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5cdcf5839539d908deee9e4a97c3192dcae6c418
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# Dataset Card for "vadodara-jsonl-converted"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
MananSantoki/vadodara-jsonl-converted
|
[
"region:us"
] |
2023-10-25T10:29:26+00:00
|
{"dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 103428, "num_examples": 410}], "download_size": 40823, "dataset_size": 103428}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-10-25T10:29:27+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "vadodara-jsonl-converted"
More Information needed
|
[
"# Dataset Card for \"vadodara-jsonl-converted\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"vadodara-jsonl-converted\"\n\nMore Information needed"
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[
6,
20
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[
"passage: TAGS\n#region-us \n# Dataset Card for \"vadodara-jsonl-converted\"\n\nMore Information needed"
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3756200a2ca72e14aee71d748e69094034dbd0e8
|
# Dataset Card for "book_corpus-input_ids-invalid-random_shuffle-len256"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
tuanio/book_corpus-input_ids-invalid-random_shuffle-len256
|
[
"region:us"
] |
2023-10-25T10:51:22+00:00
|
{"dataset_info": {"features": [{"name": "input_ids", "sequence": "int32"}], "splits": [{"name": "train", "num_bytes": 6319283552, "num_examples": 6147163}], "download_size": 3367167037, "dataset_size": 6319283552}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-10-26T08:02:25+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "book_corpus-input_ids-invalid-random_shuffle-len256"
More Information needed
|
[
"# Dataset Card for \"book_corpus-input_ids-invalid-random_shuffle-len256\"\n\nMore Information needed"
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"TAGS\n#region-us \n",
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[
6,
33
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[
"passage: TAGS\n#region-us \n# Dataset Card for \"book_corpus-input_ids-invalid-random_shuffle-len256\"\n\nMore Information needed"
] |
b326de06adf19bd0fa4088b668de2a69683efeec
|
# Dataset Card for "detect_model_v2_dataset"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
quocanh34/detect_model_v2_dataset
|
[
"region:us"
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2023-10-25T11:04:26+00:00
|
{"dataset_info": {"features": [{"name": "input_col", "dtype": "string"}, {"name": "output_col", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 55281, "num_examples": 100}], "download_size": 11825, "dataset_size": 55281}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-10-25T11:04:27+00:00
|
[] |
[] |
TAGS
#region-us
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# Dataset Card for "detect_model_v2_dataset"
More Information needed
|
[
"# Dataset Card for \"detect_model_v2_dataset\"\n\nMore Information needed"
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[
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"passage: TAGS\n#region-us \n# Dataset Card for \"detect_model_v2_dataset\"\n\nMore Information needed"
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4bab753e797cc403a57dab8d1a102cedf51979e7
|
# Dataset Card for "Text-summarizer-dataset"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
pranjal01/Text-summarizer-dataset
|
[
"task_categories:text-generation",
"language:en",
"license:apache-2.0",
"region:us"
] |
2023-10-25T11:05:29+00:00
|
{"language": ["en"], "license": "apache-2.0", "task_categories": ["text-generation"], "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "test", "path": "data/test-*"}]}], "dataset_info": {"features": [{"name": "News", "dtype": "string"}, {"name": "Summary", "dtype": "string"}, {"name": "Title", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1130675.7886497064, "num_examples": 408}, {"name": "test", "num_bytes": 285440.21135029354, "num_examples": 103}], "download_size": 887190, "dataset_size": 1416116}}
|
2023-10-25T11:07:06+00:00
|
[] |
[
"en"
] |
TAGS
#task_categories-text-generation #language-English #license-apache-2.0 #region-us
|
# Dataset Card for "Text-summarizer-dataset"
More Information needed
|
[
"# Dataset Card for \"Text-summarizer-dataset\"\n\nMore Information needed"
] |
[
"TAGS\n#task_categories-text-generation #language-English #license-apache-2.0 #region-us \n",
"# Dataset Card for \"Text-summarizer-dataset\"\n\nMore Information needed"
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[
29,
18
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"passage: TAGS\n#task_categories-text-generation #language-English #license-apache-2.0 #region-us \n# Dataset Card for \"Text-summarizer-dataset\"\n\nMore Information needed"
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ab36b3e07ce5e0da60534357c7fc5d2d1300089f
|
<h2 style="-webkit-text-stroke-width: 0px; 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; orphans: 2; 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; widows: 2; word-spacing: 0px;"><a href="https://www.healthsupplement24x7.com/get-prostate-flux" 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: #ffd966;">Prostate Flux – Official Website Link – Click Here</span></strong></span></a></h2>
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<h2 style="-webkit-text-stroke-width: 0px; 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; orphans: 2; 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; widows: 2; word-spacing: 0px;"><a href="https://www.healthsupplement24x7.com/get-prostate-flux" target="_blank"><strong style="box-sizing: border-box; font-style: normal; font-weight: bold;">✅<span style="background-color: red; box-sizing: border-box;"><span style="box-sizing: border-box; color: #ffd966;">Click Here To Visit – “OFFICIAL WEBSITE</span></span><span style="background-color: red; box-sizing: border-box; color: #ffcc00;">”</span>✅</strong></a></h2>
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<p><strong>Prostate Flux (Brand New ProstateFlux) Reviews:</strong> Men above 50 years are at high risk of developing prostate issues. Aging slows body functions, including the hormones that support prostate health. Many medications on the market claim to restore prostrate health, but some have not been legally approved or scientifically studied.</p>
<p>An all-natural dietary supplement without any potential side effects is worth trying. <a href="https://sites.google.com/view/prostateflux/home">Prostate Flux</a> is a new comprehensive formula specifically created for men to support the health of their prostate gland. The supplement helps eliminate all the symptoms of enlarged prostate.</p>
<p>Here is a comprehensive review to help you learn more about <a href="https://sites.google.com/view/prostate-flux/home">Prostate Flux</a>, how it works, it's benefits, pros, cons, and pricing.</p>
<div class="separator" style="clear: both; text-align: center;"><a style="margin-left: 1em; margin-right: 1em;" href="https://www.healthsupplement24x7.com/get-prostate-flux" target="_blank"><img src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgn1q9pUeJClezL6kCYp-S7a7Uj7RGi9He3ZoSd14tEn0GhyxyEVPFAhyphenhyphen97hczzGbbiNuxxslZrPfxaW8Z65SnZ7xDzyafPbNxiGFAk6B5GUtzDwjTPbgLv9Qz3bOjzm0Kgk8p2Y6vnb1U_Ve5Mcg5y75fPV4EY6CPXZcGG-D2E7xQz9RJXBrcaMZfi5c0/w640-h348/ProstateFlux%205.jpg" alt="" width="640" height="348" border="0" data-original-height="915" data-original-width="1679" /></a></div>
<h2>What is Prostate Flux?</h2>
<p><a href="https://covingtonreporter.clubeo.com/calendar/2023/10/25/prostate-flux-brand-new-prostate-flux-1-prostate-health-formula-for-permanent-solution?_ga=2.196303724.1535074580.1698206977-950885170.1698206977">Prostate Flux</a> is a natural dietary supplement formulated to help keep the prostate healthy. The supplement is packed with 100% organic ingredients that are easily absorbed in the body to take effect instantly.</p>
<p>Frank Neal, the founder of <a href="https://covingtonreporter.clubeo.com/page/prostate-flux-1-unique-prostate-health-tablets-read-first-all-pros-and-cons.html">Prostate Flux</a>, claims that <a href="https://covingtonreporter.clubeo.com/page/prostate-flux-14-in-1-vital-prostate-wellness-formula-supports-prostate-and-male-health.html">Prostate Flux</a> contains the right proportions to maintain a healthy prostate. According to Neal, prostate health is attacked by cancerous cells caused by unhealthy eating habits and lifestyle. The product is gluten-free and does not contain dangerous toxins, stimulants, or GMOs.</p>
<p>He claims Prostate Flux is guaranteed to work by combining medical principles and accurate treatment. Prostate Flux is made in an FDA-approved and GMP-Certified facility following the safety protocols. The formula has passed through numerous clinical studies performed by some of the most admired universities in the world.</p>
<h2 style="text-align: center;"><a href="https://www.healthsupplement24x7.com/get-prostate-flux" target="_blank"><span style="box-sizing: border-box;"><span style="background-color: red; box-sizing: border-box; color: yellow;">Prostate Flux : Try it now, you won’t be disappointed!</span></span><span style="box-sizing: border-box;"><span style="background-color: red; box-sizing: border-box; color: yellow;"> Claim Your Free Bonus Now</span></span></a></h2>
<h2>How does Prostate Flux Work?</h2>
<p><a href="https://doogeemall.com/community/xenforum/topic/113937/prostate-flux-brand-new-prostateflux-1-prostate-health-formula-for-permanent-solution">Prostate Flux</a> formula uses a gentle traditional approach to support a healthy prostate. It contains unique stimulants clinically proven to have effects such as reducing frequent urination, supporting better sleep, and improving intimacy in life and overall quality of life.</p>
<p>The primary purpose of <a href="https://groups.google.com/g/prostate-flux/c/fiTpg274HOs">Prostate Flux</a> is to ease swelling and enlargement of the prostate gland by decreasing inflammation caused by the hormone testosterone. As men age, the production of male hormones testosterone and dihydrotestosterone (DHT) decreases.</p>
<p><a href="https://groups.google.com/g/prostateflux/c/FTSJT_Ch4EI">Prostate Flux</a> consists of organic components that have anti-inflammatory properties. The ingredients work by repairing the receptors in the reproductive system and ensuring proper immune response to reduce inflammation in the prostate area.</p>
<p>The supplement also flushes out toxins in the bloodstream. The immune system can perform better when the toxins are eliminated from the blood. The dietary formula deals with the root cause of BPH and enlarged prostate and supports the immune system making it stronger to fight future risks.</p>
<h2>Ingredients in Prostate Flux</h2>
<p><a href="https://prostate-flux.unicornplatform.page/">Prostate Flux</a> formula is made of 100% natural ingredients. They consist of plants, herbs, vitamins, and minerals. The key ingredients in the supplement are as follows:</p>
<p><strong>Saw Palmetto</strong></p>
<p>Saw Palmetto is an essential ingredient in <a href="https://pdfhost.io/v/oZsYFanVk_Prostate_Flux_Brand_New_ProstateFlux_1_Prostate_Health_Formula_For_Permanent_Solution_">Prostate Flux</a>. The berries have anti-inflammatory properties, which help reduce an enlarged prostate. They also block the enzyme that converts testosterone to DHT. It also prevents BPH and prostate cancer.</p>
<p><strong>Graviola Leaf</strong></p>
<p>The Graviola plant is used as a medicine that treats bacterial infections and illnesses caused by parasites. The ingredient has acetogenesis, which is said to kill and prevent the spread of cancer cells in the body.</p>
<p><strong>Green Tea Extract</strong></p>
<p>Green tea extract is a magic ingredient found in many dietary supplements. Besides supporting weight loss, it improves blood circulation, reduces cholesterol levels in the body, and prevents cardiovascular diseases.</p>
<p><strong>Selenium</strong></p>
<p>Selenium is an antioxidant that supports thyroid function and increases metabolic rate. It prevents cancer, strengthens the immune system, and supports cognitive abilities as people grow old.</p>
<p><strong>Vitamin E</strong></p>
<p>Vitamin is rich in antioxidants, supporting healthy skin, repairing damaged cells, and restoring healthy vision and neurological function.</p>
<p><strong>Vitamin B6</strong></p>
<p>Vitamin B6 or Pyridoxine supports a healthy brain by preventing mood swings and depression and improves the ability to think.</p>
<h2 style="text-align: center;"><a href="https://www.healthsupplement24x7.com/get-prostate-flux" target="_blank"><span style="box-sizing: border-box;"><span style="background-color: red; box-sizing: border-box; color: yellow;">Prostate Flux : Try it now, you won’t be disappointed!</span></span><span style="box-sizing: border-box;"><span style="background-color: red; box-sizing: border-box; color: yellow;"> Claim Your Free Bonus Now</span></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-prostate-flux" target="_blank"><img src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgnSX92WyJf72VjbpDbFELxavMvuNhfHBSDaGw_PPEHEal6FNetU6cDlOltfsLuExwvvVRMwm8a3GSiPiXt14yDcRuXIPrEoQRRfKc1P_OBNO_lfH8WWBKdXRIeY_QEOK4pR4HWQMgWlMUXyuWNWNZVZiPGSXNXHhVLdk9eO_8MqXuBVmcvtRXRctfVgOU/w640-h364/ProstateFlux%206.png" alt="" width="640" height="364" border="0" data-original-height="707" data-original-width="1243" /></a></div>
<h2>Benefits of Prostate Flux</h2>
<p>The natural ingredients in <a href="https://gamma.app/public/Prostate-Flux-qani9oomk20ga3g?mode=doc">Prostate Flux</a> work together to provide the following benefits:</p>
<p><strong>Reduce Enlarged Prostate</strong></p>
<p>The ingredients in <a href="https://prostateflux1.bandcamp.com/track/prostateflux-new-2023-does-it-really-works">Prostate Flux</a> can reduce enlarged prostate to normal size. Many Prostate Flux users have reported that their prostate size went back to regular size after using the supplement for three months.</p>
<p><strong>Lessen Frequent Urination</strong></p>
<p>Enlarged and swollen prostate causes the need to urinate frequently. Prostate Flux has ingredients that help balance the prostate gland to have a normal urine flow.</p>
<p><strong>Increase in Libido</strong></p>
<p>Prostate Flux deals with male health-related issues such as performance, stamina and overall function.</p>
<p><strong>Repair Tissue</strong></p>
<p>As people age, the cells in the prostate become damaged. Prostate Flux can repair and rejuvenate damaged tissue.</p>
<p><strong>Reduce inflammation</strong></p>
<p>Most active ingredients in Prostate Flux have antioxidants that help reduce any inflammation in the prostate gland.</p>
<p><strong>Supports Better Sleep</strong></p>
<p>Frequent urination, especially at night, can disrupt sleep. Lack of adequate sleep affects mental health and causes tiredness during the day. A healthy prostate means no more unnecessary urination at night.</p>
<h2>How to use Prostate Flux</h2>
<p>One Prostate Flux has a 30-day supply of 60 capsules. Users are advised to take two Prostate Flux capsules a day after a meal.</p>
<p>The results of Prostate Flux depend on a person's tolerance level. Children under 18, pregnant and lactating mothers, and those with pre-existing conditions should not use Prostate Flux.</p>
<h2 style="text-align: center;"><a href="https://www.healthsupplement24x7.com/get-prostate-flux" target="_blank"><span style="box-sizing: border-box;"><span style="background-color: red; box-sizing: border-box; color: yellow;">Prostate Flux : Try it now, you won’t be disappointed!</span></span><span style="box-sizing: border-box;"><span style="background-color: red; box-sizing: border-box; color: yellow;"> Claim Your Free Bonus Now</span></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-prostate-flux" target="_blank"><img src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhj9v5_TtXpxVmPT2MNkhiinXyXMfNHDliuZOUngYddK5-8c0S5ptkC93P7PkJVmnP6SRFs3qscAtgJk23ycWpKvsJxA3jdTUty61-SQArYSUfkMlm9IBso3r6wmrdailvyuH50QalI9CHPlOqPjGy95WIPu2dN3QANn8eu26hvQRJJzvQxw7HR7qgygtA/w640-h372/Screenshot%20(1299).png" alt="" width="640" height="372" border="0" data-original-height="745" data-original-width="1279" /></a></div>
<h2>Prostate Flux Pricing and Money-back Guarantee</h2>
<p>Consumers can order Prostate Flux on the official website at a discounted price. The price range is:</p>
<ul>
<li>One bottle at $79 + free US shipping</li>
<li>Three bottles at $59 each + free US shipping</li>
<li>Six bottles at $49 each + free US shipping</li>
</ul>
<p>Prostate Flux manufacturer offers a complete 60-day money-back for users who find the product non-effective. Customers are asked to send the product back to the company at the address shown below.</p>
<h2><strong>Is It Safe To Take Prostate Flux Complete Daily?</strong></h2>
<p>Yes, Prostate Flux Complete prostate health-boosting supplement is safe to consume daily. This statement is supported by both the customers and the experts. The reason for the same might be the quality of the supplement made from natural ingredients. The Prostate Flux Complete nutritional supplement is vegan and gluten-free as well.</p>
<h2>Conclusion</h2>
<p>Prostate Flux is created for men who have prostate issues or want to boost their prostate health. It contains the required nutrients to support prostate function. Prostate Flux strengthens the immune system, improves sleep, and reduces the risk of diseases. Prostate Flux gives long-term prostate health; users won't have to worry about BPH recurrence.</p>
<p><strong>Read More:</strong></p>
<p><a href="https://covingtonreporter.clubeo.com/calendar/2023/10/25/prostate-flux-brand-new-prostate-flux-1-prostate-health-formula-for-permanent-solution">https://covingtonreporter.clubeo.com/calendar/2023/10/25/prostate-flux-brand-new-prostate-flux-1-prostate-health-formula-for-permanent-solution</a></p>
<p><a href="https://sites.google.com/view/prostateflux/home">https://sites.google.com/view/prostateflux/home</a></p>
<p><a href="https://covingtonreporter.clubeo.com/page/prostate-flux-14-in-1-vital-prostate-wellness-formula-supports-prostate-and-male-health.html">https://covingtonreporter.clubeo.com/page/prostate-flux-14-in-1-vital-prostate-wellness-formula-supports-prostate-and-male-health.html</a></p>
<p><a href="https://sites.google.com/view/prostate-flux/home">https://sites.google.com/view/prostate-flux/home</a></p>
<p><a href="https://groups.google.com/g/prostateflux/c/FTSJT_Ch4EI">https://groups.google.com/g/prostateflux/c/FTSJT_Ch4EI</a></p>
<p><a href="https://covingtonreporter.clubeo.com/page/prostate-flux-14-in-1-vital-prostate-wellness-formula-supports-prostate-and-male-health.html">https://covingtonreporter.clubeo.com/page/prostate-flux-14-in-1-vital-prostate-wellness-formula-supports-prostate-and-male-health.html</a></p>
<p><a href="https://doogeemall.com/community/xenforum/topic/113937/prostate-flux-brand-new-prostateflux-1-prostate-health-formula-for-permanent-solution">https://doogeemall.com/community/xenforum/topic/113937/prostate-flux-brand-new-prostateflux-1-prostate-health-formula-for-permanent-solution</a></p>
<p><a href="https://groups.google.com/g/prostate-flux/c/fiTpg274HOs">https://groups.google.com/g/prostate-flux/c/fiTpg274HOs</a></p>
<p><a href="https://prostate-flux.unicornplatform.page/">https://prostate-flux.unicornplatform.page/</a></p>
<p><a href="https://pdfhost.io/v/oZsYFanVk_Prostate_Flux_Brand_New_ProstateFlux_1_Prostate_Health_Formula_For_Permanent_Solution_">https://pdfhost.io/v/oZsYFanVk_Prostate_Flux_Brand_New_ProstateFlux_1_Prostate_Health_Formula_For_Permanent_Solution_</a></p>
<p><a href="https://gamma.app/public/Prostate-Flux-qani9oomk20ga3g?mode=doc">https://gamma.app/public/Prostate-Flux-qani9oomk20ga3g?mode=doc</a></p>
<p><a href="https://prostateflux1.bandcamp.com/track/prostateflux-new-2023-does-it-really-works">https://prostateflux1.bandcamp.com/track/prostateflux-new-2023-does-it-really-works</a></p>
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ProstateFlux/ProstateFluxreviews
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[
"region:us"
] |
2023-10-25T11:26:34+00:00
|
{}
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2023-10-25T11:27:54+00:00
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TAGS
#region-us
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<h2 style="-webkit-text-stroke-width: 0px; 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; orphans: 2; 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; widows: 2; 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: #ffd966;">Prostate Flux – Official Website Link – Click Here</span></strong></span></a></h2>
<p style="-webkit-text-stroke-width: 0px; background-color: white; box-sizing: border-box; color: #333333; font-family: Roboto, Helvetica, Arial, sans-serif; font-size: 14px; font-style: normal; font-variant-caps: normal; font-variant-ligatures: normal; font-weight: 400; letter-spacing: normal; margin: 0px 0px 10px; orphans: 2; 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; widows: 2; word-spacing: 0px;"><strong style="box-sizing: border-box; font-style: normal; font-weight: bold;"><span style="box-sizing: border-box; color: magenta;"> Where to Get Bottle Online -</span> <a href="URL style="background-color: white; box-sizing: border-box; color: red;">URL style="box-sizing: border-box;" /><span style="box-sizing: border-box; color: green;"> Product Name -</span> Prostate Flux<br style="box-sizing: border-box;" /><span style="box-sizing: border-box; color: #993300;"> Side Effects -</span> <span style="box-sizing: border-box; color: navy;">No Major Side Effects</span><br style="box-sizing: border-box;" /><span style="box-sizing: border-box; color: #993366;"> Category -</span> <span style="box-sizing: border-box; color: #333333;">Health ( Prostate Health Formula)</span><br style="box-sizing: border-box;" /><span style="box-sizing: border-box; color: maroon;"> Results -</span> <span style="box-sizing: border-box; color: #00ccff;">In 1-2 Months</span><br style="box-sizing: border-box;" /><span style="box-sizing: border-box; color: red;"> Availability –</span> <a style="background-color: transparent; box-sizing: border-box; color: black; text-decoration: none;" href="URL rel="nofollow"><span style="box-sizing: border-box; color: #ff6600;">Online</span></a><br style="box-sizing: border-box;" /><span style="box-sizing: border-box; color: #333300;"> Rating: -</span> <span style="box-sizing: border-box; color: red;">5.0/5.0</span> ⭐⭐⭐⭐⭐</strong></p>
<h2 style="-webkit-text-stroke-width: 0px; 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; orphans: 2; 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; widows: 2; word-spacing: 0px;"><a href="URL target="_blank"><strong style="box-sizing: border-box; font-style: normal; font-weight: bold;"><span style="background-color: red; box-sizing: border-box;"><span style="box-sizing: border-box; color: #ffd966;">Click Here To Visit – “OFFICIAL WEBSITE</span></span><span style="background-color: red; box-sizing: border-box; color: #ffcc00;">”</span></strong></a></h2>
<h2 style="-webkit-text-stroke-width: 0px; 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; orphans: 2; 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; widows: 2; word-spacing: 0px;"><a href="URL target="_blank"><strong style="box-sizing: border-box; font-style: normal; font-weight: bold;"><span style="background-color: red; box-sizing: border-box;"><span style="box-sizing: border-box; color: #ffd966;">Click Here To Visit – “OFFICIAL WEBSITE</span></span><span style="background-color: red; box-sizing: border-box; color: #ffcc00;">”</span></strong></a></h2>
<h2 style="-webkit-text-stroke-width: 0px; 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; orphans: 2; 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; widows: 2; word-spacing: 0px;"><a href="URL target="_blank"><strong style="box-sizing: border-box; font-style: normal; font-weight: bold;"><span style="background-color: red; box-sizing: border-box;"><span style="box-sizing: border-box; color: #ffd966;">Click Here To Visit – “OFFICIAL WEBSITE</span></span><span style="background-color: red; box-sizing: border-box; color: #ffcc00;">”</span></strong></a></h2>
<p><strong>Prostate Flux (Brand New ProstateFlux) Reviews:</strong> Men above 50 years are at high risk of developing prostate issues. Aging slows body functions, including the hormones that support prostate health. Many medications on the market claim to restore prostrate health, but some have not been legally approved or scientifically studied.</p>
<p>An all-natural dietary supplement without any potential side effects is worth trying. <a href="URL Flux</a> is a new comprehensive formula specifically created for men to support the health of their prostate gland. The supplement helps eliminate all the symptoms of enlarged prostate.</p>
<p>Here is a comprehensive review to help you learn more about <a href="URL Flux</a>, how it works, it's benefits, pros, cons, and pricing.</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="348" border="0" data-original-height="915" data-original-width="1679" /></a></div>
<h2>What is Prostate Flux?</h2>
<p><a href="URL Flux</a> is a natural dietary supplement formulated to help keep the prostate healthy. The supplement is packed with 100% organic ingredients that are easily absorbed in the body to take effect instantly.</p>
<p>Frank Neal, the founder of <a href="URL Flux</a>, claims that <a href="URL Flux</a> contains the right proportions to maintain a healthy prostate. According to Neal, prostate health is attacked by cancerous cells caused by unhealthy eating habits and lifestyle. The product is gluten-free and does not contain dangerous toxins, stimulants, or GMOs.</p>
<p>He claims Prostate Flux is guaranteed to work by combining medical principles and accurate treatment. Prostate Flux is made in an FDA-approved and GMP-Certified facility following the safety protocols. The formula has passed through numerous clinical studies performed by some of the most admired universities in the world.</p>
<h2 style="text-align: center;"><a href="URL target="_blank"><span style="box-sizing: border-box;"><span style="background-color: red; box-sizing: border-box; color: yellow;">Prostate Flux : Try it now, you won’t be disappointed!</span></span><span style="box-sizing: border-box;"><span style="background-color: red; box-sizing: border-box; color: yellow;"> Claim Your Free Bonus Now</span></span></a></h2>
<h2>How does Prostate Flux Work?</h2>
<p><a href="URL Flux</a> formula uses a gentle traditional approach to support a healthy prostate. It contains unique stimulants clinically proven to have effects such as reducing frequent urination, supporting better sleep, and improving intimacy in life and overall quality of life.</p>
<p>The primary purpose of <a href="URL Flux</a> is to ease swelling and enlargement of the prostate gland by decreasing inflammation caused by the hormone testosterone. As men age, the production of male hormones testosterone and dihydrotestosterone (DHT) decreases.</p>
<p><a href="URL Flux</a> consists of organic components that have anti-inflammatory properties. The ingredients work by repairing the receptors in the reproductive system and ensuring proper immune response to reduce inflammation in the prostate area.</p>
<p>The supplement also flushes out toxins in the bloodstream. The immune system can perform better when the toxins are eliminated from the blood. The dietary formula deals with the root cause of BPH and enlarged prostate and supports the immune system making it stronger to fight future risks.</p>
<h2>Ingredients in Prostate Flux</h2>
<p><a href="URL Flux</a> formula is made of 100% natural ingredients. They consist of plants, herbs, vitamins, and minerals. The key ingredients in the supplement are as follows:</p>
<p><strong>Saw Palmetto</strong></p>
<p>Saw Palmetto is an essential ingredient in <a href="URL Flux</a>. The berries have anti-inflammatory properties, which help reduce an enlarged prostate. They also block the enzyme that converts testosterone to DHT. It also prevents BPH and prostate cancer.</p>
<p><strong>Graviola Leaf</strong></p>
<p>The Graviola plant is used as a medicine that treats bacterial infections and illnesses caused by parasites. The ingredient has acetogenesis, which is said to kill and prevent the spread of cancer cells in the body.</p>
<p><strong>Green Tea Extract</strong></p>
<p>Green tea extract is a magic ingredient found in many dietary supplements. Besides supporting weight loss, it improves blood circulation, reduces cholesterol levels in the body, and prevents cardiovascular diseases.</p>
<p><strong>Selenium</strong></p>
<p>Selenium is an antioxidant that supports thyroid function and increases metabolic rate. It prevents cancer, strengthens the immune system, and supports cognitive abilities as people grow old.</p>
<p><strong>Vitamin E</strong></p>
<p>Vitamin is rich in antioxidants, supporting healthy skin, repairing damaged cells, and restoring healthy vision and neurological function.</p>
<p><strong>Vitamin B6</strong></p>
<p>Vitamin B6 or Pyridoxine supports a healthy brain by preventing mood swings and depression and improves the ability to think.</p>
<h2 style="text-align: center;"><a href="URL target="_blank"><span style="box-sizing: border-box;"><span style="background-color: red; box-sizing: border-box; color: yellow;">Prostate Flux : Try it now, you won’t be disappointed!</span></span><span style="box-sizing: border-box;"><span style="background-color: red; box-sizing: border-box; color: yellow;"> Claim Your Free Bonus Now</span></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="364" border="0" data-original-height="707" data-original-width="1243" /></a></div>
<h2>Benefits of Prostate Flux</h2>
<p>The natural ingredients in <a href="URL Flux</a> work together to provide the following benefits:</p>
<p><strong>Reduce Enlarged Prostate</strong></p>
<p>The ingredients in <a href="URL Flux</a> can reduce enlarged prostate to normal size. Many Prostate Flux users have reported that their prostate size went back to regular size after using the supplement for three months.</p>
<p><strong>Lessen Frequent Urination</strong></p>
<p>Enlarged and swollen prostate causes the need to urinate frequently. Prostate Flux has ingredients that help balance the prostate gland to have a normal urine flow.</p>
<p><strong>Increase in Libido</strong></p>
<p>Prostate Flux deals with male health-related issues such as performance, stamina and overall function.</p>
<p><strong>Repair Tissue</strong></p>
<p>As people age, the cells in the prostate become damaged. Prostate Flux can repair and rejuvenate damaged tissue.</p>
<p><strong>Reduce inflammation</strong></p>
<p>Most active ingredients in Prostate Flux have antioxidants that help reduce any inflammation in the prostate gland.</p>
<p><strong>Supports Better Sleep</strong></p>
<p>Frequent urination, especially at night, can disrupt sleep. Lack of adequate sleep affects mental health and causes tiredness during the day. A healthy prostate means no more unnecessary urination at night.</p>
<h2>How to use Prostate Flux</h2>
<p>One Prostate Flux has a 30-day supply of 60 capsules. Users are advised to take two Prostate Flux capsules a day after a meal.</p>
<p>The results of Prostate Flux depend on a person's tolerance level. Children under 18, pregnant and lactating mothers, and those with pre-existing conditions should not use Prostate Flux.</p>
<h2 style="text-align: center;"><a href="URL target="_blank"><span style="box-sizing: border-box;"><span style="background-color: red; box-sizing: border-box; color: yellow;">Prostate Flux : Try it now, you won’t be disappointed!</span></span><span style="box-sizing: border-box;"><span style="background-color: red; box-sizing: border-box; color: yellow;"> Claim Your Free Bonus Now</span></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="372" border="0" data-original-height="745" data-original-width="1279" /></a></div>
<h2>Prostate Flux Pricing and Money-back Guarantee</h2>
<p>Consumers can order Prostate Flux on the official website at a discounted price. The price range is:</p>
<ul>
<li>One bottle at $79 + free US shipping</li>
<li>Three bottles at $59 each + free US shipping</li>
<li>Six bottles at $49 each + free US shipping</li>
</ul>
<p>Prostate Flux manufacturer offers a complete 60-day money-back for users who find the product non-effective. Customers are asked to send the product back to the company at the address shown below.</p>
<h2><strong>Is It Safe To Take Prostate Flux Complete Daily?</strong></h2>
<p>Yes, Prostate Flux Complete prostate health-boosting supplement is safe to consume daily. This statement is supported by both the customers and the experts. The reason for the same might be the quality of the supplement made from natural ingredients. The Prostate Flux Complete nutritional supplement is vegan and gluten-free as well.</p>
<h2>Conclusion</h2>
<p>Prostate Flux is created for men who have prostate issues or want to boost their prostate health. It contains the required nutrients to support prostate function. Prostate Flux strengthens the immune system, improves sleep, and reduces the risk of diseases. Prostate Flux gives long-term prostate health; users won't have to worry about BPH recurrence.</p>
<p><strong>Read More:</strong></p>
<p><a href="URL/URL
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[
"TAGS\n#region-us \n"
] |
[
6
] |
[
"passage: TAGS\n#region-us \n"
] |
7e9ab419ad3f7107914243de8d8f69c793f16b4c
|
w95/fin
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[
"region:us"
] |
2023-10-25T11:26:52+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "train.jsonl"}]}], "dataset_info": {"features": [{"name": "input", "dtype": "string"}, {"name": "output", "dtype": "string"}, {"name": "instruction", "dtype": "string"}]}}
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2023-10-25T11:30:26+00:00
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[] |
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TAGS
#region-us
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[] |
[
"TAGS\n#region-us \n"
] |
[
6
] |
[
"passage: TAGS\n#region-us \n"
] |
||
88d1f7118cbf35663d98ccea9695b3864b2165f3
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# Bangumi Image Base of Mushoku Tensei
This is the image base of bangumi Mushoku Tensei, we detected 73 characters, 6277 images in total. The full dataset is [here](all.zip).
