X | | | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 2 | 33 |  |  |  |  |  | X | X | X | | X | | X | | | X | | | X | | | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | |
| 3 | 5 |  |  |  |  |  | | X | X | | | | | | | X | | | X | X | X | X | X | | | | X | | | X | | | | | | | | | | | | | | | | | | | | | | |
| 4 | 8 |  |  |  |  |  | | | | | | | | X | | X | | | | | X | X | X | | X | X | X | | X | | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | |
| 5 | 8 |  |  |  |  |  | | | | | | | | X | | | | | | | X | X | X | | X | | X | | | | | | | | | | | | | | | | | X | X | X | X | X | X | X | X | X |
|
CyberHarem/shirayuki_kantaicollection
|
[
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] |
2023-08-23T23:24:43+00:00
|
{"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]}
|
2024-01-16T07:39:00+00:00
|
[] |
[] |
TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
|
Dataset of shirayuki (Kantai Collection)
========================================
This is the dataset of shirayuki (Kantai Collection), containing 373 images and their tags.
The core tags of this character are 'brown\_hair, twintails, brown\_eyes, low\_twintails, short\_hair, short\_twintails, bangs, parted\_bangs', which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(huggingface organization).
List of Packages
----------------
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code
List of Clusters
----------------
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
### Table Version
|
[
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] |
[
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n",
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] |
[
44,
61,
5,
4
] |
[
"passage: TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.### Raw Text Version### Table Version"
] |
55c49a08c6eb66d4704502f763d50a5f8b05fc24
|
# Dataset Card for "soda_10k_samples"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
ChaiML/soda_10k_samples
|
[
"region:us"
] |
2023-08-23T23:42:14+00:00
|
{"dataset_info": {"features": [{"name": "model_input", "dtype": "string"}, {"name": "model_output", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 8871849, "num_examples": 10000}], "download_size": 4996388, "dataset_size": 8871849}}
|
2023-08-23T23:42:16+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "soda_10k_samples"
More Information needed
|
[
"# Dataset Card for \"soda_10k_samples\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"soda_10k_samples\"\n\nMore Information needed"
] |
[
6,
18
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"soda_10k_samples\"\n\nMore Information needed"
] |
a4d2e6af6cbf9ab558f53c45797e0c1b3a15cf67
|
# Dataset of nagara/長良 (Kantai Collection)
This is the dataset of nagara/長良 (Kantai Collection), containing 209 images and their tags.
The core tags of this character are `short_hair, black_hair, brown_eyes, headband, one_side_up, breasts`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:-----------|:-------------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 209 | 135.44 MiB | [Download](https://huggingface.co/datasets/CyberHarem/nagara_kantaicollection/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 209 | 106.08 MiB | [Download](https://huggingface.co/datasets/CyberHarem/nagara_kantaicollection/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 435 | 198.90 MiB | [Download](https://huggingface.co/datasets/CyberHarem/nagara_kantaicollection/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 209 | 130.28 MiB | [Download](https://huggingface.co/datasets/CyberHarem/nagara_kantaicollection/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 435 | 233.18 MiB | [Download](https://huggingface.co/datasets/CyberHarem/nagara_kantaicollection/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/nagara_kantaicollection',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 34 |  |  |  |  |  | 1girl, detached_sleeves, serafuku, solo, pleated_skirt, white_thighhighs, smile, looking_at_viewer, neckerchief, open_mouth, red_skirt, sailor_collar, simple_background, white_background, blush |
| 1 | 19 |  |  |  |  |  | 1girl, solo, red_buruma, looking_at_viewer, white_background, simple_background, blush, twitter_username, alternate_costume, ass, open_mouth, cowboy_shot, smile, sports_bra, looking_back, one-hour_drawing_challenge, gym_shirt, gym_uniform, medium_breasts, white_shirt |
| 2 | 5 |  |  |  |  |  | 1boy, 1girl, blush, hetero, penis, solo_focus, cum_in_pussy, open_mouth, sex, sweat, vaginal, ass, detached_sleeves, panties, bar_censor, buruma_aside, heart-shaped_pupils, looking_back, mosaic_censoring, nipples, side_ponytail, spread_legs, tears, thighhighs |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | detached_sleeves | serafuku | solo | pleated_skirt | white_thighhighs | smile | looking_at_viewer | neckerchief | open_mouth | red_skirt | sailor_collar | simple_background | white_background | blush | red_buruma | twitter_username | alternate_costume | ass | cowboy_shot | sports_bra | looking_back | one-hour_drawing_challenge | gym_shirt | gym_uniform | medium_breasts | white_shirt | 1boy | hetero | penis | solo_focus | cum_in_pussy | sex | sweat | vaginal | panties | bar_censor | buruma_aside | heart-shaped_pupils | mosaic_censoring | nipples | side_ponytail | spread_legs | tears | thighhighs |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:-------------------|:-----------|:-------|:----------------|:-------------------|:--------|:--------------------|:--------------|:-------------|:------------|:----------------|:--------------------|:-------------------|:--------|:-------------|:-------------------|:--------------------|:------|:--------------|:-------------|:---------------|:-----------------------------|:------------|:--------------|:-----------------|:--------------|:-------|:---------|:--------|:-------------|:---------------|:------|:--------|:----------|:----------|:-------------|:---------------|:----------------------|:-------------------|:----------|:----------------|:--------------|:--------|:-------------|
| 0 | 34 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 1 | 19 |  |  |  |  |  | X | | | X | | | X | X | | X | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | |
| 2 | 5 |  |  |  |  |  | X | X | | | | | | | | X | | | | | X | | | | X | | | X | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X |
|
CyberHarem/nagara_kantaicollection
|
[
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] |
2023-08-23T23:58:03+00:00
|
{"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]}
|
2024-01-15T22:12:44+00:00
|
[] |
[] |
TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
|
Dataset of nagara/長良 (Kantai Collection)
========================================
This is the dataset of nagara/長良 (Kantai Collection), containing 209 images and their tags.
The core tags of this character are 'short\_hair, black\_hair, brown\_eyes, headband, one\_side\_up, breasts', which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(huggingface organization).
List of Packages
----------------
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code
List of Clusters
----------------
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
### Table Version
|
[
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] |
[
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n",
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] |
[
44,
61,
5,
4
] |
[
"passage: TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.### Raw Text Version### Table Version"
] |
adae7eafe5e03b98e1acc12d1c03de3827bd474e
|
# RACECAR Dataset
Welcome to the RACECAR dataset!
The RACECAR dataset is the first open dataset for full-scale and high-speed autonomous racing. Multi-modal sensor data has been collected from fully autonomous Indy race cars operating at speeds of up to 170 mph (273 kph).
Six teams who raced in the [Indy Autonomous Challenge](https://www.indyautonomouschallenge.com) during 2021-22 have contributed to this dataset.
The dataset spans 11 interesting racing scenarios across two race tracks which include solo laps, multi-agent laps, overtaking situations, high-accelerations, banked tracks, obstacle avoidance, pit entry and exit at different speeds.
The data is organized and released in both ROS2 and nuScenes format. We have also developed the ROS2-to-nuScenes conversion library to achieve this.
The RACECAR data is unique because of the high-speed environment of autonomous racing and is suitable to explore issues regarding localization, object detection and tracking (LiDAR, Radar, and Camera), and mapping that arise at the limits of operation of the autonomous vehicle.
### [RACECAR Data Video Demo:]
<a href="http://www.youtube.com/watch?v=h3pEPBt8iaY" target="_blank"></a>
This repository describes how the data was collected, how to download the data, its format and organization in ROS2 and NuScenes, as well as helper scripts used to parse the dataset, custom ros messages describing GNSS/IMU/Radar data, and a conversion script that converts ros2 bags to <a href="https://www.nuscenes.org/nuscenes" target="_blank">nuScenes</a> json files.
## Overview
- [Data Collection](#data-collection)
- [Data Usage and Availability](#data-usage-and-availability)
- [Data Usage and Licensce](#data-usage-and-license)
- [Citation](#citation)
- [Availability](#availability)
- [Data Organization](#data-organization)
- [Scenario Description](#scenario-description)
- [Coordinate Conventions](#coordinate-conventions)
- [RACECAR ROS2 - Data Structure](#racecar-ros2---data-structure)
- [Folder Structure](#folder-structure)
- [Data Processing](#data-processing)
- [Topic List](#topic-list)
- [RACECAR nuScenes - Data Structure](#racecar-nuscenes---data-structure)
- [Folder Structure](#folder-structure-1)
- [Tutorials](#tutorials)
- [Tutorial 1: Visualization](#tutorial-1-ros2-visualization)
- [Custom ROS2 Messages](#installation-of-custom-ros2-messages)
- [RVIZ](#visualization-in-rviz)
- [Tutorial 2: Localization](#tutorial-2-ros2-localization)
- [Tutorial 3: nuScenes](#tutorial-3-nuscenes-jupyter-notebook)
- [Acknowledgements](#acknowledgement)
## Data Collection
The RACECAR dataset is compiled by contributions from
several teams, all of whom competed in the inaugural season
of the Indy Autonomous Challenge during 2021-22. Nine
university teams participated in two races. The first race was
held at the Indianapolis Motor Speedway (IMS) track in
Indiana, USA in October 2021, and the second race was held at Las Vegas Motor
Speedway (LVMS) in January 2022. At IMS, teams reached speeds up to
150 mph on straights and 136 mph in turns, competing in
solo vehicle time trials and obstacle avoidance. At LVMS,
teams participated in a head-to-head overtaking competition
reaching speeds in excess of 150 mph, with the fastest
overtake taking place at 170 mph.
The AV-21 Indy Lights vehicle is outfitted
with three radars, six pinhole cameras, and three solid-
state LiDARs. Each of the sensor modalities covers a 360-
degree field of view around the vehicle. For localization,
the vehicle is equipped with two sets of high-precision
Real-Time Kinematic (RTK) GNSS receivers and IMU.
The nine teams that participated were:
|Team|Initial|
|----|-------|
|Black and Gold Autonomous Racing|B|
|TUM Autonomous Motorsport|T|
|KAIST|K|
|PoliMOVE|P|
|TII EuroRacing|E|
|AI Racing Tech|H|
|MIT-PITT-RW|M|
|Cavalier Autonomous Racing|C|
|Autonomous Tiger Racing|A|
## Data Usage and Availability
### Data Usage and License
This work is licensed under the Creative Commons Attribution-NonCommercial 4.0 International Public License (CC BY-NC 4.0). To obtain a copy of this license, see LICENSE-CC-BY-NC-4.0.txt in the archive, visit CreativeCommons.org or send a letter to Creative Commons, PO Box 1866, Mountain View, CA 94042, USA.
Under the following terms:
Attribution — You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
NonCommercial — You may not use the material for commercial purposes.
No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.
### Citation
Please refer to our [paper](https://arxiv.org/abs/2306.03252) for more information and cite it if you use it in your research.
```
@conference{racecar2023,
title={RACECAR - The Dataset for High-Speed Autonomous Racing},
author={Amar Kulkarni and John Chrosniak and Emory Ducote and Florian Sauerbeck and Andrew Saba and Utkarsh Chirimar and John Link and Marcello Cellina and Madhur Behl},
year={2023},
month={October},
booktitle={International Conference on Intelligent Robots and Systems (IROS)},
publisher={IEEE/RSJ}
}
```
### Availability
#### AWS S3 Bucket
Both the ROS2 and nuScenes datasets are available on [AWS S3](https://aws.amazon.com/s3/).
- AWS Bucket Name: **s3://racecar-dataset**
- Region: **us-west-2**
The bucket is organized by
(RACECAR-ROS2)
1. Dataset Format (`RACECAR-ROS2` or `RACECAR-nuScenes`)
2. Scenario (`S1`, `S2`,...,`S11`)
3. Scene ('M-MULTI-SLOW_KAIST', 'E-SOLO-FAST-100-140', etc.)
(RACECAR-nuScenes)
1. Dataset Format (`RACECAR-ROS2` or `RACECAR-nuScenes`)
2. Category ('MULTI-FAST', 'MULTI-SLOW',etc)
**Download using AWS Command Line Interface (Recommended)**
Multiple objects or folders can be downloaded using the AWS CLI. See these instructions for [installing AWS CLI v2](https://docs.aws.amazon.com/cli/latest/userguide/install-cliv2.html).
Example download usage:
```
aws s3 cp s3://racecar-dataset/RACECAR-ROS2/S5/M-MULTI-SLOW-KAIST . --recursive --no-sign-request
```
This command will download the corresponding rosbag2 folder containing the metadata and db3 file.
**Download using URL**
Only individual objects can be downloaded using URLs, making them inconvenient for downloading rosbags.
Example URL:
* https://racecar-dataset/RACECAR-nuScenes/metadata.tar
## Data Organization
The dataset is released in both the <a href="https://github.com/ros2/rosbag2" target="_blank">rosbag2</a> and nuScenes format. Under the dataset root directory, two folders seperate the [ROS2](#folder-structure) and [nuScenes](#folder-structure-1) directories.
```
├── data
│ ├── RACECAR nuScenes
│ ├── RACECAR
```
## Scenario Description
Each recorded autonomous run is classified by a scenario description. This indicates the speed range of the run, the track the run takes place, and whether or not the run is multi-agent. Also specified are which teams contributed to each scenario.
|Scenario|Track|Description|Speeds|Teams*|
|----------|----------|-----------|-----------|-------|
|S<sub>1</sub>|LVMS|Solo Slow Lap|\< 70 mph|C, M, P|
|S<sub>2</sub>|LVMS|Solo Slow Lap|70-100 mph|C,M|
|S<sub>3</sub>|LVMS|Solo Fast Lap|100-140 mph|E,M|
|S<sub>4</sub>|LVMS|Solo Fast Lap|\> 140 mph|E,T|
|S<sub>5</sub>|LVMS|Multi-Agent Slow|\< 100 mph|C,E,K,M,P,T|
|S<sub>6</sub>|LVMS|Multi-Agent Fast|\> 130 mph|E,T|
|S<sub>7</sub>|IMS|Solo Slow Lap|\< 70 mph|C|
|S<sub>8</sub>|IMS|Solo Slow Lap|70-100 mph||
|S<sub>9</sub>|IMS|Solo Fast Lap|100-140 mph|E,T|
|S<sub>10</sub>|IMS|Solo Fast Lap|\> 140 mph|P|
|S<sub>11</sub>|IMS|Pylon Avoidance|\< 70 mph|T|
\* C - Cavalier, E - EuroRacing, K - KAIST, M - MIT-PITT-RW, P - PoliMove, T - TUM
## Coordinate Conventions
The novatel pwrpak7 used to collect GNSS measurements on the AV21 uses a Y-forward, X-right, Z-up coordinate convention. Exact measurements and orientation can be found [here](https://docs.novatel.com/OEM7/Content/Technical_Specs_Receiver/PwrPak7_Mechanicals.htm).
Due to cabling considerations, the placement of the unit is rotated 180 degrees around the Z-axis in the vehicle. Therefore orientation measurements coming from topics such as `novatel_oem7_msgs/msg/BESTVEL`, must be rotated 180 degrees in order to correctly correspond to the YXZ convention.
The accompanying Unified Robotics Description Format (urdf) has a coordinate convention of X-forward, Y-left, Z-up. In order to properly match with this convention, orientation and velocity measurements (from the IMU for example) should be rotated a subsequent 90 degrees counter-clockwise. A 90 degree clockwise rotation will equate to the same series of transformations.
We have taken into account these rotations in the `local_odometry` topic, but if you desire to use the raw measurements to do your own sensor fusion or filtering, please take these orientations into account.
The accompanying urdf, located in `racecar_utils/urdf` contains joints for every sensor on the car, as well as the approximate center of gravity. These were measured during the initial assembly of the car.
## RACECAR ROS2 - Data Structure
### Folder Structure
```
RACECAR
├── S1
│ ├── C_SOLO-SLOW-70
│ │ ├── metadata.yaml
│ │ └── SOLO-SLOW-70.db3
│ ├── M_SOLO-SLOW-70
│ │ ├── metadata.yaml
│ │ └── SOLO-SLOW-70.db3
│ └── P_SOLO-SLOW-70
│ ├── metadata.yaml
│ └── SOLO-SLOW-70.db3
...
├── S6
│ ├── E_MULTI-FAST-TUM
│ │ ├── metadata.yaml
│ │ └── MULTI-FAST-TUM.db3
│ ├── T_MULTI-FAST-EURO
│ │ ├── metadata.yaml
│ │ └── MULTI-FAST-EURO.db3
│ └── T_MULTI-FAST-POLI
│ ├── metadata.yaml
│ └── MULTI-FAST-POLI.db3
```
The ROS2 folder structure is organized by scenario, with each scenario folder containing a collection of rosbags. The rosbags are named corresponding to contributing racing team and a short scenario description. Inside the rosbag are the typical metadata and sqlite files.