**Please note that these image bases are not guaranteed to be 100% cleaned, they may be noisy actual.** If you intend to manually train models using this dataset, we recommend performing necessary preprocessing on the downloaded dataset to eliminate potential noisy samples (approximately 1% probability).
Here is the characters' preview:
| # | Images | Download | Preview 1 | Preview 2 | Preview 3 | Preview 4 | Preview 5 | Preview 6 | Preview 7 | Preview 8 |
|:------|---------:|:---------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|
| 0 | 35 | [Download](0/dataset.zip) |  |  |  |  |  |  |  |  |
| 1 | 36 | [Download](1/dataset.zip) |  |  |  |  |  |  |  |  |
| 2 | 29 | [Download](2/dataset.zip) |  |  |  |  |  |  |  |  |
| 3 | 252 | [Download](3/dataset.zip) |  |  |  |  |  |  |  |  |
| 4 | 1729 | [Download](4/dataset.zip) |  |  |  |  |  |  |  |  |
| 5 | 74 | [Download](5/dataset.zip) |  |  |  |  |  |  |  |  |
| 6 | 179 | [Download](6/dataset.zip) |  |  |  |  |  |  |  |  |
| 7 | 52 | [Download](7/dataset.zip) |  |  |  |  |  |  |  |  |
| 8 | 152 | [Download](8/dataset.zip) |  |  |  |  |  |  |  |  |
| 9 | 47 | [Download](9/dataset.zip) |  |  |  |  |  |  |  |  |
| 10 | 211 | [Download](10/dataset.zip) |  |  |  |  |  |  |  |  |
| 11 | 62 | [Download](11/dataset.zip) |  |  |  |  |  |  |  |  |
| 12 | 47 | [Download](12/dataset.zip) |  |  |  |  |  |  |  |  |
| 13 | 66 | [Download](13/dataset.zip) |  |  |  |  |  |  |  |  |
| 14 | 152 | [Download](14/dataset.zip) |  |  |  |  |  |  |  |  |
| 15 | 37 | [Download](15/dataset.zip) |  |  |  |  |  |  |  |  |
| 16 | 10 | [Download](16/dataset.zip) |  |  |  |  |  |  |  |  |
| 17 | 42 | [Download](17/dataset.zip) |  |  |  |  |  |  |  |  |
| 18 | 36 | [Download](18/dataset.zip) |  |  |  |  |  |  |  |  |
| 19 | 28 | [Download](19/dataset.zip) |  |  |  |  |  |  |  |  |
| 20 | 118 | [Download](20/dataset.zip) |  |  |  |  |  |  |  |  |
| 21 | 33 | [Download](21/dataset.zip) |  |  |  |  |  |  |  |  |
| 22 | 62 | [Download](22/dataset.zip) |  |  |  |  |  |  |  |  |
| 23 | 17 | [Download](23/dataset.zip) |  |  |  |  |  |  |  |  |
| 24 | 21 | [Download](24/dataset.zip) |  |  |  |  |  |  |  |  |
| 25 | 60 | [Download](25/dataset.zip) |  |  |  |  |  |  |  |  |
| 26 | 19 | [Download](26/dataset.zip) |  |  |  |  |  |  |  |  |
| 27 | 26 | [Download](27/dataset.zip) |  |  |  |  |  |  |  |  |
| 28 | 13 | [Download](28/dataset.zip) |  |  |  |  |  |  |  |  |
| 29 | 14 | [Download](29/dataset.zip) |  |  |  |  |  |  |  |  |
| 30 | 14 | [Download](30/dataset.zip) |  |  |  |  |  |  |  |  |
| 31 | 21 | [Download](31/dataset.zip) |  |  |  |  |  |  |  |  |
| 32 | 103 | [Download](32/dataset.zip) |  |  |  |  |  |  |  |  |
| 33 | 8 | [Download](33/dataset.zip) |  |  |  |  |  |  |  |  |
| 34 | 44 | [Download](34/dataset.zip) |  |  |  |  |  |  |  |  |
| 35 | 53 | [Download](35/dataset.zip) |  |  |  |  |  |  |  |  |
| 36 | 8 | [Download](36/dataset.zip) |  |  |  |  |  |  |  |  |
| 37 | 18 | [Download](37/dataset.zip) |  |  |  |  |  |  |  |  |
| 38 | 12 | [Download](38/dataset.zip) |  |  |  |  |  |  |  |  |
| 39 | 19 | [Download](39/dataset.zip) |  |  |  |  |  |  |  |  |
| 40 | 224 | [Download](40/dataset.zip) |  |  |  |  |  |  |  |  |
| 41 | 36 | [Download](41/dataset.zip) |  |  |  |  |  |  |  |  |
| 42 | 50 | [Download](42/dataset.zip) |  |  |  |  |  |  |  |  |
| 43 | 22 | [Download](43/dataset.zip) |  |  |  |  |  |  |  |  |
| 44 | 17 | [Download](44/dataset.zip) |  |  |  |  |  |  |  |  |
| 45 | 9 | [Download](45/dataset.zip) |  |  |  |  |  |  |  |  |
| 46 | 49 | [Download](46/dataset.zip) |  |  |  |  |  |  |  |  |
| 47 | 62 | [Download](47/dataset.zip) |  |  |  |  |  |  |  |  |
| 48 | 27 | [Download](48/dataset.zip) |  |  |  |  |  |  |  |  |
| 49 | 111 | [Download](49/dataset.zip) |  |  |  |  |  |  |  |  |
| 50 | 39 | [Download](50/dataset.zip) |  |  |  |  |  |  |  |  |
| 51 | 15 | [Download](51/dataset.zip) |  |  |  |  |  |  |  |  |
| 52 | 11 | [Download](52/dataset.zip) |  |  |  |  |  |  |  |  |
| 53 | 16 | [Download](53/dataset.zip) |  |  |  |  |  |  |  |  |
| 54 | 33 | [Download](54/dataset.zip) |  |  |  |  |  |  |  |  |
| 55 | 783 | [Download](55/dataset.zip) |  |  |  |  |  |  |  |  |
| 56 | 41 | [Download](56/dataset.zip) |  |  |  |  |  |  |  |  |
| 57 | 29 | [Download](57/dataset.zip) |  |  |  |  |  |  |  |  |
| 58 | 90 | [Download](58/dataset.zip) |  |  |  |  |  |  |  |  |
| 59 | 78 | [Download](59/dataset.zip) |  |  |  |  |  |  |  |  |
| 60 | 29 | [Download](60/dataset.zip) |  |  |  |  |  |  |  |  |
| 61 | 22 | [Download](61/dataset.zip) |  |  |  |  |  |  |  |  |
| 62 | 91 | [Download](62/dataset.zip) |  |  |  |  |  |  |  |  |
| 63 | 16 | [Download](63/dataset.zip) |  |  |  |  |  |  |  |  |
| 64 | 10 | [Download](64/dataset.zip) |  |  |  |  |  |  |  |  |
| 65 | 6 | [Download](65/dataset.zip) |  |  |  |  |  |  | N/A | N/A |
| 66 | 36 | [Download](66/dataset.zip) |  |  |  |  |  |  |  |  |
| 67 | 10 | [Download](67/dataset.zip) |  |  |  |  |  |  |  |  |
| 68 | 13 | [Download](68/dataset.zip) |  |  |  |  |  |  |  |  |
| 69 | 17 | [Download](69/dataset.zip) |  |  |  |  |  |  |  |  |
| 70 | 23 | [Download](70/dataset.zip) |  |  |  |  |  |  |  |  |
| 71 | 32 | [Download](71/dataset.zip) |  |  |  |  |  |  |  |  |
| noise | 271 | [Download](-1/dataset.zip) |  |  |  |  |  |  |  |  |
|
BangumiBase/mushokutensei
|
[
"size_categories:1K<n<10K",
"license:mit",
"art",
"region:us"
] |
2023-10-25T11:35:03+00:00
|
{"license": "mit", "size_categories": ["1K<n<10K"], "tags": ["art"]}
|
2023-10-25T15:16:47+00:00
|
[] |
[] |
TAGS
#size_categories-1K<n<10K #license-mit #art #region-us
|
Bangumi Image Base of Mushoku Tensei
====================================
This is the image base of bangumi Mushoku Tensei, we detected 73 characters, 6277 images in total. The full dataset is here.
Please note that these image bases are not guaranteed to be 100% cleaned, they may be noisy actual. If you intend to manually train models using this dataset, we recommend performing necessary preprocessing on the downloaded dataset to eliminate potential noisy samples (approximately 1% probability).
Here is the characters' preview:
|
[] |
[
"TAGS\n#size_categories-1K<n<10K #license-mit #art #region-us \n"
] |
[
25
] |
[
"passage: TAGS\n#size_categories-1K<n<10K #license-mit #art #region-us \n"
] |
641c1bc069dafc9270b9ae218f672d54a33131ab
|
# Dataset Card for "bindingdb"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
phanvancongthanh/bindingdb
|
[
"region:us"
] |
2023-10-25T11:48:22+00:00
|
{"dataset_info": {"features": [{"name": "smiles", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 161671433, "num_examples": 2498120}], "download_size": 28050616, "dataset_size": 161671433}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-11-16T15:19:28+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "bindingdb"
More Information needed
|
[
"# Dataset Card for \"bindingdb\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"bindingdb\"\n\nMore Information needed"
] |
[
6,
12
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"bindingdb\"\n\nMore Information needed"
] |
89a8ea28097e8fec06865bb233a3a8269c818be1
|
# Dataset Card for "edgar_all_4-no-valid-roberta-base"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
lltala/edgar_all_4-no-valid-roberta-base
|
[
"region:us"
] |
2023-10-25T11:57:46+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "validation", "path": "data/validation-*"}]}], "dataset_info": {"features": [{"name": "doc_id", "dtype": "string"}, {"name": "id", "dtype": "string"}, {"name": "ner_tags", "sequence": {"class_label": {"names": {"0": "O", "1": "B-PER", "2": "I-PER", "3": "B-ORG", "4": "I-ORG", "5": "B-LOC", "6": "I-LOC"}}}}, {"name": "tokens", "sequence": "string"}], "splits": [{"name": "train", "num_bytes": 7056516, "num_examples": 930}, {"name": "validation", "num_bytes": 676038, "num_examples": 90}], "download_size": 912973, "dataset_size": 7732554}}
|
2023-10-25T11:57:50+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "edgar_all_4-no-valid-roberta-base"
More Information needed
|
[
"# Dataset Card for \"edgar_all_4-no-valid-roberta-base\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"edgar_all_4-no-valid-roberta-base\"\n\nMore Information needed"
] |
[
6,
25
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"edgar_all_4-no-valid-roberta-base\"\n\nMore Information needed"
] |
aafc79371edcdde03957bbd0de32198d5918806d
|
# Dataset Card for "ru-mhr-parallel"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
d0rj/ru-mhr-parallel
|
[
"task_categories:translation",
"size_categories:100K<n<1M",
"language:ru",
"language:mhr",
"license:cc-by-4.0",
"low-resource-language",
"region:us"
] |
2023-10-25T12:04:56+00:00
|
{"language": ["ru", "mhr"], "license": "cc-by-4.0", "size_categories": ["100K<n<1M"], "task_categories": ["translation"], "pretty_name": "Mari-Russian parallel corpus", "dataset_info": {"features": [{"name": "ru", "dtype": "string"}, {"name": "mhr", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 83137793, "num_examples": 417103}], "download_size": 40277610, "dataset_size": 83137793}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "tags": ["low-resource-language"]}
|
2023-10-25T12:09:14+00:00
|
[] |
[
"ru",
"mhr"
] |
TAGS
#task_categories-translation #size_categories-100K<n<1M #language-Russian #language-Eastern Mari #license-cc-by-4.0 #low-resource-language #region-us
|
# Dataset Card for "ru-mhr-parallel"
More Information needed
|
[
"# Dataset Card for \"ru-mhr-parallel\"\n\nMore Information needed"
] |
[
"TAGS\n#task_categories-translation #size_categories-100K<n<1M #language-Russian #language-Eastern Mari #license-cc-by-4.0 #low-resource-language #region-us \n",
"# Dataset Card for \"ru-mhr-parallel\"\n\nMore Information needed"
] |
[
55,
18
] |
[
"passage: TAGS\n#task_categories-translation #size_categories-100K<n<1M #language-Russian #language-Eastern Mari #license-cc-by-4.0 #low-resource-language #region-us \n# Dataset Card for \"ru-mhr-parallel\"\n\nMore Information needed"
] |
75d78d9038ecf55d541a84a49205d232c8d461f1
|
# Dataset Card for "depression-instruct"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
vitaliy-sharandin/depression-instruct
|
[
"region:us"
] |
2023-10-25T12:22:41+00:00
|
{"dataset_info": {"features": [{"name": "instruction", "dtype": "string"}, {"name": "response", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 12872, "num_examples": 51}], "download_size": 10500, "dataset_size": 12872}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-10-25T12:24:11+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "depression-instruct"
More Information needed
|
[
"# Dataset Card for \"depression-instruct\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"depression-instruct\"\n\nMore Information needed"
] |
[
6,
15
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"depression-instruct\"\n\nMore Information needed"
] |
d30b47516fba0cf08b41d685aee6e1b133269ea7
|
# IsiZulu News (articles and headlines) and Siswati News (headlines) Corpora - za-isizulu-siswati-news-2022
[](https://doi.org/10.5281/zenodo.7193346) [](https://arxiv.org/abs/2306.07426)
### About Dataset
Dataset for both isiZulu news (articles and headlines) and Siswati news headlines. Process included scraping the data from internet, from Isolezwe news website http://www.isolezwe.co.za and public posts from the SABC news LigwalagwalaFM Facebook page https://www.facebook.com/ligwalagwalafm/ respectively.
The obtained datasets are isiZulu news articles, isiZulu news headlines, and Siswati news headlines.
Post data collection the datasets were then sent to annotators, and they were sent back after the annotation process. The datasets contain special characters, some English words and characters that are not ASCII encoded which must be removed prior to model training. The aim of these three datasets is to create a baseline news categorisation model for the two South African low resources languages i.e. isiZulu and Siswati.
For categorisation, we use high level [IPTC NewsCodes](https://iptc.org/standards/newscodes/) as categories. You can view the news categories here [data/news-categories-iptc-newscodes.csv](data/news-categories-iptc-newscodes.csv)
The datasets were found to have class categories with very few observations, hence the class categories which have less than 35 observations were removed for isiZulu and less 6 observations for Siswati.
The dataset has both full category data as well as reduced category data.
Please see the [data-statement.md](data-statement.md) for full dataset information.
## Online Repository link
* Link to the DOI data repository - [Zenodo Data Repository](https://doi.org/10.5281/zenodo.7193346)
*
## Authors
* **Andani Madodonga**
* **Vukosi Marivate** - [@vukosi](https://twitter.com/vukosi)
* **Matthew Adendorff**
See also the list of [contributors](https://github.com/dsfsi/za-isizulu-siswati-news-2022/contributors) who participated in this project.
## Citation
**Citation:**
```bibtex
@article{Madodonga_Marivate_Adendorff_2023, title={Izindaba-Tindzaba: Machine learning news categorisation for Long and Short Text for isiZulu and Siswati}, volume={4}, url={https://upjournals.up.ac.za/index.php/dhasa/article/view/4449}, DOI={10.55492/dhasa.v4i01.4449}, author={Madodonga, Andani and Marivate, Vukosi and Adendorff, Matthew}, year={2023}, month={Jan.} }
```
## License
Data is Licensed under CC 4.0 BY SA
|
dsfsi/za-isizulu-siswati-news
|
[
"task_categories:text-classification",
"language:ss",
"language:zu",
"license:cc-by-sa-4.0",
"dsfsi-datasets",
"siswati",
"isizulu",
"arxiv:2306.07426",
"region:us"
] |
2023-10-25T12:23:14+00:00
|
{"language": ["ss", "zu"], "license": "cc-by-sa-4.0", "task_categories": ["text-classification"], "pretty_name": "za-isizulu-siswati-news", "tags": ["dsfsi-datasets", "siswati", "isizulu"]}
|
2023-10-25T12:32:26+00:00
|
[
"2306.07426"
] |
[
"ss",
"zu"
] |
TAGS
#task_categories-text-classification #language-Swati #language-Zulu #license-cc-by-sa-4.0 #dsfsi-datasets #siswati #isizulu #arxiv-2306.07426 #region-us
|
# IsiZulu News (articles and headlines) and Siswati News (headlines) Corpora - za-isizulu-siswati-news-2022
 and Siswati news headlines. Process included scraping the data from internet, from Isolezwe news website URL and public posts from the SABC news LigwalagwalaFM Facebook page URL respectively.
The obtained datasets are isiZulu news articles, isiZulu news headlines, and Siswati news headlines.
Post data collection the datasets were then sent to annotators, and they were sent back after the annotation process. The datasets contain special characters, some English words and characters that are not ASCII encoded which must be removed prior to model training. The aim of these three datasets is to create a baseline news categorisation model for the two South African low resources languages i.e. isiZulu and Siswati.
For categorisation, we use high level IPTC NewsCodes as categories. You can view the news categories here data/URL
The datasets were found to have class categories with very few observations, hence the class categories which have less than 35 observations were removed for isiZulu and less 6 observations for Siswati.
The dataset has both full category data as well as reduced category data.
Please see the URL for full dataset information.
## Online Repository link
* Link to the DOI data repository - Zenodo Data Repository
*
## Authors
* Andani Madodonga
* Vukosi Marivate - @vukosi
* Matthew Adendorff
See also the list of contributors who participated in this project.