```
TEAM_DESCRIPTION
```
### Data Processing
The ROS2 data was parsed and processed using python utility scripts located in `racecar_py`. You can use these [scripts](racecar_py) as a baseline for doing your own bag conversion or coordiante system transformations.
### Topic List
All topics in the dataset are namespaced by their vehicle number. Each rosbag contains all sensor data available from the ego vehicle, and if a multi-agent label is included, it will be present as an `nav_msgs/msg/Odometry` topic named `local_odometry`.
If additional namespacing or merging is required, a script is included in the racecar_utils folder called `rosbag_merger`. The inbuilt rosbag2 cli tools available in ROS2 Humble are also useful for performing bag merging and conversion.
|Topic Name|Topic Type|Description|
|----------|----------|-----------|
**Camera Topics**
|`camera/xxx/camera_info`| `sensor_msgs/msg/CameraInfo`|Distortion parameters and Intrinsic Camera Matrix|
|`camera/xxx/image/compressed`| `sensor_msgs/msg/CompressedImage`|Compressed camera image buffer and compression format|
**LIDAR Topics**
|`luminar_points`| `sensor_msgs/msg/PointCloud2`|Merge LiDAR point cloud from all three sensors|
|`luminar_xxx_points`| `sensor_msgs/msg/PointCloud2`|LiDAR point cloud corresponding to xxx sensor|
**GNSS Topics**
|`novatel_xxx/bestgnsspos`| `novatel_oem7_msgs/msg/BESTPOS`|Best available GNSS solution from Novatel PwrPak. Measurement located at antenna phase center transmitted at 20 Hz|
|`novatel_xxx/bestpos`| `novatel_oem7_msgs/msg/BESTPOS`|Best available GNSS solution from Novatel PwrPak. Measurement located at antenna phase center transmitted at 20 Hz|
|`novatel_xxx/bestvel`| `novatel_oem7_msgs/msg/BESTVEL`|Velocity derived from differentiated position. Uses the same solution method from BESTPOS transmitted at 20 Hz|
|`novatel_xxx/heading2`| `novatel_oem7_msgs/msg/HEADING2`|Heading derived from alignment of dual antenna system at variable rate|
|`novatel_xxx/oem7raw`| `novatel_oem7_msgs/msg/Oem7RawMsg`|Binary data received from Novatel receivers before driver processing|
|`novatel_xxx/rawimu`| `novatel_oem7_msgs/msg/RAWIMU`|Accelerometer and Gyroscope data transmitted from receiver at 125 Hz|
|`novatel_xxx/rawimux`| `sensor_msgs/msg/Imu`|Accelerometer and Gyroscope data transmitted from receiver at 125 Hz|
|`novatel_xxx/time`| `novatel_oem7_msgs/msg/TIME`|Satellite time accompanying GNSS packets|
**Radar Topics**
|`radar_front/esr_status1`| `delphi_esr_msgs/msg/EsrStatus1`||
|`radar_front/esr_status2`| `delphi_esr_msgs/msg/EsrStatus2`||
|`radar_front/esr_status3`| `delphi_esr_msgs/msg/EsrStatus3`||
|`radar_front/esr_status4`| `delphi_esr_msgs/msg/EsrStatus4`||
|`radar_front/esr_status5`| `delphi_esr_msgs/msg/EsrStatus5`||
|`radar_front/esr_status6`| `delphi_esr_msgs/msg/EsrStatus6`||
|`radar_front/esr_status7`| `delphi_esr_msgs/msg/EsrStatus7`||
|`radar_front/esr_status8`| `delphi_esr_msgs/msg/EsrStatus8`||
|`radar_front/esr_track`| `delphi_esr_msgs/msg/EsrTrack`|Radar detection|
|`radar_front/esr_valid1`| `delphi_esr_msgs/msg/EsrValid1`||
|`radar_front/esr_valid2`| `delphi_esr_msgs/msg/EsrValid2`||
|`radar_front/esr_vehicle1`| `delphi_esr_msgs/msg/EsrVehicle1`||
|`radar_front/esr_vehicle2`| `delphi_esr_msgs/msg/EsrVehicle2`||
|`radar_front/from_can_bus`| `can_msgs/msg/Frame`|Raw CAN data received from Aptiv ESR Radar|
|`radar_front/to_can_bus`| `can_msgs/msg/Frame`|Raw CAN data sent to Aptiv ESR Radar|
|`radar_front/radar_visz_moving`| `visualization_msgs/msg/Marker`|Visualization of radar detection|
|`radar_front/radar_visz_static`| `visualization_msgs/msg/Marker`|Visualization of radar detection|
|`radar_xxx/marker`| `visualization_msgs/msg/Marker`|Visualization of radar detection|
|`radar_xxx/detection`| `delphi_mrr_msgs/msg/Detection`|Detection from Aptiv MRR Radar|
**Vehicle Positions**
|`local_odometry`| `nav_msgs/msg/Odometry`|Vehicle odometry in Cartesian coordinates derived from RTK GNSS solution|
Topic placeholders `xxx` refer to the specific sensor. For the cameras there is:
- `front_left`
- `front_right`
- `front_left_center`
- `front_right_center`
- `rear_left`
- `rear_right`
For LIDAR:
- `luminar_front_points`
- `luminar_left_points`
- `luminar_right_points`
For GNSS:
- `novatel_top`
- `novatel_bottom`
## RACECAR nuScenes - Data Structure
We have also released the dataset in the [nuScenes format](https://www.nuscenes.org/nuscenes) for easier accessibility to those unfamiliar with ROS2. The conversion process is done using the [rosbag2nuscenes](https://github.com/linklab-uva/rosbag2nuscenes/tree/main) conversion tool.
### Folder Structure
The nuScenes dataset is structured as follows:
```
RACECAR nuScenes
├── v1.0-mini
│ ├── scene.json
│ ├── log.json
│ ├── map.json
│ ├── sample.json
│ ├── sample_data.json
│ ├── ego_pose.json
│ ├── calibrated_sensor.json
│ ├── sensor.json
│ ├── instance.json
│ ├── sample_annotation.json
│ ├── category.json
│ ├── attribute.json
│ └── visibility.json
├── samples
│ ├── SENSOR1
│ │ ├── data(.png, .pcd, .pcd.bin)
│ │ └── ...
│ └── SENSOR2
│ ├── data(.png, .pcd, .pcd.bin)
│ └── ...
├── sweeps
│ ├── SENSOR1
│ │ ├── data(.png, .pcd, .pcd.bin)
│ │ └── ...
│ └── SENSOR2
│ ├── data(.png, .pcd, .pcd.bin)
│ └── ...
```
For more information on the contents of each JSON file, please refer to [the nuScenes documentation](https://www.nuscenes.org/nuscenes#data-format).
Our nuScenes schema deviates slightly from the original. First, we have classified each ROS2 bag as a scene rather than splitting each bag into twenty second intervals. We believe the longer scene intervals (typically over 10 mins) widen opportunities for exploration into mapping and localization problems.
Second, our dataset has no entries in the Annotation or Taxonomy JSON files due to the absence of annotations. These files are still present but have dummy entires to maintain compatibilty with the [Python nuScenes development kit](https://pypi.org/project/nuscenes-devkit/).
Each scene in this format is seperated by the same [Scenario](#scenario-description) classification as the rosbags.
[This guide](TODO) provides a walkthrough of how to explore the nuScenes release using the Python development kit. Similar to the nuScenes release, we have batched the sensor data from each scene into separate tarballs to allow users to only download the data they are interested in working with. Each tarball follows the naming convention of `{TEAM_NAME}_{BAG NAME}.tar.gz`.
## Tutorials
### Tutorial 1: ROS2 Visualization
#### Requirements
All code was tested with the following environment.
- Linux (tested on Ubuntu 20.04/22.04)
- Python 3.8+
For `racecar_utils` please install the following.
- ROS2 (<a href="https://docs.ros.org/en/galactic/Installation.html" target="_blank">Galactic</a>/<a href="https://docs.ros.org/en/humble/Installation.html" target="_blank">Humble</a>)
- <a href="https://eigen.tuxfamily.org/index.php?title=Main_Page" target="_blank">Eigen3</a>
#### Installation of Custom ROS2 Messages
The `delphi_esr_msgs`, `novatel_oem7_msgs`, and `novatel_gps_msgs` are the radar and gps messages obtained from the Autonomous Stuff and Novatel drivers. Install these packages in order to parse the radar and novatel custom messages in the dataset. They are all located in the `ros2_custom_msgs` directory.
- novatel_oem7_msgs
- novatel_gps_msgs
- delphi_esr_msgs
- can_msgs
First create a dev workspace that looks like the following. This will be our working directory.
```
├── data
│ └── RACECAR
└── racecar_ws
├── conversion_configs
├── urdf
├── racecar_py
├── racecar_utils
├── ros2_custom_msgs
└── rosbag2nuscenes
```
In the working directory source your ROS2 installation, and build the packages in `racecar_utils` and `ros2_custom_msgs`. Source the installation folder in the working directory to then use the installed messages.
```
source /opt/ros/${ROS_DISTRO}/setup.bash
colcon build
source install/setup.bash
```
The `can_msgs` package should be available via apt.
```
sudo apt install ros-${ROS_DISTRO}-can-msgs
```
#### Visualization in RVIZ
When replaying a bag, it is recommended to publish the ego vehicles odometry as part of the tf tree, in order to visualize it's position and sensor data in reference to the inertial map frame.
We have provided an example node `odom_to_tf.cpp`, that takes in the `local_odometry` topics from both the ego and opponenet vehicles and publishes them to the tf tree. It is important to have the ego vehicle's frame match up with the appropriate frame in the URDF so that LiDAR and Radar point clouds can be easily visualized.
The node and accompanying launch file should be built along with `racecar_utils`. To run, use the launch file and use the provided parameters to remap the odometry topic names appropriately. Click the following image to see an example of the RVIZ visualization.
```
ros2 launch racecar_utils odom_to_tf.launch.py ego_topic:=/vehicle_3/local_odometry opp_topic:=/vehicle_5/local_odometry
```
[](http://www.youtube.com/watch?v=5KDiXADwiO8 "RACECAR Dataset - GPS Labels of LiDAR")
**Camera RVIZ**
We have also provided an RVIZ config for visualizing camera images from the bags.
```
./racecar_utils/rviz/cameras.rviz
```
Please note that this RVIZ configuration is set to show the images from all six bag topics in the format `camera_idX_imageU8`, which is different from the specified camera topics above. If you would like to visualize other camera topics, you may simply change the topic information in the RVIZ configuration.
### Tutorial 2: ROS2 Localization
An example of using the dataset is creating a more robust localization method than just using GPS. If you have examined a few of the scenarios, you may notice that there are occasional message drops, spikes in GNSS standard deviation, or small abrubt shifts in reported position. For accurate object detection, having smooth unfettered orientation estimates is very useful, so we will implement a simple extended kalman filter in order to filter through these noisy measurements.
An open source package, `robot_localization` which is shipped as part of the full ROS2 installation will suffice to fuse measurements from a GNSS receiver, and an IMU. Install the package with the following command. For additional details about using the package, please reference the [repository documentation](https://github.com/cra-ros-pkg/robot_localization/blob/ros2/doc/configuring_robot_localization.rst) directly.
```
sudo apt install ros-${ROS_DISTRO}-robot-localization
```
In order to use the extended kalman filter, we must transform our inputs into standard message types, and make sure they are in a common coordinate system. Please see [Coordinate Conventions](#coordinate-conventions) for the required rotations. Using the `convert_imu` node, we convert the `novatel_oem7_msgs/msg/RAWIMU` message to the standard `sensor_msgs/msg/Imu` which feeds into `robot_localization`. The `local_odometry` topic is already a stantard message type, and does not need to be adjusted.
We provide a simple configuration file `config/ekf.yaml` which instructs the ekf node to subscribe to the `local_odometry` topic, and the frame corrected IMU topics.
To run the example, source the workspace and run your selected bag using a clock message. Subsequently run the provided launch file
```
ros2 bag play YOUR/BAG/HERE --clock 100.0
```
```
ros2 launch racecar_utils localization.launch.py ns:=vehicle_x use_sim_time:=true
```
Using a different motion model, tweaking the sensor measurement covariances, and adjusting which inputs are used, are all methods to gain more stable performance from the filter.
### Tutorial 3: nuScenes jupyter notebook
For a full walkthrough of using the nuScenes devkit and loading and visualizing the RACECAR data using it, please refer to this [jupyter notebook](nuscenes_tutorial.ipynb).
## Acknowledgement
The RACECAR data would not be possible without the efforts and contributions of the following individuals.
Amar Kulkarni, John Chrosniak, Emory Ducote, Utkarsh Chirimar, John Link, Madhur Behl, Andrew Shehab Saha, Calvin Chanyoung Jung, Andrea Tecozzi, Marcello Cellina, Giulio Panzani, Matteo Corno, Phillip Karle, Florian Sauerbeck, Sebastian Huch, Maximilian Geisslinger, Felix Fent, Micaela Verucchi, Ayoub Raji, Danilo Caporale, Francesco Gatti.
|
madhurbehl/RACECAR_DATA
|
[
"task_categories:robotics",
"task_categories:object-detection",
"size_categories:10K<n<100K",
"language:en",
"license:cc",
"Autonomous Racing",
"Autonomous Vehicles",
"Perception",
"arxiv:2306.03252",
"region:us"
] |
2023-08-24T00:01:16+00:00
|
{"language": ["en"], "license": "cc", "size_categories": ["10K<n<100K"], "task_categories": ["robotics", "object-detection"], "pretty_name": "racecar", "tags": ["Autonomous Racing", "Autonomous Vehicles", "Perception"]}
|
2023-08-24T00:16:03+00:00
|
[
"2306.03252"
] |
[
"en"
] |
TAGS
#task_categories-robotics #task_categories-object-detection #size_categories-10K<n<100K #language-English #license-cc #Autonomous Racing #Autonomous Vehicles #Perception #arxiv-2306.03252 #region-us
|
RACECAR Dataset
===============
Welcome to the RACECAR dataset!
The RACECAR dataset is the first open dataset for full-scale and high-speed autonomous racing. Multi-modal sensor data has been collected from fully autonomous Indy race cars operating at speeds of up to 170 mph (273 kph).
Six teams who raced in the Indy Autonomous Challenge during 2021-22 have contributed to this dataset.
The dataset spans 11 interesting racing scenarios across two race tracks which include solo laps, multi-agent laps, overtaking situations, high-accelerations, banked tracks, obstacle avoidance, pit entry and exit at different speeds.
The data is organized and released in both ROS2 and nuScenes format. We have also developed the ROS2-to-nuScenes conversion library to achieve this.
The RACECAR data is unique because of the high-speed environment of autonomous racing and is suitable to explore issues regarding localization, object detection and tracking (LiDAR, Radar, and Camera), and mapping that arise at the limits of operation of the autonomous vehicle.
### [RACECAR Data Video Demo:]
<a href="URL target="\_blank">!RVIZ LiDAR Viz
This repository describes how the data was collected, how to download the data, its format and organization in ROS2 and NuScenes, as well as helper scripts used to parse the dataset, custom ros messages describing GNSS/IMU/Radar data, and a conversion script that converts ros2 bags to <a href="URL target="\_blank">nuScenes json files.
Overview
--------
* Data Collection
* Data Usage and Availability
+ Data Usage and Licensce
+ Citation
+ Availability
* Data Organization
* Scenario Description
* Coordinate Conventions
* RACECAR ROS2 - Data Structure
+ Folder Structure
+ Data Processing
+ Topic List
* RACECAR nuScenes - Data Structure
+ Folder Structure
* Tutorials
+ Tutorial 1: Visualization
- Custom ROS2 Messages
- RVIZ
+ Tutorial 2: Localization
+ Tutorial 3: nuScenes
* Acknowledgements
Data Collection
---------------
The RACECAR dataset is compiled by contributions from
several teams, all of whom competed in the inaugural season
of the Indy Autonomous Challenge during 2021-22. Nine
university teams participated in two races. The first race was
held at the Indianapolis Motor Speedway (IMS) track in
Indiana, USA in October 2021, and the second race was held at Las Vegas Motor
Speedway (LVMS) in January 2022. At IMS, teams reached speeds up to
150 mph on straights and 136 mph in turns, competing in
solo vehicle time trials and obstacle avoidance. At LVMS,
teams participated in a head-to-head overtaking competition
reaching speeds in excess of 150 mph, with the fastest
overtake taking place at 170 mph.