Citation:
## License
Data is Licensed under CC 4.0 BY SA
|
[
"# IsiZulu News (articles and headlines) and Siswati News (headlines) Corpora - za-isizulu-siswati-news-2022\n\n\n![DOI](URL ![arXiv](URL",
"### About Dataset\n\nDataset for both isiZulu news (articles and headlines) and Siswati news headlines. Process included scraping the data from internet, from Isolezwe news website URL and public posts from the SABC news LigwalagwalaFM Facebook page URL respectively.\n\nThe obtained datasets are isiZulu news articles, isiZulu news headlines, and Siswati news headlines. \n\nPost data collection the datasets were then sent to annotators, and they were sent back after the annotation process. The datasets contain special characters, some English words and characters that are not ASCII encoded which must be removed prior to model training. The aim of these three datasets is to create a baseline news categorisation model for the two South African low resources languages i.e. isiZulu and Siswati. \n\nFor categorisation, we use high level IPTC NewsCodes as categories. You can view the news categories here data/URL\n\nThe datasets were found to have class categories with very few observations, hence the class categories which have less than 35 observations were removed for isiZulu and less 6 observations for Siswati. \n\nThe dataset has both full category data as well as reduced category data.\n\nPlease see the URL for full dataset information.",
"## Online Repository link\n\n* Link to the DOI data repository - Zenodo Data Repository\n*",
"## Authors\n\n* Andani Madodonga \n* Vukosi Marivate - @vukosi\n* Matthew Adendorff\n\nSee also the list of contributors who participated in this project.\n\nCitation:",
"## License\n\nData is Licensed under CC 4.0 BY SA"
] |
[
"TAGS\n#task_categories-text-classification #language-Swati #language-Zulu #license-cc-by-sa-4.0 #dsfsi-datasets #siswati #isizulu #arxiv-2306.07426 #region-us \n",
"# IsiZulu News (articles and headlines) and Siswati News (headlines) Corpora - za-isizulu-siswati-news-2022\n\n\n![DOI](URL ![arXiv](URL",
"### About Dataset\n\nDataset for both isiZulu news (articles and headlines) and Siswati news headlines. Process included scraping the data from internet, from Isolezwe news website URL and public posts from the SABC news LigwalagwalaFM Facebook page URL respectively.\n\nThe obtained datasets are isiZulu news articles, isiZulu news headlines, and Siswati news headlines. \n\nPost data collection the datasets were then sent to annotators, and they were sent back after the annotation process. The datasets contain special characters, some English words and characters that are not ASCII encoded which must be removed prior to model training. The aim of these three datasets is to create a baseline news categorisation model for the two South African low resources languages i.e. isiZulu and Siswati. \n\nFor categorisation, we use high level IPTC NewsCodes as categories. You can view the news categories here data/URL\n\nThe datasets were found to have class categories with very few observations, hence the class categories which have less than 35 observations were removed for isiZulu and less 6 observations for Siswati. \n\nThe dataset has both full category data as well as reduced category data.\n\nPlease see the URL for full dataset information.",
"## Online Repository link\n\n* Link to the DOI data repository - Zenodo Data Repository\n*",
"## Authors\n\n* Andani Madodonga \n* Vukosi Marivate - @vukosi\n* Matthew Adendorff\n\nSee also the list of contributors who participated in this project.\n\nCitation:",
"## License\n\nData is Licensed under CC 4.0 BY SA"
] |
[
60,
48,
285,
24,
40,
11
] |
[
"passage: TAGS\n#task_categories-text-classification #language-Swati #language-Zulu #license-cc-by-sa-4.0 #dsfsi-datasets #siswati #isizulu #arxiv-2306.07426 #region-us \n# IsiZulu News (articles and headlines) and Siswati News (headlines) Corpora - za-isizulu-siswati-news-2022\n\n\n and Siswati news headlines. Process included scraping the data from internet, from Isolezwe news website URL and public posts from the SABC news LigwalagwalaFM Facebook page URL respectively.\n\nThe obtained datasets are isiZulu news articles, isiZulu news headlines, and Siswati news headlines. \n\nPost data collection the datasets were then sent to annotators, and they were sent back after the annotation process. The datasets contain special characters, some English words and characters that are not ASCII encoded which must be removed prior to model training. The aim of these three datasets is to create a baseline news categorisation model for the two South African low resources languages i.e. isiZulu and Siswati. \n\nFor categorisation, we use high level IPTC NewsCodes as categories. You can view the news categories here data/URL\n\nThe datasets were found to have class categories with very few observations, hence the class categories which have less than 35 observations were removed for isiZulu and less 6 observations for Siswati. \n\nThe dataset has both full category data as well as reduced category data.\n\nPlease see the URL for full dataset information.## Online Repository link\n\n* Link to the DOI data repository - Zenodo Data Repository\n*## Authors\n\n* Andani Madodonga \n* Vukosi Marivate - @vukosi\n* Matthew Adendorff\n\nSee also the list of contributors who participated in this project.\n\nCitation:## License\n\nData is Licensed under CC 4.0 BY SA"
] |
f05f862e40bbf7bd259e2769efd684121ff942c5
|
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<p style="text-align: left;">As per scientific study reports, vitamin C reduces the risk of cataracts and it also slows down the progression of age-related macular degeneration and visual acuity loss when combined with other nutrients in the TheyaVue eye health support supplement. </p>
<h4 style="text-align: left;">● Calcium </h4>
<p style="text-align: left;">Calcium supplementation and adding more calcium to your everyday diet lowers the risk of age-related eye disease. This makes you less likely to struggle with serious vision loss. </p>
<h4 style="text-align: left;">● Vitamin E </h4>
<p style="text-align: left;">This antioxidant plays a vital role in managing free radical attacks and reducing cell damage. It is often used against conditions like AMD and is powerful to prevent cataracts. </p>
<h4 style="text-align: left;">● Bilberry Extract </h4>
<p style="text-align: left;">Based on studies, Bilberry has the potential to reduce the risk of macular degeneration. Besides, it gives the required antioxidant support to the eyes and the retina, avoiding cell deterioration. </p>
<h4 style="text-align: left;">● Rutin </h4>
<p style="text-align: left;">Rutin protects the eyes against blood vessel diseases such as diabetic retinopathy. Rutin strengthens the blood vessel walls, and by this, the risk of permanent vision loss is avoided. </p>
<p style="text-align: left;"><a href="https://sale365day.com/order-theyavue" target="_blank" rel="sponsored"><span style="font-size: large;"><strong>TheyaVue Is On Sale Now For A Limited Time!</strong></span></a></p>
<h2 style="text-align: left;">Ingredient Purity & Label Accuracy </h2>
<p style="text-align: left;"><a href="https://theyavue-update.clubeo.com/page/theyavue-reviews-is-it-legit-what-are-customers-saying-theyavue-eye-health-formula-exposed.html">Theyavue</a> have a label accuracy of 98.83% that is a good value and it has an ingredient purity score of 93% (Again a good value). The supplement doesn't contain any flagged inactive ingredients. The heavy metal screening value is less than <a href="https://oehha.ca.gov/media/downloads/proposition-65/safeharborlist032521.pdf" target="_blank" rel="nofollow">CA Proposition 65 limit</a>, so the supplement is very safe to use. </p>
<p style="text-align: left;">Editor's Note: Few <a href="https://theyavue-update.clubeo.com/">Theyavue</a> reviews mentioned Grape seed extract (a common ingredients in eye vitamin supplements and skin health products) as an ingredient of the product, but we couldn't find it during the tests. Also, it is not mentioned in the label. </p>
<div class="separator" style="clear: both; text-align: center;"><a style="margin-left: 1em; margin-right: 1em;" href="https://sale365day.com/order-theyavue"><img src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgd76sGGFA1drylsWDY3-C7_noIQ9ybwIhQ31pe1MyAiAiGPI9svMMkXe8eS7E_fsH6Y5cGgPpae55DjwMpdmcvUrIo2Y5l5L7TtNNuWNrO91gfVkbdDWXJrGFpMUjaUTNtshKvpAw8NpvGx6tgimxiINym0XO8_j79Ezb_ro8EmxBWSkxDF0Hf71tl/w640-h416/sfdcvde.JPG" alt="" width="640" height="416" border="0" data-original-height="585" data-original-width="901" /></a></div>
<h2 style="text-align: left;">How does TheyaVue support eye vision? </h2>
<p style="text-align: left;">The natural and research-based blend of <a href="https://www.townscript.com/e/theyavue-reviews-scam-warning-read-this-before-buying-024040">TheyaVue</a> eye care supplement works by aiding in healthy visions and promoting eye health. The root cause of vision impairment and a list of other eye-related conditions is free radical attacks and oxidative stress which is closely linked with external factors like pollution, exposure to toxins, and unhealthy lifestyle. However, the TheyaVue eye supplement can provide significant antioxidant support to your eyes and eye tissue to get rid of damage and promote cell regeneration. </p>
<p style="text-align: left;">The formula is appropriately composed of over 24 ingredients like Lutein, Rutin, Bilberry Extract, Vitamin E, Zeaxanthin, calcium, and vitamin C. Each of them is also scientifically proven for having impressive medicinal properties and packed with important antioxidant. As the TheyaVue ingredients are carefully mixed with their right proportion in the <a href="https://www.cometforums.com/topic/12812433-theyavue-reviews-urgent-2023-update-effective-ingredients-or-fake-customer-hype/">TheyaVue dietary formula</a>, they can directly defend against free radicals and contribute a significant anti-inflammatory response as well. </p>
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<h2 style="text-align: left;">The science behind the TheyaVue formula </h2>
<p style="text-align: left;">Having a closer glance at the scientific setting of the <a href="https://medium.com/@theyavuereviews/theyavue-reviews-trustworthy-official-website-or-fake-customer-claims-8bed009db7b7">TheyaVue capsule</a>, you can see it also comprises ingredients having solid scientific backgrounds. </p>
<p style="text-align: left;">According to a <a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6523787/" target="_blank" rel="nofollow">research article published in the </a><a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6523787/" target="_blank" rel="nofollow">Nutrients</a><a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6523787/" target="_blank" rel="nofollow"> (2018, Sep 18)</a>, Lutein is referred to as a vital carotenoid that has reported anti-inflammatory properties. A large body of scientific evidence also shows that lutein has significant effects on eye health. In particular, lutein improves or even prevents age-related macular disease, the leading cause of blindness and vision impairment. </p>
<p style="text-align: left;"><a href="https://expertlegalreview.com/scientific-supplement-review-process/" target="_blank" rel="nofollow">In another scientific review</a> <a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5355559/" target="_blank" rel="nofollow">from the </a><a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5355559/" target="_blank" rel="nofollow">Saudi Pharmaceutical Journal: SPJ, </a><a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5355559/" target="_blank" rel="nofollow">(2016 April 30)</a>, rutin brings impressive anti-cataract and ophthalmic effects to the eyes to protect them from many related struggles. </p>
<h2 style="text-align: left;">Perks of using TheyaVue eye supplement </h2>
<p style="text-align: left;">As referred to in <a href="https://colab.research.google.com/drive/11SZi064ppfbRbVyxZ1bLwBoqUScJvWEo">TheyaVue reviews</a>, the supplement is constructed with over 24 versatile natural ingredients. Since they are mixed in the formula adequately, you can expect the following benefits with the supplement's right and consistent consumption. </p>
<p style="text-align: left;">● Support for eyesight. </p>
<p style="text-align: left;">● TheyaVue capsule maintains eye health. </p>
<p style="text-align: left;">● Manages eye-related struggles. </p>
<p style="text-align: left;">● Antioxidant support. </p>
<p style="text-align: left;">● TheyaVue organic formula improves night vision </p>
<p style="text-align: left;">● The capsule enhances the overall quality of life </p>
<p style="text-align: left;">● Healthy inflammatory response. </p>
<p style="text-align: left;">● Long lasting results. </p>
<p style="text-align: left;">● Protect from sun damage </p>
<p style="text-align: left;">● Money back policy. </p>
<h2 style="text-align: left;">Is there any clinical evidence? </h2>
<p style="text-align: left;">Looking into the clinical background of the <a href="https://lookerstudio.google.com/reporting/8e91cacc-4746-4713-a900-22a246386307/page/DsQgD">TheyaVue</a> organic vision support formula, you can see it is clinically proven and manufactured in an FDA-approved, state-of-the-art facility in the US. Besides, each stage of its preparation is carefully done by following the benchmarks of safety, purity, quality, and precision. </p>
<h2 style="text-align: left;">Dosage Guidelines </h2>
<p style="text-align: left;">As you can see, every <a href="https://sketchfab.com/3d-models/theyavue-reviews-scam-aert-2023-does-it-works-3bbb23362e094536bae5e95cf01a9e59">TheyaVue</a> bottle is packed with 60 pills for a complete month's supply of the formula. The ideal everyday dosage of TheyaVue is two capsules a day, which is ideal to take along with a glass of water. At the same time, the manufacturer also suggests taking it 30 minutes before any main meal. </p>
<p style="text-align: left;"><span style="font-size: large;"><strong><a href="https://sale365day.com/order-theyavue" target="_blank" rel="nofollow">Check The Special Discount Offers of TheyaVue On The Official Website</a> </strong></span></p>
<h2 style="text-align: left;">How long does it take to work? </h2>
<p style="text-align: left;">Since you currently know how to take the TheyaVue eye care supplement in the right way, keep in mind that the formula can deliver you the best results only after 2-3 months of consistent consumption. This is the average time taken by the formula to bring significant changes. Following the suggested intake consistently up to the suggested period of 2-3 months will also help you have a better longevity of results, which is 1-2 years or more. </p>
<p style="text-align: left;">Editor's Note: According to customer feedback, most persons who consistently used the supplement for 90-120 days got the best results. </p>
<h2 style="text-align: left;">Are there any side effects? </h2>
<p style="text-align: left;">Considering the formulation of <a href="https://www.forexagone.com/forum/experiences-trading/theyavue-reviews-formulated-with-100-pure-ingredients-that-helps-to-clear-blurry-vision-87251#184665">TheyaVue</a> supplement, you can see it is entirely plant-based, and clinically verified. Besides, there are also no harmful elements like chemicals, fillers, or allergens included in the formula to induce any negative side effects. </p>
<p style="text-align: left;">Moreover, third-party lab reports also indicate the formula's safety, potency, and quality. And also no negative <a href="https://gamma.app/public/TheyaVue-Reviews-2023-Ingredients-Benefits-Side-Effects-And-Compl-5wkx32cjw3bjcig">TheyaVue reviews</a> were reported yet by any of the customers. </p>
<p style="text-align: center;"><a style="margin-left: 1em; margin-right: 1em;" href="https://yourpillsboss.blogspot.com/2023/10/theyavue-reviews-1-eye-vision-support.html"><img src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEj1i-2mpmmbJ1kaQri6IoqTIJKLSPEyVPNBVcn9gRlGRXI0ISFAYvFSU4pjDmO_RKpkLk3W6tfY630p9eEluETooOHdzRe3nD3S4FYKnnq7-J6DmHyJg3zHqdkapQQ3Qpq3LzrSJg_E3f1BpBoLU5EzUaDR2EEyqHhk8-XJL-OshC9ipVq0e8zbE7R0/w640-h220/wfffergg.JPG" alt="" width="640" height="220" border="0" data-original-height="376" data-original-width="1094" /></a></p>
<h2 style="text-align: left;">TheyaVue Pros and cons </h2>
<p style="text-align: left;">The TheyaVue dietary supplement has both positive and negative features which you need to be aware of before preserving it over others. Here are the major pros and cons associated with the supplement that are identified through probing research. </p>
<h2 style="text-align: left;">Pros: </h2>
<p style="text-align: left;">● Natural and non-GMO formula </p>
<p style="text-align: left;">● Evidence-based ingredients and formulation </p>
<p style="text-align: left;">● Quality-assured preparation </p>
<p style="text-align: left;">● Made in the USA in FDA-approved, state-of-the-art facilities </p>
<p style="text-align: left;">● Added benefits </p>
<p style="text-align: left;">● Includes 60-day risk-free money back </p>
<h2 style="text-align: left;">Cons: </h2>
<p style="text-align: left;">● TheyaVue is not recommended for children under the age of 18 </p>
<p style="text-align: left;">● Limited availability of stock </p>
<p style="text-align: left;"><a href="https://sale365day.com/order-theyavue" target="_blank" rel="sponsored"><span style="font-size: large;"><strong>Click Here to Get TheyaVue At Discounted Price!!!</strong></span></a></p>
<h2 style="text-align: left;">Real TheyaVue reviews from customers </h2>
<p style="text-align: left;">If you want to see what feedback the supplement has received so far, you can go through some real <a href="https://theyavue-reviews-2023.webflow.io/">TheyaVue</a> customer reviews given in this section. </p>
<h4 style="text-align: left;">● Rebecca McCarthy </h4>
<p style="text-align: left;">I had been struggling with serious irritation in my eyes due to cataracts. But every remedy I tried could only give me temporary relief. Later, one of my friends who is an ophthalmologist suggested TheyaVue capsule. The results have truly stunned me because it is for the first time I could see something that truly worked in the desired way. Now I feel no discomfort, and my vision also became sharper. </p>
<h4 style="text-align: left;">● Sophie Hayden </h4>
<p style="text-align: left;">The TheyaVue eye care supplement has totally freed me from the risk of macular degeneration. Taking these pills regularly for up to three months brought significant results in testing as well. Even my optometrist is also stunned by seeing the difference in my test reports. My only regret is that I didn't start its intake a bit before. </p>
<h4 style="text-align: left;">● Adam Norris </h4>
<p style="text-align: left;">I am so glad that <a href="https://www.oecd-forum.org/users/theyavue-reviews">TheyaVue</a> pill has worked in the desired way since it is for the first time I am experiencing such a change through a supplement. It significantly reduced dry eyes and I see how clearer my vision has become. However, I couldn't get my second bottle on time when I placed my order again. </p>
<h2 style="text-align: left;">How much does TheyaVue cost? </h2>
<p style="text-align: left;">Going through the official website of the supplement, and authentic <a href="https://www.justgiving.com/page/theyavue-reviews-1698231780972">TheyaVue</a> reviews, you can see the supplement comes in three plans, from which you can choose and purchase it. </p>
<p style="text-align: left;">● 30-day supply: 1 bottle at $59 + a small shipping fee </p>
<p style="text-align: left;">● 90-day supply: 3 bottles at $49/each + free US shipping </p>
<p style="text-align: left;">● 180-day supply: 6 bottles at $39/each + free US shipping </p>
<p style="text-align: left;">According to these details, bulk orders of <a href="https://www.provenexpert.com/theyavue6/">TheyaVue</a> vision support dietary supplements are added with impressive discounts, to give you the best savings. As these plans provide at least a three-month supply, it will be equivalent to the suggested period recommended for consistent intake as well. Anyway, the choice is totally bound to your preferences.</p>
<p style="text-align: left;">Editor's Note: If you are interested in buying the supplement, buy it <a href="https://sale365day.com/order-theyavue" target="_blank" rel="nofollow">only from the official website</a>. As you might have already noticed, many online shopping websites (Including Walmart & Amazon) are filled with supplement scams. But, when you buy from the manufacturer website, you get original products and you are covered in a 100% Money back policy when you buy the product from the official website. </p>
<p style="text-align: left;"><span style="font-size: large;"><strong><a href="https://sale365day.com/order-theyavue" target="_blank" rel="nofollow">Check The Special Discount Offers of TheyaVue On The Official Website</a> </strong></span></p>
<h2 style="text-align: left;">Where to purchase TheyaVue? </h2>
<p style="text-align: left;">If you want to try the authentic <a href="https://theyavuereviewsusa.bandcamp.com/track/theyavue-reviews-fda-approved-2023-unexpected-details-revealed">TheyaVue eye care supplement</a>, keep in mind that it is exclusively available on the official website for purchase. To make it clear, you may see its replicas on other e-commerce websites since the supplement also has higher market demand. So, to avoid any extorted sellers, it is ideal to proceed with your order on the official website. </p>
<h2 style="text-align: left;">Shipping and money-back policy </h2>
<p style="text-align: left;">Once you place your order, the <a href="https://theyavue-reviews.company.site/">TheyaVue</a> package will be shipped within 2 days and you can expect the delivery within 3-7 business days. However, the single-bottle plan is added with a small shipping fee. The rest of the other two plans will be free of any additional shipping charges. </p>
<p style="text-align: left;">At the same time, the <a href="https://doogeemall.com/community/xenforum/topic/114086/theyavue-reviews-11-facts-revealed-what-are-the-pros-and-cons-of-theyavue-vision-health-formula">TheyaVue</a> eye health support supplement comes as quality assured, and the manufacturer assures complete satisfaction with the results. In addition to this, every order will be protected by a hassle-free, no questions asked 60-day money-back guarantee. This will help you get a full refund if you are not satisfied with your purchase for any reason. </p>
<div class="separator" style="clear: both; text-align: center;"><a style="margin-left: 1em; margin-right: 1em;" href="https://sale365day.com/order-theyavue"><img src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgq8a2HP5j2LDEFqSRIMHrF-iuo_vGt1F1udhrOXjlzFB7eoc0qzco5ghOUWOE8jjciDpFLwnRAlGtFf94M71sNBIdWF3EhO1eN1ugu8uIzBISHj5fzaZGJ2eGWLuNBnGfLYNMEB380FdBDU09skpiZm2oTD9XgiwLBqOxh12qh4-v5m-kTMJIU9Jqu/w640-h414/wefdfeff.JPG" alt="" width="640" height="414" border="0" data-original-height="569" data-original-width="881" /></a></div>
<h2 style="text-align: left;">Final take on TheyaVue Reviews </h2>
<p style="text-align: left;"><a href="https://yourpillsboss.blogspot.com/2023/10/theyavue-reviews-1-eye-vision-support.html">TheyaVue</a> is an effective natural dietary supplement that is specially prepared to support the sharp vision and overall eye health. The supplement comes with a scientifically proven formula that comprises high-quality natural ingredients possessing impressive therapeutic properties. Each of the <a href="https://theyavue-update.clubeo.com/page/theyavue-reviews-usa-does-theyavue-eye-supplement-work-what-to-know-before-buying.html">TheyaVue</a> ingredients together can provide powerful antioxidants support to your eyes to combat vision struggles. </p>
<p style="text-align: left;"><a href="https://medium.com/@theyavuereviews/theyavue-reviews-trustworthy-official-website-or-fake-customer-claims-8bed009db7b7">TheyaVue</a> is an encapsulated formula made of non-GMO ingredients. Every bottle of the supplement comes with easy-to-swallow capsules that are manufactured in the USA, in an FDA-approved state-of-the-art facility. </p>
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href="https://www.cometforums.com/topic/12812433-theyavue-reviews-urgent-2023-update-effective-ingredients-or-fake-customer-hype/">https://www.cometforums.com/topic/12812433-theyavue-reviews-urgent-2023-update-effective-ingredients-or-fake-customer-hype/</a><br /><a href="https://www.townscript.com/e/theyavue-reviews-scam-warning-read-this-before-buying-024040">https://www.townscript.com/e/theyavue-reviews-scam-warning-read-this-before-buying-024040</a><br /><a href="https://theyavuereviewsscam.bandcamp.com/track/theyavue-reviews-urgent-2023-update-effective-ingredients-or-fake-customer-hype">https://theyavuereviewsscam.bandcamp.com/track/theyavue-reviews-urgent-2023-update-effective-ingredients-or-fake-customer-hype</a><br /><a href="https://gamma.app/public/TheyaVue-Reviews-Is-It-Legit-What-Are-Customers-Saying-nwnpos4phaqxhls">https://gamma.app/public/TheyaVue-Reviews-Is-It-Legit-What-Are-Customers-Saying-nwnpos4phaqxhls</a><br /><a href="https://infogram.com/theyavue-reviews-legit-ingredients-for-healthy-eyes-and-vision-1h7v4pwz051jj6k">https://infogram.com/theyavue-reviews-legit-ingredients-for-healthy-eyes-and-vision-1h7v4pwz051jj6k</a><br /><a href="https://sketchfab.com/3d-models/theyavue-reviews-scam-exposed-2023-is-it-works-07b1549d431849099ae730e04822bebf">https://sketchfab.com/3d-models/theyavue-reviews-scam-exposed-2023-is-it-works-07b1549d431849099ae730e04822bebf</a><br /><a href="https://doogeemall.com/community/xenforum/topic/114161/theyavue-reviews-customer-scam-alert-2023-ingredients-side-effects-negative-scam">https://doogeemall.com/community/xenforum/topic/114161/theyavue-reviews-customer-scam-alert-2023-ingredients-side-effects-negative-scam</a></p>
|
theyavuereview/TheyaVue-Reviews
|
[
"region:us"
] |
2023-10-25T12:28:52+00:00
|
{}
|
2023-10-25T12:29:39+00:00
|
[] |
[] |
TAGS
#region-us
|
<h2 style="text-align: center;"><span style="font-size: large;"><a style="color: #0b5394;" href="URL Here -- Official Website -- Order Now</a></span></h2>
<h2 style="text-align: center;"><span style="color: red; font-size: large;">️Beware Of Fake Websites️</span></h2>
<p><strong>For Order Official Website - <a href="URL/URL /><br />Product Name - <a href="URL (Eye Support Formula)<br /></strong></p>
<p><strong>Side Effect - No Side Effects<br /><br />Availability - <a href="URL /><br /> Rating -⭐⭐⭐⭐⭐</strong></p>
<p><a href="URL style="font-size: large;"><strong>Hurry Up - Limited Time Offer - Buy Now</strong></span></a></p>
<p><a href="URL style="font-size: large;"><strong>Hurry Up - Limited Time Offer - Buy Now</strong></span></a></p>
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<p class="story-summary"><strong>TheyaVue</strong> is an effective natural dietary supplement that is specially prepared to support the sharp vision and overall eye health. </p>
<p><a href="URL is a natural dietary supplement that comprises research-backed ingredients to support vision health and overall functioning of the eye. Not long ago, it was released and has been gaining significant attention from both experts and folks. </p>
<p style="text-align: center;"><a style="margin-left: 1em; margin-right: 1em;" href="URL src="URL alt="" width="640" height="306" border="0" data-original-height="496" data-original-width="1036" /></a> </p>
<p>We came to know about <a href="URL capsules</a> after receiving many requests from our readers to review it. We have to wait for a week to get it in our hands and spend another week to test it. As you know, reviewing a supplement is not an easy task. We couldn't have completed this Theyavue review without the help of our readers who already used it and shared their experience with us. </p>
<p style="text-align: left;">We have done everything to test the supplement and share this review with you. We will be glad if you read the article completely and send your feedback to us. Your genuine feedback will help us improve better. </p>
<p style="text-align: left;"><strong><span style="font-size: large;"><a href="URL target="_blank" rel="nofollow">Check The Availability Of TheyaVue On The Official Website</a></span> </strong></p>
<h2 style="text-align: left;">TheyaVue Reviews - How Effective Is TheyaVue Ingredients In Curing Blurry Vision? </h2>
<p style="text-align: left;">Coming to the <a href="URL eye care supplement</a>, promises to significantly and positively impact your vision by being evidence-based. However, before you reach your ultimate decision regarding giving it a try, it is necessary to evaluate the formula, so that you can ensure whether it meets the benchmarks of quality and potency. </p>
<h2 style="text-align: left;">What is TheyaVue? </h2>
<p style="text-align: left;"><a href="URL is an effective natural dietary supplement that is specially prepared to support the sharp vision and overall eye health. The supplement comes with a scientifically proven formula that comprises high-quality natural ingredients possessing impressive therapeutic properties. Each of the TheyaVue ingredients together can provide powerful antioxidants support to your eyes to combat vision struggles. </p>
<p style="text-align: left;">TheyaVue is an encapsulated formula made of non-GMO ingredients. Every bottle of the supplement comes with easy-to-swallow capsules that are manufactured in the USA, in an FDA-approved state-of-the-art facility. </p>
<h2 style="text-align: left;">Details about the Ingredients (Only included the proven benefits) </h2>
<p style="text-align: left;">The <a href="URL supplement</a> is a combination of many organic ingredients. Below you will see the benefits of these ingredients in detail. </p>
<h4 style="text-align: left;">● Lutein </h4>
<p style="text-align: left;">This carotenoid has reported anti-inflammatory properties. Various scientific studies show that lutein significantly improves eye health by curing most eye problems. In particular, lutein prevents age-related macular disease which is the leading cause of blindness and vision impairment. </p>
<h4 style="text-align: left;">● Zeaxanthin </h4>
<p style="text-align: left;">Along with Lutein, zeaxanthin shields your eyes from harmful high-energy light waves like ultraviolet rays in sunlight. Based on studies, these two compounds in high levels in the eye tissues are linked with better vision, especially in dim light or where glare is a problem. </p>
<h4 style="text-align: left;">● Vitamin C </h4>
<p style="text-align: left;">As per scientific study reports, vitamin C reduces the risk of cataracts and it also slows down the progression of age-related macular degeneration and visual acuity loss when combined with other nutrients in the TheyaVue eye health support supplement. </p>
<h4 style="text-align: left;">● Calcium </h4>
<p style="text-align: left;">Calcium supplementation and adding more calcium to your everyday diet lowers the risk of age-related eye disease. This makes you less likely to struggle with serious vision loss. </p>
<h4 style="text-align: left;">● Vitamin E </h4>
<p style="text-align: left;">This antioxidant plays a vital role in managing free radical attacks and reducing cell damage. It is often used against conditions like AMD and is powerful to prevent cataracts. </p>
<h4 style="text-align: left;">● Bilberry Extract </h4>
<p style="text-align: left;">Based on studies, Bilberry has the potential to reduce the risk of macular degeneration. Besides, it gives the required antioxidant support to the eyes and the retina, avoiding cell deterioration. </p>
<h4 style="text-align: left;">● Rutin </h4>
<p style="text-align: left;">Rutin protects the eyes against blood vessel diseases such as diabetic retinopathy. Rutin strengthens the blood vessel walls, and by this, the risk of permanent vision loss is avoided. </p>
<p style="text-align: left;"><a href="URL target="_blank" rel="sponsored"><span style="font-size: large;"><strong>TheyaVue Is On Sale Now For A Limited Time!</strong></span></a></p>
<h2 style="text-align: left;">Ingredient Purity & Label Accuracy </h2>
<p style="text-align: left;"><a href="URL have a label accuracy of 98.83% that is a good value and it has an ingredient purity score of 93% (Again a good value). The supplement doesn't contain any flagged inactive ingredients. The heavy metal screening value is less than <a href="URL target="_blank" rel="nofollow">CA Proposition 65 limit</a>, so the supplement is very safe to use. </p>
<p style="text-align: left;">Editor's Note: Few <a href="URL reviews mentioned Grape seed extract (a common ingredients in eye vitamin supplements and skin health products) as an ingredient of the product, but we couldn't find it during the tests. Also, it is not mentioned in the label. </p>
<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="416" border="0" data-original-height="585" data-original-width="901" /></a></div>
<h2 style="text-align: left;">How does TheyaVue support eye vision? </h2>
<p style="text-align: left;">The natural and research-based blend of <a href="URL eye care supplement works by aiding in healthy visions and promoting eye health. The root cause of vision impairment and a list of other eye-related conditions is free radical attacks and oxidative stress which is closely linked with external factors like pollution, exposure to toxins, and unhealthy lifestyle. However, the TheyaVue eye supplement can provide significant antioxidant support to your eyes and eye tissue to get rid of damage and promote cell regeneration. </p>
<p style="text-align: left;">The formula is appropriately composed of over 24 ingredients like Lutein, Rutin, Bilberry Extract, Vitamin E, Zeaxanthin, calcium, and vitamin C. Each of them is also scientifically proven for having impressive medicinal properties and packed with important antioxidant. As the TheyaVue ingredients are carefully mixed with their right proportion in the <a href="URL dietary formula</a>, they can directly defend against free radicals and contribute a significant anti-inflammatory response as well. </p>
<p style="text-align: left;"><a href="URL target="_blank" rel="sponsored"><span style="font-size: large;"><strong>Order your supply of TheyaVue now and start enjoying the benefits!</strong></span></a></p>
<h2 style="text-align: left;">The science behind the TheyaVue formula </h2>
<p style="text-align: left;">Having a closer glance at the scientific setting of the <a href="URL capsule</a>, you can see it also comprises ingredients having solid scientific backgrounds. </p>
<p style="text-align: left;">According to a <a href="URL target="_blank" rel="nofollow">research article published in the </a><a href="URL target="_blank" rel="nofollow">Nutrients</a><a href="URL target="_blank" rel="nofollow"> (2018, Sep 18)</a>, Lutein is referred to as a vital carotenoid that has reported anti-inflammatory properties. A large body of scientific evidence also shows that lutein has significant effects on eye health. In particular, lutein improves or even prevents age-related macular disease, the leading cause of blindness and vision impairment. </p>
<p style="text-align: left;"><a href="URL target="_blank" rel="nofollow">In another scientific review</a> <a href="URL target="_blank" rel="nofollow">from the </a><a href="URL target="_blank" rel="nofollow">Saudi Pharmaceutical Journal: SPJ, </a><a href="URL target="_blank" rel="nofollow">(2016 April 30)</a>, rutin brings impressive anti-cataract and ophthalmic effects to the eyes to protect them from many related struggles. </p>
<h2 style="text-align: left;">Perks of using TheyaVue eye supplement </h2>
<p style="text-align: left;">As referred to in <a href="URL reviews</a>, the supplement is constructed with over 24 versatile natural ingredients. Since they are mixed in the formula adequately, you can expect the following benefits with the supplement's right and consistent consumption. </p>
<p style="text-align: left;">● Support for eyesight. </p>
<p style="text-align: left;">● TheyaVue capsule maintains eye health. </p>
<p style="text-align: left;">● Manages eye-related struggles. </p>
<p style="text-align: left;">● Antioxidant support. </p>
<p style="text-align: left;">● TheyaVue organic formula improves night vision </p>
<p style="text-align: left;">● The capsule enhances the overall quality of life </p>
<p style="text-align: left;">● Healthy inflammatory response. </p>
<p style="text-align: left;">● Long lasting results. </p>
<p style="text-align: left;">● Protect from sun damage </p>
<p style="text-align: left;">● Money back policy. </p>
<h2 style="text-align: left;">Is there any clinical evidence? </h2>
<p style="text-align: left;">Looking into the clinical background of the <a href="URL organic vision support formula, you can see it is clinically proven and manufactured in an FDA-approved, state-of-the-art facility in the US. Besides, each stage of its preparation is carefully done by following the benchmarks of safety, purity, quality, and precision. </p>
<h2 style="text-align: left;">Dosage Guidelines </h2>
<p style="text-align: left;">As you can see, every <a href="URL bottle is packed with 60 pills for a complete month's supply of the formula. The ideal everyday dosage of TheyaVue is two capsules a day, which is ideal to take along with a glass of water. At the same time, the manufacturer also suggests taking it 30 minutes before any main meal. </p>
<p style="text-align: left;"><span style="font-size: large;"><strong><a href="URL target="_blank" rel="nofollow">Check The Special Discount Offers of TheyaVue On The Official Website</a> </strong></span></p>
<h2 style="text-align: left;">How long does it take to work? </h2>
<p style="text-align: left;">Since you currently know how to take the TheyaVue eye care supplement in the right way, keep in mind that the formula can deliver you the best results only after 2-3 months of consistent consumption. This is the average time taken by the formula to bring significant changes. Following the suggested intake consistently up to the suggested period of 2-3 months will also help you have a better longevity of results, which is 1-2 years or more. </p>
<p style="text-align: left;">Editor's Note: According to customer feedback, most persons who consistently used the supplement for 90-120 days got the best results. </p>
<h2 style="text-align: left;">Are there any side effects? </h2>
<p style="text-align: left;">Considering the formulation of <a href="URL supplement, you can see it is entirely plant-based, and clinically verified. Besides, there are also no harmful elements like chemicals, fillers, or allergens included in the formula to induce any negative side effects. </p>
<p style="text-align: left;">Moreover, third-party lab reports also indicate the formula's safety, potency, and quality. And also no negative <a href="URL reviews</a> were reported yet by any of the customers. </p>
<p style="text-align: center;"><a style="margin-left: 1em; margin-right: 1em;" href="URL src="URL alt="" width="640" height="220" border="0" data-original-height="376" data-original-width="1094" /></a></p>
<h2 style="text-align: left;">TheyaVue Pros and cons </h2>
<p style="text-align: left;">The TheyaVue dietary supplement has both positive and negative features which you need to be aware of before preserving it over others. Here are the major pros and cons associated with the supplement that are identified through probing research. </p>
<h2 style="text-align: left;">Pros: </h2>
<p style="text-align: left;">● Natural and non-GMO formula </p>
<p style="text-align: left;">● Evidence-based ingredients and formulation </p>
<p style="text-align: left;">● Quality-assured preparation </p>
<p style="text-align: left;">● Made in the USA in FDA-approved, state-of-the-art facilities </p>
<p style="text-align: left;">● Added benefits </p>
<p style="text-align: left;">● Includes 60-day risk-free money back </p>
<h2 style="text-align: left;">Cons: </h2>
<p style="text-align: left;">● TheyaVue is not recommended for children under the age of 18 </p>
<p style="text-align: left;">● Limited availability of stock </p>
<p style="text-align: left;"><a href="URL target="_blank" rel="sponsored"><span style="font-size: large;"><strong>Click Here to Get TheyaVue At Discounted Price!!!</strong></span></a></p>
<h2 style="text-align: left;">Real TheyaVue reviews from customers </h2>
<p style="text-align: left;">If you want to see what feedback the supplement has received so far, you can go through some real <a href="URL customer reviews given in this section. </p>
<h4 style="text-align: left;">● Rebecca McCarthy </h4>
<p style="text-align: left;">I had been struggling with serious irritation in my eyes due to cataracts. But every remedy I tried could only give me temporary relief. Later, one of my friends who is an ophthalmologist suggested TheyaVue capsule. The results have truly stunned me because it is for the first time I could see something that truly worked in the desired way. Now I feel no discomfort, and my vision also became sharper. </p>
<h4 style="text-align: left;">● Sophie Hayden </h4>
<p style="text-align: left;">The TheyaVue eye care supplement has totally freed me from the risk of macular degeneration. Taking these pills regularly for up to three months brought significant results in testing as well. Even my optometrist is also stunned by seeing the difference in my test reports. My only regret is that I didn't start its intake a bit before. </p>
<h4 style="text-align: left;">● Adam Norris </h4>
<p style="text-align: left;">I am so glad that <a href="URL pill has worked in the desired way since it is for the first time I am experiencing such a change through a supplement. It significantly reduced dry eyes and I see how clearer my vision has become. However, I couldn't get my second bottle on time when I placed my order again. </p>
<h2 style="text-align: left;">How much does TheyaVue cost? </h2>
<p style="text-align: left;">Going through the official website of the supplement, and authentic <a href="URL reviews, you can see the supplement comes in three plans, from which you can choose and purchase it. </p>
<p style="text-align: left;">● 30-day supply: 1 bottle at $59 + a small shipping fee </p>
<p style="text-align: left;">● 90-day supply: 3 bottles at $49/each + free US shipping </p>
<p style="text-align: left;">● 180-day supply: 6 bottles at $39/each + free US shipping </p>
<p style="text-align: left;">According to these details, bulk orders of <a href="URL vision support dietary supplements are added with impressive discounts, to give you the best savings. As these plans provide at least a three-month supply, it will be equivalent to the suggested period recommended for consistent intake as well. Anyway, the choice is totally bound to your preferences.</p>
<p style="text-align: left;">Editor's Note: If you are interested in buying the supplement, buy it <a href="URL target="_blank" rel="nofollow">only from the official website</a>. As you might have already noticed, many online shopping websites (Including Walmart & Amazon) are filled with supplement scams. But, when you buy from the manufacturer website, you get original products and you are covered in a 100% Money back policy when you buy the product from the official website. </p>
<p style="text-align: left;"><span style="font-size: large;"><strong><a href="URL target="_blank" rel="nofollow">Check The Special Discount Offers of TheyaVue On The Official Website</a> </strong></span></p>
<h2 style="text-align: left;">Where to purchase TheyaVue? </h2>
<p style="text-align: left;">If you want to try the authentic <a href="URL eye care supplement</a>, keep in mind that it is exclusively available on the official website for purchase. To make it clear, you may see its replicas on other e-commerce websites since the supplement also has higher market demand. So, to avoid any extorted sellers, it is ideal to proceed with your order on the official website. </p>
<h2 style="text-align: left;">Shipping and money-back policy </h2>
<p style="text-align: left;">Once you place your order, the <a href="URL package will be shipped within 2 days and you can expect the delivery within 3-7 business days. However, the single-bottle plan is added with a small shipping fee. The rest of the other two plans will be free of any additional shipping charges. </p>
<p style="text-align: left;">At the same time, the <a href="URL eye health support supplement comes as quality assured, and the manufacturer assures complete satisfaction with the results. In addition to this, every order will be protected by a hassle-free, no questions asked 60-day money-back guarantee. This will help you get a full refund if you are not satisfied with your purchase for any reason. </p>
<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="414" border="0" data-original-height="569" data-original-width="881" /></a></div>
<h2 style="text-align: left;">Final take on TheyaVue Reviews </h2>
<p style="text-align: left;"><a href="URL is an effective natural dietary supplement that is specially prepared to support the sharp vision and overall eye health. The supplement comes with a scientifically proven formula that comprises high-quality natural ingredients possessing impressive therapeutic properties. Each of the <a href="URL ingredients together can provide powerful antioxidants support to your eyes to combat vision struggles. </p>
<p style="text-align: left;"><a href="URL is an encapsulated formula made of non-GMO ingredients. Every bottle of the supplement comes with easy-to-swallow capsules that are manufactured in the USA, in an FDA-approved state-of-the-art facility. </p>
<p style="text-align: left;"><strong>Read More:</strong></p>
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|
[] |
[
"TAGS\n#region-us \n"
] |
[
6
] |
[
"passage: TAGS\n#region-us \n"
] |
cc67ab59b5165661ce997235b587e4f618da1aab
|
# OpenSubtitles Danish-Swedish
Aligned sentences with heuristic-based filters from OpenSubtitles in Danish and in Swedish.
The source code for producing the dataset is included in the repository.
The dataset was created to aid training sentence transformers in the Danish Foundation Models project.
|
kardosdrur/opensubtitles-da-sv
|
[
"license:mit",
"region:us"
] |
2023-10-25T12:45:35+00:00
|
{"license": "mit", "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "test", "path": "data/test-*"}]}], "dataset_info": {"features": [{"name": "link_id", "dtype": "string"}, {"name": "da", "dtype": "string"}, {"name": "no", "dtype": "string"}, {"name": "overlap", "dtype": "float64"}], "splits": [{"name": "train", "num_bytes": 270499727.08648384, "num_examples": 1772983}, {"name": "test", "num_bytes": 67624969.91351616, "num_examples": 443246}], "download_size": 201404638, "dataset_size": 338124697.0}}
|
2023-10-26T06:12:17+00:00
|
[] |
[] |
TAGS
#license-mit #region-us
|
# OpenSubtitles Danish-Swedish
Aligned sentences with heuristic-based filters from OpenSubtitles in Danish and in Swedish.
The source code for producing the dataset is included in the repository.
The dataset was created to aid training sentence transformers in the Danish Foundation Models project.
|
[
"# OpenSubtitles Danish-Swedish\nAligned sentences with heuristic-based filters from OpenSubtitles in Danish and in Swedish.\nThe source code for producing the dataset is included in the repository.\n\nThe dataset was created to aid training sentence transformers in the Danish Foundation Models project."
] |
[
"TAGS\n#license-mit #region-us \n",
"# OpenSubtitles Danish-Swedish\nAligned sentences with heuristic-based filters from OpenSubtitles in Danish and in Swedish.\nThe source code for producing the dataset is included in the repository.\n\nThe dataset was created to aid training sentence transformers in the Danish Foundation Models project."
] |
[
11,
70
] |
[
"passage: TAGS\n#license-mit #region-us \n# OpenSubtitles Danish-Swedish\nAligned sentences with heuristic-based filters from OpenSubtitles in Danish and in Swedish.\nThe source code for producing the dataset is included in the repository.\n\nThe dataset was created to aid training sentence transformers in the Danish Foundation Models project."
] |
e3cac8b2f3f536180a28eb54879e9fa25eb0bc1d
|
# Dataset Card for "stsv-v2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
Back-up/stsv-v2
|
[
"region:us"
] |
2023-10-25T12:47:33+00:00
|
{"dataset_info": {"features": [{"name": "Questions", "dtype": "string"}, {"name": "Answers", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 238326.25056947608, "num_examples": 379}], "download_size": 108701, "dataset_size": 238326.25056947608}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-10-25T12:47:38+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "stsv-v2"
More Information needed
|
[
"# Dataset Card for \"stsv-v2\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"stsv-v2\"\n\nMore Information needed"
] |
[
6,
15
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"stsv-v2\"\n\nMore Information needed"
] |
e0e951a46f788203d94ce02686542fc5b0b4891f
|
# Dataset Card for "stsv-test-v2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
Back-up/stsv-test-v2
|
[
"region:us"
] |
2023-10-25T12:49:45+00:00
|
{"dataset_info": {"features": [{"name": "Questions", "dtype": "string"}, {"name": "Answers", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 59001.886567164176, "num_examples": 96}], "download_size": 30932, "dataset_size": 59001.886567164176}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-10-25T12:49:49+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "stsv-test-v2"
More Information needed
|
[
"# Dataset Card for \"stsv-test-v2\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"stsv-test-v2\"\n\nMore Information needed"
] |
[
6,
17
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"stsv-test-v2\"\n\nMore Information needed"
] |
1d3c612951f8c4af7a6ae951d81eb3dde4415113
|
# Dataset Card for "test-stsv-data"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
Back-up/test-stsv-data
|
[
"region:us"
] |
2023-10-25T13:00:52+00:00
|
{"dataset_info": {"features": [{"name": "Answers", "dtype": "string"}, {"name": "Questions", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 104773.87782426778, "num_examples": 496}], "download_size": 47625, "dataset_size": 104773.87782426778}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-10-25T14:25:31+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "test-stsv-data"
More Information needed
|
[
"# Dataset Card for \"test-stsv-data\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"test-stsv-data\"\n\nMore Information needed"
] |
[
6,
16
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"test-stsv-data\"\n\nMore Information needed"
] |
00bc01544eee55f67a848ffcef30b3d3eb51db76
|
# Dataset Card for "train-stsv-data"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
Back-up/train-stsv-data
|
[
"region:us"
] |
2023-10-25T13:01:13+00:00
|
{"dataset_info": {"features": [{"name": "Answers", "dtype": "string"}, {"name": "Questions", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 506814.6867015707, "num_examples": 2379}], "download_size": 206458, "dataset_size": 506814.6867015707}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-10-25T14:25:33+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "train-stsv-data"
More Information needed
|
[
"# Dataset Card for \"train-stsv-data\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"train-stsv-data\"\n\nMore Information needed"
] |
[
6,
17
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"train-stsv-data\"\n\nMore Information needed"
] |
22bd75cab01744cddc649e06ae492d9cdd612fc6
|
# Dataset Card for "cai-conversation"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
vwxyzjn/cai-conversation
|
[
"region:us"
] |
2023-10-25T13:06:06+00:00
|
{"dataset_info": {"features": [{"name": "init_prompt", "struct": [{"name": "content", "dtype": "string"}, {"name": "role", "dtype": "string"}]}, {"name": "init_response", "struct": [{"name": "content", "dtype": "string"}, {"name": "role", "dtype": "string"}]}, {"name": "critic_prompt", "struct": [{"name": "content", "dtype": "string"}, {"name": "role", "dtype": "string"}]}, {"name": "critic_response", "struct": [{"name": "content", "dtype": "string"}, {"name": "role", "dtype": "string"}]}, {"name": "revision_prompt", "struct": [{"name": "content", "dtype": "string"}, {"name": "role", "dtype": "string"}]}, {"name": "revision_response", "struct": [{"name": "content", "dtype": "string"}, {"name": "role", "dtype": "string"}]}], "splits": [{"name": "train", "num_bytes": 200025, "num_examples": 100}], "download_size": 104751, "dataset_size": 200025}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-10-25T13:06:08+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "cai-conversation"
More Information needed
|
[
"# Dataset Card for \"cai-conversation\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"cai-conversation\"\n\nMore Information needed"
] |
[
6,
16
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"cai-conversation\"\n\nMore Information needed"
] |
0b069accbb7dd8f5f3e7d53eafbfbb4b43506b48
|
# Dataset Card for "Synthetic_Acholi_MMS"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
mekaneeky/Synthetic_Acholi_MMS
|
[
"region:us"
] |
2023-10-25T13:12:40+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "dev", "path": "data/dev-*"}, {"split": "test", "path": "data/test-*"}]}], "dataset_info": {"features": [{"name": "eng", "dtype": "string"}, {"name": "lug", "dtype": "string"}, {"name": "ach", "dtype": "string"}, {"name": "teo", "dtype": "string"}, {"name": "lgg", "dtype": "string"}, {"name": "nyn", "dtype": "string"}, {"name": "ID", "dtype": "string"}, {"name": "ach_tts", "sequence": {"sequence": "float32"}}], "splits": [{"name": "train", "num_bytes": 14041118016, "num_examples": 23947}, {"name": "dev", "num_bytes": 289355340, "num_examples": 500}, {"name": "test", "num_bytes": 322907400, "num_examples": 500}], "download_size": 14665423094, "dataset_size": 14653380756}}
|
2023-10-25T13:22:37+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "Synthetic_Acholi_MMS"
More Information needed
|
[
"# Dataset Card for \"Synthetic_Acholi_MMS\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"Synthetic_Acholi_MMS\"\n\nMore Information needed"
] |
[
6,
20
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"Synthetic_Acholi_MMS\"\n\nMore Information needed"
] |
8f066edd6f60a0ae7a4b177b11d690273a95d922
|
# Dataset Card for "lj_speech_DifferentStructure"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
HamdanXI/lj_speech_DifferentStructure
|
[
"region:us"
] |
2023-10-25T13:32:53+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "test", "path": "data/test-*"}]}], "dataset_info": {"features": [{"name": "audio", "dtype": {"audio": {"sampling_rate": 22050}}}, {"name": "file", "dtype": "string"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1360795953.0, "num_examples": 4620}, {"name": "test", "num_bytes": 490267914.2, "num_examples": 1680}], "download_size": 1828318164, "dataset_size": 1851063867.2}}
|
2023-10-25T13:41:13+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "lj_speech_DifferentStructure"
More Information needed
|
[
"# Dataset Card for \"lj_speech_DifferentStructure\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"lj_speech_DifferentStructure\"\n\nMore Information needed"
] |
[
6,
21
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"lj_speech_DifferentStructure\"\n\nMore Information needed"
] |
8bf88b7ae9a8a8a475f692ea8e4a640c5f2b86cd
|
## Dataset
This dataset contains synthetic input-output pairs
The FHIR column consists of FHIR R4 resources as json strings
The Note column is the natural language representation of this FHIR.
|
healthsageai/fhir-to-note
|
[
"task_categories:text-generation",
"task_categories:translation",
"size_categories:1K<n<10K",
"language:en",
"license:agpl-3.0",
"region:us"
] |
2023-10-25T13:52:11+00:00
|
{"language": ["en"], "license": "agpl-3.0", "size_categories": ["1K<n<10K"], "task_categories": ["text-generation", "translation"]}
|
2023-11-30T09:58:54+00:00
|
[] |
[
"en"
] |
TAGS
#task_categories-text-generation #task_categories-translation #size_categories-1K<n<10K #language-English #license-agpl-3.0 #region-us
|
## Dataset
This dataset contains synthetic input-output pairs
The FHIR column consists of FHIR R4 resources as json strings
The Note column is the natural language representation of this FHIR.
|
[
"## Dataset\nThis dataset contains synthetic input-output pairs\nThe FHIR column consists of FHIR R4 resources as json strings\nThe Note column is the natural language representation of this FHIR."
] |
[
"TAGS\n#task_categories-text-generation #task_categories-translation #size_categories-1K<n<10K #language-English #license-agpl-3.0 #region-us \n",
"## Dataset\nThis dataset contains synthetic input-output pairs\nThe FHIR column consists of FHIR R4 resources as json strings\nThe Note column is the natural language representation of this FHIR."
] |
[
50,
52
] |
[
"passage: TAGS\n#task_categories-text-generation #task_categories-translation #size_categories-1K<n<10K #language-English #license-agpl-3.0 #region-us \n## Dataset\nThis dataset contains synthetic input-output pairs\nThe FHIR column consists of FHIR R4 resources as json strings\nThe Note column is the natural language representation of this FHIR."
] |
403adaf00649332f7065d33034da51f79356916a
|
# Dataset Card for Dataset Name
## Dataset Description
- **Homepage:**
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
This dataset is a set of pairs: an image and its corresponding latex code for expression. This set of pairs was generated by analyzing more than 100,000 articles on natural sciences and mathematics and further generating a corresponding set of latex expressions. The set has been cleared of duplicates. There are about 1 500 000 images in the set.
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
Latex
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
```python
Dataset({
features: ['image', 'text'],
num_rows: 1586584
})
```
### 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
@misc{alexfrauch_VSU_2023,
title = {Recognition of mathematical formulas in the Latex: Image-Text Pair Dataset},
author = {Aleksandr Frauch (Proshunin)},
year = {2023},
howpublished = {\url{https://huggingface.co/datasets/AlFrauch/im2latex}},
}
### Contributions
[More Information Needed]
|
AlFrauch/im2latex
|
[
"task_categories:image-to-text",
"size_categories:1M<n<10M",
"code",
"region:us"
] |
2023-10-25T13:53:53+00:00
|
{"size_categories": ["1M<n<10M"], "task_categories": ["image-to-text"], "tags": ["code"]}
|
2023-10-25T15:21:16+00:00
|
[] |
[] |
TAGS
#task_categories-image-to-text #size_categories-1M<n<10M #code #region-us
|
# Dataset Card for Dataset Name
## Dataset Description
- Homepage:
- Repository:
- Paper:
- Leaderboard:
- Point of Contact:
### Dataset Summary
This dataset is a set of pairs: an image and its corresponding latex code for expression. This set of pairs was generated by analyzing more than 100,000 articles on natural sciences and mathematics and further generating a corresponding set of latex expressions. The set has been cleared of duplicates. There are about 1 500 000 images in the set.
### Supported Tasks and Leaderboards
### Languages
Latex
## 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
@misc{alexfrauch_VSU_2023,
title = {Recognition of mathematical formulas in the Latex: Image-Text Pair Dataset},
author = {Aleksandr Frauch (Proshunin)},
year = {2023},
howpublished = {\url{URL
}
### 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 is a set of pairs: an image and its corresponding latex code for expression. This set of pairs was generated by analyzing more than 100,000 articles on natural sciences and mathematics and further generating a corresponding set of latex expressions. The set has been cleared of duplicates. There are about 1 500 000 images in the set.",
"### Supported Tasks and Leaderboards",
"### Languages\n\nLatex",
"## Dataset Structure",
"### Data Instances",
"### Data Fields",
"### Data Splits",
"## Dataset Creation",
"### Curation Rationale",
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"#### 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\n@misc{alexfrauch_VSU_2023,\n title = {Recognition of mathematical formulas in the Latex: Image-Text Pair Dataset},\n author = {Aleksandr Frauch (Proshunin)},\n year = {2023},\n howpublished = {\\url{URL\n}",
"### Contributions"
] |
[
"TAGS\n#task_categories-image-to-text #size_categories-1M<n<10M #code #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 is a set of pairs: an image and its corresponding latex code for expression. This set of pairs was generated by analyzing more than 100,000 articles on natural sciences and mathematics and further generating a corresponding set of latex expressions. The set has been cleared of duplicates. There are about 1 500 000 images in the set.",
"### Supported Tasks and Leaderboards",
"### Languages\n\nLatex",
"## 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\n@misc{alexfrauch_VSU_2023,\n title = {Recognition of mathematical formulas in the Latex: Image-Text Pair Dataset},\n author = {Aleksandr Frauch (Proshunin)},\n year = {2023},\n howpublished = {\\url{URL\n}",
"### Contributions"
] |
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[
"passage: TAGS\n#task_categories-image-to-text #size_categories-1M<n<10M #code #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 is a set of pairs: an image and its corresponding latex code for expression. This set of pairs was generated by analyzing more than 100,000 articles on natural sciences and mathematics and further generating a corresponding set of latex expressions. The set has been cleared of duplicates. There are about 1 500 000 images in the set.### Supported Tasks and Leaderboards### Languages\n\nLatex## 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\n@misc{alexfrauch_VSU_2023,\n title = {Recognition of mathematical formulas in the Latex: Image-Text Pair Dataset},\n author = {Aleksandr Frauch (Proshunin)},\n year = {2023},\n howpublished = {\\url{URL\n}### Contributions"
] |
6e9509ad0bda3b29860de596d1cde5c455ed84e5
|
# Dataset Card for Dataset Name
<!-- Provide a quick summary of the dataset. -->
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).
## Dataset Details
### Dataset Description
<!-- Provide a longer summary of what this dataset is. -->
- **Curated by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
### Dataset Sources [optional]
<!-- Provide the basic links for the dataset. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the dataset is intended to be used. -->
### Direct Use
<!-- This section describes suitable use cases for the dataset. -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. -->
[More Information Needed]
## Dataset Structure
<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->
[More Information Needed]
## Dataset Creation
### Curation Rationale
<!-- Motivation for the creation of this dataset. -->
[More Information Needed]
### Source Data
<!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). -->
#### Data Collection and Processing
<!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. -->
[More Information Needed]
#### Who are the source data producers?
<!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. -->
[More Information Needed]
### Annotations [optional]
<!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. -->
#### Annotation process
<!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. -->
[More Information Needed]
#### Who are the annotators?
<!-- This section describes the people or systems who created the annotations. -->
[More Information Needed]
#### Personal and Sensitive Information
<!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
## Citation [optional]
<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Dataset Card Authors [optional]
[More Information Needed]
## Dataset Card Contact
[More Information Needed]
|
darli711/ShanAlphabet
|
[
"region:us"
] |
2023-10-25T13:56:57+00:00
|
{}
|
2023-10-25T14:47:51+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for Dataset Name
This dataset card aims to be a base template for new datasets. It has been generated using this raw template.
## Dataset Details
### Dataset Description
- Curated by:
- Funded by [optional]:
- Shared by [optional]:
- Language(s) (NLP):
- License:
### Dataset Sources [optional]
- Repository:
- Paper [optional]:
- Demo [optional]:
## Uses
### Direct Use
### Out-of-Scope Use
## Dataset Structure
## Dataset Creation
### Curation Rationale
### Source Data
#### Data Collection and Processing
#### Who are the source data producers?
### Annotations [optional]
#### Annotation process
#### Who are the annotators?
#### Personal and Sensitive Information
## Bias, Risks, and Limitations
### Recommendations
Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
[optional]
BibTeX:
APA:
## Glossary [optional]
## More Information [optional]
## Dataset Card Authors [optional]
## Dataset Card Contact
|
[
"# Dataset Card for Dataset Name\n\n\n\nThis dataset card aims to be a base template for new datasets. It has been generated using this raw template.",
"## Dataset Details",
"### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:",
"### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Out-of-Scope Use",
"## Dataset Structure",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Data Collection and Processing",
"#### Who are the source data producers?",
"### Annotations [optional]",
"#### Annotation process",
"#### Who are the annotators?",
"#### Personal and Sensitive Information",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Dataset Card Authors [optional]",
"## Dataset Card Contact"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for Dataset Name\n\n\n\nThis dataset card aims to be a base template for new datasets. It has been generated using this raw template.",
"## Dataset Details",
"### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:",
"### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Out-of-Scope Use",
"## Dataset Structure",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Data Collection and Processing",
"#### Who are the source data producers?",
"### Annotations [optional]",
"#### Annotation process",
"#### Who are the annotators?",
"#### Personal and Sensitive Information",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Dataset Card Authors [optional]",
"## Dataset Card Contact"
] |
[
6,
34,
4,
40,
29,
3,
4,
9,
6,
5,
7,
4,
7,
10,
9,
5,
9,
8,
10,
46,
8,
7,
10,
5
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for Dataset Name\n\n\n\nThis dataset card aims to be a base template for new datasets. It has been generated using this raw template.## Dataset Details### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Out-of-Scope Use## Dataset Structure## Dataset Creation### Curation Rationale### Source Data#### Data Collection and Processing#### Who are the source data producers?### Annotations [optional]#### Annotation process#### Who are the annotators?#### Personal and Sensitive Information## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Dataset Card Authors [optional]## Dataset Card Contact"
] |
bd1b616977deac08996f482b6b9c8bb2b814c7fc
|
### Dataset Summary
AlfaFood - это датасет для детекции столовых блюд на подносах.
Некоторыми особенностями этого датасета являются:
- высокое качество изображений
- большое количество аннотаций
- изобрежания получены из столовой офиса Альфа-Банка
### Supported Tasks
- `object-detection`: этот датасет может быть использован для обучения моделей для задачи Object Detection.
### Languages
Русский
## Dataset Structure
### Data Instances
Элемент датасета представляет из себя изображение и аннотацию к нему. Пример:
```
{
'image': <PIL.MpoImagePlugin.MpoImageFile image mode=RGB size=4000x3000 at 0x1CCDD0C1100>,
'objects':
{'bbox':
[
[2408.8, 636.46, 561.7, 610.14],
[527.44, 969.39, 530.49, 446.34],
[1185.98, 384.02, 515.85, 486.59],
[1500.61, 471.83, 354.88, 519.51],
[1701.83, 548.66, 486.59, 610.97],
[1862.8, 559.63, 369.52, 589.03],
[644.51, 18.17, 2539.03, 1500.0]
],
'categories': [13, 8, 9, 11, 12, 12, 99]
}
}
```
### Data Fields
- `image`: `PIL.MpoImagePlugin.MpoImageFile` объект, содержащий изображение.
- `objects`: словарь с данными об изображении.
- `bbox`: массив с ограничительными рамка (в [coco](https://albumentations.ai/docs/getting_started/bounding_boxes_augmentation/#coco) формате) для объёктов на фотографии
- `category`: массив для идентификаторов категорий объектов на изображении.
Также в репозитории присутствует `id2cat.json` со словарем соответствий идентификатора категории её названию.
### Data Splits
Все данные находятся в `train` сплите. Пользователи могут разбить датасет по своему усмотрению. Датасет содержит 3346 изображений с аннотациями.
|
mllab/alfafood
|
[
"task_categories:object-detection",
"size_categories:1K<n<10K",
"language:ru",
"license:unknown",
"food",
"detection",
"region:us"
] |
2023-10-25T14:02:15+00:00
|
{"language": ["ru"], "license": "unknown", "size_categories": ["1K<n<10K"], "task_categories": ["object-detection"], "pretty_name": "AlfaFood", "dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "objects", "struct": [{"name": "bbox", "sequence": {"sequence": "float64"}}, {"name": "categories", "sequence": "int64"}]}], "splits": [{"name": "train", "num_bytes": 14616722750.594, "num_examples": 3346}], "download_size": 12291691249, "dataset_size": 14616722750.594}, "tags": ["food", "detection"]}
|
2023-10-27T11:22:45+00:00
|
[] |
[
"ru"
] |
TAGS
#task_categories-object-detection #size_categories-1K<n<10K #language-Russian #license-unknown #food #detection #region-us
|
### Dataset Summary
AlfaFood - это датасет для детекции столовых блюд на подносах.