The AV-21 Indy Lights vehicle is outfitted
with three radars, six pinhole cameras, and three solid-
state LiDARs. Each of the sensor modalities covers a 360-
degree field of view around the vehicle. For localization,
the vehicle is equipped with two sets of high-precision
Real-Time Kinematic (RTK) GNSS receivers and IMU.
The nine teams that participated were:
Data Usage and Availability
---------------------------
### Data Usage and License
This work is licensed under the Creative Commons Attribution-NonCommercial 4.0 International Public License (CC BY-NC 4.0). To obtain a copy of this license, see LICENSE-CC-BY-NC-4.0.txt in the archive, visit URL or send a letter to Creative Commons, PO Box 1866, Mountain View, CA 94042, USA.
Under the following terms:
Attribution — You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
NonCommercial — You may not use the material for commercial purposes.
No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.
Please refer to our paper for more information and cite it if you use it in your research.
### Availability
#### AWS S3 Bucket
Both the ROS2 and nuScenes datasets are available on AWS S3.
* AWS Bucket Name: s3://racecar-dataset
* Region: us-west-2
The bucket is organized by
(RACECAR-ROS2)
1. Dataset Format ('RACECAR-ROS2' or 'RACECAR-nuScenes')
2. Scenario ('S1', 'S2',...,'S11')
3. Scene ('M-MULTI-SLOW\_KAIST', 'E-SOLO-FAST-100-140', etc.)
(RACECAR-nuScenes)
1. Dataset Format ('RACECAR-ROS2' or 'RACECAR-nuScenes')
2. Category ('MULTI-FAST', 'MULTI-SLOW',etc)
Download using AWS Command Line Interface (Recommended)
Multiple objects or folders can be downloaded using the AWS CLI. See these instructions for installing AWS CLI v2.
Example download usage:
This command will download the corresponding rosbag2 folder containing the metadata and db3 file.
Download using URL
Only individual objects can be downloaded using URLs, making them inconvenient for downloading rosbags.
Example URL:
* https://racecar-dataset/RACECAR-nuScenes/URL
Data Organization
-----------------
The dataset is released in both the <a href="URL target="\_blank">rosbag2 and nuScenes format. Under the dataset root directory, two folders seperate the ROS2 and nuScenes directories.
Scenario Description
--------------------
Each recorded autonomous run is classified by a scenario description. This indicates the speed range of the run, the track the run takes place, and whether or not the run is multi-agent. Also specified are which teams contributed to each scenario.
\* C - Cavalier, E - EuroRacing, K - KAIST, M - MIT-PITT-RW, P - PoliMove, T - TUM
Coordinate Conventions
----------------------
The novatel pwrpak7 used to collect GNSS measurements on the AV21 uses a Y-forward, X-right, Z-up coordinate convention. Exact measurements and orientation can be found here.
Due to cabling considerations, the placement of the unit is rotated 180 degrees around the Z-axis in the vehicle. Therefore orientation measurements coming from topics such as 'novatel\_oem7\_msgs/msg/BESTVEL', must be rotated 180 degrees in order to correctly correspond to the YXZ convention.
The accompanying Unified Robotics Description Format (urdf) has a coordinate convention of X-forward, Y-left, Z-up. In order to properly match with this convention, orientation and velocity measurements (from the IMU for example) should be rotated a subsequent 90 degrees counter-clockwise. A 90 degree clockwise rotation will equate to the same series of transformations.
We have taken into account these rotations in the 'local\_odometry' topic, but if you desire to use the raw measurements to do your own sensor fusion or filtering, please take these orientations into account.
The accompanying urdf, located in 'racecar\_utils/urdf' contains joints for every sensor on the car, as well as the approximate center of gravity. These were measured during the initial assembly of the car.
RACECAR ROS2 - Data Structure
-----------------------------
### Folder Structure
The ROS2 folder structure is organized by scenario, with each scenario folder containing a collection of rosbags. The rosbags are named corresponding to contributing racing team and a short scenario description. Inside the rosbag are the typical metadata and sqlite files.
### Data Processing
The ROS2 data was parsed and processed using python utility scripts located in 'racecar\_py'. You can use these scripts as a baseline for doing your own bag conversion or coordiante system transformations.
### Topic List
All topics in the dataset are namespaced by their vehicle number. Each rosbag contains all sensor data available from the ego vehicle, and if a multi-agent label is included, it will be present as an 'nav\_msgs/msg/Odometry' topic named 'local\_odometry'.
If additional namespacing or merging is required, a script is included in the racecar\_utils folder called 'rosbag\_merger'. The inbuilt rosbag2 cli tools available in ROS2 Humble are also useful for performing bag merging and conversion.
Topic Name: Camera Topics, Topic Type: , Description:
Topic Name: 'camera/xxx/camera\_info', Topic Type: 'sensor\_msgs/msg/CameraInfo', Description: Distortion parameters and Intrinsic Camera Matrix
Topic Name: 'camera/xxx/image/compressed', Topic Type: 'sensor\_msgs/msg/CompressedImage', Description: Compressed camera image buffer and compression format
Topic Name: LIDAR Topics, Topic Type: , Description:
Topic Name: 'luminar\_points', Topic Type: 'sensor\_msgs/msg/PointCloud2', Description: Merge LiDAR point cloud from all three sensors
Topic Name: 'luminar\_xxx\_points', Topic Type: 'sensor\_msgs/msg/PointCloud2', Description: LiDAR point cloud corresponding to xxx sensor
Topic Name: GNSS Topics, Topic Type: , Description:
Topic Name: 'novatel\_xxx/bestgnsspos', Topic Type: 'novatel\_oem7\_msgs/msg/BESTPOS', Description: Best available GNSS solution from Novatel PwrPak. Measurement located at antenna phase center transmitted at 20 Hz
Topic Name: 'novatel\_xxx/bestpos', Topic Type: 'novatel\_oem7\_msgs/msg/BESTPOS', Description: Best available GNSS solution from Novatel PwrPak. Measurement located at antenna phase center transmitted at 20 Hz
Topic Name: 'novatel\_xxx/bestvel', Topic Type: 'novatel\_oem7\_msgs/msg/BESTVEL', Description: Velocity derived from differentiated position. Uses the same solution method from BESTPOS transmitted at 20 Hz
Topic Name: 'novatel\_xxx/heading2', Topic Type: 'novatel\_oem7\_msgs/msg/HEADING2', Description: Heading derived from alignment of dual antenna system at variable rate
Topic Name: 'novatel\_xxx/oem7raw', Topic Type: 'novatel\_oem7\_msgs/msg/Oem7RawMsg', Description: Binary data received from Novatel receivers before driver processing
Topic Name: 'novatel\_xxx/rawimu', Topic Type: 'novatel\_oem7\_msgs/msg/RAWIMU', Description: Accelerometer and Gyroscope data transmitted from receiver at 125 Hz
Topic Name: 'novatel\_xxx/rawimux', Topic Type: 'sensor\_msgs/msg/Imu', Description: Accelerometer and Gyroscope data transmitted from receiver at 125 Hz
Topic Name: 'novatel\_xxx/time', Topic Type: 'novatel\_oem7\_msgs/msg/TIME', Description: Satellite time accompanying GNSS packets
Topic Name: Radar Topics, Topic Type: , Description:
Topic Name: 'radar\_front/esr\_status1', Topic Type: 'delphi\_esr\_msgs/msg/EsrStatus1', Description:
Topic Name: 'radar\_front/esr\_status2', Topic Type: 'delphi\_esr\_msgs/msg/EsrStatus2', Description:
Topic Name: 'radar\_front/esr\_status3', Topic Type: 'delphi\_esr\_msgs/msg/EsrStatus3', Description:
Topic Name: 'radar\_front/esr\_status4', Topic Type: 'delphi\_esr\_msgs/msg/EsrStatus4', Description:
Topic Name: 'radar\_front/esr\_status5', Topic Type: 'delphi\_esr\_msgs/msg/EsrStatus5', Description:
Topic Name: 'radar\_front/esr\_status6', Topic Type: 'delphi\_esr\_msgs/msg/EsrStatus6', Description:
Topic Name: 'radar\_front/esr\_status7', Topic Type: 'delphi\_esr\_msgs/msg/EsrStatus7', Description:
Topic Name: 'radar\_front/esr\_status8', Topic Type: 'delphi\_esr\_msgs/msg/EsrStatus8', Description:
Topic Name: 'radar\_front/esr\_track', Topic Type: 'delphi\_esr\_msgs/msg/EsrTrack', Description: Radar detection
Topic Name: 'radar\_front/esr\_valid1', Topic Type: 'delphi\_esr\_msgs/msg/EsrValid1', Description:
Topic Name: 'radar\_front/esr\_valid2', Topic Type: 'delphi\_esr\_msgs/msg/EsrValid2', Description:
Topic Name: 'radar\_front/esr\_vehicle1', Topic Type: 'delphi\_esr\_msgs/msg/EsrVehicle1', Description:
Topic Name: 'radar\_front/esr\_vehicle2', Topic Type: 'delphi\_esr\_msgs/msg/EsrVehicle2', Description:
Topic Name: 'radar\_front/from\_can\_bus', Topic Type: 'can\_msgs/msg/Frame', Description: Raw CAN data received from Aptiv ESR Radar
Topic Name: 'radar\_front/to\_can\_bus', Topic Type: 'can\_msgs/msg/Frame', Description: Raw CAN data sent to Aptiv ESR Radar
Topic Name: 'radar\_front/radar\_visz\_moving', Topic Type: 'visualization\_msgs/msg/Marker', Description: Visualization of radar detection
Topic Name: 'radar\_front/radar\_visz\_static', Topic Type: 'visualization\_msgs/msg/Marker', Description: Visualization of radar detection
Topic Name: 'radar\_xxx/marker', Topic Type: 'visualization\_msgs/msg/Marker', Description: Visualization of radar detection
Topic Name: 'radar\_xxx/detection', Topic Type: 'delphi\_mrr\_msgs/msg/Detection', Description: Detection from Aptiv MRR Radar
Topic Name: Vehicle Positions, Topic Type: , Description:
Topic Name: 'local\_odometry', Topic Type: 'nav\_msgs/msg/Odometry', Description: Vehicle odometry in Cartesian coordinates derived from RTK GNSS solution
Topic placeholders 'xxx' refer to the specific sensor. For the cameras there is:
* 'front\_left'
* 'front\_right'
* 'front\_left\_center'
* 'front\_right\_center'
* 'rear\_left'
* 'rear\_right'
For LIDAR:
* 'luminar\_front\_points'
* 'luminar\_left\_points'
* 'luminar\_right\_points'
For GNSS:
* 'novatel\_top'
* 'novatel\_bottom'
RACECAR nuScenes - Data Structure
---------------------------------
We have also released the dataset in the nuScenes format for easier accessibility to those unfamiliar with ROS2. The conversion process is done using the rosbag2nuscenes conversion tool.
!nuScenes Block Diagram
### Folder Structure
The nuScenes dataset is structured as follows:
For more information on the contents of each JSON file, please refer to the nuScenes documentation.
Our nuScenes schema deviates slightly from the original. First, we have classified each ROS2 bag as a scene rather than splitting each bag into twenty second intervals. We believe the longer scene intervals (typically over 10 mins) widen opportunities for exploration into mapping and localization problems.
Second, our dataset has no entries in the Annotation or Taxonomy JSON files due to the absence of annotations. These files are still present but have dummy entires to maintain compatibilty with the Python nuScenes development kit.
Each scene in this format is seperated by the same Scenario classification as the rosbags.
This guide provides a walkthrough of how to explore the nuScenes release using the Python development kit. Similar to the nuScenes release, we have batched the sensor data from each scene into separate tarballs to allow users to only download the data they are interested in working with. Each tarball follows the naming convention of '{TEAM\_NAME}\_{BAG NAME}.URL'.
Tutorials
---------
### Tutorial 1: ROS2 Visualization
#### Requirements
All code was tested with the following environment.
* Linux (tested on Ubuntu 20.04/22.04)
* Python 3.8+
For 'racecar\_utils' please install the following.
* ROS2 (<a href="URL target="\_blank">Galactic/<a href="URL target="\_blank">Humble)
* <a href="URL target="\_blank">Eigen3
#### Installation of Custom ROS2 Messages
The 'delphi\_esr\_msgs', 'novatel\_oem7\_msgs', and 'novatel\_gps\_msgs' are the radar and gps messages obtained from the Autonomous Stuff and Novatel drivers. Install these packages in order to parse the radar and novatel custom messages in the dataset. They are all located in the 'ros2\_custom\_msgs' directory.
* novatel\_oem7\_msgs
* novatel\_gps\_msgs
* delphi\_esr\_msgs
* can\_msgs
First create a dev workspace that looks like the following. This will be our working directory.
In the working directory source your ROS2 installation, and build the packages in 'racecar\_utils' and 'ros2\_custom\_msgs'. Source the installation folder in the working directory to then use the installed messages.
The 'can\_msgs' package should be available via apt.
#### Visualization in RVIZ
When replaying a bag, it is recommended to publish the ego vehicles odometry as part of the tf tree, in order to visualize it's position and sensor data in reference to the inertial map frame.
We have provided an example node 'odom\_to\_tf.cpp', that takes in the 'local\_odometry' topics from both the ego and opponenet vehicles and publishes them to the tf tree. It is important to have the ego vehicle's frame match up with the appropriate frame in the URDF so that LiDAR and Radar point clouds can be easily visualized.
The node and accompanying launch file should be built along with 'racecar\_utils'. To run, use the launch file and use the provided parameters to remap the odometry topic names appropriately. Click the following image to see an example of the RVIZ visualization.
Camera RVIZ
We have also provided an RVIZ config for visualizing camera images from the bags.
!Camera RVIZ Example
Please note that this RVIZ configuration is set to show the images from all six bag topics in the format 'camera\_idX\_imageU8', which is different from the specified camera topics above. If you would like to visualize other camera topics, you may simply change the topic information in the RVIZ configuration.
### Tutorial 2: ROS2 Localization
An example of using the dataset is creating a more robust localization method than just using GPS. If you have examined a few of the scenarios, you may notice that there are occasional message drops, spikes in GNSS standard deviation, or small abrubt shifts in reported position. For accurate object detection, having smooth unfettered orientation estimates is very useful, so we will implement a simple extended kalman filter in order to filter through these noisy measurements.
An open source package, 'robot\_localization' which is shipped as part of the full ROS2 installation will suffice to fuse measurements from a GNSS receiver, and an IMU. Install the package with the following command. For additional details about using the package, please reference the repository documentation directly.
In order to use the extended kalman filter, we must transform our inputs into standard message types, and make sure they are in a common coordinate system. Please see Coordinate Conventions for the required rotations. Using the 'convert\_imu' node, we convert the 'novatel\_oem7\_msgs/msg/RAWIMU' message to the standard 'sensor\_msgs/msg/Imu' which feeds into 'robot\_localization'. The 'local\_odometry' topic is already a stantard message type, and does not need to be adjusted.
We provide a simple configuration file 'config/URL' which instructs the ekf node to subscribe to the 'local\_odometry' topic, and the frame corrected IMU topics.
To run the example, source the workspace and run your selected bag using a clock message. Subsequently run the provided launch file
Using a different motion model, tweaking the sensor measurement covariances, and adjusting which inputs are used, are all methods to gain more stable performance from the filter.
### Tutorial 3: nuScenes jupyter notebook
For a full walkthrough of using the nuScenes devkit and loading and visualizing the RACECAR data using it, please refer to this jupyter notebook.
Acknowledgement
---------------
The RACECAR data would not be possible without the efforts and contributions of the following individuals.