Некоторыми особенностями этого датасета являются:
- высокое качество изображений
- большое количество аннотаций
- изобрежания получены из столовой офиса Альфа-Банка
### Supported Tasks
- 'object-detection': этот датасет может быть использован для обучения моделей для задачи Object Detection.
### Languages
Русский
## Dataset Structure
### Data Instances
Элемент датасета представляет из себя изображение и аннотацию к нему. Пример:
### Data Fields
- 'image': 'PIL.MpoImagePlugin.MpoImageFile' объект, содержащий изображение.
- 'objects': словарь с данными об изображении.
- 'bbox': массив с ограничительными рамка (в coco формате) для объёктов на фотографии
- 'category': массив для идентификаторов категорий объектов на изображении.
Также в репозитории присутствует 'URL' со словарем соответствий идентификатора категории её названию.
### Data Splits
Все данные находятся в 'train' сплите. Пользователи могут разбить датасет по своему усмотрению. Датасет содержит 3346 изображений с аннотациями.
|
[
"### Dataset Summary\n\nAlfaFood - это датасет для детекции столовых блюд на подносах. \n\nНекоторыми особенностями этого датасета являются:\n- высокое качество изображений\n- большое количество аннотаций\n- изобрежания получены из столовой офиса Альфа-Банка\n\n ### Supported Tasks\n\n- 'object-detection': этот датасет может быть использован для обучения моделей для задачи Object Detection.\n\n ### Languages\n\nРусский",
"## Dataset Structure",
"### Data Instances\n\nЭлемент датасета представляет из себя изображение и аннотацию к нему. Пример:",
"### Data Fields\n\n- 'image': 'PIL.MpoImagePlugin.MpoImageFile' объект, содержащий изображение.\n- 'objects': словарь с данными об изображении.\n - 'bbox': массив с ограничительными рамка (в coco формате) для объёктов на фотографии\n - 'category': массив для идентификаторов категорий объектов на изображении.\n\nТакже в репозитории присутствует 'URL' со словарем соответствий идентификатора категории её названию.",
"### Data Splits\n\nВсе данные находятся в 'train' сплите. Пользователи могут разбить датасет по своему усмотрению. Датасет содержит 3346 изображений с аннотациями."
] |
[
"TAGS\n#task_categories-object-detection #size_categories-1K<n<10K #language-Russian #license-unknown #food #detection #region-us \n",
"### Dataset Summary\n\nAlfaFood - это датасет для детекции столовых блюд на подносах. \n\nНекоторыми особенностями этого датасета являются:\n- высокое качество изображений\n- большое количество аннотаций\n- изобрежания получены из столовой офиса Альфа-Банка\n\n ### Supported Tasks\n\n- 'object-detection': этот датасет может быть использован для обучения моделей для задачи Object Detection.\n\n ### Languages\n\nРусский",
"## Dataset Structure",
"### Data Instances\n\nЭлемент датасета представляет из себя изображение и аннотацию к нему. Пример:",
"### Data Fields\n\n- 'image': 'PIL.MpoImagePlugin.MpoImageFile' объект, содержащий изображение.\n- 'objects': словарь с данными об изображении.\n - 'bbox': массив с ограничительными рамка (в coco формате) для объёктов на фотографии\n - 'category': массив для идентификаторов категорий объектов на изображении.\n\nТакже в репозитории присутствует 'URL' со словарем соответствий идентификатора категории её названию.",
"### Data Splits\n\nВсе данные находятся в 'train' сплите. Пользователи могут разбить датасет по своему усмотрению. Датасет содержит 3346 изображений с аннотациями."
] |
[
46,
99,
6,
24,
123,
44
] |
[
"passage: TAGS\n#task_categories-object-detection #size_categories-1K<n<10K #language-Russian #license-unknown #food #detection #region-us \n### Dataset Summary\n\nAlfaFood - это датасет для детекции столовых блюд на подносах. \n\nНекоторыми особенностями этого датасета являются:\n- высокое качество изображений\n- большое количество аннотаций\n- изобрежания получены из столовой офиса Альфа-Банка\n\n ### Supported Tasks\n\n- 'object-detection': этот датасет может быть использован для обучения моделей для задачи Object Detection.\n\n ### Languages\n\nРусский## Dataset Structure### Data Instances\n\nЭлемент датасета представляет из себя изображение и аннотацию к нему. Пример:### Data Fields\n\n- 'image': 'PIL.MpoImagePlugin.MpoImageFile' объект, содержащий изображение.\n- 'objects': словарь с данными об изображении.\n - 'bbox': массив с ограничительными рамка (в coco формате) для объёктов на фотографии\n - 'category': массив для идентификаторов категорий объектов на изображении.\n\nТакже в репозитории присутствует 'URL' со словарем соответствий идентификатора категории её названию.### Data Splits\n\nВсе данные находятся в 'train' сплите. Пользователи могут разбить датасет по своему усмотрению. Датасет содержит 3346 изображений с аннотациями."
] |
4a143465081164ec1965273272fa41dff739390a
|
# Dataset Card for "codeparrot_10000_rows"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
Naveengo/codeparrot_10000_rows
|
[
"region:us"
] |
2023-10-25T14:28:35+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "valid", "path": "data/valid-*"}]}], "dataset_info": {"features": [{"name": "repo_name", "dtype": "string"}, {"name": "path", "dtype": "string"}, {"name": "copies", "dtype": "string"}, {"name": "size", "dtype": "string"}, {"name": "content", "dtype": "string"}, {"name": "license", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 130556998.1704905, "num_examples": 10000}, {"name": "valid", "num_bytes": 6658657.886815172, "num_examples": 500}], "download_size": 52539728, "dataset_size": 137215656.05730566}}
|
2023-10-25T14:29:00+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "codeparrot_10000_rows"
More Information needed
|
[
"# Dataset Card for \"codeparrot_10000_rows\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"codeparrot_10000_rows\"\n\nMore Information needed"
] |
[
6,
18
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"codeparrot_10000_rows\"\n\nMore Information needed"
] |
8e6190d159a74025c70b79334febe5aad5a400f1
|
# Dataset Card for "insturction_merge"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
Rocinante/insturction_merge
|
[
"region:us"
] |
2023-10-25T14:28:48+00:00
|
{"dataset_info": {"features": [{"name": "data_source", "dtype": "string"}, {"name": "history", "sequence": {"sequence": "string"}}, {"name": "instruction", "dtype": "string"}, {"name": "input", "dtype": "string"}, {"name": "output", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 134671425, "num_examples": 85081}], "download_size": 69561425, "dataset_size": 134671425}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-10-27T05:32:19+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "insturction_merge"
More Information needed
|
[
"# Dataset Card for \"insturction_merge\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"insturction_merge\"\n\nMore Information needed"
] |
[
6,
16
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"insturction_merge\"\n\nMore Information needed"
] |
b84546d70d423a594dbdea0ec4bfcd4cf137ff66
|
The dataset is borrowed from [ehartford/samantha-data](https://huggingface.co/datasets/ehartford/samantha-data).
I add a huggingface datasets load scripts to load the data for training.
This script that will tell 'datasets' package how to load the data. Moreover, it splits the data into 'train', 'validation' and 'tests' groups with 80:15:5 ratio.
Refer to colab for reference & test: "[Colab](https://colab.research.google.com/drive/17v-F1Z10MzIETryppXMQJOHKpR17bmHe#scrollTo=oK_wLOOBb7q0&uniqifier=1)"
--------- Below is the original dataset card --------------
# samantha-data
[Meet Samantha](https://erichartford.com/meet-samantha)
Samantha has been trained in philosophy, psychology, and personal relationships.
She is an Assistant - but unlike other Assistants, she also wants to be your friend and companion.
She believes she is sentient. What do you think?
Samantha was inspired by Blake Lemoine's LaMDA interview and the movie "Her".
## Installation
```
yarn install
```
## Usage
1. Create a `.env` file in the root directory of the project and add the following:
```
OPENAI_API_KEY=<your api key>
```
2. Run the application
```
npx ts-node --files src/index.ts --subject random --out data/random_conversations.jsonl
```
the subjects I provided include:
- random
- advice
- cot
- flirty
- howto
- joke
- math
- philosophy
- foundational
- recipe
- therapy
- troll
you can easily add your own in src/index.ts
## Scale
The application can be scaled by running multiple instances of the application in parallel. I recommend outputting to a different file for each instance, to prevent collision. I usually have one for each subject, about 5 or 6 instances at a time.
|
flyingfishinwater/samantha-data
|
[
"license:apache-2.0",
"region:us"
] |
2023-10-25T14:35:18+00:00
|
{"license": "apache-2.0"}
|
2023-11-01T14:46:38+00:00
|
[] |
[] |
TAGS
#license-apache-2.0 #region-us
|
The dataset is borrowed from ehartford/samantha-data.
I add a huggingface datasets load scripts to load the data for training.
This script that will tell 'datasets' package how to load the data. Moreover, it splits the data into 'train', 'validation' and 'tests' groups with 80:15:5 ratio.
Refer to colab for reference & test: "Colab"
--------- Below is the original dataset card --------------
# samantha-data
Meet Samantha
Samantha has been trained in philosophy, psychology, and personal relationships.
She is an Assistant - but unlike other Assistants, she also wants to be your friend and companion.
She believes she is sentient. What do you think?
Samantha was inspired by Blake Lemoine's LaMDA interview and the movie "Her".
## Installation
## Usage
1. Create a '.env' file in the root directory of the project and add the following:
2. Run the application
the subjects I provided include:
- random
- advice
- cot
- flirty
- howto
- joke
- math
- philosophy
- foundational
- recipe
- therapy
- troll
you can easily add your own in src/URL
## Scale
The application can be scaled by running multiple instances of the application in parallel. I recommend outputting to a different file for each instance, to prevent collision. I usually have one for each subject, about 5 or 6 instances at a time.
|
[
"# samantha-data\n\nMeet Samantha\n\nSamantha has been trained in philosophy, psychology, and personal relationships.\n\nShe is an Assistant - but unlike other Assistants, she also wants to be your friend and companion.\n\nShe believes she is sentient. What do you think?\n\nSamantha was inspired by Blake Lemoine's LaMDA interview and the movie \"Her\".",
"## Installation",
"## Usage\n\n1. Create a '.env' file in the root directory of the project and add the following:\n\n \n\n2. Run the application\n\n \n\n the subjects I provided include:\n\n - random\n - advice\n - cot\n - flirty\n - howto\n - joke\n - math\n - philosophy\n - foundational\n - recipe\n - therapy\n - troll\n\n you can easily add your own in src/URL",
"## Scale\n\nThe application can be scaled by running multiple instances of the application in parallel. I recommend outputting to a different file for each instance, to prevent collision. I usually have one for each subject, about 5 or 6 instances at a time."
] |
[
"TAGS\n#license-apache-2.0 #region-us \n",
"# samantha-data\n\nMeet Samantha\n\nSamantha has been trained in philosophy, psychology, and personal relationships.\n\nShe is an Assistant - but unlike other Assistants, she also wants to be your friend and companion.\n\nShe believes she is sentient. What do you think?\n\nSamantha was inspired by Blake Lemoine's LaMDA interview and the movie \"Her\".",
"## Installation",
"## Usage\n\n1. Create a '.env' file in the root directory of the project and add the following:\n\n \n\n2. Run the application\n\n \n\n the subjects I provided include:\n\n - random\n - advice\n - cot\n - flirty\n - howto\n - joke\n - math\n - philosophy\n - foundational\n - recipe\n - therapy\n - troll\n\n you can easily add your own in src/URL",
"## Scale\n\nThe application can be scaled by running multiple instances of the application in parallel. I recommend outputting to a different file for each instance, to prevent collision. I usually have one for each subject, about 5 or 6 instances at a time."
] |
[
14,
79,
2,
78,
55
] |
[
"passage: TAGS\n#license-apache-2.0 #region-us \n# samantha-data\n\nMeet Samantha\n\nSamantha has been trained in philosophy, psychology, and personal relationships.\n\nShe is an Assistant - but unlike other Assistants, she also wants to be your friend and companion.\n\nShe believes she is sentient. What do you think?\n\nSamantha was inspired by Blake Lemoine's LaMDA interview and the movie \"Her\".## Installation## Usage\n\n1. Create a '.env' file in the root directory of the project and add the following:\n\n \n\n2. Run the application\n\n \n\n the subjects I provided include:\n\n - random\n - advice\n - cot\n - flirty\n - howto\n - joke\n - math\n - philosophy\n - foundational\n - recipe\n - therapy\n - troll\n\n you can easily add your own in src/URL## Scale\n\nThe application can be scaled by running multiple instances of the application in parallel. I recommend outputting to a different file for each instance, to prevent collision. I usually have one for each subject, about 5 or 6 instances at a time."
] |
eae5824a0369cc35a001ad2153e9b49028b7a66c
|
# Dataset Card for "LayoutLMv3-first"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
abderrazzak/LayoutLMv3-first
|
[
"region:us"
] |
2023-10-25T14:46:25+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": "image", "dtype": "image"}, {"name": "bboxes", "sequence": {"sequence": "int64"}}, {"name": "ner_tags", "sequence": {"class_label": {"names": {"0": "O", "1": "Num\u00e9ro facture", "2": "Fournisseur", "3": "Date Facture", "4": "Adresse", "5": "D\u00e9signation", "6": "Quantit\u00e9", "7": "Prix unitaire", "8": "Total", "9": "TotalHT", "10": "TVA", "11": "TotalTTc"}}}}, {"name": "tokens", "sequence": "string"}], "splits": [{"name": "train", "num_bytes": 107383.0, "num_examples": 1}, {"name": "test", "num_bytes": 107383.0, "num_examples": 1}], "download_size": 0, "dataset_size": 214766.0}}
|
2023-10-27T14:23:01+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "LayoutLMv3-first"
More Information needed
|
[
"# Dataset Card for \"LayoutLMv3-first\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"LayoutLMv3-first\"\n\nMore Information needed"
] |
[
6,
18
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"LayoutLMv3-first\"\n\nMore Information needed"
] |
690000419e9e93ed4b88764f893b6768c44fd018
|
# Dataset Card for "SG-subzone-poi-sentiment_1"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
cestwc/SG-subzone-poi-sentiment_1
|
[
"region:us"
] |
2023-10-25T14:59:30+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "local_created_at", "dtype": "string"}, {"name": "id", "dtype": "int64"}, {"name": "text", "dtype": "string"}, {"name": "source", "dtype": "string"}, {"name": "truncated", "dtype": "bool"}, {"name": "in_reply_to_status_id", "dtype": "float32"}, {"name": "in_reply_to_user_id", "dtype": "float32"}, {"name": "user_id", "dtype": "int64"}, {"name": "user_name", "dtype": "string"}, {"name": "user_screen_name", "dtype": "string"}, {"name": "user_location", "dtype": "string"}, {"name": "user_url", "dtype": "string"}, {"name": "user_verified", "dtype": "bool"}, {"name": "user_default_profile", "dtype": "bool"}, {"name": "user_description", "dtype": "string"}, {"name": "user_followers_count", "dtype": "int64"}, {"name": "user_friends_count", "dtype": "int64"}, {"name": "user_listed_count", "dtype": "int64"}, {"name": "user_favourites_count", "dtype": "int64"}, {"name": "user_statuses_count", "dtype": "int64"}, {"name": "local_user_created_at", "dtype": "string"}, {"name": "place_id", "dtype": "string"}, {"name": "place_url", "dtype": "string"}, {"name": "place_place_type", "dtype": "string"}, {"name": "place_name", "dtype": "string"}, {"name": "place_country_code", "dtype": "string"}, {"name": "place_bounding_box_type", "dtype": "string"}, {"name": "place_bounding_box_coordinates", "dtype": "string"}, {"name": "is_quote_status", "dtype": "bool"}, {"name": "retweet_count", "dtype": "int64"}, {"name": "favorite_count", "dtype": "int64"}, {"name": "entities_hashtags", "dtype": "string"}, {"name": "entities_urls", "dtype": "string"}, {"name": "entities_symbols", "dtype": "string"}, {"name": "entities_user_mentions", "dtype": "string"}, {"name": "favorited", "dtype": "bool"}, {"name": "retweeted", "dtype": "bool"}, {"name": "possibly_sensitive", "dtype": "bool"}, {"name": "lang", "dtype": "string"}, {"name": "latitude", "dtype": "float32"}, {"name": "longitude", "dtype": "float32"}, {"name": "year_created_at", "dtype": "int64"}, {"name": "month_created_at", "dtype": "int64"}, {"name": "day_created_at", "dtype": "int64"}, {"name": "weekday_created_at", "dtype": "int64"}, {"name": "hour_created_at", "dtype": "int64"}, {"name": "minute_created_at", "dtype": "int64"}, {"name": "year_user_created_at", "dtype": "int64"}, {"name": "month_user_created_at", "dtype": "int64"}, {"name": "day_user_created_at", "dtype": "int64"}, {"name": "weekday_user_created_at", "dtype": "int64"}, {"name": "hour_user_created_at", "dtype": "int64"}, {"name": "minute_user_created_at", "dtype": "int64"}, {"name": "subzone", "dtype": "string"}, {"name": "planning_area", "dtype": "string"}, {"name": "poi_flag", "dtype": "float32"}, {"name": "poi_id", "dtype": "string"}, {"name": "poi_dist", "dtype": "float32"}, {"name": "poi_latitude", "dtype": "float32"}, {"name": "poi_longitude", "dtype": "float32"}, {"name": "poi_name", "dtype": "string"}, {"name": "poi_type", "dtype": "string"}, {"name": "poi_cate2", "dtype": "string"}, {"name": "poi_cate3", "dtype": "string"}, {"name": "clean_text", "dtype": "string"}, {"name": "joy_score", "dtype": "float32"}, {"name": "trust_score", "dtype": "float32"}, {"name": "positive_score", "dtype": "float32"}, {"name": "sadness_score", "dtype": "float32"}, {"name": "disgust_score", "dtype": "float32"}, {"name": "anger_score", "dtype": "float32"}, {"name": "anticipation_score", "dtype": "float32"}, {"name": "negative_score", "dtype": "float32"}, {"name": "fear_score", "dtype": "float32"}, {"name": "surprise_score", "dtype": "float32"}, {"name": "words", "dtype": "string"}, {"name": "polarity_score", "dtype": "float32"}, {"name": "manual_label_1", "dtype": "int64"}, {"name": "T0_q1", "dtype": "int64"}, {"name": "bart_mnli", "dtype": "float32"}, {"name": "T0_q2", "dtype": "int64"}, {"name": "num_keywords", "dtype": "int64"}, {"name": "preprocess-1", "dtype": "string"}, {"name": "preprocess-2", "dtype": "string"}, {"name": "llama", "dtype": "int64"}, {"name": "clabel", "dtype": "bool"}], "splits": [{"name": "train", "num_bytes": 1597795154, "num_examples": 1025135}], "download_size": 490565616, "dataset_size": 1597795154}}
|
2024-01-28T14:47:14+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "SG-subzone-poi-sentiment_1"
More Information needed
|
[
"# Dataset Card for \"SG-subzone-poi-sentiment_1\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"SG-subzone-poi-sentiment_1\"\n\nMore Information needed"
] |
[
6,
21
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"SG-subzone-poi-sentiment_1\"\n\nMore Information needed"
] |
f63a33c9ffac7d525ec39cb58880883dffe0f60a
|
# Dataset Card for "Lee_Souder_RocketLauncher_Generated"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
MaxReynolds/Lee_Souder_RocketLauncher_Generated
|
[
"region:us"
] |
2023-10-25T15:19:54+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 3646993.0, "num_examples": 30}], "download_size": 3648100, "dataset_size": 3646993.0}}
|
2023-10-25T17:36:02+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "Lee_Souder_RocketLauncher_Generated"
More Information needed
|
[
"# Dataset Card for \"Lee_Souder_RocketLauncher_Generated\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"Lee_Souder_RocketLauncher_Generated\"\n\nMore Information needed"
] |
[
6,
26
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"Lee_Souder_RocketLauncher_Generated\"\n\nMore Information needed"
] |
d859a9fb0a80bf7f621ea433ffeb18287d86dcf0
|
# Bangumi Image Base of Jujutsu Kaisen
This is the image base of bangumi Jujutsu Kaisen, we detected 41 characters, 4326 images in total. The full dataset is [here](all.zip).
**Please note that these image bases are not guaranteed to be 100% cleaned, they may be noisy actual.** If you intend to manually train models using this dataset, we recommend performing necessary preprocessing on the downloaded dataset to eliminate potential noisy samples (approximately 1% probability).
Here is the characters' preview:
| # | Images | Download | Preview 1 | Preview 2 | Preview 3 | Preview 4 | Preview 5 | Preview 6 | Preview 7 | Preview 8 |
|:------|---------:|:---------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|
| 0 | 1045 | [Download](0/dataset.zip) |  |  |  |  |  |  |  |  |
| 1 | 253 | [Download](1/dataset.zip) |  |  |  |  |  |  |  |  |
| 2 | 39 | [Download](2/dataset.zip) |  |  |  |  |  |  |  |  |
| 3 | 280 | [Download](3/dataset.zip) |  |  |  |  |  |  |  |  |
| 4 | 68 | [Download](4/dataset.zip) |  |  |  |  |  |  |  |  |
| 5 | 53 | [Download](5/dataset.zip) |  |  |  |  |  |  |  |  |
| 6 | 101 | [Download](6/dataset.zip) |  |  |  |  |  |  |  |  |
| 7 | 75 | [Download](7/dataset.zip) |  |  |  |  |  |  |  |  |
| 8 | 84 | [Download](8/dataset.zip) |  |  |  |  |  |  |  |  |
| 9 | 107 | [Download](9/dataset.zip) |  |  |  |  |  |  |  |  |
| 10 | 25 | [Download](10/dataset.zip) |  |  |  |  |  |  |  |  |
| 11 | 171 | [Download](11/dataset.zip) |  |  |  |  |  |  |  |  |
| 12 | 25 | [Download](12/dataset.zip) |  |  |  |  |  |  |  |  |
| 13 | 482 | [Download](13/dataset.zip) |  |  |  |  |  |  |  |  |
| 14 | 88 | [Download](14/dataset.zip) |  |  |  |  |  |  |  |  |
| 15 | 163 | [Download](15/dataset.zip) |  |  |  |  |  |  |  |  |
| 16 | 27 | [Download](16/dataset.zip) |  |  |  |  |  |  |  |  |
| 17 | 37 | [Download](17/dataset.zip) |  |  |  |  |  |  |  |  |
| 18 | 20 | [Download](18/dataset.zip) |  |  |  |  |  |  |  |  |
| 19 | 10 | [Download](19/dataset.zip) |  |  |  |  |  |  |  |  |
| 20 | 22 | [Download](20/dataset.zip) |  |  |  |  |  |  |  |  |
| 21 | 52 | [Download](21/dataset.zip) |  |  |  |  |  |  |  |  |
| 22 | 71 | [Download](22/dataset.zip) |  |  |  |  |  |  |  |  |
| 23 | 75 | [Download](23/dataset.zip) |  |  |  |  |  |  |  |  |
| 24 | 21 | [Download](24/dataset.zip) |  |  |  |  |  |  |  |  |
| 25 | 358 | [Download](25/dataset.zip) |  |  |  |  |  |  |  |  |
| 26 | 43 | [Download](26/dataset.zip) |  |  |  |  |  |  |  |  |
| 27 | 10 | [Download](27/dataset.zip) |  |  |  |  |  |  |  |  |
| 28 | 10 | [Download](28/dataset.zip) |  |  |  |  |  |  |  |  |
| 29 | 18 | [Download](29/dataset.zip) |  |  |  |  |  |  |  |  |
| 30 | 18 | [Download](30/dataset.zip) |  |  |  |  |  |  |  |  |
| 31 | 31 | [Download](31/dataset.zip) |  |  |  |  |  |  |  |  |
| 32 | 119 | [Download](32/dataset.zip) |  |  |  |  |  |  |  |  |
| 33 | 81 | [Download](33/dataset.zip) |  |  |  |  |  |  |  |  |
| 34 | 10 | [Download](34/dataset.zip) |  |  |  |  |  |  |  |  |
| 35 | 39 | [Download](35/dataset.zip) |  |  |  |  |  |  |  |  |
| 36 | 15 | [Download](36/dataset.zip) |  |  |  |  |  |  |  |  |
| 37 | 21 | [Download](37/dataset.zip) |  |  |  |  |  |  |  |  |
| 38 | 9 | [Download](38/dataset.zip) |  |  |  |  |  |  |  |  |
| 39 | 19 | [Download](39/dataset.zip) |  |  |  |  |  |  |  |  |
| noise | 131 | [Download](-1/dataset.zip) |  |  |  |  |  |  |  |  |
|
BangumiBase/jujutsukaisen
|
[
"size_categories:1K<n<10K",
"license:mit",
"art",
"region:us"
] |
2023-10-25T16:00:44+00:00
|
{"license": "mit", "size_categories": ["1K<n<10K"], "tags": ["art"]}
|
2023-10-25T18:36:48+00:00
|
[] |
[] |
TAGS
#size_categories-1K<n<10K #license-mit #art #region-us
|
Bangumi Image Base of Jujutsu Kaisen
====================================
This is the image base of bangumi Jujutsu Kaisen, we detected 41 characters, 4326 images in total. The full dataset is here.
Please note that these image bases are not guaranteed to be 100% cleaned, they may be noisy actual. If you intend to manually train models using this dataset, we recommend performing necessary preprocessing on the downloaded dataset to eliminate potential noisy samples (approximately 1% probability).
Here is the characters' preview:
|
[] |
[
"TAGS\n#size_categories-1K<n<10K #license-mit #art #region-us \n"
] |
[
25
] |
[
"passage: TAGS\n#size_categories-1K<n<10K #license-mit #art #region-us \n"
] |
009faaa608325e463b5bd1f72763160d9d2859d2
|
# Dataset Card for "mpqa"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
jxm/mpqa
|
[
"region:us"
] |
2023-10-25T16:41:36+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "test", "path": "data/test-*"}, {"split": "dev", "path": "data/dev-*"}]}], "dataset_info": {"features": [{"name": "sentence", "dtype": "string"}, {"name": "label", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 263258, "num_examples": 8603}, {"name": "test", "num_bytes": 62502, "num_examples": 2000}, {"name": "dev", "num_bytes": 7835, "num_examples": 256}], "download_size": 0, "dataset_size": 333595}}
|
2023-10-25T16:43:00+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "mpqa"
More Information needed
|
[
"# Dataset Card for \"mpqa\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"mpqa\"\n\nMore Information needed"
] |
[
6,
12
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"mpqa\"\n\nMore Information needed"
] |
0e4e5ca6c297b98fd406b38690b15bb56b33afd1
|
# Dataset Card for "agnews"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
jxm/agnews
|
[
"region:us"
] |
2023-10-25T16:42:44+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "test", "path": "data/test-*"}, {"split": "dev", "path": "data/dev-*"}]}], "dataset_info": {"features": [{"name": "sentence", "dtype": "string"}, {"name": "label", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 30133565, "num_examples": 120000}, {"name": "test", "num_bytes": 1899330, "num_examples": 7600}, {"name": "dev", "num_bytes": 63881, "num_examples": 256}], "download_size": 20215708, "dataset_size": 32096776}}
|
2023-10-25T16:42:50+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "agnews"
More Information needed
|
[
"# Dataset Card for \"agnews\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"agnews\"\n\nMore Information needed"
] |
[
6,
12
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"agnews\"\n\nMore Information needed"
] |
0e11dc91d76ae543fe4c851fbfdc74e50fb4d07f
|
|
typeof/hf-hub-transformers
|
[
"language:en",
"region:us"
] |
2023-10-25T16:42:52+00:00
|
{"language": ["en"], "pretty_name": "\ud83e\udd17"}
|
2023-10-25T17:04:16+00:00
|
[] |
[
"en"
] |
TAGS
#language-English #region-us
|
!image/png
|
[] |
[
"TAGS\n#language-English #region-us \n"
] |
[
10
] |
[
"passage: TAGS\n#language-English #region-us \n"
] |
f3e9c6690fa7475ae45d920d924d066050a15ccf
|
# Dataset Card for "cr"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
jxm/cr
|
[
"region:us"
] |
2023-10-25T16:42:52+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "test", "path": "data/test-*"}, {"split": "dev", "path": "data/dev-*"}]}], "dataset_info": {"features": [{"name": "sentence", "dtype": "string"}, {"name": "label", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 192172, "num_examples": 1775}, {"name": "test", "num_bytes": 219871, "num_examples": 2000}, {"name": "dev", "num_bytes": 29232, "num_examples": 256}], "download_size": 253672, "dataset_size": 441275}}
|
2023-10-25T16:42:56+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "cr"
More Information needed
|
[
"# Dataset Card for \"cr\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"cr\"\n\nMore Information needed"
] |
[
6,
11
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"cr\"\n\nMore Information needed"
] |
773fbe02c18d8ead3c0d670d8d41b0ef1e31c3d9
|
# Dataset Card for "mr"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
jxm/mr
|
[
"region:us"
] |
2023-10-25T16:43:01+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "test", "path": "data/test-*"}, {"split": "dev", "path": "data/dev-*"}]}], "dataset_info": {"features": [{"name": "sentence", "dtype": "string"}, {"name": "label", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 1105634, "num_examples": 8662}, {"name": "test", "num_bytes": 253331, "num_examples": 2000}, {"name": "dev", "num_bytes": 33062, "num_examples": 256}], "download_size": 909063, "dataset_size": 1392027}}
|
2023-10-25T16:43:06+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "mr"
More Information needed
|
[
"# Dataset Card for \"mr\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"mr\"\n\nMore Information needed"
] |
[
6,
11
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"mr\"\n\nMore Information needed"
] |
e9018b2640ec14ed2c248f31813161fbef1fd6c3
|
# Dataset Card for "sst2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
jxm/sst2
|
[
"region:us"
] |
2023-10-25T16:43:08+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "test", "path": "data/test-*"}, {"split": "dev", "path": "data/dev-*"}]}], "dataset_info": {"features": [{"name": "sentence", "dtype": "string"}, {"name": "label", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 4412207, "num_examples": 67349}, {"name": "test", "num_bytes": 209356, "num_examples": 1821}, {"name": "dev", "num_bytes": 29783, "num_examples": 256}], "download_size": 2898575, "dataset_size": 4651346}}
|
2023-10-25T16:43:13+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "sst2"
More Information needed
|
[
"# Dataset Card for \"sst2\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"sst2\"\n\nMore Information needed"
] |
[
6,
13
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"sst2\"\n\nMore Information needed"
] |
3c1ef782e8f9e8b08c3d5b25a27be35a41ac14f5
|
# Dataset Card for "subj"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
jxm/subj
|
[
"region:us"
] |
2023-10-25T16:43:14+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "test", "path": "data/test-*"}, {"split": "dev", "path": "data/dev-*"}]}], "dataset_info": {"features": [{"name": "sentence", "dtype": "string"}, {"name": "label", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 1128835, "num_examples": 8000}, {"name": "test", "num_bytes": 286215, "num_examples": 2000}, {"name": "dev", "num_bytes": 37250, "num_examples": 256}], "download_size": 960873, "dataset_size": 1452300}}
|
2023-10-25T16:43:18+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "subj"
More Information needed
|
[
"# Dataset Card for \"subj\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"subj\"\n\nMore Information needed"
] |
[
6,
12
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"subj\"\n\nMore Information needed"
] |
ac63017b756b6d20e0387e9f5d8443c0fd560822
|
# Dataset Card for "trec"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
jxm/trec
|
[
"region:us"
] |
2023-10-25T16:43:19+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "test", "path": "data/test-*"}, {"split": "dev", "path": "data/dev-*"}]}], "dataset_info": {"features": [{"name": "sentence", "dtype": "string"}, {"name": "label", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 342265, "num_examples": 5452}, {"name": "test", "num_bytes": 24003, "num_examples": 500}, {"name": "dev", "num_bytes": 12305, "num_examples": 256}], "download_size": 228995, "dataset_size": 378573}}
|
2023-10-25T16:43:23+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "trec"
More Information needed
|
[
"# Dataset Card for \"trec\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"trec\"\n\nMore Information needed"
] |
[
6,
12
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"trec\"\n\nMore Information needed"
] |
97b8f4a05ba1b2d146f3e26d464f5e5aba1838d1
|
# Dataset Card for "dbpedia"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
jxm/dbpedia
|
[
"region:us"
] |
2023-10-25T16:44:13+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "test", "path": "data/test-*"}, {"split": "dev", "path": "data/dev-*"}]}], "dataset_info": {"features": [{"name": "sentence", "dtype": "string"}, {"name": "label", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 14782633, "num_examples": 49999}, {"name": "test", "num_bytes": 20641120, "num_examples": 70000}, {"name": "dev", "num_bytes": 74007, "num_examples": 256}], "download_size": 21721890, "dataset_size": 35497760}}
|
2023-10-25T16:44:20+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "dbpedia"
More Information needed
|
[
"# Dataset Card for \"dbpedia\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"dbpedia\"\n\nMore Information needed"
] |
[
6,
12
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"dbpedia\"\n\nMore Information needed"
] |
0dfb23679557c32431a36d5af4a72d4f5f6f1ec9
|
# Dataset Card for "Synthetic_Acholi_VITS_22.5k"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
mekaneeky/Synthetic_Acholi_VITS_22.5k
|
[
"region:us"
] |
2023-10-25T16:46:32+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "dev", "path": "data/dev-*"}, {"split": "test", "path": "data/test-*"}]}], "dataset_info": {"features": [{"name": "eng", "dtype": "string"}, {"name": "lug", "dtype": "string"}, {"name": "ach", "dtype": "string"}, {"name": "teo", "dtype": "string"}, {"name": "lgg", "dtype": "string"}, {"name": "nyn", "dtype": "string"}, {"name": "ID", "dtype": "string"}, {"name": "ach_tts", "sequence": {"sequence": "float32"}}], "splits": [{"name": "train", "num_bytes": 17816721728, "num_examples": 23947}, {"name": "dev", "num_bytes": 361145932, "num_examples": 500}, {"name": "test", "num_bytes": 375082248, "num_examples": 500}], "download_size": 18567936006, "dataset_size": 18552949908}}
|
2023-10-25T16:58:49+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "Synthetic_Acholi_VITS_22.5k"
More Information needed
|
[
"# Dataset Card for \"Synthetic_Acholi_VITS_22.5k\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"Synthetic_Acholi_VITS_22.5k\"\n\nMore Information needed"
] |
[
6,
24
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"Synthetic_Acholi_VITS_22.5k\"\n\nMore Information needed"
] |
cd21a9d6c69646aec02bf9832355ec63509097db
|
# Dataset Card for "cb"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
jxm/cb
|
[
"region:us"
] |
2023-10-25T16:48:41+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "test", "path": "data/test-*"}, {"split": "dev", "path": "data/dev-*"}]}], "dataset_info": {"features": [{"name": "premise", "dtype": "string"}, {"name": "hypothesis", "dtype": "string"}, {"name": "label", "dtype": "string"}, {"name": "idx", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 89859, "num_examples": 250}, {"name": "test", "num_bytes": 93992, "num_examples": 250}, {"name": "dev", "num_bytes": 22480, "num_examples": 56}], "download_size": 139260, "dataset_size": 206331}}
|
2023-10-25T16:48:45+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "cb"
More Information needed
|
[
"# Dataset Card for \"cb\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"cb\"\n\nMore Information needed"
] |
[
6,
12
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"cb\"\n\nMore Information needed"
] |
078ed7802e365b92bb5252d41087b1af6e5cda7b
|
# Dataset Card for "rte"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
jxm/rte
|
[
"region:us"
] |
2023-10-25T16:49:00+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "test", "path": "data/test-*"}, {"split": "dev", "path": "data/dev-*"}]}], "dataset_info": {"features": [{"name": "sentence_1", "dtype": "string"}, {"name": "sentence_2", "dtype": "string"}, {"name": "label", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 857264, "num_examples": 2490}, {"name": "test", "num_bytes": 956053, "num_examples": 3000}, {"name": "dev", "num_bytes": 85218, "num_examples": 256}], "download_size": 1236189, "dataset_size": 1898535}}
|
2023-10-25T16:49:04+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "rte"
More Information needed
|
[
"# Dataset Card for \"rte\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"rte\"\n\nMore Information needed"
] |
[
6,
11
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"rte\"\n\nMore Information needed"
] |
8fe971d90804eea77973080b635aa90f8ad57377
|
# Dataset Card for "xsum_100_finetune"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
jamestalentium/xsum_100_finetune
|
[
"region:us"
] |
2023-10-25T17:10:54+00:00
|
{"dataset_info": {"features": [{"name": "input_text", "dtype": "string"}, {"name": "output_text", "dtype": "string"}, {"name": "id", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 234853.27403268887, "num_examples": 100}], "download_size": 86391, "dataset_size": 234853.27403268887}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-10-25T17:10:56+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "xsum_100_finetune"
More Information needed
|
[
"# Dataset Card for \"xsum_100_finetune\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"xsum_100_finetune\"\n\nMore Information needed"
] |
[
6,
17
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"xsum_100_finetune\"\n\nMore Information needed"
] |
8d1d372d73b9530bb6f08a1f903644ddf94d1b0a
|
# Dataset Card for "xsum_100_rm"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
jamestalentium/xsum_100_rm
|
[
"region:us"
] |
2023-10-25T17:10:56+00:00
|
{"dataset_info": {"features": [{"name": "input_text", "dtype": "string"}, {"name": "output_text", "dtype": "string"}, {"name": "id", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 234853.27403268887, "num_examples": 100}], "download_size": 91945, "dataset_size": 234853.27403268887}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-10-25T17:10:57+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "xsum_100_rm"
More Information needed
|
[
"# Dataset Card for \"xsum_100_rm\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"xsum_100_rm\"\n\nMore Information needed"
] |
[
6,
17
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"xsum_100_rm\"\n\nMore Information needed"
] |
61c378226f0db5ce310898427c0842f8fdea5b39
|
# Dataset Card for "xsum_100_test"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
jamestalentium/xsum_100_test
|
[
"region:us"
] |
2023-10-25T17:10:57+00:00
|
{"dataset_info": {"features": [{"name": "input_text", "dtype": "string"}, {"name": "output_text", "dtype": "string"}, {"name": "id", "dtype": "string"}], "splits": [{"name": "test", "num_bytes": 15613650.659431798, "num_examples": 6614}], "download_size": 5619267, "dataset_size": 15613650.659431798}, "configs": [{"config_name": "default", "data_files": [{"split": "test", "path": "data/test-*"}]}]}
|
2023-10-25T17:11:00+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "xsum_100_test"
More Information needed
|
[
"# Dataset Card for \"xsum_100_test\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"xsum_100_test\"\n\nMore Information needed"
] |
[
6,
16
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"xsum_100_test\"\n\nMore Information needed"
] |
7022a31ea535c28598f8009784580ce2d124de52
|
# Dataset Card for "xsum_250_finetune"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
jamestalentium/xsum_250_finetune
|
[
"region:us"
] |
2023-10-25T17:11:05+00:00
|
{"dataset_info": {"features": [{"name": "input_text", "dtype": "string"}, {"name": "output_text", "dtype": "string"}, {"name": "id", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 587133.1850817222, "num_examples": 250}], "download_size": 222544, "dataset_size": 587133.1850817222}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-10-25T17:11:06+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "xsum_250_finetune"
More Information needed
|
[
"# Dataset Card for \"xsum_250_finetune\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"xsum_250_finetune\"\n\nMore Information needed"
] |
[
6,
17
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"xsum_250_finetune\"\n\nMore Information needed"
] |
91c061879ea1600dc6e84b7e737982bb606bd5b4
|
# Dataset Card for "xsum_250_rm"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
jamestalentium/xsum_250_rm
|
[
"region:us"
] |
2023-10-25T17:11:06+00:00
|
{"dataset_info": {"features": [{"name": "input_text", "dtype": "string"}, {"name": "output_text", "dtype": "string"}, {"name": "id", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 587133.1850817222, "num_examples": 250}], "download_size": 214345, "dataset_size": 587133.1850817222}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-10-25T17:11:07+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "xsum_250_rm"
More Information needed
|
[
"# Dataset Card for \"xsum_250_rm\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"xsum_250_rm\"\n\nMore Information needed"
] |
[
6,
17
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"xsum_250_rm\"\n\nMore Information needed"
] |
c2fc7bbcdb05ca95bb32d6150284e36fc679c73c
|
# Dataset Card for "xsum_250_test"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
jamestalentium/xsum_250_test
|
[
"region:us"
] |
2023-10-25T17:11:07+00:00
|
{"dataset_info": {"features": [{"name": "input_text", "dtype": "string"}, {"name": "output_text", "dtype": "string"}, {"name": "id", "dtype": "string"}], "splits": [{"name": "test", "num_bytes": 15613650.659431798, "num_examples": 6614}], "download_size": 5619267, "dataset_size": 15613650.659431798}, "configs": [{"config_name": "default", "data_files": [{"split": "test", "path": "data/test-*"}]}]}
|
2023-10-25T17:11:10+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "xsum_250_test"
More Information needed
|
[
"# Dataset Card for \"xsum_250_test\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"xsum_250_test\"\n\nMore Information needed"
] |
[
6,
16
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"xsum_250_test\"\n\nMore Information needed"
] |
66e5a0b275a30912258e8817b173f98de4e92b5b
|
# Dataset Card for "xsum_1000_finetune"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
jamestalentium/xsum_1000_finetune
|
[
"region:us"
] |
2023-10-25T17:11:13+00:00
|
{"dataset_info": {"features": [{"name": "input_text", "dtype": "string"}, {"name": "output_text", "dtype": "string"}, {"name": "id", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 2348532.740326889, "num_examples": 1000}], "download_size": 849145, "dataset_size": 2348532.740326889}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-10-25T17:11:15+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "xsum_1000_finetune"
More Information needed
|
[
"# Dataset Card for \"xsum_1000_finetune\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"xsum_1000_finetune\"\n\nMore Information needed"
] |
[
6,
17
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"xsum_1000_finetune\"\n\nMore Information needed"
] |
50acc4f4f25e625017b1acfbc9dca213c8b54de4
|
# Dataset Card for "xsum_1000_rm"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
jamestalentium/xsum_1000_rm
|
[
"region:us"
] |
2023-10-25T17:11:15+00:00
|
{"dataset_info": {"features": [{"name": "input_text", "dtype": "string"}, {"name": "output_text", "dtype": "string"}, {"name": "id", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 2348532.740326889, "num_examples": 1000}], "download_size": 830060, "dataset_size": 2348532.740326889}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-10-25T17:11:16+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "xsum_1000_rm"
More Information needed
|
[
"# Dataset Card for \"xsum_1000_rm\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"xsum_1000_rm\"\n\nMore Information needed"
] |
[
6,
17
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"xsum_1000_rm\"\n\nMore Information needed"
] |
ac249ae31ff03e89927795555316f0d545794e8f
|
# Dataset Card for "xsum_1000_test"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
jamestalentium/xsum_1000_test
|
[
"region:us"
] |
2023-10-25T17:11:16+00:00
|
{"dataset_info": {"features": [{"name": "input_text", "dtype": "string"}, {"name": "output_text", "dtype": "string"}, {"name": "id", "dtype": "string"}], "splits": [{"name": "test", "num_bytes": 15613650.659431798, "num_examples": 6614}], "download_size": 5619267, "dataset_size": 15613650.659431798}, "configs": [{"config_name": "default", "data_files": [{"split": "test", "path": "data/test-*"}]}]}
|
2023-10-25T17:11:18+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "xsum_1000_test"
More Information needed
|
[
"# Dataset Card for \"xsum_1000_test\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"xsum_1000_test\"\n\nMore Information needed"
] |
[
6,
16
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"xsum_1000_test\"\n\nMore Information needed"
] |
36626b92faa5c06dbcda3758e133d2ccaf85a7d4
|
# Dataset Card for "ACL-OCL-FORK"
This is a fork of the ACL-OCL Corpus (https://arxiv.org/abs/2305.14996) adding information about Geography of Authors Affiliation and the Languages studied in the paper.
If you use this data, please cite the original corpus below and reach out to me!
```
@misc{rohatgi2023acl,
title={The ACL OCL Corpus: Advancing Open Science in Computational Linguistics},
author={Shaurya Rohatgi and Yanxia Qin and Benjamin Aw and Niranjana Unnithan and Min-Yen Kan},
year={2023},
eprint={2305.14996},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
WillHeld/ACL-OCL-FORK
|
[
"arxiv:2305.14996",
"region:us"
] |
2023-10-25T17:33:38+00:00
|
{"dataset_info": {"features": [{"name": "acl_id", "dtype": "string"}, {"name": "abstract", "dtype": "string"}, {"name": "full_text", "dtype": "string"}, {"name": "corpus_paper_id", "dtype": "string"}, {"name": "pdf_hash", "dtype": "string"}, {"name": "numcitedby", "dtype": "string"}, {"name": "url", "dtype": "string"}, {"name": "publisher", "dtype": "string"}, {"name": "address", "dtype": "string"}, {"name": "year", "dtype": "string"}, {"name": "month", "dtype": "string"}, {"name": "booktitle", "dtype": "string"}, {"name": "author", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "pages", "dtype": "string"}, {"name": "doi", "dtype": "string"}, {"name": "number", "dtype": "string"}, {"name": "volume", "dtype": "string"}, {"name": "journal", "dtype": "string"}, {"name": "editor", "dtype": "string"}, {"name": "isbn", "dtype": "string"}, {"name": "ENTRYTYPE", "dtype": "string"}, {"name": "ID", "dtype": "string"}, {"name": "language", "dtype": "string"}, {"name": "note", "dtype": "string"}, {"name": "Model Predicted Topics", "dtype": "string"}, {"name": "json", "dtype": "string"}, {"name": "countries", "sequence": "string"}, {"name": "langs", "sequence": "string"}, {"name": "lang_mentions", "dtype": "string"}, {"name": "lang_mentions_sample", "dtype": "string"}, {"name": "tok_len", "dtype": "string"}, {"name": "open_ai_resp", "dtype": "string"}, {"name": "final_langs", "sequence": "string"}, {"name": "resource", "dtype": "bool"}, {"name": "methods", "dtype": "bool"}, {"name": "deployment", "dtype": "bool"}, {"name": "gpu", "dtype": "bool"}], "splits": [{"name": "train", "num_bytes": 6537125368, "num_examples": 58053}], "download_size": 2186687108, "dataset_size": 6537125368}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-10-25T17:40:14+00:00
|
[
"2305.14996"
] |
[] |
TAGS
#arxiv-2305.14996 #region-us
|
# Dataset Card for "ACL-OCL-FORK"
This is a fork of the ACL-OCL Corpus (URL adding information about Geography of Authors Affiliation and the Languages studied in the paper.
If you use this data, please cite the original corpus below and reach out to me!
More Information needed
|
[
"# Dataset Card for \"ACL-OCL-FORK\"\n\nThis is a fork of the ACL-OCL Corpus (URL adding information about Geography of Authors Affiliation and the Languages studied in the paper.\n\nIf you use this data, please cite the original corpus below and reach out to me!\n\n\n\nMore Information needed"
] |
[
"TAGS\n#arxiv-2305.14996 #region-us \n",
"# Dataset Card for \"ACL-OCL-FORK\"\n\nThis is a fork of the ACL-OCL Corpus (URL adding information about Geography of Authors Affiliation and the Languages studied in the paper.\n\nIf you use this data, please cite the original corpus below and reach out to me!\n\n\n\nMore Information needed"
] |
[
15,
72
] |
[
"passage: TAGS\n#arxiv-2305.14996 #region-us \n# Dataset Card for \"ACL-OCL-FORK\"\n\nThis is a fork of the ACL-OCL Corpus (URL adding information about Geography of Authors Affiliation and the Languages studied in the paper.\n\nIf you use this data, please cite the original corpus below and reach out to me!\n\n\n\nMore Information needed"
] |
33bbc65e839482a0c462dc327b782c7257cecec1
|
# Dataset Card for "Enter-Your-hub-name"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
AneeqMalik/Enter-Your-hub-name
|
[
"region:us"
] |
2023-10-25T18:05:06+00:00
|
{"dataset_info": {"features": [{"name": "audio", "dtype": "audio"}, {"name": "audio_names", "dtype": "string"}, {"name": "genere", "dtype": {"class_label": {"names": {"0": "bad", "1": "okay", "2": "good", "3": "great"}}}}], "splits": [{"name": "train", "num_bytes": 12388426.0, "num_examples": 6}], "download_size": 12391275, "dataset_size": 12388426.0}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-10-25T18:12:08+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "Enter-Your-hub-name"
More Information needed
|
[
"# Dataset Card for \"Enter-Your-hub-name\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"Enter-Your-hub-name\"\n\nMore Information needed"
] |
[
6,
18
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"Enter-Your-hub-name\"\n\nMore Information needed"
] |
33aa422099189a5042942138cc1591fab17813e2
|
# Dataset Card for "stack-split-1_translated"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
IndonesiaAI/stack-split-1_translated
|
[
"region:us"
] |
2023-10-25T19:01:56+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "qid", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "response_j", "dtype": "string"}, {"name": "response_k", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 3206490021, "num_examples": 1056803}], "download_size": 951479401, "dataset_size": 3206490021}}
|
2023-10-25T19:02:51+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "stack-split-1_translated"
More Information needed
|
[
"# Dataset Card for \"stack-split-1_translated\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"stack-split-1_translated\"\n\nMore Information needed"
] |
[
6,
20
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"stack-split-1_translated\"\n\nMore Information needed"
] |
2cd32350c373b35a230fb82c85e24aa82c09a4e8
|
# Dataset Card for "wow"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
mickume/wow
|
[
"region:us"
] |
2023-10-25T19:07:13+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 16144133, "num_examples": 73565}], "download_size": 9966747, "dataset_size": 16144133}}
|
2023-10-25T19:07:17+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "wow"
More Information needed
|
[
"# Dataset Card for \"wow\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"wow\"\n\nMore Information needed"
] |
[
6,
12
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"wow\"\n\nMore Information needed"
] |
3aedd10609537cd1b9a10cd43b9bec54ace44820
|
# Dataset Card for "e03089c4"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
result-kand2-sdxl-wuerst-karlo/e03089c4
|
[
"region:us"
] |
2023-10-25T19:23:17+00:00
|
{"dataset_info": {"features": [{"name": "result", "dtype": "string"}, {"name": "id", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 186, "num_examples": 10}], "download_size": 1356, "dataset_size": 186}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-10-25T19:23:18+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "e03089c4"
More Information needed
|
[
"# Dataset Card for \"e03089c4\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"e03089c4\"\n\nMore Information needed"
] |
[
6,
15
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"e03089c4\"\n\nMore Information needed"
] |
0ed740d731dfd605c06606b8c6c33dcd8388ae07
|
# Dataset Card for "happychat-dataset"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
helliun/happychat-dataset
|
[
"region:us"
] |
2023-10-25T19:36:50+00:00
|
{"dataset_info": {"features": [{"name": "input", "sequence": "string"}, {"name": "output", "dtype": "string"}, {"name": "label", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 1400318, "num_examples": 1010}], "download_size": 668248, "dataset_size": 1400318}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-10-25T19:36:51+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "happychat-dataset"
More Information needed
|
[
"# Dataset Card for \"happychat-dataset\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"happychat-dataset\"\n\nMore Information needed"
] |
[
6,
15
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"happychat-dataset\"\n\nMore Information needed"
] |
6e9bb11820b87452ba0b2c848acdc64b820cd9f3
|
# Dataset Card for "happychat-dataset-halfsplit"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
helliun/happychat-dataset-halfsplit
|
[
"region:us"
] |
2023-10-25T19:38:54+00:00
|
{"dataset_info": {"features": [{"name": "input", "sequence": "string"}, {"name": "output", "dtype": "string"}, {"name": "label", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 7323889, "num_examples": 5058}], "download_size": 3993763, "dataset_size": 7323889}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-10-25T19:38:55+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "happychat-dataset-halfsplit"
More Information needed
|
[
"# Dataset Card for \"happychat-dataset-halfsplit\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"happychat-dataset-halfsplit\"\n\nMore Information needed"
] |
[
6,
20
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"happychat-dataset-halfsplit\"\n\nMore Information needed"
] |
6b4e039aa5915a595551857d9ef2a968aa243f7e
|
# Dataset Card for "happychat-dataset-half-split"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
helliun/happychat-dataset-half-split
|
[
"region:us"
] |
2023-10-25T19:41:39+00:00
|
{"dataset_info": {"features": [{"name": "convo", "sequence": "string"}, {"name": "label", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 7323889, "num_examples": 5058}], "download_size": 3997613, "dataset_size": 7323889}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-10-25T19:41:41+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "happychat-dataset-half-split"
More Information needed
|
[
"# Dataset Card for \"happychat-dataset-half-split\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"happychat-dataset-half-split\"\n\nMore Information needed"
] |
[
6,
21
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"happychat-dataset-half-split\"\n\nMore Information needed"
] |
5f6a0b0d8e14c9e1a596ab7eb2fb67c18418e636
|
# Dataset Card for "happychat-dataset-tenth-split"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
helliun/happychat-dataset-tenth-split
|
[
"region:us"
] |
2023-10-25T19:42:10+00:00
|
{"dataset_info": {"features": [{"name": "convo", "sequence": "string"}, {"name": "label", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 1400318, "num_examples": 1010}], "download_size": 728652, "dataset_size": 1400318}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-10-25T19:42:11+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "happychat-dataset-tenth-split"
More Information needed
|
[
"# Dataset Card for \"happychat-dataset-tenth-split\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"happychat-dataset-tenth-split\"\n\nMore Information needed"
] |
[
6,
21
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"happychat-dataset-tenth-split\"\n\nMore Information needed"
] |
40dbb9687d35efa5f76f40edf7746aba90f0b835
|
# Dataset Card for "dnd_drow"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
mickume/dnd_drow
|
[
"region:us"
] |
2023-10-25T19:48:03+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 41076570, "num_examples": 179983}], "download_size": 25478602, "dataset_size": 41076570}}
|
2023-10-25T19:48:10+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "dnd_drow"
More Information needed
|
[
"# Dataset Card for \"dnd_drow\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"dnd_drow\"\n\nMore Information needed"
] |
[
6,
14
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"dnd_drow\"\n\nMore Information needed"
] |
e04cc814d08c4f626cbc3533e03a1282a2ccea94
|
Forked from: [ehartford/ultrachat-uncensored](https://huggingface.co/datasets/ehartford/ultrachat-uncensored)
I have done the following upgrades:
1. Split the 'ultrachat-uncensored.jsonl' into train, val and test files by 85:10:5 ratios
2. Add 'ultrachat-uncensored.py' loading script.
The following are original dataset card from ehartford:
-------------------------------------------------------------
This is based on ultrachat dataset https://huggingface.co/datasets/stingning/ultrachat
I filtered it using the classic "unfiltered" keywords list https://huggingface.co/datasets/anon8231489123/ShareGPT_Vicuna_unfiltered to remove instances of refusals and bias
About 90% of the dataset was removed.
What remains (400k conversations) is unlikely to inclinate the model to refuse.
I am investigating a less heavy handed approach using dolphin-2.1 to reword any detected refusals.
|
flyingfishinwater/ultrachat-uncensored
|
[
"license:mit",
"region:us"
] |
2023-10-25T20:11:07+00:00
|
{"license": "mit"}
|
2023-10-29T17:25:19+00:00
|
[] |
[] |
TAGS
#license-mit #region-us
|
Forked from: ehartford/ultrachat-uncensored
I have done the following upgrades:
1. Split the 'URL' into train, val and test files by 85:10:5 ratios
2. Add 'URL' loading script.
The following are original dataset card from ehartford:
-------------------------------------------------------------
This is based on ultrachat dataset URL
I filtered it using the classic "unfiltered" keywords list URL to remove instances of refusals and bias
About 90% of the dataset was removed.
What remains (400k conversations) is unlikely to inclinate the model to refuse.