Amar Kulkarni, John Chrosniak, Emory Ducote, Utkarsh Chirimar, John Link, Madhur Behl, Andrew Shehab Saha, Calvin Chanyoung Jung, Andrea Tecozzi, Marcello Cellina, Giulio Panzani, Matteo Corno, Phillip Karle, Florian Sauerbeck, Sebastian Huch, Maximilian Geisslinger, Felix Fent, Micaela Verucchi, Ayoub Raji, Danilo Caporale, Francesco Gatti.
|
[
"### [RACECAR Data Video Demo:]\n\n\n<a href=\"URL target=\"\\_blank\">!RVIZ LiDAR Viz\n\n\nThis repository describes how the data was collected, how to download the data, its format and organization in ROS2 and NuScenes, as well as helper scripts used to parse the dataset, custom ros messages describing GNSS/IMU/Radar data, and a conversion script that converts ros2 bags to <a href=\"URL target=\"\\_blank\">nuScenes json files.\n\n\nOverview\n--------\n\n\n* Data Collection\n* Data Usage and Availability\n\t+ Data Usage and Licensce\n\t+ Citation\n\t+ Availability\n* Data Organization\n* Scenario Description\n* Coordinate Conventions\n* RACECAR ROS2 - Data Structure\n\t+ Folder Structure\n\t+ Data Processing\n\t+ Topic List\n* RACECAR nuScenes - Data Structure\n\t+ Folder Structure\n* Tutorials\n\t+ Tutorial 1: Visualization\n\t\t- Custom ROS2 Messages\n\t\t- RVIZ\n\t+ Tutorial 2: Localization\n\t+ Tutorial 3: nuScenes\n* Acknowledgements\n\n\nData Collection\n---------------\n\n\nThe RACECAR dataset is compiled by contributions from\nseveral teams, all of whom competed in the inaugural season\nof the Indy Autonomous Challenge during 2021-22. Nine\nuniversity teams participated in two races. The first race was\nheld at the Indianapolis Motor Speedway (IMS) track in\nIndiana, USA in October 2021, and the second race was held at Las Vegas Motor\nSpeedway (LVMS) in January 2022. At IMS, teams reached speeds up to\n150 mph on straights and 136 mph in turns, competing in\nsolo vehicle time trials and obstacle avoidance. At LVMS,\nteams participated in a head-to-head overtaking competition\nreaching speeds in excess of 150 mph, with the fastest\novertake taking place at 170 mph.\n\n\nThe AV-21 Indy Lights vehicle is outfitted\nwith three radars, six pinhole cameras, and three solid-\nstate LiDARs. Each of the sensor modalities covers a 360-\ndegree field of view around the vehicle. For localization,\nthe vehicle is equipped with two sets of high-precision\nReal-Time Kinematic (RTK) GNSS receivers and IMU.\n\n\nThe nine teams that participated were:\n\n\n\nData Usage and Availability\n---------------------------",
"### Data Usage and License\n\n\nThis work is licensed under the Creative Commons Attribution-NonCommercial 4.0 International Public License (CC BY-NC 4.0). To obtain a copy of this license, see LICENSE-CC-BY-NC-4.0.txt in the archive, visit URL or send a letter to Creative Commons, PO Box 1866, Mountain View, CA 94042, USA.\n\n\nUnder the following terms:\n\n\nAttribution — You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.\nNonCommercial — You may not use the material for commercial purposes.\nNo additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.\n\n\nPlease refer to our paper for more information and cite it if you use it in your research.",
"### Availability",
"#### AWS S3 Bucket\n\n\nBoth the ROS2 and nuScenes datasets are available on AWS S3.\n\n\n* AWS Bucket Name: s3://racecar-dataset\n* Region: us-west-2\n\n\nThe bucket is organized by\n\n\n(RACECAR-ROS2)\n\n\n1. Dataset Format ('RACECAR-ROS2' or 'RACECAR-nuScenes')\n2. Scenario ('S1', 'S2',...,'S11')\n3. Scene ('M-MULTI-SLOW\\_KAIST', 'E-SOLO-FAST-100-140', etc.)\n\n\n(RACECAR-nuScenes)\n\n\n1. Dataset Format ('RACECAR-ROS2' or 'RACECAR-nuScenes')\n2. Category ('MULTI-FAST', 'MULTI-SLOW',etc)\n\n\nDownload using AWS Command Line Interface (Recommended)\n\n\nMultiple objects or folders can be downloaded using the AWS CLI. See these instructions for installing AWS CLI v2.\n\n\nExample download usage:\n\n\nThis command will download the corresponding rosbag2 folder containing the metadata and db3 file.\n\n\nDownload using URL\n\n\nOnly individual objects can be downloaded using URLs, making them inconvenient for downloading rosbags.\n\n\nExample URL:\n\n\n* https://racecar-dataset/RACECAR-nuScenes/URL\n\n\nData Organization\n-----------------\n\n\nThe dataset is released in both the <a href=\"URL target=\"\\_blank\">rosbag2 and nuScenes format. Under the dataset root directory, two folders seperate the ROS2 and nuScenes directories.\n\n\nScenario Description\n--------------------\n\n\nEach recorded autonomous run is classified by a scenario description. This indicates the speed range of the run, the track the run takes place, and whether or not the run is multi-agent. Also specified are which teams contributed to each scenario.\n\n\n\n\\* C - Cavalier, E - EuroRacing, K - KAIST, M - MIT-PITT-RW, P - PoliMove, T - TUM\n\n\nCoordinate Conventions\n----------------------\n\n\nThe novatel pwrpak7 used to collect GNSS measurements on the AV21 uses a Y-forward, X-right, Z-up coordinate convention. Exact measurements and orientation can be found here.\n\n\nDue to cabling considerations, the placement of the unit is rotated 180 degrees around the Z-axis in the vehicle. Therefore orientation measurements coming from topics such as 'novatel\\_oem7\\_msgs/msg/BESTVEL', must be rotated 180 degrees in order to correctly correspond to the YXZ convention.\n\n\nThe accompanying Unified Robotics Description Format (urdf) has a coordinate convention of X-forward, Y-left, Z-up. In order to properly match with this convention, orientation and velocity measurements (from the IMU for example) should be rotated a subsequent 90 degrees counter-clockwise. A 90 degree clockwise rotation will equate to the same series of transformations.\n\n\n\n\n\nWe have taken into account these rotations in the 'local\\_odometry' topic, but if you desire to use the raw measurements to do your own sensor fusion or filtering, please take these orientations into account.\n\n\nThe accompanying urdf, located in 'racecar\\_utils/urdf' contains joints for every sensor on the car, as well as the approximate center of gravity. These were measured during the initial assembly of the car.\n\n\n\n\n\nRACECAR ROS2 - Data Structure\n-----------------------------",
"### Folder Structure\n\n\nThe ROS2 folder structure is organized by scenario, with each scenario folder containing a collection of rosbags. The rosbags are named corresponding to contributing racing team and a short scenario description. Inside the rosbag are the typical metadata and sqlite files.",
"### Data Processing\n\n\nThe ROS2 data was parsed and processed using python utility scripts located in 'racecar\\_py'. You can use these scripts as a baseline for doing your own bag conversion or coordiante system transformations.",
"### Topic List\n\n\nAll topics in the dataset are namespaced by their vehicle number. Each rosbag contains all sensor data available from the ego vehicle, and if a multi-agent label is included, it will be present as an 'nav\\_msgs/msg/Odometry' topic named 'local\\_odometry'.\n\n\nIf additional namespacing or merging is required, a script is included in the racecar\\_utils folder called 'rosbag\\_merger'. The inbuilt rosbag2 cli tools available in ROS2 Humble are also useful for performing bag merging and conversion.\n\n\nTopic Name: Camera Topics, Topic Type: , Description: \nTopic Name: 'camera/xxx/camera\\_info', Topic Type: 'sensor\\_msgs/msg/CameraInfo', Description: Distortion parameters and Intrinsic Camera Matrix\nTopic Name: 'camera/xxx/image/compressed', Topic Type: 'sensor\\_msgs/msg/CompressedImage', Description: Compressed camera image buffer and compression format\nTopic Name: LIDAR Topics, Topic Type: , Description: \nTopic Name: 'luminar\\_points', Topic Type: 'sensor\\_msgs/msg/PointCloud2', Description: Merge LiDAR point cloud from all three sensors\nTopic Name: 'luminar\\_xxx\\_points', Topic Type: 'sensor\\_msgs/msg/PointCloud2', Description: LiDAR point cloud corresponding to xxx sensor\nTopic Name: GNSS Topics, Topic Type: , Description: \nTopic Name: 'novatel\\_xxx/bestgnsspos', Topic Type: 'novatel\\_oem7\\_msgs/msg/BESTPOS', Description: Best available GNSS solution from Novatel PwrPak. Measurement located at antenna phase center transmitted at 20 Hz\nTopic Name: 'novatel\\_xxx/bestpos', Topic Type: 'novatel\\_oem7\\_msgs/msg/BESTPOS', Description: Best available GNSS solution from Novatel PwrPak. Measurement located at antenna phase center transmitted at 20 Hz\nTopic Name: 'novatel\\_xxx/bestvel', Topic Type: 'novatel\\_oem7\\_msgs/msg/BESTVEL', Description: Velocity derived from differentiated position. Uses the same solution method from BESTPOS transmitted at 20 Hz\nTopic Name: 'novatel\\_xxx/heading2', Topic Type: 'novatel\\_oem7\\_msgs/msg/HEADING2', Description: Heading derived from alignment of dual antenna system at variable rate\nTopic Name: 'novatel\\_xxx/oem7raw', Topic Type: 'novatel\\_oem7\\_msgs/msg/Oem7RawMsg', Description: Binary data received from Novatel receivers before driver processing\nTopic Name: 'novatel\\_xxx/rawimu', Topic Type: 'novatel\\_oem7\\_msgs/msg/RAWIMU', Description: Accelerometer and Gyroscope data transmitted from receiver at 125 Hz\nTopic Name: 'novatel\\_xxx/rawimux', Topic Type: 'sensor\\_msgs/msg/Imu', Description: Accelerometer and Gyroscope data transmitted from receiver at 125 Hz\nTopic Name: 'novatel\\_xxx/time', Topic Type: 'novatel\\_oem7\\_msgs/msg/TIME', Description: Satellite time accompanying GNSS packets\nTopic Name: Radar Topics, Topic Type: , Description: \nTopic Name: 'radar\\_front/esr\\_status1', Topic Type: 'delphi\\_esr\\_msgs/msg/EsrStatus1', Description: \nTopic Name: 'radar\\_front/esr\\_status2', Topic Type: 'delphi\\_esr\\_msgs/msg/EsrStatus2', Description: \nTopic Name: 'radar\\_front/esr\\_status3', Topic Type: 'delphi\\_esr\\_msgs/msg/EsrStatus3', Description: \nTopic Name: 'radar\\_front/esr\\_status4', Topic Type: 'delphi\\_esr\\_msgs/msg/EsrStatus4', Description: \nTopic Name: 'radar\\_front/esr\\_status5', Topic Type: 'delphi\\_esr\\_msgs/msg/EsrStatus5', Description: \nTopic Name: 'radar\\_front/esr\\_status6', Topic Type: 'delphi\\_esr\\_msgs/msg/EsrStatus6', Description: \nTopic Name: 'radar\\_front/esr\\_status7', Topic Type: 'delphi\\_esr\\_msgs/msg/EsrStatus7', Description: \nTopic Name: 'radar\\_front/esr\\_status8', Topic Type: 'delphi\\_esr\\_msgs/msg/EsrStatus8', Description: \nTopic Name: 'radar\\_front/esr\\_track', Topic Type: 'delphi\\_esr\\_msgs/msg/EsrTrack', Description: Radar detection\nTopic Name: 'radar\\_front/esr\\_valid1', Topic Type: 'delphi\\_esr\\_msgs/msg/EsrValid1', Description: \nTopic Name: 'radar\\_front/esr\\_valid2', Topic Type: 'delphi\\_esr\\_msgs/msg/EsrValid2', Description: \nTopic Name: 'radar\\_front/esr\\_vehicle1', Topic Type: 'delphi\\_esr\\_msgs/msg/EsrVehicle1', Description: \nTopic Name: 'radar\\_front/esr\\_vehicle2', Topic Type: 'delphi\\_esr\\_msgs/msg/EsrVehicle2', Description: \nTopic Name: 'radar\\_front/from\\_can\\_bus', Topic Type: 'can\\_msgs/msg/Frame', Description: Raw CAN data received from Aptiv ESR Radar\nTopic Name: 'radar\\_front/to\\_can\\_bus', Topic Type: 'can\\_msgs/msg/Frame', Description: Raw CAN data sent to Aptiv ESR Radar\nTopic Name: 'radar\\_front/radar\\_visz\\_moving', Topic Type: 'visualization\\_msgs/msg/Marker', Description: Visualization of radar detection\nTopic Name: 'radar\\_front/radar\\_visz\\_static', Topic Type: 'visualization\\_msgs/msg/Marker', Description: Visualization of radar detection\nTopic Name: 'radar\\_xxx/marker', Topic Type: 'visualization\\_msgs/msg/Marker', Description: Visualization of radar detection\nTopic Name: 'radar\\_xxx/detection', Topic Type: 'delphi\\_mrr\\_msgs/msg/Detection', Description: Detection from Aptiv MRR Radar\nTopic Name: Vehicle Positions, Topic Type: , Description: \nTopic Name: 'local\\_odometry', Topic Type: 'nav\\_msgs/msg/Odometry', Description: Vehicle odometry in Cartesian coordinates derived from RTK GNSS solution\n\n\nTopic placeholders 'xxx' refer to the specific sensor. For the cameras there is:\n\n\n* 'front\\_left'\n* 'front\\_right'\n* 'front\\_left\\_center'\n* 'front\\_right\\_center'\n* 'rear\\_left'\n* 'rear\\_right'\n\n\nFor LIDAR:\n\n\n* 'luminar\\_front\\_points'\n* 'luminar\\_left\\_points'\n* 'luminar\\_right\\_points'\n\n\nFor GNSS:\n\n\n* 'novatel\\_top'\n* 'novatel\\_bottom'\n\n\nRACECAR nuScenes - Data Structure\n---------------------------------\n\n\nWe have also released the dataset in the nuScenes format for easier accessibility to those unfamiliar with ROS2. The conversion process is done using the rosbag2nuscenes conversion tool.\n\n\n!nuScenes Block Diagram",
"### Folder Structure\n\n\nThe nuScenes dataset is structured as follows:\n\n\nFor more information on the contents of each JSON file, please refer to the nuScenes documentation.\n\n\nOur nuScenes schema deviates slightly from the original. First, we have classified each ROS2 bag as a scene rather than splitting each bag into twenty second intervals. We believe the longer scene intervals (typically over 10 mins) widen opportunities for exploration into mapping and localization problems.\nSecond, our dataset has no entries in the Annotation or Taxonomy JSON files due to the absence of annotations. These files are still present but have dummy entires to maintain compatibilty with the Python nuScenes development kit.\nEach scene in this format is seperated by the same Scenario classification as the rosbags.\n\n\nThis guide provides a walkthrough of how to explore the nuScenes release using the Python development kit. Similar to the nuScenes release, we have batched the sensor data from each scene into separate tarballs to allow users to only download the data they are interested in working with. Each tarball follows the naming convention of '{TEAM\\_NAME}\\_{BAG NAME}.URL'.\n\n\nTutorials\n---------",
"### Tutorial 1: ROS2 Visualization",
"#### Requirements\n\n\nAll code was tested with the following environment.\n\n\n* Linux (tested on Ubuntu 20.04/22.04)\n* Python 3.8+\n\n\nFor 'racecar\\_utils' please install the following.\n\n\n* ROS2 (<a href=\"URL target=\"\\_blank\">Galactic/<a href=\"URL target=\"\\_blank\">Humble)\n* <a href=\"URL target=\"\\_blank\">Eigen3",
"#### Installation of Custom ROS2 Messages\n\n\nThe 'delphi\\_esr\\_msgs', 'novatel\\_oem7\\_msgs', and 'novatel\\_gps\\_msgs' are the radar and gps messages obtained from the Autonomous Stuff and Novatel drivers. Install these packages in order to parse the radar and novatel custom messages in the dataset. They are all located in the 'ros2\\_custom\\_msgs' directory.\n\n\n* novatel\\_oem7\\_msgs\n* novatel\\_gps\\_msgs\n* delphi\\_esr\\_msgs\n* can\\_msgs\n\n\nFirst create a dev workspace that looks like the following. This will be our working directory.\n\n\nIn the working directory source your ROS2 installation, and build the packages in 'racecar\\_utils' and 'ros2\\_custom\\_msgs'. Source the installation folder in the working directory to then use the installed messages.\n\n\nThe 'can\\_msgs' package should be available via apt.",
"#### Visualization in RVIZ\n\n\nWhen replaying a bag, it is recommended to publish the ego vehicles odometry as part of the tf tree, in order to visualize it's position and sensor data in reference to the inertial map frame.\n\n\nWe have provided an example node 'odom\\_to\\_tf.cpp', that takes in the 'local\\_odometry' topics from both the ego and opponenet vehicles and publishes them to the tf tree. It is important to have the ego vehicle's frame match up with the appropriate frame in the URDF so that LiDAR and Radar point clouds can be easily visualized.\n\n\nThe node and accompanying launch file should be built along with 'racecar\\_utils'. To run, use the launch file and use the provided parameters to remap the odometry topic names appropriately. Click the following image to see an example of the RVIZ visualization.\n\n\n![RVIZ LiDAR Viz](URL \"RACECAR Dataset - GPS Labels of LiDAR\")\n\n\nCamera RVIZ\n\n\nWe have also provided an RVIZ config for visualizing camera images from the bags.\n\n\n!Camera RVIZ Example\n\n\nPlease note that this RVIZ configuration is set to show the images from all six bag topics in the format 'camera\\_idX\\_imageU8', which is different from the specified camera topics above. If you would like to visualize other camera topics, you may simply change the topic information in the RVIZ configuration.",
"### Tutorial 2: ROS2 Localization\n\n\nAn example of using the dataset is creating a more robust localization method than just using GPS. If you have examined a few of the scenarios, you may notice that there are occasional message drops, spikes in GNSS standard deviation, or small abrubt shifts in reported position. For accurate object detection, having smooth unfettered orientation estimates is very useful, so we will implement a simple extended kalman filter in order to filter through these noisy measurements.\n\n\nAn open source package, 'robot\\_localization' which is shipped as part of the full ROS2 installation will suffice to fuse measurements from a GNSS receiver, and an IMU. Install the package with the following command. For additional details about using the package, please reference the repository documentation directly.\n\n\nIn order to use the extended kalman filter, we must transform our inputs into standard message types, and make sure they are in a common coordinate system. Please see Coordinate Conventions for the required rotations. Using the 'convert\\_imu' node, we convert the 'novatel\\_oem7\\_msgs/msg/RAWIMU' message to the standard 'sensor\\_msgs/msg/Imu' which feeds into 'robot\\_localization'. The 'local\\_odometry' topic is already a stantard message type, and does not need to be adjusted.\n\n\nWe provide a simple configuration file 'config/URL' which instructs the ekf node to subscribe to the 'local\\_odometry' topic, and the frame corrected IMU topics.\n\n\nTo run the example, source the workspace and run your selected bag using a clock message. Subsequently run the provided launch file\n\n\nUsing a different motion model, tweaking the sensor measurement covariances, and adjusting which inputs are used, are all methods to gain more stable performance from the filter.",
"### Tutorial 3: nuScenes jupyter notebook\n\n\nFor a full walkthrough of using the nuScenes devkit and loading and visualizing the RACECAR data using it, please refer to this jupyter notebook.\n\n\nAcknowledgement\n---------------\n\n\nThe RACECAR data would not be possible without the efforts and contributions of the following individuals.\n\n\nAmar Kulkarni, John Chrosniak, Emory Ducote, Utkarsh Chirimar, John Link, Madhur Behl, Andrew Shehab Saha, Calvin Chanyoung Jung, Andrea Tecozzi, Marcello Cellina, Giulio Panzani, Matteo Corno, Phillip Karle, Florian Sauerbeck, Sebastian Huch, Maximilian Geisslinger, Felix Fent, Micaela Verucchi, Ayoub Raji, Danilo Caporale, Francesco Gatti."