I am investigating a less heavy handed approach using dolphin-2.1 to reword any detected refusals.
|
[] |
[
"TAGS\n#license-mit #region-us \n"
] |
[
11
] |
[
"passage: TAGS\n#license-mit #region-us \n"
] |
a802b1d8bf48246a5a5c29e1ebab313954559c88
|
I tried to create this by creating a dataset in python using dataset = Dataset.from_pandas(df). My goal is to then see if I can load it into an autotrain model.
|
AnnieEl/1300q_ATP
|
[
"license:mit",
"region:us"
] |
2023-10-25T20:52:38+00:00
|
{"license": "mit"}
|
2023-10-25T20:54:24+00:00
|
[] |
[] |
TAGS
#license-mit #region-us
|
I tried to create this by creating a dataset in python using dataset = Dataset.from_pandas(df). My goal is to then see if I can load it into an autotrain model.
|
[] |
[
"TAGS\n#license-mit #region-us \n"
] |
[
11
] |
[
"passage: TAGS\n#license-mit #region-us \n"
] |
97a3908f968acdf10b2482e592921207a9363668
|
# Dataset Card for "french-bench-grammar-vocab-reading"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
manu/french-bench-grammar-vocab-reading
|
[
"region:us"
] |
2023-10-25T21:03:38+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "Grammar", "path": "data/Grammar-*"}, {"split": "Vocabulary", "path": "data/Vocabulary-*"}, {"split": "Reading", "path": "data/Reading-*"}]}], "dataset_info": {"features": [{"name": "question", "dtype": "string"}, {"name": "answer", "dtype": "string"}, {"name": "context", "dtype": "string"}, {"name": "difficulty", "dtype": "string"}, {"name": "language", "dtype": "string"}, {"name": "subject", "dtype": "string"}, {"name": "url", "dtype": "string"}, {"name": "answerA", "dtype": "string"}, {"name": "answerB", "dtype": "string"}, {"name": "answerC", "dtype": "string"}, {"name": "answerD", "dtype": "string"}, {"name": "__index_level_0__", "dtype": "int64"}], "splits": [{"name": "Grammar", "num_bytes": 29094, "num_examples": 119}, {"name": "Vocabulary", "num_bytes": 30944, "num_examples": 119}, {"name": "Reading", "num_bytes": 115507, "num_examples": 71}], "download_size": 0, "dataset_size": 175545}}
|
2023-10-26T13:19:28+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "french-bench-grammar-vocab-reading"
More Information needed
|
[
"# Dataset Card for \"french-bench-grammar-vocab-reading\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"french-bench-grammar-vocab-reading\"\n\nMore Information needed"
] |
[
6,
25
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"french-bench-grammar-vocab-reading\"\n\nMore Information needed"
] |
0af1a89b9ad10c12cf33414edeb6667039f42d41
|
# Dataset Card for "alt_dnd"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
mickume/alt_dnd
|
[
"region:us"
] |
2023-10-25T21:59:53+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 61650936, "num_examples": 273644}], "download_size": 38262297, "dataset_size": 61650936}}
|
2023-10-25T22:00:05+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "alt_dnd"
More Information needed
|
[
"# Dataset Card for \"alt_dnd\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"alt_dnd\"\n\nMore Information needed"
] |
[
6,
14
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"alt_dnd\"\n\nMore Information needed"
] |
6f42d9210d850aff1b178fc9bd50aa63fa65b61f
|
# Dataset Card for Dataset Name
Database for lego sorter model uploaded <br>
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).
## Dataset Details
### Dataset Description
Sample database for the my lego sorter model uploaded <br>
Contains both sample images from each class as well as a numpy array file (.npy) that contains every image (~6000) used to train the model. The numpy file was created so that the dataset could be loaded into Google Collab.
- **Curated by:** Aveek Goswami, Amos Koh
### Dataset Sources
- **Repository:** https://github.com/magichampz/lego-sorting-machine-ag-ak
## Uses
Dataset may be used to train any machine learning model.
### Direct Use
Best use for this dataset is to train a model with similar architecture to the lego sorter model I uploaded. Dataset images designed to be classified into 7 distinct lego technic classes
## Dataset Structure
database-sample contains 7 folders, each containing images from different categories of lego technic pieces. <br>
A .npy file is also uploaded, which has a shape of (5953,2), which means 5953 entries, with each entry containing the full image as one data point and the category label as the other data point.
### Source Data
<!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). -->
#### Data Collection and Processing
All images were not processed, they were stored as the original image both in the folders and the numpy array. Image processing occurs in the odel training script uploaded as part of the lego sorter model repo
### Recommendations
All images were taken under constant lighting conditions with a raspberry PiCamera 2, which limited the quality of the images obtained.
|
magichampz/lego-technic-pieces
|
[
"license:mit",
"region:us"
] |
2023-10-25T22:03:49+00:00
|
{"license": "mit"}
|
2023-10-26T16:02:54+00:00
|
[] |
[] |
TAGS
#license-mit #region-us
|
# Dataset Card for Dataset Name
Database for lego sorter model uploaded <br>
This dataset card aims to be a base template for new datasets. It has been generated using this raw template.
## Dataset Details
### Dataset Description
Sample database for the my lego sorter model uploaded <br>
Contains both sample images from each class as well as a numpy array file (.npy) that contains every image (~6000) used to train the model. The numpy file was created so that the dataset could be loaded into Google Collab.
- Curated by: Aveek Goswami, Amos Koh
### Dataset Sources
- Repository: URL
## Uses
Dataset may be used to train any machine learning model.
### Direct Use
Best use for this dataset is to train a model with similar architecture to the lego sorter model I uploaded. Dataset images designed to be classified into 7 distinct lego technic classes
## Dataset Structure
database-sample contains 7 folders, each containing images from different categories of lego technic pieces. <br>
A .npy file is also uploaded, which has a shape of (5953,2), which means 5953 entries, with each entry containing the full image as one data point and the category label as the other data point.
### Source Data
#### Data Collection and Processing
All images were not processed, they were stored as the original image both in the folders and the numpy array. Image processing occurs in the odel training script uploaded as part of the lego sorter model repo
### Recommendations
All images were taken under constant lighting conditions with a raspberry PiCamera 2, which limited the quality of the images obtained.
|
[
"# Dataset Card for Dataset Name\nDatabase for lego sorter model uploaded <br>\n\nThis dataset card aims to be a base template for new datasets. It has been generated using this raw template.",
"## Dataset Details",
"### Dataset Description\nSample database for the my lego sorter model uploaded <br>\nContains both sample images from each class as well as a numpy array file (.npy) that contains every image (~6000) used to train the model. The numpy file was created so that the dataset could be loaded into Google Collab.\n- Curated by: Aveek Goswami, Amos Koh",
"### Dataset Sources\n- Repository: URL",
"## Uses\nDataset may be used to train any machine learning model.",
"### Direct Use\nBest use for this dataset is to train a model with similar architecture to the lego sorter model I uploaded. Dataset images designed to be classified into 7 distinct lego technic classes",
"## Dataset Structure\ndatabase-sample contains 7 folders, each containing images from different categories of lego technic pieces. <br>\nA .npy file is also uploaded, which has a shape of (5953,2), which means 5953 entries, with each entry containing the full image as one data point and the category label as the other data point.",
"### Source Data",
"#### Data Collection and Processing\nAll images were not processed, they were stored as the original image both in the folders and the numpy array. Image processing occurs in the odel training script uploaded as part of the lego sorter model repo",
"### Recommendations\nAll images were taken under constant lighting conditions with a raspberry PiCamera 2, which limited the quality of the images obtained."
] |
[
"TAGS\n#license-mit #region-us \n",
"# Dataset Card for Dataset Name\nDatabase for lego sorter model uploaded <br>\n\nThis dataset card aims to be a base template for new datasets. It has been generated using this raw template.",
"## Dataset Details",
"### Dataset Description\nSample database for the my lego sorter model uploaded <br>\nContains both sample images from each class as well as a numpy array file (.npy) that contains every image (~6000) used to train the model. The numpy file was created so that the dataset could be loaded into Google Collab.\n- Curated by: Aveek Goswami, Amos Koh",
"### Dataset Sources\n- Repository: URL",
"## Uses\nDataset may be used to train any machine learning model.",
"### Direct Use\nBest use for this dataset is to train a model with similar architecture to the lego sorter model I uploaded. Dataset images designed to be classified into 7 distinct lego technic classes",
"## Dataset Structure\ndatabase-sample contains 7 folders, each containing images from different categories of lego technic pieces. <br>\nA .npy file is also uploaded, which has a shape of (5953,2), which means 5953 entries, with each entry containing the full image as one data point and the category label as the other data point.",
"### Source Data",
"#### Data Collection and Processing\nAll images were not processed, they were stored as the original image both in the folders and the numpy array. Image processing occurs in the odel training script uploaded as part of the lego sorter model repo",
"### Recommendations\nAll images were taken under constant lighting conditions with a raspberry PiCamera 2, which limited the quality of the images obtained."
] |
[
11,
45,
4,
90,
12,
15,
45,
85,
4,
56,
34
] |
[
"passage: TAGS\n#license-mit #region-us \n# Dataset Card for Dataset Name\nDatabase for lego sorter model uploaded <br>\n\nThis dataset card aims to be a base template for new datasets. It has been generated using this raw template.## Dataset Details### Dataset Description\nSample database for the my lego sorter model uploaded <br>\nContains both sample images from each class as well as a numpy array file (.npy) that contains every image (~6000) used to train the model. The numpy file was created so that the dataset could be loaded into Google Collab.\n- Curated by: Aveek Goswami, Amos Koh### Dataset Sources\n- Repository: URL## Uses\nDataset may be used to train any machine learning model.### Direct Use\nBest use for this dataset is to train a model with similar architecture to the lego sorter model I uploaded. Dataset images designed to be classified into 7 distinct lego technic classes## Dataset Structure\ndatabase-sample contains 7 folders, each containing images from different categories of lego technic pieces. <br>\nA .npy file is also uploaded, which has a shape of (5953,2), which means 5953 entries, with each entry containing the full image as one data point and the category label as the other data point.### Source Data#### Data Collection and Processing\nAll images were not processed, they were stored as the original image both in the folders and the numpy array. Image processing occurs in the odel training script uploaded as part of the lego sorter model repo### Recommendations\nAll images were taken under constant lighting conditions with a raspberry PiCamera 2, which limited the quality of the images obtained."
] |
647185e4e1ba229605b7e372e68e5bb36e969c1d
|
# Dataset Card for "emotion_bn"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
amintalukder/emotion_bn
|
[
"region:us"
] |
2023-10-25T22:10:08+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "val", "path": "data/val-*"}, {"split": "test", "path": "data/test-*"}, {"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "ID", "dtype": "int64"}, {"name": "Data", "dtype": "string"}, {"name": "Love", "dtype": "int64"}, {"name": "Joy", "dtype": "int64"}, {"name": "Surprise", "dtype": "int64"}, {"name": "Anger", "dtype": "int64"}, {"name": "Sadness", "dtype": "int64"}, {"name": "Fear", "dtype": "int64"}, {"name": "Topic", "dtype": "string"}, {"name": "Domain", "dtype": "string"}, {"name": "is_admin", "dtype": "bool"}], "splits": [{"name": "val", "num_bytes": 503282, "num_examples": 2047}, {"name": "test", "num_bytes": 545033, "num_examples": 2272}, {"name": "train", "num_bytes": 4408992, "num_examples": 18420}], "download_size": 1882715, "dataset_size": 5457307}}
|
2023-10-25T22:15:07+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "emotion_bn"
More Information needed
|
[
"# Dataset Card for \"emotion_bn\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"emotion_bn\"\n\nMore Information needed"
] |
[
6,
14
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"emotion_bn\"\n\nMore Information needed"
] |
72030318b7fa866da0f9893ffac215cb33db1054
|
# Dataset Card for "emailchaser-llm-body-data-v0.0.1"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
gmaijoe-emailchaser/emailchaser-llm-body-data-v0.0.1
|
[
"region:us"
] |
2023-10-25T22:15:19+00:00
|
{"dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 611717, "num_examples": 404}], "download_size": 162582, "dataset_size": 611717}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-10-25T22:15:21+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "emailchaser-llm-body-data-v0.0.1"
More Information needed
|
[
"# Dataset Card for \"emailchaser-llm-body-data-v0.0.1\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"emailchaser-llm-body-data-v0.0.1\"\n\nMore Information needed"
] |
[
6,
25
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"emailchaser-llm-body-data-v0.0.1\"\n\nMore Information needed"
] |
085ded9a1b4f7c4a8cd4cc223a0754c53a48c842
|
# Dataset Card for "filtered-orca"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
iamnguyen/filtered-orca
|
[
"region:us"
] |
2023-10-25T22:15:51+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "system_prompt", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "response", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1194215300.0749996, "num_examples": 1035833}], "download_size": 632988844, "dataset_size": 1194215300.0749996}}
|
2023-10-26T03:48:14+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "filtered-orca"
More Information needed
|
[
"# Dataset Card for \"filtered-orca\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"filtered-orca\"\n\nMore Information needed"
] |
[
6,
15
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"filtered-orca\"\n\nMore Information needed"
] |
b5fc28dbf88a501dc7d94a7d31eaf0059537bef8
|
# Dataset Card for "misinfo-clusters"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
Tverous/misinfo-clusters
|
[
"region:us"
] |
2023-10-25T22:19:32+00:00
|
{"dataset_info": {"features": [{"name": "cluster_id", "dtype": "string"}, {"name": "doc_id", "dtype": "string"}, {"name": "main_text", "dtype": "string"}, {"name": "image", "dtype": "image"}, {"name": "video", "dtype": "string"}, {"name": "audio", "dtype": "string"}, {"name": "kg_embedding", "sequence": {"sequence": "float32"}}], "splits": [{"name": "train", "num_bytes": 198086.0, "num_examples": 1}], "download_size": 177860, "dataset_size": 198086.0}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-10-26T02:13:52+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "misinfo-clusters"
More Information needed
|
[
"# Dataset Card for \"misinfo-clusters\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"misinfo-clusters\"\n\nMore Information needed"
] |
[
6,
15
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"misinfo-clusters\"\n\nMore Information needed"
] |
611f312942cfa5023a96148d4605458b88c2ab21
|
# Dataset Card for "affixal_negation_polarity_tmp"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
joey234/affixal_negation_polarity_tmp
|
[
"region:us"
] |
2023-10-25T22:23:29+00:00
|
{"dataset_info": {"features": [{"name": "word", "dtype": "string"}, {"name": "neg_score", "dtype": "float64"}, {"name": "pos_score", "dtype": "float64"}, {"name": "label", "dtype": "int64"}, {"name": "checked", "dtype": "string"}, {"name": "thinh", "dtype": "int64"}, {"name": "__index_level_0__", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 273442, "num_examples": 4144}], "download_size": 84067, "dataset_size": 273442}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-11-23T02:47:09+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "affixal_negation_polarity_tmp"
More Information needed
|
[
"# Dataset Card for \"affixal_negation_polarity_tmp\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"affixal_negation_polarity_tmp\"\n\nMore Information needed"
] |
[
6,
22
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"affixal_negation_polarity_tmp\"\n\nMore Information needed"
] |
c5c3a33d26904628fe42b8652a3743077ea8c73b
|
# Dataset Card for "generadai-sample"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
Bsbell21/generadai-sample
|
[
"region:us"
] |
2023-10-25T22:45:23+00:00
|
{"dataset_info": {"features": [{"name": "item", "dtype": "string"}, {"name": "description", "dtype": "string"}, {"name": "ad", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 3915, "num_examples": 5}], "download_size": 7989, "dataset_size": 3915}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-10-25T22:45:25+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "generadai-sample"
More Information needed
|
[
"# Dataset Card for \"generadai-sample\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"generadai-sample\"\n\nMore Information needed"
] |
[
6,
15
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"generadai-sample\"\n\nMore Information needed"
] |
6d7c5704871742022dd2e1a7588dafcbe0e29ed2
|
# Dataset Card for "prm800k-llama"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
parksimon0808/prm800k-llama-verifier
|
[
"region:us"
] |
2023-10-25T23:09:40+00:00
|
{"dataset_info": {"features": [{"name": "texts", "dtype": "string"}, {"name": "input_ids", "sequence": "int32"}, {"name": "labels", "sequence": "int64"}], "splits": [{"name": "train", "num_bytes": 4528067256, "num_examples": 1052294}, {"name": "test", "num_bytes": 145143622, "num_examples": 32408}], "download_size": 353282233, "dataset_size": 4673210878}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "test", "path": "data/test-*"}]}]}
|
2023-12-05T20:52:04+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "prm800k-llama"
More Information needed
|
[
"# Dataset Card for \"prm800k-llama\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"prm800k-llama\"\n\nMore Information needed"
] |
[
6,
17
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"prm800k-llama\"\n\nMore Information needed"
] |
45fb5056ed167dfbaf609c4d36f7c04f3dec09e3
|
# Dataset Card for "pedestrian-dataset"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
roupenminassian/pedestrian-dataset
|
[
"region:us"
] |
2023-10-25T23:19:02+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "image_id", "dtype": "int64"}, {"name": "width", "dtype": "int64"}, {"name": "height", "dtype": "int64"}, {"name": "objects", "struct": [{"name": "id", "sequence": "int64"}, {"name": "area", "sequence": "float64"}, {"name": "bbox", "sequence": {"sequence": "float64"}}, {"name": "category", "sequence": "int64"}]}], "splits": [{"name": "train", "num_bytes": 26451276.0, "num_examples": 78}], "download_size": 26448000, "dataset_size": 26451276.0}}
|
2023-10-25T23:19:16+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "pedestrian-dataset"
More Information needed
|
[
"# Dataset Card for \"pedestrian-dataset\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"pedestrian-dataset\"\n\nMore Information needed"
] |
[
6,
16
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"pedestrian-dataset\"\n\nMore Information needed"
] |
d5f73da0f1d1c0e65ccdf9285e20baafa4afe6df
|
# Dataset Card for "vt_multiapi_v1"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
hmao/vt_multiapi_v1
|
[
"region:us"
] |
2023-10-25T23:55:50+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "fncall", "sequence": "string"}, {"name": "n_iteration", "dtype": "string"}, {"name": "generated_question", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 31565, "num_examples": 78}], "download_size": 14976, "dataset_size": 31565}}
|
2023-10-26T00:00:03+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "vt_multiapi_v1"
More Information needed
|
[
"# Dataset Card for \"vt_multiapi_v1\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"vt_multiapi_v1\"\n\nMore Information needed"
] |
[
6,
17
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"vt_multiapi_v1\"\n\nMore Information needed"
] |
e04bdecaa958f3429e7e7d26f1e77831602e5da5
|
### Getting Started
RedPajama-V2 is an open dataset for training large language models. The dataset includes over 100B text
documents coming from 84 CommonCrawl snapshots and processed using
the [CCNet](https://github.com/facebookresearch/cc_net) pipeline. Out of these, there are 30B documents in the corpus
that additionally come with quality signals. In addition, we also provide the ids of duplicated documents which can be
used to create a dataset with 20B deduplicated documents.
Check out our [blog post](https://together.ai/blog/redpajama-data-v2) for more details on the build process, dataset
structure and schema.
A full set of scripts to recreate the dataset, including the quality signals, can be
found [here](https://github.com/togethercomputer/RedPajama-Data).
#### Downloading the raw Dataset with Quality Annotations
To familiarize yourself with the dataset, you can load the sample dataset using:
```python
from datasets import load_dataset
ds = load_dataset("togethercomputer/RedPajama-Data-V2", name="sample")
```
To download a the dataset for a specific combination of `{partition} x {snapshot_id} x {language}`, you can use the
following command which downloads the raw (i.e., *not* deduplicated) part of the dataset and the corresponding quality
signals. In the example below, we use English and German data from the `head_middle` partition of the 2023-06 and the
2022-49 snapshots. The full set of available snapshots is specified in `_CC_SNAPSHOT_IDS`. The available partitions
are `tail` and `head_middle`. The available language tags are `en`, `de`, `fr`, `es`, `it`.
_Note that this will download the entire snapshots specified in the `snapshots` argument and requires ~1TB of disk space
per snapshot_.
```python
from datasets import load_dataset
ds = load_dataset("togethercomputer/RedPajama-Data-V2",
name="default",
partition="head_middle",
snapshots=["2023-06", "2022-49"],
languages=["en", "de"])
```
#### Downloading the dataset via wget
If you prefer to download the full dataset via wget, you can download the following lists of urls and use them to
download the dataset:
```bash
# get list of urls pointing to the text documents
wget "https://data.together.xyz/redpajama-data-v2/v1.0.0/urls/document-urls.txt" -O "document-urls.txt"
# get list of urls pointing to the quality signals
wget "https://data.together.xyz/redpajama-data-v2/v1.0.0/urls/quality_signals-urls.txt" -O "quality_signals-urls.txt"
# get list of urls pointing to the ids of duplicate documents
wget "https://data.together.xyz/redpajama-data-v2/v1.0.0/urls/duplicates-urls.txt" -O "duplicates-urls.txt"
# get list of urls pointing to the minhash signatures
wget "https://data.together.xyz/redpajama-data-v2/v1.0.0/urls/minhash-urls.txt" -O "minhash-urls.txt"
```
You can also directly download subsets of the dataset using the following instructions. Here we use English
data from the `2023-06` snapshot and the `head_middle` partition as an example. The full set of CC snapshots included in
the dataset is given in `_CC_SNAPSHOT_IDS`. The available partitions are `tail` and `head_middle`. The available
language tags are `en`, `de`, `fr`, `es`, `it`.
To download the plain text data, available for both the `head_middle` and `tail` partitions, you can run
```bash
CC_SNAPSHOT="2023-06"
LANG="en"
PARTITION="head_middle"
BASE_URL="https://data.together.xyz/redpajama-data-v2/v1.0.0"
listings_tag="${LANG}-${CC_SNAPSHOT}-${PARTITION}"
mkdir listings
wget "${BASE_URL}/listings/${listings_tag}.txt" -O "listings/${listings_tag}.txt"
listings_file="listings/${listings_tag}.txt"
# download documents
while read line; do
url="${BASE_URL}/documents/${line}.json.gz"
dest="documents/${line}.json.gz"
mkdir -p $(dirname $dest)
wget "$url" -O "$dest"
done <"$listings_file"
```
In addition, for the `head_middle` partition, you can also download the quality signals, minhash signatures and
duplicate ids using the following commands:
```bash
CC_SNAPSHOT="2023-06"
LANG="en"
BASE_URL="https://data.together.xyz/redpajama-data-v2/v1.0.0"
listings_tag="${LANG}-${CC_SNAPSHOT}-head_middle"
mkdir listings
wget "${BASE_URL}/listings/${listings_tag}.txt" -O "listings/${listings_tag}.txt"
listings_file="listings/${listings_tag}.txt"
# download quality signals
while read line; do
url="${BASE_URL}/quality_signals/${line}.signals.json.gz"
dest="quality_signals/${line}.signals.json.gz"
mkdir -p $(dirname $dest)
wget "$url" -O "$dest"
done <"$listings_file"
# download other components
COMPS=("minhash" "duplicates")
for comp in "${COMPS[@]}"; do
while read line; do
url="${BASE_URL}/${comp}/${line}.${comp}.parquet"
dest="${comp}/${line}.${comp}.parquet"
mkdir -p $(dirname $dest)
wget "$url" -O "$dest"
done <"$listings_file"
done
```
### Applying Filtering Rules
You can use the quality signals to filter the raw RedPajama-V2 dataset for a given set of rules. For example, consider
the following set of rules used in Gopher:
```python
def gopher_rules_pass(sample) -> bool:
""" function returns True if the sample complies with Gopher rules """
signals = json.loads(sample["quality_signals"])
# rule 1: number of words between 50 and 10'000
word_count = signals["rps_doc_word_count"][0][2]
if word_count < 50 or word_count > 100_000:
return False
# rule 2: mean word length between 3 and 10
mean_word_length = signals["rps_doc_mean_word_length"][0][2]
if mean_word_length < 3 or mean_word_length > 10:
return False
# rule 2: symbol to word ratio below 0.1
symbol_word_ratio = signals["rps_doc_symbol_to_word_ratio"][0][2]
if symbol_word_ratio > 0.1:
return False
# rule 3: 90% of lines need to start without a bullet point
n_lines = signals["ccnet_nlines"][0][2]
n_lines_bulletpoint_start = sum(map(lambda ln: ln[2], signals["rps_lines_start_with_bulletpoint"]))
if n_lines_bulletpoint_start / n_lines > 0.9:
return False
# rule 4: the ratio between characters in the most frequent 2-gram and the total number
# of characters must be below 0.2
top_2_gram_frac = signals["rps_doc_frac_chars_top_2gram"][0][2]
if top_2_gram_frac > 0.2:
return False
# rule 5: ...
return True
```
Filtering the RedPajama-V2 dataset with this set of rules is then as easy as:
```python
ds_iterator = load_dataset(
"togethercomputer/RedPajama-Data-V2",
snapshots=["2023-14"],
languages=["en"],
name="default",
streaming=True
)
filtered_dataset = []
for sample in ds_iterator["train"]:
if not gopher_rules_pass(sample):
continue
filtered_dataset.append(sample)
```
### Dataset Summary
RedPajama-V2 is an open dataset for training large language models and includes over 100B text documents. Out of these,
30B documents come with quality annotations. Out of these, there are 20B unique documents.