] |
[
"TAGS\n#task_categories-robotics #task_categories-object-detection #size_categories-10K<n<100K #language-English #license-cc #Autonomous Racing #Autonomous Vehicles #Perception #arxiv-2306.03252 #region-us \n",
"### [RACECAR Data Video Demo:]\n\n\n<a href=\"URL target=\"\\_blank\">!RVIZ LiDAR Viz\n\n\nThis repository describes how the data was collected, how to download the data, its format and organization in ROS2 and NuScenes, as well as helper scripts used to parse the dataset, custom ros messages describing GNSS/IMU/Radar data, and a conversion script that converts ros2 bags to <a href=\"URL target=\"\\_blank\">nuScenes json files.\n\n\nOverview\n--------\n\n\n* Data Collection\n* Data Usage and Availability\n\t+ Data Usage and Licensce\n\t+ Citation\n\t+ Availability\n* Data Organization\n* Scenario Description\n* Coordinate Conventions\n* RACECAR ROS2 - Data Structure\n\t+ Folder Structure\n\t+ Data Processing\n\t+ Topic List\n* RACECAR nuScenes - Data Structure\n\t+ Folder Structure\n* Tutorials\n\t+ Tutorial 1: Visualization\n\t\t- Custom ROS2 Messages\n\t\t- RVIZ\n\t+ Tutorial 2: Localization\n\t+ Tutorial 3: nuScenes\n* Acknowledgements\n\n\nData Collection\n---------------\n\n\nThe RACECAR dataset is compiled by contributions from\nseveral teams, all of whom competed in the inaugural season\nof the Indy Autonomous Challenge during 2021-22. Nine\nuniversity teams participated in two races. The first race was\nheld at the Indianapolis Motor Speedway (IMS) track in\nIndiana, USA in October 2021, and the second race was held at Las Vegas Motor\nSpeedway (LVMS) in January 2022. At IMS, teams reached speeds up to\n150 mph on straights and 136 mph in turns, competing in\nsolo vehicle time trials and obstacle avoidance. At LVMS,\nteams participated in a head-to-head overtaking competition\nreaching speeds in excess of 150 mph, with the fastest\novertake taking place at 170 mph.\n\n\nThe AV-21 Indy Lights vehicle is outfitted\nwith three radars, six pinhole cameras, and three solid-\nstate LiDARs. Each of the sensor modalities covers a 360-\ndegree field of view around the vehicle. For localization,\nthe vehicle is equipped with two sets of high-precision\nReal-Time Kinematic (RTK) GNSS receivers and IMU.\n\n\nThe nine teams that participated were:\n\n\n\nData Usage and Availability\n---------------------------",
"### Data Usage and License\n\n\nThis work is licensed under the Creative Commons Attribution-NonCommercial 4.0 International Public License (CC BY-NC 4.0). To obtain a copy of this license, see LICENSE-CC-BY-NC-4.0.txt in the archive, visit URL or send a letter to Creative Commons, PO Box 1866, Mountain View, CA 94042, USA.\n\n\nUnder the following terms:\n\n\nAttribution — You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.\nNonCommercial — You may not use the material for commercial purposes.\nNo additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.\n\n\nPlease refer to our paper for more information and cite it if you use it in your research.",
"### Availability",
"#### AWS S3 Bucket\n\n\nBoth the ROS2 and nuScenes datasets are available on AWS S3.\n\n\n* AWS Bucket Name: s3://racecar-dataset\n* Region: us-west-2\n\n\nThe bucket is organized by\n\n\n(RACECAR-ROS2)\n\n\n1. Dataset Format ('RACECAR-ROS2' or 'RACECAR-nuScenes')\n2. Scenario ('S1', 'S2',...,'S11')\n3. Scene ('M-MULTI-SLOW\\_KAIST', 'E-SOLO-FAST-100-140', etc.)\n\n\n(RACECAR-nuScenes)\n\n\n1. Dataset Format ('RACECAR-ROS2' or 'RACECAR-nuScenes')\n2. Category ('MULTI-FAST', 'MULTI-SLOW',etc)\n\n\nDownload using AWS Command Line Interface (Recommended)\n\n\nMultiple objects or folders can be downloaded using the AWS CLI. See these instructions for installing AWS CLI v2.\n\n\nExample download usage:\n\n\nThis command will download the corresponding rosbag2 folder containing the metadata and db3 file.\n\n\nDownload using URL\n\n\nOnly individual objects can be downloaded using URLs, making them inconvenient for downloading rosbags.\n\n\nExample URL:\n\n\n* https://racecar-dataset/RACECAR-nuScenes/URL\n\n\nData Organization\n-----------------\n\n\nThe dataset is released in both the <a href=\"URL target=\"\\_blank\">rosbag2 and nuScenes format. Under the dataset root directory, two folders seperate the ROS2 and nuScenes directories.\n\n\nScenario Description\n--------------------\n\n\nEach recorded autonomous run is classified by a scenario description. This indicates the speed range of the run, the track the run takes place, and whether or not the run is multi-agent. Also specified are which teams contributed to each scenario.\n\n\n\n\\* C - Cavalier, E - EuroRacing, K - KAIST, M - MIT-PITT-RW, P - PoliMove, T - TUM\n\n\nCoordinate Conventions\n----------------------\n\n\nThe novatel pwrpak7 used to collect GNSS measurements on the AV21 uses a Y-forward, X-right, Z-up coordinate convention. Exact measurements and orientation can be found here.\n\n\nDue to cabling considerations, the placement of the unit is rotated 180 degrees around the Z-axis in the vehicle. Therefore orientation measurements coming from topics such as 'novatel\\_oem7\\_msgs/msg/BESTVEL', must be rotated 180 degrees in order to correctly correspond to the YXZ convention.\n\n\nThe accompanying Unified Robotics Description Format (urdf) has a coordinate convention of X-forward, Y-left, Z-up. In order to properly match with this convention, orientation and velocity measurements (from the IMU for example) should be rotated a subsequent 90 degrees counter-clockwise. A 90 degree clockwise rotation will equate to the same series of transformations.\n\n\n\n\n\nWe have taken into account these rotations in the 'local\\_odometry' topic, but if you desire to use the raw measurements to do your own sensor fusion or filtering, please take these orientations into account.\n\n\nThe accompanying urdf, located in 'racecar\\_utils/urdf' contains joints for every sensor on the car, as well as the approximate center of gravity. These were measured during the initial assembly of the car.\n\n\n\n\n\nRACECAR ROS2 - Data Structure\n-----------------------------",
"### Folder Structure\n\n\nThe ROS2 folder structure is organized by scenario, with each scenario folder containing a collection of rosbags. The rosbags are named corresponding to contributing racing team and a short scenario description. Inside the rosbag are the typical metadata and sqlite files.",
"### Data Processing\n\n\nThe ROS2 data was parsed and processed using python utility scripts located in 'racecar\\_py'. You can use these scripts as a baseline for doing your own bag conversion or coordiante system transformations.",
"### Topic List\n\n\nAll topics in the dataset are namespaced by their vehicle number. Each rosbag contains all sensor data available from the ego vehicle, and if a multi-agent label is included, it will be present as an 'nav\\_msgs/msg/Odometry' topic named 'local\\_odometry'.\n\n\nIf additional namespacing or merging is required, a script is included in the racecar\\_utils folder called 'rosbag\\_merger'. The inbuilt rosbag2 cli tools available in ROS2 Humble are also useful for performing bag merging and conversion.\n\n\nTopic Name: Camera Topics, Topic Type: , Description: \nTopic Name: 'camera/xxx/camera\\_info', Topic Type: 'sensor\\_msgs/msg/CameraInfo', Description: Distortion parameters and Intrinsic Camera Matrix\nTopic Name: 'camera/xxx/image/compressed', Topic Type: 'sensor\\_msgs/msg/CompressedImage', Description: Compressed camera image buffer and compression format\nTopic Name: LIDAR Topics, Topic Type: , Description: \nTopic Name: 'luminar\\_points', Topic Type: 'sensor\\_msgs/msg/PointCloud2', Description: Merge LiDAR point cloud from all three sensors\nTopic Name: 'luminar\\_xxx\\_points', Topic Type: 'sensor\\_msgs/msg/PointCloud2', Description: LiDAR point cloud corresponding to xxx sensor\nTopic Name: GNSS Topics, Topic Type: , Description: \nTopic Name: 'novatel\\_xxx/bestgnsspos', Topic Type: 'novatel\\_oem7\\_msgs/msg/BESTPOS', Description: Best available GNSS solution from Novatel PwrPak. Measurement located at antenna phase center transmitted at 20 Hz\nTopic Name: 'novatel\\_xxx/bestpos', Topic Type: 'novatel\\_oem7\\_msgs/msg/BESTPOS', Description: Best available GNSS solution from Novatel PwrPak. Measurement located at antenna phase center transmitted at 20 Hz\nTopic Name: 'novatel\\_xxx/bestvel', Topic Type: 'novatel\\_oem7\\_msgs/msg/BESTVEL', Description: Velocity derived from differentiated position. Uses the same solution method from BESTPOS transmitted at 20 Hz\nTopic Name: 'novatel\\_xxx/heading2', Topic Type: 'novatel\\_oem7\\_msgs/msg/HEADING2', Description: Heading derived from alignment of dual antenna system at variable rate\nTopic Name: 'novatel\\_xxx/oem7raw', Topic Type: 'novatel\\_oem7\\_msgs/msg/Oem7RawMsg', Description: Binary data received from Novatel receivers before driver processing\nTopic Name: 'novatel\\_xxx/rawimu', Topic Type: 'novatel\\_oem7\\_msgs/msg/RAWIMU', Description: Accelerometer and Gyroscope data transmitted from receiver at 125 Hz\nTopic Name: 'novatel\\_xxx/rawimux', Topic Type: 'sensor\\_msgs/msg/Imu', Description: Accelerometer and Gyroscope data transmitted from receiver at 125 Hz\nTopic Name: 'novatel\\_xxx/time', Topic Type: 'novatel\\_oem7\\_msgs/msg/TIME', Description: Satellite time accompanying GNSS packets\nTopic Name: Radar Topics, Topic Type: , Description: \nTopic Name: 'radar\\_front/esr\\_status1', Topic Type: 'delphi\\_esr\\_msgs/msg/EsrStatus1', Description: \nTopic Name: 'radar\\_front/esr\\_status2', Topic Type: 'delphi\\_esr\\_msgs/msg/EsrStatus2', Description: \nTopic Name: 'radar\\_front/esr\\_status3', Topic Type: 'delphi\\_esr\\_msgs/msg/EsrStatus3', Description: \nTopic Name: 'radar\\_front/esr\\_status4', Topic Type: 'delphi\\_esr\\_msgs/msg/EsrStatus4', Description: \nTopic Name: 'radar\\_front/esr\\_status5', Topic Type: 'delphi\\_esr\\_msgs/msg/EsrStatus5', Description: \nTopic Name: 'radar\\_front/esr\\_status6', Topic Type: 'delphi\\_esr\\_msgs/msg/EsrStatus6', Description: \nTopic Name: 'radar\\_front/esr\\_status7', Topic Type: 'delphi\\_esr\\_msgs/msg/EsrStatus7', Description: \nTopic Name: 'radar\\_front/esr\\_status8', Topic Type: 'delphi\\_esr\\_msgs/msg/EsrStatus8', Description: \nTopic Name: 'radar\\_front/esr\\_track', Topic Type: 'delphi\\_esr\\_msgs/msg/EsrTrack', Description: Radar detection\nTopic Name: 'radar\\_front/esr\\_valid1', Topic Type: 'delphi\\_esr\\_msgs/msg/EsrValid1', Description: \nTopic Name: 'radar\\_front/esr\\_valid2', Topic Type: 'delphi\\_esr\\_msgs/msg/EsrValid2', Description: \nTopic Name: 'radar\\_front/esr\\_vehicle1', Topic Type: 'delphi\\_esr\\_msgs/msg/EsrVehicle1', Description: \nTopic Name: 'radar\\_front/esr\\_vehicle2', Topic Type: 'delphi\\_esr\\_msgs/msg/EsrVehicle2', Description: \nTopic Name: 'radar\\_front/from\\_can\\_bus', Topic Type: 'can\\_msgs/msg/Frame', Description: Raw CAN data received from Aptiv ESR Radar\nTopic Name: 'radar\\_front/to\\_can\\_bus', Topic Type: 'can\\_msgs/msg/Frame', Description: Raw CAN data sent to Aptiv ESR Radar\nTopic Name: 'radar\\_front/radar\\_visz\\_moving', Topic Type: 'visualization\\_msgs/msg/Marker', Description: Visualization of radar detection\nTopic Name: 'radar\\_front/radar\\_visz\\_static', Topic Type: 'visualization\\_msgs/msg/Marker', Description: Visualization of radar detection\nTopic Name: 'radar\\_xxx/marker', Topic Type: 'visualization\\_msgs/msg/Marker', Description: Visualization of radar detection\nTopic Name: 'radar\\_xxx/detection', Topic Type: 'delphi\\_mrr\\_msgs/msg/Detection', Description: Detection from Aptiv MRR Radar\nTopic Name: Vehicle Positions, Topic Type: , Description: \nTopic Name: 'local\\_odometry', Topic Type: 'nav\\_msgs/msg/Odometry', Description: Vehicle odometry in Cartesian coordinates derived from RTK GNSS solution\n\n\nTopic placeholders 'xxx' refer to the specific sensor. For the cameras there is:\n\n\n* 'front\\_left'\n* 'front\\_right'\n* 'front\\_left\\_center'\n* 'front\\_right\\_center'\n* 'rear\\_left'\n* 'rear\\_right'\n\n\nFor LIDAR:\n\n\n* 'luminar\\_front\\_points'\n* 'luminar\\_left\\_points'\n* 'luminar\\_right\\_points'\n\n\nFor GNSS:\n\n\n* 'novatel\\_top'\n* 'novatel\\_bottom'\n\n\nRACECAR nuScenes - Data Structure\n---------------------------------\n\n\nWe have also released the dataset in the nuScenes format for easier accessibility to those unfamiliar with ROS2. The conversion process is done using the rosbag2nuscenes conversion tool.\n\n\n!nuScenes Block Diagram",
"### Folder Structure\n\n\nThe nuScenes dataset is structured as follows:\n\n\nFor more information on the contents of each JSON file, please refer to the nuScenes documentation.\n\n\nOur nuScenes schema deviates slightly from the original. First, we have classified each ROS2 bag as a scene rather than splitting each bag into twenty second intervals. We believe the longer scene intervals (typically over 10 mins) widen opportunities for exploration into mapping and localization problems.\nSecond, our dataset has no entries in the Annotation or Taxonomy JSON files due to the absence of annotations. These files are still present but have dummy entires to maintain compatibilty with the Python nuScenes development kit.\nEach scene in this format is seperated by the same Scenario classification as the rosbags.\n\n\nThis guide provides a walkthrough of how to explore the nuScenes release using the Python development kit. Similar to the nuScenes release, we have batched the sensor data from each scene into separate tarballs to allow users to only download the data they are interested in working with. Each tarball follows the naming convention of '{TEAM\\_NAME}\\_{BAG NAME}.URL'.\n\n\nTutorials\n---------",
"### Tutorial 1: ROS2 Visualization",
"#### Requirements\n\n\nAll code was tested with the following environment.\n\n\n* Linux (tested on Ubuntu 20.04/22.04)\n* Python 3.8+\n\n\nFor 'racecar\\_utils' please install the following.\n\n\n* ROS2 (<a href=\"URL target=\"\\_blank\">Galactic/<a href=\"URL target=\"\\_blank\">Humble)\n* <a href=\"URL target=\"\\_blank\">Eigen3",
"#### Installation of Custom ROS2 Messages\n\n\nThe 'delphi\\_esr\\_msgs', 'novatel\\_oem7\\_msgs', and 'novatel\\_gps\\_msgs' are the radar and gps messages obtained from the Autonomous Stuff and Novatel drivers. Install these packages in order to parse the radar and novatel custom messages in the dataset. They are all located in the 'ros2\\_custom\\_msgs' directory.\n\n\n* novatel\\_oem7\\_msgs\n* novatel\\_gps\\_msgs\n* delphi\\_esr\\_msgs\n* can\\_msgs\n\n\nFirst create a dev workspace that looks like the following. This will be our working directory.\n\n\nIn the working directory source your ROS2 installation, and build the packages in 'racecar\\_utils' and 'ros2\\_custom\\_msgs'. Source the installation folder in the working directory to then use the installed messages.\n\n\nThe 'can\\_msgs' package should be available via apt.",
"#### Visualization in RVIZ\n\n\nWhen replaying a bag, it is recommended to publish the ego vehicles odometry as part of the tf tree, in order to visualize it's position and sensor data in reference to the inertial map frame.\n\n\nWe have provided an example node 'odom\\_to\\_tf.cpp', that takes in the 'local\\_odometry' topics from both the ego and opponenet vehicles and publishes them to the tf tree. It is important to have the ego vehicle's frame match up with the appropriate frame in the URDF so that LiDAR and Radar point clouds can be easily visualized.\n\n\nThe node and accompanying launch file should be built along with 'racecar\\_utils'. To run, use the launch file and use the provided parameters to remap the odometry topic names appropriately. Click the following image to see an example of the RVIZ visualization.\n\n\n![RVIZ LiDAR Viz](URL \"RACECAR Dataset - GPS Labels of LiDAR\")\n\n\nCamera RVIZ\n\n\nWe have also provided an RVIZ config for visualizing camera images from the bags.\n\n\n!Camera RVIZ Example\n\n\nPlease note that this RVIZ configuration is set to show the images from all six bag topics in the format 'camera\\_idX\\_imageU8', which is different from the specified camera topics above. If you would like to visualize other camera topics, you may simply change the topic information in the RVIZ configuration.",
"### Tutorial 2: ROS2 Localization\n\n\nAn example of using the dataset is creating a more robust localization method than just using GPS. If you have examined a few of the scenarios, you may notice that there are occasional message drops, spikes in GNSS standard deviation, or small abrubt shifts in reported position. For accurate object detection, having smooth unfettered orientation estimates is very useful, so we will implement a simple extended kalman filter in order to filter through these noisy measurements.\n\n\nAn open source package, 'robot\\_localization' which is shipped as part of the full ROS2 installation will suffice to fuse measurements from a GNSS receiver, and an IMU. Install the package with the following command. For additional details about using the package, please reference the repository documentation directly.\n\n\nIn order to use the extended kalman filter, we must transform our inputs into standard message types, and make sure they are in a common coordinate system. Please see Coordinate Conventions for the required rotations. Using the 'convert\\_imu' node, we convert the 'novatel\\_oem7\\_msgs/msg/RAWIMU' message to the standard 'sensor\\_msgs/msg/Imu' which feeds into 'robot\\_localization'. The 'local\\_odometry' topic is already a stantard message type, and does not need to be adjusted.\n\n\nWe provide a simple configuration file 'config/URL' which instructs the ekf node to subscribe to the 'local\\_odometry' topic, and the frame corrected IMU topics.\n\n\nTo run the example, source the workspace and run your selected bag using a clock message. Subsequently run the provided launch file\n\n\nUsing a different motion model, tweaking the sensor measurement covariances, and adjusting which inputs are used, are all methods to gain more stable performance from the filter.",
"### Tutorial 3: nuScenes jupyter notebook\n\n\nFor a full walkthrough of using the nuScenes devkit and loading and visualizing the RACECAR data using it, please refer to this jupyter notebook.\n\n\nAcknowledgement\n---------------\n\n\nThe RACECAR data would not be possible without the efforts and contributions of the following individuals.\n\n\nAmar Kulkarni, John Chrosniak, Emory Ducote, Utkarsh Chirimar, John Link, Madhur Behl, Andrew Shehab Saha, Calvin Chanyoung Jung, Andrea Tecozzi, Marcello Cellina, Giulio Panzani, Matteo Corno, Phillip Karle, Florian Sauerbeck, Sebastian Huch, Maximilian Geisslinger, Felix Fent, Micaela Verucchi, Ayoub Raji, Danilo Caporale, Francesco Gatti."
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[
"passage: TAGS\n#task_categories-robotics #task_categories-object-detection #size_categories-10K<n<100K #language-English #license-cc #Autonomous Racing #Autonomous Vehicles #Perception #arxiv-2306.03252 #region-us \n",
"passage: ### [RACECAR Data Video Demo:]\n\n\n<a href=\"URL target=\"\\_blank\">!RVIZ LiDAR Viz\n\n\nThis repository describes how the data was collected, how to download the data, its format and organization in ROS2 and NuScenes, as well as helper scripts used to parse the dataset, custom ros messages describing GNSS/IMU/Radar data, and a conversion script that converts ros2 bags to <a href=\"URL target=\"\\_blank\">nuScenes json files.\n\n\nOverview\n--------\n\n\n* Data Collection\n* Data Usage and Availability\n\t+ Data Usage and Licensce\n\t+ Citation\n\t+ Availability\n* Data Organization\n* Scenario Description\n* Coordinate Conventions\n* RACECAR ROS2 - Data Structure\n\t+ Folder Structure\n\t+ Data Processing\n\t+ Topic List\n* RACECAR nuScenes - Data Structure\n\t+ Folder Structure\n* Tutorials\n\t+ Tutorial 1: Visualization\n\t\t- Custom ROS2 Messages\n\t\t- RVIZ\n\t+ Tutorial 2: Localization\n\t+ Tutorial 3: nuScenes\n* Acknowledgements\n\n\nData Collection\n---------------\n\n\nThe RACECAR dataset is compiled by contributions from\nseveral teams, all of whom competed in the inaugural season\nof the Indy Autonomous Challenge during 2021-22. Nine\nuniversity teams participated in two races. The first race was\nheld at the Indianapolis Motor Speedway (IMS) track in\nIndiana, USA in October 2021, and the second race was held at Las Vegas Motor\nSpeedway (LVMS) in January 2022. At IMS, teams reached speeds up to\n150 mph on straights and 136 mph in turns, competing in\nsolo vehicle time trials and obstacle avoidance. At LVMS,\nteams participated in a head-to-head overtaking competition\nreaching speeds in excess of 150 mph, with the fastest\novertake taking place at 170 mph.\n\n\nThe AV-21 Indy Lights vehicle is outfitted\nwith three radars, six pinhole cameras, and three solid-\nstate LiDARs. Each of the sensor modalities covers a 360-\ndegree field of view around the vehicle. For localization,\nthe vehicle is equipped with two sets of high-precision\nReal-Time Kinematic (RTK) GNSS receivers and IMU.\n\n\nThe nine teams that participated were:\n\n\n\nData Usage and Availability\n---------------------------### Data Usage and License\n\n\nThis work is licensed under the Creative Commons Attribution-NonCommercial 4.0 International Public License (CC BY-NC 4.0). To obtain a copy of this license, see LICENSE-CC-BY-NC-4.0.txt in the archive, visit URL or send a letter to Creative Commons, PO Box 1866, Mountain View, CA 94042, USA.\n\n\nUnder the following terms:\n\n\nAttribution — You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.\nNonCommercial — You may not use the material for commercial purposes.\nNo additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.\n\n\nPlease refer to our paper for more information and cite it if you use it in your research.### Availability",
"passage: #### AWS S3 Bucket\n\n\nBoth the ROS2 and nuScenes datasets are available on AWS S3.\n\n\n* AWS Bucket Name: s3://racecar-dataset\n* Region: us-west-2\n\n\nThe bucket is organized by\n\n\n(RACECAR-ROS2)\n\n\n1. Dataset Format ('RACECAR-ROS2' or 'RACECAR-nuScenes')\n2. Scenario ('S1', 'S2',...,'S11')\n3. Scene ('M-MULTI-SLOW\\_KAIST', 'E-SOLO-FAST-100-140', etc.)\n\n\n(RACECAR-nuScenes)\n\n\n1. Dataset Format ('RACECAR-ROS2' or 'RACECAR-nuScenes')\n2. Category ('MULTI-FAST', 'MULTI-SLOW',etc)\n\n\nDownload using AWS Command Line Interface (Recommended)\n\n\nMultiple objects or folders can be downloaded using the AWS CLI. See these instructions for installing AWS CLI v2.\n\n\nExample download usage:\n\n\nThis command will download the corresponding rosbag2 folder containing the metadata and db3 file.\n\n\nDownload using URL\n\n\nOnly individual objects can be downloaded using URLs, making them inconvenient for downloading rosbags.\n\n\nExample URL:\n\n\n* https://racecar-dataset/RACECAR-nuScenes/URL\n\n\nData Organization\n-----------------\n\n\nThe dataset is released in both the <a href=\"URL target=\"\\_blank\">rosbag2 and nuScenes format. Under the dataset root directory, two folders seperate the ROS2 and nuScenes directories.\n\n\nScenario Description\n--------------------\n\n\nEach recorded autonomous run is classified by a scenario description. This indicates the speed range of the run, the track the run takes place, and whether or not the run is multi-agent. Also specified are which teams contributed to each scenario.\n\n\n\n\\* C - Cavalier, E - EuroRacing, K - KAIST, M - MIT-PITT-RW, P - PoliMove, T - TUM\n\n\nCoordinate Conventions\n----------------------\n\n\nThe novatel pwrpak7 used to collect GNSS measurements on the AV21 uses a Y-forward, X-right, Z-up coordinate convention. Exact measurements and orientation can be found here.\n\n\nDue to cabling considerations, the placement of the unit is rotated 180 degrees around the Z-axis in the vehicle. Therefore orientation measurements coming from topics such as 'novatel\\_oem7\\_msgs/msg/BESTVEL', must be rotated 180 degrees in order to correctly correspond to the YXZ convention.\n\n\nThe accompanying Unified Robotics Description Format (urdf) has a coordinate convention of X-forward, Y-left, Z-up. In order to properly match with this convention, orientation and velocity measurements (from the IMU for example) should be rotated a subsequent 90 degrees counter-clockwise. A 90 degree clockwise rotation will equate to the same series of transformations.\n\n\n\n\n\nWe have taken into account these rotations in the 'local\\_odometry' topic, but if you desire to use the raw measurements to do your own sensor fusion or filtering, please take these orientations into account.\n\n\nThe accompanying urdf, located in 'racecar\\_utils/urdf' contains joints for every sensor on the car, as well as the approximate center of gravity. These were measured during the initial assembly of the car.\n\n\n\n\n\nRACECAR ROS2 - Data Structure\n-----------------------------### Folder Structure\n\n\nThe ROS2 folder structure is organized by scenario, with each scenario folder containing a collection of rosbags. The rosbags are named corresponding to contributing racing team and a short scenario description. Inside the rosbag are the typical metadata and sqlite files.### Data Processing\n\n\nThe ROS2 data was parsed and processed using python utility scripts located in 'racecar\\_py'. You can use these scripts as a baseline for doing your own bag conversion or coordiante system transformations.",
"passage: ### Topic List\n\n\nAll topics in the dataset are namespaced by their vehicle number. Each rosbag contains all sensor data available from the ego vehicle, and if a multi-agent label is included, it will be present as an 'nav\\_msgs/msg/Odometry' topic named 'local\\_odometry'.\n\n\nIf additional namespacing or merging is required, a script is included in the racecar\\_utils folder called 'rosbag\\_merger'. The inbuilt rosbag2 cli tools available in ROS2 Humble are also useful for performing bag merging and conversion.\n\n\nTopic Name: Camera Topics, Topic Type: , Description: \nTopic Name: 'camera/xxx/camera\\_info', Topic Type: 'sensor\\_msgs/msg/CameraInfo', Description: Distortion parameters and Intrinsic Camera Matrix\nTopic Name: 'camera/xxx/image/compressed', Topic Type: 'sensor\\_msgs/msg/CompressedImage', Description: Compressed camera image buffer and compression format\nTopic Name: LIDAR Topics, Topic Type: , Description: \nTopic Name: 'luminar\\_points', Topic Type: 'sensor\\_msgs/msg/PointCloud2', Description: Merge LiDAR point cloud from all three sensors\nTopic Name: 'luminar\\_xxx\\_points', Topic Type: 'sensor\\_msgs/msg/PointCloud2', Description: LiDAR point cloud corresponding to xxx sensor\nTopic Name: GNSS Topics, Topic Type: , Description: \nTopic Name: 'novatel\\_xxx/bestgnsspos', Topic Type: 'novatel\\_oem7\\_msgs/msg/BESTPOS', Description: Best available GNSS solution from Novatel PwrPak. Measurement located at antenna phase center transmitted at 20 Hz\nTopic Name: 'novatel\\_xxx/bestpos', Topic Type: 'novatel\\_oem7\\_msgs/msg/BESTPOS', Description: Best available GNSS solution from Novatel PwrPak. Measurement located at antenna phase center transmitted at 20 Hz\nTopic Name: 'novatel\\_xxx/bestvel', Topic Type: 'novatel\\_oem7\\_msgs/msg/BESTVEL', Description: Velocity derived from differentiated position. Uses the same solution method from BESTPOS transmitted at 20 Hz\nTopic Name: 'novatel\\_xxx/heading2', Topic Type: 'novatel\\_oem7\\_msgs/msg/HEADING2', Description: Heading derived from alignment of dual antenna system at variable rate\nTopic Name: 'novatel\\_xxx/oem7raw', Topic Type: 'novatel\\_oem7\\_msgs/msg/Oem7RawMsg', Description: Binary data received from Novatel receivers before driver processing\nTopic Name: 'novatel\\_xxx/rawimu', Topic Type: 'novatel\\_oem7\\_msgs/msg/RAWIMU', Description: Accelerometer and Gyroscope data transmitted from receiver at 125 Hz\nTopic Name: 'novatel\\_xxx/rawimux', Topic Type: 'sensor\\_msgs/msg/Imu', Description: Accelerometer and Gyroscope data transmitted from receiver at 125 Hz\nTopic Name: 'novatel\\_xxx/time', Topic Type: 'novatel\\_oem7\\_msgs/msg/TIME', Description: Satellite time accompanying GNSS packets\nTopic Name: Radar Topics, Topic Type: , Description: \nTopic Name: 'radar\\_front/esr\\_status1', Topic Type: 'delphi\\_esr\\_msgs/msg/EsrStatus1', Description: \nTopic Name: 'radar\\_front/esr\\_status2', Topic Type: 'delphi\\_esr\\_msgs/msg/EsrStatus2', Description: \nTopic Name: 'radar\\_front/esr\\_status3', Topic Type: 'delphi\\_esr\\_msgs/msg/EsrStatus3', Description: \nTopic Name: 'radar\\_front/esr\\_status4', Topic Type: 'delphi\\_esr\\_msgs/msg/EsrStatus4', Description: \nTopic Name: 'radar\\_front/esr\\_status5', Topic Type: 'delphi\\_esr\\_msgs/msg/EsrStatus5', Description: \nTopic Name: 'radar\\_front/esr\\_status6', Topic Type: 'delphi\\_esr\\_msgs/msg/EsrStatus6', Description: \nTopic Name: 'radar\\_front/esr\\_status7', Topic Type: 'delphi\\_esr\\_msgs/msg/EsrStatus7', Description: \nTopic Name: 'radar\\_front/esr\\_status8', Topic Type: 'delphi\\_esr\\_msgs/msg/EsrStatus8', Description: \nTopic Name: 'radar\\_front/esr\\_track', Topic Type: 'delphi\\_esr\\_msgs/msg/EsrTrack', Description: Radar detection\nTopic Name: 'radar\\_front/esr\\_valid1', Topic Type: 'delphi\\_esr\\_msgs/msg/EsrValid1', Description: \nTopic Name: 'radar\\_front/esr\\_valid2', Topic Type: 'delphi\\_esr\\_msgs/msg/EsrValid2', Description: \nTopic Name: 'radar\\_front/esr\\_vehicle1', Topic Type: 'delphi\\_esr\\_msgs/msg/EsrVehicle1', Description: \nTopic Name: 'radar\\_front/esr\\_vehicle2', Topic Type: 'delphi\\_esr\\_msgs/msg/EsrVehicle2', Description: \nTopic Name: 'radar\\_front/from\\_can\\_bus', Topic Type: 'can\\_msgs/msg/Frame', Description: Raw CAN data received from Aptiv ESR Radar\nTopic Name: 'radar\\_front/to\\_can\\_bus', Topic Type: 'can\\_msgs/msg/Frame', Description: Raw CAN data sent to Aptiv ESR Radar\nTopic Name: 'radar\\_front/radar\\_visz\\_moving', Topic Type: 'visualization\\_msgs/msg/Marker', Description: Visualization of radar detection\nTopic Name: 'radar\\_front/radar\\_visz\\_static', Topic Type: 'visualization\\_msgs/msg/Marker', Description: Visualization of radar detection\nTopic Name: 'radar\\_xxx/marker', Topic Type: 'visualization\\_msgs/msg/Marker', Description: Visualization of radar detection\nTopic Name: 'radar\\_xxx/detection', Topic Type: 'delphi\\_mrr\\_msgs/msg/Detection', Description: Detection from Aptiv MRR Radar\nTopic Name: Vehicle Positions, Topic Type: , Description: \nTopic Name: 'local\\_odometry', Topic Type: 'nav\\_msgs/msg/Odometry', Description: Vehicle odometry in Cartesian coordinates derived from RTK GNSS solution\n\n\nTopic placeholders 'xxx' refer to the specific sensor. For the cameras there is:\n\n\n* 'front\\_left'\n* 'front\\_right'\n* 'front\\_left\\_center'\n* 'front\\_right\\_center'\n* 'rear\\_left'\n* 'rear\\_right'\n\n\nFor LIDAR:\n\n\n* 'luminar\\_front\\_points'\n* 'luminar\\_left\\_points'\n* 'luminar\\_right\\_points'\n\n\nFor GNSS:\n\n\n* 'novatel\\_top'\n* 'novatel\\_bottom'\n\n\nRACECAR nuScenes - Data Structure\n---------------------------------\n\n\nWe have also released the dataset in the nuScenes format for easier accessibility to those unfamiliar with ROS2. The conversion process is done using the rosbag2nuscenes conversion tool.\n\n\n!nuScenes Block Diagram### Folder Structure\n\n\nThe nuScenes dataset is structured as follows:\n\n\nFor more information on the contents of each JSON file, please refer to the nuScenes documentation.\n\n\nOur nuScenes schema deviates slightly from the original. First, we have classified each ROS2 bag as a scene rather than splitting each bag into twenty second intervals. We believe the longer scene intervals (typically over 10 mins) widen opportunities for exploration into mapping and localization problems.\nSecond, our dataset has no entries in the Annotation or Taxonomy JSON files due to the absence of annotations. These files are still present but have dummy entires to maintain compatibilty with the Python nuScenes development kit.\nEach scene in this format is seperated by the same Scenario classification as the rosbags.\n\n\nThis guide provides a walkthrough of how to explore the nuScenes release using the Python development kit. Similar to the nuScenes release, we have batched the sensor data from each scene into separate tarballs to allow users to only download the data they are interested in working with. Each tarball follows the naming convention of '{TEAM\\_NAME}\\_{BAG NAME}.URL'.\n\n\nTutorials\n---------### Tutorial 1: ROS2 Visualization#### Requirements\n\n\nAll code was tested with the following environment.\n\n\n* Linux (tested on Ubuntu 20.04/22.04)\n* Python 3.8+\n\n\nFor 'racecar\\_utils' please install the following.\n\n\n* ROS2 (<a href=\"URL target=\"\\_blank\">Galactic/<a href=\"URL target=\"\\_blank\">Humble)\n* <a href=\"URL target=\"\\_blank\">Eigen3",
"passage: #### Installation of Custom ROS2 Messages\n\n\nThe 'delphi\\_esr\\_msgs', 'novatel\\_oem7\\_msgs', and 'novatel\\_gps\\_msgs' are the radar and gps messages obtained from the Autonomous Stuff and Novatel drivers. Install these packages in order to parse the radar and novatel custom messages in the dataset. They are all located in the 'ros2\\_custom\\_msgs' directory.\n\n\n* novatel\\_oem7\\_msgs\n* novatel\\_gps\\_msgs\n* delphi\\_esr\\_msgs\n* can\\_msgs\n\n\nFirst create a dev workspace that looks like the following. This will be our working directory.\n\n\nIn the working directory source your ROS2 installation, and build the packages in 'racecar\\_utils' and 'ros2\\_custom\\_msgs'. Source the installation folder in the working directory to then use the installed messages.\n\n\nThe 'can\\_msgs' package should be available via apt.#### Visualization in RVIZ\n\n\nWhen replaying a bag, it is recommended to publish the ego vehicles odometry as part of the tf tree, in order to visualize it's position and sensor data in reference to the inertial map frame.\n\n\nWe have provided an example node 'odom\\_to\\_tf.cpp', that takes in the 'local\\_odometry' topics from both the ego and opponenet vehicles and publishes them to the tf tree. It is important to have the ego vehicle's frame match up with the appropriate frame in the URDF so that LiDAR and Radar point clouds can be easily visualized.\n\n\nThe node and accompanying launch file should be built along with 'racecar\\_utils'. To run, use the launch file and use the provided parameters to remap the odometry topic names appropriately. Click the following image to see an example of the RVIZ visualization.\n\n\n![RVIZ LiDAR Viz](URL \"RACECAR Dataset - GPS Labels of LiDAR\")\n\n\nCamera RVIZ\n\n\nWe have also provided an RVIZ config for visualizing camera images from the bags.\n\n\n!Camera RVIZ Example\n\n\nPlease note that this RVIZ configuration is set to show the images from all six bag topics in the format 'camera\\_idX\\_imageU8', which is different from the specified camera topics above. If you would like to visualize other camera topics, you may simply change the topic information in the RVIZ configuration."
] |
e120ee7a0b68b6c3a7e932dadfaf1f834adc448d
|
# Dataset Card for "dense_summ"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
griffin/dense_summ
|
[
"region:us"
] |
2023-08-24T00:28:50+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "prompt", "dtype": "string"}, {"name": "completion", "dtype": "string"}, {"name": "step", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 3878873, "num_examples": 798}], "download_size": 1757801, "dataset_size": 3878873}}
|
2023-08-29T18:14:08+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "dense_summ"
More Information needed
|
[
"# Dataset Card for \"dense_summ\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"dense_summ\"\n\nMore Information needed"
] |
[
6,
14
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"dense_summ\"\n\nMore Information needed"
] |
7f9d7640589d7396c9a41ac7ab5e40ae10ad17e2
|
# Dataset Card for "distilled-ccmatrix-en-es"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
thesistranslation/distilled-ccmatrix-en-es
|
[
"language:es",
"language:en",
"region:us"
] |
2023-08-24T00:32:46+00:00
|
{"language": ["es", "en"], "dataset_info": {"features": [{"name": "id", "dtype": "int32"}, {"name": "translation", "dtype": {"translation": {"languages": ["en", "es"]}}}], "splits": [{"name": "train", "num_bytes": 7084246805, "num_examples": 30000000}], "download_size": 4913968666, "dataset_size": 7084246805}}
|
2023-10-03T08:21:40+00:00
|
[] |
[
"es",
"en"
] |
TAGS
#language-Spanish #language-English #region-us
|
# Dataset Card for "distilled-ccmatrix-en-es"
More Information needed
|
[
"# Dataset Card for \"distilled-ccmatrix-en-es\"\n\nMore Information needed"
] |
[
"TAGS\n#language-Spanish #language-English #region-us \n",
"# Dataset Card for \"distilled-ccmatrix-en-es\"\n\nMore Information needed"
] |
[
15,
21
] |
[
"passage: TAGS\n#language-Spanish #language-English #region-us \n# Dataset Card for \"distilled-ccmatrix-en-es\"\n\nMore Information needed"
] |
fdec265994814cf06affaaff1b4c0bd41fb1e98b
|
# Dataset of hiyou/飛鷹 (Kantai Collection)
This is the dataset of hiyou/飛鷹 (Kantai Collection), containing 228 images and their tags.
The core tags of this character are `long_hair, ribbon, hair_ribbon, black_hair, white_ribbon, breasts, hime_cut, large_breasts, red_eyes, brown_eyes`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:-----------|:------------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 228 | 211.99 MiB | [Download](https://huggingface.co/datasets/CyberHarem/hiyou_kantaicollection/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 228 | 143.61 MiB | [Download](https://huggingface.co/datasets/CyberHarem/hiyou_kantaicollection/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 496 | 285.18 MiB | [Download](https://huggingface.co/datasets/CyberHarem/hiyou_kantaicollection/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 228 | 195.64 MiB | [Download](https://huggingface.co/datasets/CyberHarem/hiyou_kantaicollection/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 496 | 361.14 MiB | [Download](https://huggingface.co/datasets/CyberHarem/hiyou_kantaicollection/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/hiyou_kantaicollection',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 7 |  |  |  |  |  | 1girl, blouse, dress_shirt, japanese_clothes, magatama, solo, upper_body, bangs, looking_at_viewer, long_sleeves, simple_background, white_background, red_shirt, blush, twitter_username |
| 1 | 20 |  |  |  |  |  | 1girl, blouse, dress_shirt, hakama_skirt, magatama, red_hakama, solo, long_sleeves, looking_at_viewer, smile, simple_background, white_background, open_mouth, red_shirt, shikigami, red_skirt |
| 2 | 8 |  |  |  |  |  | 1girl, magatama, onmyouji, shikigami, skirt, solo, dress_shirt, scroll, blouse, airplane, hakama, open_mouth |
| 3 | 6 |  |  |  |  |  | 1girl, solo, blush, cleavage, navel, simple_background, bra, looking_at_viewer, panties, underwear_only, white_background |
| 4 | 7 |  |  |  |  |  | 1girl, cleavage, looking_at_viewer, navel, solo, open_mouth, smile, white_bikini, adapted_costume, blush, brown_hair, magatama |
| 5 | 5 |  |  |  |  |  | 1girl, alternate_costume, bangs, long_sleeves, obi, solo, wide_sleeves, holding_umbrella, looking_at_viewer, oil-paper_umbrella, brown_hair, red_kimono |
| 6 | 6 |  |  |  |  |  | 1girl, detached_collar, fake_animal_ears, playboy_bunny, rabbit_ears, solo, wrist_cuffs, ass, looking_at_viewer, simple_background, strapless_leotard, white_background, blush, cowboy_shot, medium_breasts, rabbit_tail, red_leotard, black_pantyhose, bowtie, looking_back, open_mouth |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | blouse | dress_shirt | japanese_clothes | magatama | solo | upper_body | bangs | looking_at_viewer | long_sleeves | simple_background | white_background | red_shirt | blush | twitter_username | hakama_skirt | red_hakama | smile | open_mouth | shikigami | red_skirt | onmyouji | skirt | scroll | airplane | hakama | cleavage | navel | bra | panties | underwear_only | white_bikini | adapted_costume | brown_hair | alternate_costume | obi | wide_sleeves | holding_umbrella | oil-paper_umbrella | red_kimono | detached_collar | fake_animal_ears | playboy_bunny | rabbit_ears | wrist_cuffs | ass | strapless_leotard | cowboy_shot | medium_breasts | rabbit_tail | red_leotard | black_pantyhose | bowtie | looking_back |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:---------|:--------------|:-------------------|:-----------|:-------|:-------------|:--------|:--------------------|:---------------|:--------------------|:-------------------|:------------|:--------|:-------------------|:---------------|:-------------|:--------|:-------------|:------------|:------------|:-----------|:--------|:---------|:-----------|:---------|:-----------|:--------|:------|:----------|:-----------------|:---------------|:------------------|:-------------|:--------------------|:------|:---------------|:-------------------|:---------------------|:-------------|:------------------|:-------------------|:----------------|:--------------|:--------------|:------|:--------------------|:--------------|:-----------------|:--------------|:--------------|:------------------|:---------|:---------------|
| 0 | 7 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 1 | 20 |  |  |  |  |  | X | X | X | | X | X | | | X | X | X | X | X | | | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 2 | 8 |  |  |  |  |  | X | X | X | | X | X | | | | | | | | | | | | | X | X | | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 3 | 6 |  |  |  |  |  | X | | | | | X | | | X | | X | X | | X | | | | | | | | | | | | | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | |
| 4 | 7 |  |  |  |  |  | X | | | | X | X | | | X | | | | | X | | | | X | X | | | | | | | | X | X | | | | X | X | X | | | | | | | | | | | | | | | | | | | | |
| 5 | 5 |  |  |  |  |  | X | | | | | X | | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | X | X | X | X | X | X | X | | | | | | | | | | | | | | |
| 6 | 6 |  |  |  |  |  | X | | | | | X | | | X | | X | X | | X | | | | | X | | | | | | | | | | | | | | | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X |
|
CyberHarem/hiyou_kantaicollection
|
[
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] |
2023-08-24T00:36:53+00:00
|
{"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]}
|
2024-01-15T17:30:01+00:00
|
[] |
[] |
TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
|
Dataset of hiyou/飛鷹 (Kantai Collection)
=======================================
This is the dataset of hiyou/飛鷹 (Kantai Collection), containing 228 images and their tags.
The core tags of this character are 'long\_hair, ribbon, hair\_ribbon, black\_hair, white\_ribbon, breasts, hime\_cut, large\_breasts, red\_eyes, brown\_eyes', which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(huggingface organization).
List of Packages
----------------
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code
List of Clusters
----------------
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
### Table Version
|
[
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] |
[
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n",
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] |
[
44,
61,
5,
4
] |
[
"passage: TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.### Raw Text Version### Table Version"
] |
b0a55fded492cb7787af1ae7c23f61e4b67cbdab
|
# Dataset Card for "logits-ar-kmt-512"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
amitness/logits-ar-kmt-512
|
[
"region:us"
] |
2023-08-24T00:39:54+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "test", "path": "data/test-*"}]}], "dataset_info": {"features": [{"name": "input_ids", "sequence": "int32"}, {"name": "token_type_ids", "sequence": "int8"}, {"name": "attention_mask", "sequence": "int8"}, {"name": "labels", "sequence": "int64"}, {"name": "teacher_logits", "sequence": {"sequence": "float64"}}, {"name": "teacher_indices", "sequence": {"sequence": "int64"}}, {"name": "teacher_mask_indices", "sequence": "int64"}], "splits": [{"name": "train", "num_bytes": 26479205248.58042, "num_examples": 1698296}, {"name": "test", "num_bytes": 4672811932.077537, "num_examples": 299700}], "download_size": 10941745120, "dataset_size": 31152017180.65796}}
|
2023-08-24T02:33:17+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "logits-ar-kmt-512"
More Information needed
|
[
"# Dataset Card for \"logits-ar-kmt-512\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"logits-ar-kmt-512\"\n\nMore Information needed"
] |
[
6,
19
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"logits-ar-kmt-512\"\n\nMore Information needed"
] |
0b380421af90c72c81b0e70e25b9204fa54ac7f4
|
# Dataset Card for "patientNarratives"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
kingsley9/patientNarratives
|
[
"region:us"
] |
2023-08-24T01:09:59+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "validation", "path": "data/validation-*"}]}], "dataset_info": {"features": [{"name": "Unnamed: 0", "dtype": "int64"}, {"name": "description", "dtype": "string"}, {"name": "medical_specialty", "dtype": "string"}, {"name": "sample_name", "dtype": "string"}, {"name": "narrative", "dtype": "string"}, {"name": "keywords", "dtype": "string"}, {"name": "narrative_length", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 1547287.108, "num_examples": 439}, {"name": "validation", "num_bytes": 172704.028, "num_examples": 49}], "download_size": 943968, "dataset_size": 1719991.136}}
|
2023-08-24T01:10:02+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "patientNarratives"
More Information needed
|
[
"# Dataset Card for \"patientNarratives\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"patientNarratives\"\n\nMore Information needed"
] |
[
6,
14
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"patientNarratives\"\n\nMore Information needed"
] |
438dc7b3dfa401e4593b90b0317914da30c51ef7
|
# Dataset of maruyu/まるゆ (Kantai Collection)
This is the dataset of maruyu/まるゆ (Kantai Collection), containing 296 images and their tags.
The core tags of this character are `short_hair, black_hair, goggles_on_head, diving_mask_on_head, brown_eyes`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:-----------|:-------------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 296 | 181.98 MiB | [Download](https://huggingface.co/datasets/CyberHarem/maruyu_kantaicollection/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 296 | 131.44 MiB | [Download](https://huggingface.co/datasets/CyberHarem/maruyu_kantaicollection/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 554 | 250.55 MiB | [Download](https://huggingface.co/datasets/CyberHarem/maruyu_kantaicollection/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 296 | 170.25 MiB | [Download](https://huggingface.co/datasets/CyberHarem/maruyu_kantaicollection/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 554 | 315.59 MiB | [Download](https://huggingface.co/datasets/CyberHarem/maruyu_kantaicollection/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/maruyu_kantaicollection',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 5 |  |  |  |  |  | 1girl, open_mouth, santa_hat, smile, alternate_costume, looking_at_viewer, sack, solo, black_eyes, blush, capelet, full_body, white_thighhighs, boots, bow, box, dress, one_eye_closed |
| 1 | 5 |  |  |  |  |  | 1girl, fur-trimmed_headwear, looking_at_viewer, sack, santa_hat, solo, white_dress, white_thighhighs, alternate_costume, open_mouth, thick_eyebrows, capelet, fur_trim, green_ribbon, parted_bangs, simple_background, white_panties, blush, boots, cowboy_shot, full_body, pantyshot, pom_pom_(clothes), sitting, upper_teeth_only, white_background, white_footwear, white_headwear |
| 2 | 9 |  |  |  |  |  | 1girl, diving_mask, school_swimsuit, solo, white_one-piece_swimsuit, looking_at_viewer, black_eyes |
| 3 | 5 |  |  |  |  |  | 1girl, diving_mask, open_mouth, school_swimsuit, solo, white_one-piece_swimsuit, choker, tears |
| 4 | 7 |  |  |  |  |  | 1girl, diving_mask, school_swimsuit, solo, white_one-piece_swimsuit, handgun, smile, black_eyes |
| 5 | 23 |  |  |  |  |  | 1girl, school_swimsuit, solo, white_one-piece_swimsuit, diving_mask, thick_eyebrows, looking_at_viewer, parted_bangs, white_background, simple_background, cowboy_shot, twitter_username, one-hour_drawing_challenge, standing, flat_chest, choker, open_mouth |
| 6 | 8 |  |  |  |  |  | diving_mask, school_swimsuit, white_one-piece_swimsuit, 2girls, chibi, open_mouth, smile |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | open_mouth | santa_hat | smile | alternate_costume | looking_at_viewer | sack | solo | black_eyes | blush | capelet | full_body | white_thighhighs | boots | bow | box | dress | one_eye_closed | fur-trimmed_headwear | white_dress | thick_eyebrows | fur_trim | green_ribbon | parted_bangs | simple_background | white_panties | cowboy_shot | pantyshot | pom_pom_(clothes) | sitting | upper_teeth_only | white_background | white_footwear | white_headwear | diving_mask | school_swimsuit | white_one-piece_swimsuit | choker | tears | handgun | twitter_username | one-hour_drawing_challenge | standing | flat_chest | 2girls | chibi |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:-------------|:------------|:--------|:--------------------|:--------------------|:-------|:-------|:-------------|:--------|:----------|:------------|:-------------------|:--------|:------|:------|:--------|:-----------------|:-----------------------|:--------------|:-----------------|:-----------|:---------------|:---------------|:--------------------|:----------------|:--------------|:------------|:--------------------|:----------|:-------------------|:-------------------|:-----------------|:-----------------|:--------------|:------------------|:---------------------------|:---------|:--------|:----------|:-------------------|:-----------------------------|:-----------|:-------------|:---------|:--------|
| 0 | 5 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 1 | 5 |  |  |  |  |  | X | X | X | | X | X | X | X | | X | X | X | X | X | | | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | |
| 2 | 9 |  |  |  |  |  | X | | | | | X | | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | X | X | X | | | | | | | | | |
| 3 | 5 |  |  |  |  |  | X | X | | | | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | X | X | X | X | X | | | | | | | |
| 4 | 7 |  |  |  |  |  | X | | | X | | | | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | X | X | X | | | X | | | | | | |
| 5 | 23 |  |  |  |  |  | X | X | | | | X | | X | | | | | | | | | | | | | X | | | X | X | | X | | | | | X | | | X | X | X | X | | | X | X | X | X | | |
| 6 | 8 |  |  |  |  |  | | X | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | X | X | | | | | | | | X | X |
|
CyberHarem/maruyu_kantaicollection
|
[
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] |
2023-08-24T01:22:03+00:00
|
{"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]}
|
2024-01-15T14:50:17+00:00
|
[] |
[] |
TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
|
Dataset of maruyu/まるゆ (Kantai Collection)
=========================================
This is the dataset of maruyu/まるゆ (Kantai Collection), containing 296 images and their tags.
The core tags of this character are 'short\_hair, black\_hair, goggles\_on\_head, diving\_mask\_on\_head, brown\_eyes', which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(huggingface organization).
List of Packages
----------------
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code
List of Clusters
----------------
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
### Table Version
|
[
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] |
[
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n",
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] |
[
44,
61,
5,
4
] |
[
"passage: TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.### Raw Text Version### Table Version"
] |
597a9730aa85275af8196ac9dfb4a4f95df1eb2a
|
# Dataset of light_cruiser_oni/軽巡棲鬼 (Kantai Collection)
This is the dataset of light_cruiser_oni/軽巡棲鬼 (Kantai Collection), containing 79 images and their tags.
The core tags of this character are `black_hair, long_hair, blue_eyes, hair_bun, double_bun, breasts, glowing_eyes, colored_skin, white_skin, large_breasts`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:-----------|:------------------------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 79 | 70.74 MiB | [Download](https://huggingface.co/datasets/CyberHarem/light_cruiser_oni_kantaicollection/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 79 | 52.70 MiB | [Download](https://huggingface.co/datasets/CyberHarem/light_cruiser_oni_kantaicollection/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 177 | 102.26 MiB | [Download](https://huggingface.co/datasets/CyberHarem/light_cruiser_oni_kantaicollection/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 79 | 68.62 MiB | [Download](https://huggingface.co/datasets/CyberHarem/light_cruiser_oni_kantaicollection/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 177 | 124.41 MiB | [Download](https://huggingface.co/datasets/CyberHarem/light_cruiser_oni_kantaicollection/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/light_cruiser_oni_kantaicollection',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 10 |  |  |  |  |  | 1boy, 1girl, blush, hetero, penis, solo_focus, abyssal_ship, sweat, glowing, paizuri, cum_on_breasts, bar_censor, open_mouth, collarbone, gauntlets, gloves, grin, male_pubic_hair, mosaic_censoring, simple_background, torn_clothes |
| 1 | 52 |  |  |  |  |  | abyssal_ship, 1girl, solo, glowing, gauntlets, looking_at_viewer, skirt, cleavage, serafuku, smile |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1boy | 1girl | blush | hetero | penis | solo_focus | abyssal_ship | sweat | glowing | paizuri | cum_on_breasts | bar_censor | open_mouth | collarbone | gauntlets | gloves | grin | male_pubic_hair | mosaic_censoring | simple_background | torn_clothes | solo | looking_at_viewer | skirt | cleavage | serafuku | smile |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:-------|:--------|:--------|:---------|:--------|:-------------|:---------------|:--------|:----------|:----------|:-----------------|:-------------|:-------------|:-------------|:------------|:---------|:-------|:------------------|:-------------------|:--------------------|:---------------|:-------|:--------------------|:--------|:-----------|:-----------|:--------|
| 0 | 10 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | |
| 1 | 52 |  |  |  |  |  | | X | | | | | X | | X | | | | | | X | | | | | | | X | X | X | X | X | X |
|
CyberHarem/light_cruiser_oni_kantaicollection
|
[
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] |
2023-08-24T01:36:01+00:00
|
{"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]}
|
2024-01-15T22:57:04+00:00
|
[] |
[] |
TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
|
Dataset of light\_cruiser\_oni/軽巡棲鬼 (Kantai Collection)
=======================================================
This is the dataset of light\_cruiser\_oni/軽巡棲鬼 (Kantai Collection), containing 79 images and their tags.
The core tags of this character are 'black\_hair, long\_hair, blue\_eyes, hair\_bun, double\_bun, breasts, glowing\_eyes, colored\_skin, white\_skin, large\_breasts', which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(huggingface organization).
List of Packages
----------------
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code
List of Clusters
----------------
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
### Table Version
|
[
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] |
[
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n",
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] |
[
44,
61,
5,
4
] |
[
"passage: TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.### Raw Text Version### Table Version"
] |
e4a2662dc859bba61a5891bcd9f3c2768f80108e
|
# Dataset Card for Sketch Scene Descriptions
_Dataset used to train [Sketch Scene text to image model]()_
We advance sketch research to scenes with the first dataset of freehand scene sketches, FS-COCO. With practical applications in mind, we collect sketches that convey well scene content but can be sketched within a few minutes by a person with any sketching skills. Our dataset comprises around 10,000 freehand scene vector sketches with per-point space-time information by 100 non-expert individuals, offering both object- and scene-level abstraction. Each sketch is augmented with its text description.
For each row, the dataset contains `image` and `text` keys. `image` is a varying size PIL jpeg, and `text` is the accompanying text caption. Only a train split is provided.
## Citation
If you use this dataset, please cite it as:
```
@inproceedings{fscoco,
title={FS-COCO: Towards Understanding of Freehand Sketches of Common Objects in Context.}
author={Chowdhury, Pinaki Nath and Sain, Aneeshan and Bhunia, Ayan Kumar and Xiang, Tao and Gryaditskaya, Yulia and Song, Yi-Zhe},
booktitle={ECCV},
year={2022}
}
```
|
mozci/tinysketch
|
[
"task_categories:text-to-image",
"language_creators:machine-generated",
"multilinguality:monolingual",
"size_categories:n<10K",
"source_datasets:FS-COCO",
"language:en",
"license:cc-by-nc-sa-4.0",
"region:us"
] |
2023-08-24T01:45:44+00:00
|
{"language_creators": ["machine-generated"], "language": ["en"], "license": "cc-by-nc-sa-4.0", "multilinguality": ["monolingual"], "size_categories": ["n<10K"], "source_datasets": ["FS-COCO"], "task_categories": ["text-to-image"], "task_ids": [], "pretty_name": "Sketch Scene Descriptions", "tags": []}
|
2023-08-24T02:40:02+00:00
|
[] |
[
"en"
] |
TAGS
#task_categories-text-to-image #language_creators-machine-generated #multilinguality-monolingual #size_categories-n<10K #source_datasets-FS-COCO #language-English #license-cc-by-nc-sa-4.0 #region-us
|
# Dataset Card for Sketch Scene Descriptions
_Dataset used to train [Sketch Scene text to image model]()_
We advance sketch research to scenes with the first dataset of freehand scene sketches, FS-COCO. With practical applications in mind, we collect sketches that convey well scene content but can be sketched within a few minutes by a person with any sketching skills. Our dataset comprises around 10,000 freehand scene vector sketches with per-point space-time information by 100 non-expert individuals, offering both object- and scene-level abstraction. Each sketch is augmented with its text description.
For each row, the dataset contains 'image' and 'text' keys. 'image' is a varying size PIL jpeg, and 'text' is the accompanying text caption. Only a train split is provided.
If you use this dataset, please cite it as:
|
[
"# Dataset Card for Sketch Scene Descriptions\n\n_Dataset used to train [Sketch Scene text to image model]()_\n\nWe advance sketch research to scenes with the first dataset of freehand scene sketches, FS-COCO. With practical applications in mind, we collect sketches that convey well scene content but can be sketched within a few minutes by a person with any sketching skills. Our dataset comprises around 10,000 freehand scene vector sketches with per-point space-time information by 100 non-expert individuals, offering both object- and scene-level abstraction. Each sketch is augmented with its text description.\n\nFor each row, the dataset contains 'image' and 'text' keys. 'image' is a varying size PIL jpeg, and 'text' is the accompanying text caption. Only a train split is provided.\n\n\nIf you use this dataset, please cite it as:"
] |
[
"TAGS\n#task_categories-text-to-image #language_creators-machine-generated #multilinguality-monolingual #size_categories-n<10K #source_datasets-FS-COCO #language-English #license-cc-by-nc-sa-4.0 #region-us \n",
"# Dataset Card for Sketch Scene Descriptions\n\n_Dataset used to train [Sketch Scene text to image model]()_\n\nWe advance sketch research to scenes with the first dataset of freehand scene sketches, FS-COCO. With practical applications in mind, we collect sketches that convey well scene content but can be sketched within a few minutes by a person with any sketching skills. Our dataset comprises around 10,000 freehand scene vector sketches with per-point space-time information by 100 non-expert individuals, offering both object- and scene-level abstraction. Each sketch is augmented with its text description.\n\nFor each row, the dataset contains 'image' and 'text' keys. 'image' is a varying size PIL jpeg, and 'text' is the accompanying text caption. Only a train split is provided.\n\n\nIf you use this dataset, please cite it as:"
] |
[
75,
205
] |
[
"passage: TAGS\n#task_categories-text-to-image #language_creators-machine-generated #multilinguality-monolingual #size_categories-n<10K #source_datasets-FS-COCO #language-English #license-cc-by-nc-sa-4.0 #region-us \n# Dataset Card for Sketch Scene Descriptions\n\n_Dataset used to train [Sketch Scene text to image model]()_\n\nWe advance sketch research to scenes with the first dataset of freehand scene sketches, FS-COCO. With practical applications in mind, we collect sketches that convey well scene content but can be sketched within a few minutes by a person with any sketching skills. Our dataset comprises around 10,000 freehand scene vector sketches with per-point space-time information by 100 non-expert individuals, offering both object- and scene-level abstraction. Each sketch is augmented with its text description.\n\nFor each row, the dataset contains 'image' and 'text' keys. 'image' is a varying size PIL jpeg, and 'text' is the accompanying text caption. Only a train split is provided.\n\n\nIf you use this dataset, please cite it as:"
] |
3da16a9cece49e0454bce4e75a0c104c9fb5ca08
|
# Dataset Card for "9bf6da77"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
results-sd-v1-5-sd-v2-1-if-v1-0-karlo/9bf6da77
|
[
"region:us"
] |
2023-08-24T01:54:37+00:00
|
{"dataset_info": {"features": [{"name": "result", "dtype": "string"}, {"name": "id", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 182, "num_examples": 10}], "download_size": 1340, "dataset_size": 182}}
|
2023-08-24T01:54:39+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "9bf6da77"
More Information needed
|
[
"# Dataset Card for \"9bf6da77\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"9bf6da77\"\n\nMore Information needed"
] |
[
6,
15
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"9bf6da77\"\n\nMore Information needed"
] |
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