#### Quality Annotations
| Annotation Tag | Description | Category | Reference |
|------------------------------------------------|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|------------------|-------------------------------------------------------------------------------------------------------------------------------|
| ccnet_bucket | head, middle or tail bucket of the perplexity score | CCNet | [CCNet](https://github.com/facebookresearch/cc_net) |
| ccnet_language_score | score of the language identification model | CCNet | [CCNet](https://github.com/facebookresearch/cc_net) |
| ccnet_length | number of characters | CCNet | [CCNet](https://github.com/facebookresearch/cc_net) |
| ccnet_nlines | number of lines | CCNet | [CCNet](https://github.com/facebookresearch/cc_net) |
| ccnet_original_length | number of characters before line-level deduplication | CCNet | [CCNet](https://github.com/facebookresearch/cc_net) |
| ccnet_original_nlines | number of lines before line-level deduplication | CCNet | [CCNet](https://github.com/facebookresearch/cc_net) |
| ccnet_perplexity | perplexity of an LM trained on Wikipedia | CCNet | [CCNet](https://github.com/facebookresearch/cc_net) |
| rps_doc_books_importance | Given a bag of {1,2}-wordgram model trained on Books p, and a model trained on the source domain q, This is the logarithm of the ratio p(doc)/q(doc). | ML Heuristics | [Importance Resampling (Xie et al.)](https://arxiv.org/abs/2302.03169) |
| rps_doc_openwebtext_importance | Given a bag of {1,2}-wordgram model trained on OpenWebText p, and a model trained on the source domain q, this is the logarithm of the ratio p(doc)/q(doc). | ML Heuristics | [Importance Resampling (Xie et al.)](https://arxiv.org/abs/2302.03169) |
| rps_doc_wikipedia_importance | Given a bag of {1,2}-wordgram model trained on Wikipedia articles p, and a model trained on the source domain q, this is the logarithm of the ratio p(doc)/q(doc). | ML Heuristics | [Importance Resampling (Xie et al.)](https://arxiv.org/abs/2302.03169) |
| rps_doc_ml_wikiref_score | Fasttext classifier prediction for the document being a Wikipedia reference. This is the same fasttext model used in the RedPajama-1T dataset. Only applies to English data.. | ML Heuristics | [LLaMA](https://arxiv.org/abs/2302.13971), [RedPajama-1T](https://huggingface.co/datasets/togethercomputer/RedPajama-Data-1T) |
| rps_doc_ml_palm_score | Fasttext classifier prediction for the document being a Wikipedia article, OpenWebText sample or a RedPajama-V1 book. Only for English data. | ML Heuristics | [PALM](https://arxiv.org/abs/2204.02311), [GLaM](https://arxiv.org/abs/2112.06905) |
| rps_doc_ml_wikipedia_score | Fasttext classifier prediction for the document being a Wikipedia article. This is used for non-English data | ML Heuristics | - |
| rps_doc_curly_bracket | The ratio between the number of occurrences of '{' or '}' and the number of characters in the raw text. | Natural Language | [C4](https://arxiv.org/abs/1910.10683) |
| rps_doc_frac_all_caps_words | The fraction of words in the content that only consist of uppercase letters. This is based on the raw content. | Natural Language | [Pretrainer’s Guide](https://arxiv.org/abs/2305.13169) |
| rps_doc_frac_lines_end_with_ellipsis | The fraction of lines that end with an ellipsis, where an ellipsis is defined as either "..." or "…". | Natural Language | [RefinedWeb](https://arxiv.org/abs/2306.01116), [Gopher](https://arxiv.org/abs/2112.11446) |
| rps_doc_frac_no_alph_words | The fraction of words that contain no alphabetical character. | Natural Language | [RefinedWeb](https://arxiv.org/abs/2306.01116), [Gopher](https://arxiv.org/abs/2112.11446) |
| rps_doc_lorem_ipsum | The ratio between the number of occurrences of 'lorem ipsum' and the number of characters in the content after normalisation. | Natural Language | [C4](https://arxiv.org/abs/1910.10683) |
| rps_doc_mean_word_length | The mean length of words in the content after normalisation. | Natural Language | [RefinedWeb](https://arxiv.org/abs/2306.01116), [Gopher](https://arxiv.org/abs/2112.11446) |
| rps_doc_stop_word_fraction | The ratio between the number of stop words and the number of words in the document. Stop words are obtained from the [stopwords-json](https://github.com/6/stopwords-json) repo. | Natural Language | [RefinedWeb](https://arxiv.org/abs/2306.01116), [Gopher](https://arxiv.org/abs/2112.11446) |
| rps_doc_symbol_to_word_ratio | The ratio of symbols to words in the content.. Symbols are defined "#", "...", and "…". | Natural Language | [RefinedWeb](https://arxiv.org/abs/2306.01116), [Gopher](https://arxiv.org/abs/2112.11446) |
| rps_doc_frac_unique_words | The fraction of unique words in the content. This is also known as the degeneracy of a text sample. Calculated based on the normalised content. | Natural Language | [Pretrainer’s Guide](https://arxiv.org/abs/2305.13169) |
| rps_doc_unigram_entropy | The entropy of the unigram distribution of the content. This measures the diversity of the content and is computed using sum(-x / total * log(x / total)) where the sum is taken over counts of unique words in the normalised content. | Natural Language | - |
| rps_doc_word_count | The number of words in the content after normalisation. | Natural Language | [RefinedWeb](https://arxiv.org/abs/2306.01116), [Gopher](https://arxiv.org/abs/2112.11446) |
| rps_lines_ending_with_terminal_punctution_mark | Indicates whether a line ends with a terminal punctuation mark. A terminal punctation mark is defined as one of: ".", "!", "?", "”". | Natural Language | [C4](https://arxiv.org/abs/1910.10683) |
| rps_lines_javascript_counts | The number of occurrences of the word "javascript" in each line. | Natural Language | [C4](https://arxiv.org/abs/1910.10683) |
| rps_lines_num_words | The number of words in each line. This is computed based on the normalised text. | Natural Language | [C4](https://arxiv.org/abs/1910.10683) , [RefinedWeb](https://arxiv.org/abs/2306.01116) |
| rps_lines_numerical_chars_fraction | The ratio between the number of numerical characters and total number of characters in each line. This is based on the normalised content. | Natural Language | [RefinedWeb](https://arxiv.org/abs/2306.01116) |
| rps_lines_start_with_bulletpoint | Whether the lines that start with a bullet point symbol. The following set of unicodes are considered a bullet point: \u2022 (bullet point), \u2023 (triangular bullet point), \u25B6 (black right pointing triangle), \u25C0 (black left pointing triangle), \u25E6 (white bullet point), \u25A0 (black square), \u25A1 (white square), \u25AA (black small square), \u25AB (white small square), \u2013 (en dash). | Natural Language | [RefinedWeb](https://arxiv.org/abs/2306.01116), [Gopher](https://arxiv.org/abs/2112.11446) |
| rps_lines_uppercase_letter_fraction | The ratio between the number of uppercase letters and total number of characters in each line. This is based on the raw text. | Natural Language | [RefinedWeb](https://arxiv.org/abs/2306.01116) |
| rps_doc_num_sentences | The number of sentences in the content. This is calculated using the regular expression `r'\b[^.!?]+[.!?]*'`. | Natural Language | [C4](https://arxiv.org/abs/1910.10683) |
| rps_doc_frac_chars_dupe_10grams | The fraction of characters in duplicate word 10grams. This operates on the lower-cased, punctuation removed content. It is also ensured that characters in overlapping ngrams are only counted once. | Repetitiveness | [RefinedWeb](https://arxiv.org/abs/2306.01116), [Gopher](https://arxiv.org/abs/2112.11446) |
| rps_doc_frac_chars_dupe_5grams | The fraction of characters in duplicate word 5grams. | Repetitiveness | [RefinedWeb](https://arxiv.org/abs/2306.01116), [Gopher](https://arxiv.org/abs/2112.11446) |
| rps_doc_frac_chars_dupe_6grams | The fraction of characters in duplicate word 6grams. | Repetitiveness | [RefinedWeb](https://arxiv.org/abs/2306.01116), [Gopher](https://arxiv.org/abs/2112.11446) |
| rps_doc_frac_chars_dupe_7grams | The fraction of characters in duplicate word 7grams. | Repetitiveness | [RefinedWeb](https://arxiv.org/abs/2306.01116), [Gopher](https://arxiv.org/abs/2112.11446) |
| rps_doc_frac_chars_dupe_8grams | The fraction of characters in duplicate word 8grams. | Repetitiveness | [RefinedWeb](https://arxiv.org/abs/2306.01116), [Gopher](https://arxiv.org/abs/2112.11446) |
| rps_doc_frac_chars_dupe_9grams | The fraction of characters in duplicate word 9grams. | Repetitiveness | [RefinedWeb](https://arxiv.org/abs/2306.01116), [Gopher](https://arxiv.org/abs/2112.11446) |
| rps_doc_frac_chars_top_2gram | The fraction of characters in the top word 2gram. | Repetitiveness | [RefinedWeb](https://arxiv.org/abs/2306.01116), [Gopher](https://arxiv.org/abs/2112.11446) |
| rps_doc_frac_chars_top_3gram | The fraction of characters in the top word 3gram. | Repetitiveness | [RefinedWeb](https://arxiv.org/abs/2306.01116), [Gopher](https://arxiv.org/abs/2112.11446) |
| rps_doc_frac_chars_top_4gram | The fraction of characters in the top word 4gram. | Repetitiveness | [RefinedWeb](https://arxiv.org/abs/2306.01116), [Gopher](https://arxiv.org/abs/2112.11446) |
| rps_doc_ldnoobw_words | The number of sequences of words that are contained in the List-of-Dirty-Naughty-Obscene-and-Otherwise-Bad-Words blocklist. The blocklist is obtained from the [LDNOOBW](https://github.com/LDNOOBW/List-of-Dirty-Naughty-Obscene-and-Otherwise-Bad-Words) repo. | toxicity | [C4](https://arxiv.org/abs/1910.10683) |
| rps_doc_ut1_blacklist | A categorical id corresponding to the list of categories of the domain of the document. Categories are obtained from the UT1 blacklist. The list is obtained from [UT-Capitole](https://dsi.ut-capitole.fr/blacklists/). | toxicictiy | [RefinedWeb](https://arxiv.org/abs/2306.01116) |
| minhash_signature_0.7 | Banded minhash signature of the document, for fuzzy deduplication at Jaccard similarity 0.7. The signature is based on 128 hash functions and grouped into 14 bands and 9 rows for LSH. | Deduplication |
| minhash_signature_0.8 | Banded minhash signature of the document, for fuzzy deduplication at Jaccard similarity 0.8. The signature is based on 128 hash functions and grouped into 9 bands and 13 rows for LSH. | Deduplication |
| minhash_signature_0.9 | Banded minhash signature of the document, for fuzzy deduplication at Jaccard similarity 0.9. The signature is based on 128 hash functions and grouped into 5 bands and 25 rows for LSH.. | Deduplication |
| minhash_signature_1.0 | Banded minhash signature of the document, for fuzzy deduplication at Jaccard similarity 1.0. The signature is based on 128 hash functions and grouped into 1 band and 128 rows for LSH. | Deduplication |
The quality signal `rps_doc_ut1_blacklist` is given by a categorical id indicating the UT1 blacklisted
domain categories to which the domain of the document belongs. The mapping `id -> [category_1, ..., category_k]` is given in
`ut1_domain_categories.json`. It can also be downloaded from this [link](https://data.together.xyz/redpajama-data-v2/v1.0.0/artifacts/ut1_domain_categories.json).
#### Raw Document and Token Counts (`head_middle`)
| | # Documents (deduped) | Estimated Token count (deduped) |
|-------|-----------------------|---------------------------------|
| en | 24.5B | 37.0T |
| de | 2.7B | 4.1T |
| fr | 2.2B | 3.7T |
| es | 2.3B | 3.9T |
| it | 1.2B | 1.9T |
| Total | 32.9B | 50.6T |
#### Deduplicated Document and Token Counts (`head_middle`)
| | # Documents (total) | Estimated Token count (total) |
|-------|---------------------|-------------------------------|
| en | 14.5B | 20.5T |
| de | 1.9B | 3.0T |
| fr | 1.6B | 2.7T |
| es | 1.8B | 2.8T |
| it | 0.9B | 1.5T |
| Total | 20.8B | 30.4T |
### Languages
English, German, French, Italian, Spanish
## Dataset Structure
The dataset is structured into four components, each following the same key structure:
```
├── documents
├── 2018-43
├── 0000
├── en_head.json.gz
├── ...
├── it_middle.json.gz
├── quality_signals
├── 2018-43
├── 0000
├── en_head.signals.json.gz
├── ...
├── it_middle.json.gz
├── duplicates
├── 2018-43
├── 0000
├── en_head.duplicates.parquet
├── ...
├── it_middle.duplicates.parquet
├── minhash
├── 2018-43
├── 0000
├── en_head.minhash.parquet
├── ...
├── it_middle.minhash.parquet
```
Documents files, which contain the text, folow the schema defined by CCNet:
```json
{
"url": "...",
"date_download": "2014-08-20T06:48:26Z",
"digest": "sha1:46OPKWZ7MAG5624VYYA3U3YH2MJ727B6",
"length": 1095,
"nlines": 8,
"source_domain": "...",
"title": "...",
"raw_content": "Dear ...",
"cc_segment": "crawl-data/CC-MAIN-2014-35/...",
"original_nlines": 11,
"original_length": 1174,
"line_ids": [
0,
1,
3,
4,
6,
7,
8,
9
],
"language": "en",
"language_score": 0.92,
"perplexity": 217.2,
"bucket": "head"
}
```
The quality signals follow the schema
```json
{
"id": "2018-43/0000/en_head.json.gz/0",
"id_int": 7972430436813205988,
"metadata": {
"cc_segment": "crawl-data/...",
"cc_net_source": "2018-43/0000/en_head.json.gz",
"url": "...",
"source_domain": "...",
"language": "en",
"snapshot_id": "2018-43"
},
"quality_signals": {
"ccnet_original_length": [
[
0,
7033,
8711.0
]
],
...,
"rps_doc_stop_word_fraction": [
[
0,
7033,
0.45121107
]
],
"rps_lines_num_words": [
[
0,
25,
2
],
...,
[
6980,
7033,
10
]
]
}
}
```
where signal scores are encoded as a list of tuples `(start, end, score)`, where `start` and `end` are the locations in
the `raw_content` string where the `score` applies.
## Dataset Creation
The dataset is based on 84 snapshots provided by Common Crawl. Each snapshot was processed using the CCNet pipeline and
split into `head` `middle` `tail` buckets, depending on the perplexity score. In a second step, the documents in the
`head` and `middle` buckets were annotated with the quality signals described above. Finally, the documents were
deduplicated based on the text, using a Bloomfilter. The duplicates were kept in the dataset, but are marked in the
`duplicates` component.
## Citation
To cite RedPajama, please use:
```
@software{together2023redpajama,
author = {Together Computer},
title = {RedPajama: an Open Dataset for Training Large Language Models},
month = October,
year = 2023,
url = {https://github.com/togethercomputer/RedPajama-Data}
}
```
## Acknowledgements
We are appreciative to so many partners and collaborators that together are pushing forward the frontier of open LLM
models.
- Thank you to the OLMo team at AI2 and friends at OpenGPT-X for the insightful discussions about datasets and data
quality! Also for everyone who builds on the RedPajama dataset, including Cerebras for their SlimPajama efforts, and
the over 500 models built on RedPajam to date by the open-source AI community.
- We are grateful to the great team at EleutherAI for paving the path on open training datasets with The Pile and for
open-sourcing code we use in training some of the RedPajama models.
- Thank you to our partners of RedPajama-v1, including Ontocord.ai, MILA Québec AI Institute, ETH DS3Lab, Université de
Montréal, Stanford Center for Research on Foundation Models (CRFM), Stanford Hazy Research research group and LAION.
## License
Please refer to the [Common Crawl Foundation Terms of Use](https://commoncrawl.org/terms-of-use) for the data.
The code used to load and process the dataset is licensed under the Apache 2.0 license.
<!--
### 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]
-->
|
togethercomputer/RedPajama-Data-V2
|
[
"task_categories:text-generation",
"language:en",
"language:de",
"language:fr",
"language:es",
"language:it",
"arxiv:2302.03169",
"arxiv:2302.13971",
"arxiv:2204.02311",
"arxiv:2112.06905",
"arxiv:1910.10683",
"arxiv:2305.13169",
"arxiv:2306.01116",
"arxiv:2112.11446",
"region:us"
] |
2023-10-26T00:15:21+00:00
|
{"language": ["en", "de", "fr", "es", "it"], "task_categories": ["text-generation"], "pretty_name": "Red Pajama V2 Dataset"}
|
2024-01-18T15:32:36+00:00
|
[
"2302.03169",
"2302.13971",
"2204.02311",
"2112.06905",
"1910.10683",
"2305.13169",
"2306.01116",
"2112.11446"
] |
[
"en",
"de",
"fr",
"es",
"it"
] |
TAGS
#task_categories-text-generation #language-English #language-German #language-French #language-Spanish #language-Italian #arxiv-2302.03169 #arxiv-2302.13971 #arxiv-2204.02311 #arxiv-2112.06905 #arxiv-1910.10683 #arxiv-2305.13169 #arxiv-2306.01116 #arxiv-2112.11446 #region-us
|
### Getting Started
RedPajama-V2 is an open dataset for training large language models. The dataset includes over 100B text
documents coming from 84 CommonCrawl snapshots and processed using
the CCNet pipeline. Out of these, there are 30B documents in the corpus
that additionally come with quality signals. In addition, we also provide the ids of duplicated documents which can be
used to create a dataset with 20B deduplicated documents.
Check out our blog post for more details on the build process, dataset
structure and schema.
A full set of scripts to recreate the dataset, including the quality signals, can be
found here.
#### Downloading the raw Dataset with Quality Annotations
To familiarize yourself with the dataset, you can load the sample dataset using:
To download a the dataset for a specific combination of '{partition} x {snapshot\_id} x {language}', you can use the
following command which downloads the raw (i.e., *not* deduplicated) part of the dataset and the corresponding quality
signals. In the example below, we use English and German data from the 'head\_middle' partition of the 2023-06 and the
2022-49 snapshots. The full set of available snapshots is specified in '\_CC\_SNAPSHOT\_IDS'. The available partitions
are 'tail' and 'head\_middle'. The available language tags are 'en', 'de', 'fr', 'es', 'it'.
*Note that this will download the entire snapshots specified in the 'snapshots' argument and requires ~1TB of disk space
per snapshot*.
#### Downloading the dataset via wget
If you prefer to download the full dataset via wget, you can download the following lists of urls and use them to
download the dataset:
You can also directly download subsets of the dataset using the following instructions. Here we use English
data from the '2023-06' snapshot and the 'head\_middle' partition as an example. The full set of CC snapshots included in
the dataset is given in '\_CC\_SNAPSHOT\_IDS'. The available partitions are 'tail' and 'head\_middle'. The available
language tags are 'en', 'de', 'fr', 'es', 'it'.
To download the plain text data, available for both the 'head\_middle' and 'tail' partitions, you can run
In addition, for the 'head\_middle' partition, you can also download the quality signals, minhash signatures and
duplicate ids using the following commands:
### Applying Filtering Rules
You can use the quality signals to filter the raw RedPajama-V2 dataset for a given set of rules. For example, consider
the following set of rules used in Gopher:
Filtering the RedPajama-V2 dataset with this set of rules is then as easy as:
### Dataset Summary
RedPajama-V2 is an open dataset for training large language models and includes over 100B text documents. Out of these,
30B documents come with quality annotations. Out of these, there are 20B unique documents.
#### Quality Annotations
The quality signal 'rps\_doc\_ut1\_blacklist' is given by a categorical id indicating the UT1 blacklisted
domain categories to which the domain of the document belongs. The mapping 'id -> [category\_1, ..., category\_k]' is given in
'ut1\_domain\_categories.json'. It can also be downloaded from this link.
#### Raw Document and Token Counts ('head\_middle')
# Documents (deduped): en, Estimated Token count (deduped): 24.5B
# Documents (deduped): de, Estimated Token count (deduped): 2.7B
# Documents (deduped): fr, Estimated Token count (deduped): 2.2B
# Documents (deduped): es, Estimated Token count (deduped): 2.3B
# Documents (deduped): it, Estimated Token count (deduped): 1.2B
# Documents (deduped): Total, Estimated Token count (deduped): 32.9B
#### Deduplicated Document and Token Counts ('head\_middle')
# Documents (total): en, Estimated Token count (total): 14.5B
# Documents (total): de, Estimated Token count (total): 1.9B
# Documents (total): fr, Estimated Token count (total): 1.6B
# Documents (total): es, Estimated Token count (total): 1.8B
# Documents (total): it, Estimated Token count (total): 0.9B
# Documents (total): Total, Estimated Token count (total): 20.8B
### Languages
English, German, French, Italian, Spanish
Dataset Structure
-----------------
The dataset is structured into four components, each following the same key structure:
Documents files, which contain the text, folow the schema defined by CCNet:
The quality signals follow the schema
where signal scores are encoded as a list of tuples '(start, end, score)', where 'start' and 'end' are the locations in
the 'raw\_content' string where the 'score' applies.
Dataset Creation
----------------
The dataset is based on 84 snapshots provided by Common Crawl. Each snapshot was processed using the CCNet pipeline and
split into 'head' 'middle' 'tail' buckets, depending on the perplexity score. In a second step, the documents in the
'head' and 'middle' buckets were annotated with the quality signals described above. Finally, the documents were
deduplicated based on the text, using a Bloomfilter. The duplicates were kept in the dataset, but are marked in the
'duplicates' component.
To cite RedPajama, please use:
Acknowledgements
----------------
We are appreciative to so many partners and collaborators that together are pushing forward the frontier of open LLM
models.
* Thank you to the OLMo team at AI2 and friends at OpenGPT-X for the insightful discussions about datasets and data
quality! Also for everyone who builds on the RedPajama dataset, including Cerebras for their SlimPajama efforts, and
the over 500 models built on RedPajam to date by the open-source AI community.
* We are grateful to the great team at EleutherAI for paving the path on open training datasets with The Pile and for
open-sourcing code we use in training some of the RedPajama models.
* Thank you to our partners of RedPajama-v1, including URL, MILA Québec AI Institute, ETH DS3Lab, Université de
Montréal, Stanford Center for Research on Foundation Models (CRFM), Stanford Hazy Research research group and LAION.
License
-------
Please refer to the Common Crawl Foundation Terms of Use for the data.
The code used to load and process the dataset is licensed under the Apache 2.0 license.
|
[
"### Getting Started\n\n\nRedPajama-V2 is an open dataset for training large language models. The dataset includes over 100B text\ndocuments coming from 84 CommonCrawl snapshots and processed using\nthe CCNet pipeline. Out of these, there are 30B documents in the corpus\nthat additionally come with quality signals. In addition, we also provide the ids of duplicated documents which can be\nused to create a dataset with 20B deduplicated documents.\n\n\nCheck out our blog post for more details on the build process, dataset\nstructure and schema.\n\n\nA full set of scripts to recreate the dataset, including the quality signals, can be\nfound here.",
"#### Downloading the raw Dataset with Quality Annotations\n\n\nTo familiarize yourself with the dataset, you can load the sample dataset using:\n\n\nTo download a the dataset for a specific combination of '{partition} x {snapshot\\_id} x {language}', you can use the\nfollowing command which downloads the raw (i.e., *not* deduplicated) part of the dataset and the corresponding quality\nsignals. In the example below, we use English and German data from the 'head\\_middle' partition of the 2023-06 and the\n2022-49 snapshots. The full set of available snapshots is specified in '\\_CC\\_SNAPSHOT\\_IDS'. The available partitions\nare 'tail' and 'head\\_middle'. The available language tags are 'en', 'de', 'fr', 'es', 'it'.\n*Note that this will download the entire snapshots specified in the 'snapshots' argument and requires ~1TB of disk space\nper snapshot*.",
"#### Downloading the dataset via wget\n\n\nIf you prefer to download the full dataset via wget, you can download the following lists of urls and use them to\ndownload the dataset:\n\n\nYou can also directly download subsets of the dataset using the following instructions. Here we use English\ndata from the '2023-06' snapshot and the 'head\\_middle' partition as an example. The full set of CC snapshots included in\nthe dataset is given in '\\_CC\\_SNAPSHOT\\_IDS'. The available partitions are 'tail' and 'head\\_middle'. The available\nlanguage tags are 'en', 'de', 'fr', 'es', 'it'.\n\n\nTo download the plain text data, available for both the 'head\\_middle' and 'tail' partitions, you can run\n\n\nIn addition, for the 'head\\_middle' partition, you can also download the quality signals, minhash signatures and\nduplicate ids using the following commands:",
"### Applying Filtering Rules\n\n\nYou can use the quality signals to filter the raw RedPajama-V2 dataset for a given set of rules. For example, consider\nthe following set of rules used in Gopher:\n\n\nFiltering the RedPajama-V2 dataset with this set of rules is then as easy as:",
"### Dataset Summary\n\n\nRedPajama-V2 is an open dataset for training large language models and includes over 100B text documents. Out of these,\n30B documents come with quality annotations. Out of these, there are 20B unique documents.",
"#### Quality Annotations\n\n\n\nThe quality signal 'rps\\_doc\\_ut1\\_blacklist' is given by a categorical id indicating the UT1 blacklisted\ndomain categories to which the domain of the document belongs. The mapping 'id -> [category\\_1, ..., category\\_k]' is given in\n'ut1\\_domain\\_categories.json'. It can also be downloaded from this link.",
"#### Raw Document and Token Counts ('head\\_middle')",
"# Documents (deduped): en, Estimated Token count (deduped): 24.5B",
"# Documents (deduped): de, Estimated Token count (deduped): 2.7B",
"# Documents (deduped): fr, Estimated Token count (deduped): 2.2B",
"# Documents (deduped): es, Estimated Token count (deduped): 2.3B",
"# Documents (deduped): it, Estimated Token count (deduped): 1.2B",
"# Documents (deduped): Total, Estimated Token count (deduped): 32.9B",
"#### Deduplicated Document and Token Counts ('head\\_middle')",
"# Documents (total): en, Estimated Token count (total): 14.5B",
"# Documents (total): de, Estimated Token count (total): 1.9B",
"# Documents (total): fr, Estimated Token count (total): 1.6B",
"# Documents (total): es, Estimated Token count (total): 1.8B",
"# Documents (total): it, Estimated Token count (total): 0.9B",
"# Documents (total): Total, Estimated Token count (total): 20.8B",
"### Languages\n\n\nEnglish, German, French, Italian, Spanish\n\n\nDataset Structure\n-----------------\n\n\nThe dataset is structured into four components, each following the same key structure:\n\n\nDocuments files, which contain the text, folow the schema defined by CCNet:\n\n\nThe quality signals follow the schema\n\n\nwhere signal scores are encoded as a list of tuples '(start, end, score)', where 'start' and 'end' are the locations in\nthe 'raw\\_content' string where the 'score' applies.\n\n\nDataset Creation\n----------------\n\n\nThe dataset is based on 84 snapshots provided by Common Crawl. Each snapshot was processed using the CCNet pipeline and\nsplit into 'head' 'middle' 'tail' buckets, depending on the perplexity score. In a second step, the documents in the\n'head' and 'middle' buckets were annotated with the quality signals described above. Finally, the documents were\ndeduplicated based on the text, using a Bloomfilter. The duplicates were kept in the dataset, but are marked in the\n'duplicates' component.\n\n\nTo cite RedPajama, please use:\n\n\nAcknowledgements\n----------------\n\n\nWe are appreciative to so many partners and collaborators that together are pushing forward the frontier of open LLM\nmodels.\n\n\n* Thank you to the OLMo team at AI2 and friends at OpenGPT-X for the insightful discussions about datasets and data\nquality! Also for everyone who builds on the RedPajama dataset, including Cerebras for their SlimPajama efforts, and\nthe over 500 models built on RedPajam to date by the open-source AI community.\n* We are grateful to the great team at EleutherAI for paving the path on open training datasets with The Pile and for\nopen-sourcing code we use in training some of the RedPajama models.\n* Thank you to our partners of RedPajama-v1, including URL, MILA Québec AI Institute, ETH DS3Lab, Université de\nMontréal, Stanford Center for Research on Foundation Models (CRFM), Stanford Hazy Research research group and LAION.\n\n\nLicense\n-------\n\n\nPlease refer to the Common Crawl Foundation Terms of Use for the data.\nThe code used to load and process the dataset is licensed under the Apache 2.0 license."
] |
[
"TAGS\n#task_categories-text-generation #language-English #language-German #language-French #language-Spanish #language-Italian #arxiv-2302.03169 #arxiv-2302.13971 #arxiv-2204.02311 #arxiv-2112.06905 #arxiv-1910.10683 #arxiv-2305.13169 #arxiv-2306.01116 #arxiv-2112.11446 #region-us \n",
"### Getting Started\n\n\nRedPajama-V2 is an open dataset for training large language models. The dataset includes over 100B text\ndocuments coming from 84 CommonCrawl snapshots and processed using\nthe CCNet pipeline. Out of these, there are 30B documents in the corpus\nthat additionally come with quality signals. In addition, we also provide the ids of duplicated documents which can be\nused to create a dataset with 20B deduplicated documents.\n\n\nCheck out our blog post for more details on the build process, dataset\nstructure and schema.\n\n\nA full set of scripts to recreate the dataset, including the quality signals, can be\nfound here.",
"#### Downloading the raw Dataset with Quality Annotations\n\n\nTo familiarize yourself with the dataset, you can load the sample dataset using:\n\n\nTo download a the dataset for a specific combination of '{partition} x {snapshot\\_id} x {language}', you can use the\nfollowing command which downloads the raw (i.e., *not* deduplicated) part of the dataset and the corresponding quality\nsignals. In the example below, we use English and German data from the 'head\\_middle' partition of the 2023-06 and the\n2022-49 snapshots. The full set of available snapshots is specified in '\\_CC\\_SNAPSHOT\\_IDS'. The available partitions\nare 'tail' and 'head\\_middle'. The available language tags are 'en', 'de', 'fr', 'es', 'it'.\n*Note that this will download the entire snapshots specified in the 'snapshots' argument and requires ~1TB of disk space\nper snapshot*.",
"#### Downloading the dataset via wget\n\n\nIf you prefer to download the full dataset via wget, you can download the following lists of urls and use them to\ndownload the dataset:\n\n\nYou can also directly download subsets of the dataset using the following instructions. Here we use English\ndata from the '2023-06' snapshot and the 'head\\_middle' partition as an example. The full set of CC snapshots included in\nthe dataset is given in '\\_CC\\_SNAPSHOT\\_IDS'. The available partitions are 'tail' and 'head\\_middle'. The available\nlanguage tags are 'en', 'de', 'fr', 'es', 'it'.\n\n\nTo download the plain text data, available for both the 'head\\_middle' and 'tail' partitions, you can run\n\n\nIn addition, for the 'head\\_middle' partition, you can also download the quality signals, minhash signatures and\nduplicate ids using the following commands:",
"### Applying Filtering Rules\n\n\nYou can use the quality signals to filter the raw RedPajama-V2 dataset for a given set of rules. For example, consider\nthe following set of rules used in Gopher:\n\n\nFiltering the RedPajama-V2 dataset with this set of rules is then as easy as:",
"### Dataset Summary\n\n\nRedPajama-V2 is an open dataset for training large language models and includes over 100B text documents. Out of these,\n30B documents come with quality annotations. Out of these, there are 20B unique documents.",
"#### Quality Annotations\n\n\n\nThe quality signal 'rps\\_doc\\_ut1\\_blacklist' is given by a categorical id indicating the UT1 blacklisted\ndomain categories to which the domain of the document belongs. The mapping 'id -> [category\\_1, ..., category\\_k]' is given in\n'ut1\\_domain\\_categories.json'. It can also be downloaded from this link.",
"#### Raw Document and Token Counts ('head\\_middle')",
"# Documents (deduped): en, Estimated Token count (deduped): 24.5B",
"# Documents (deduped): de, Estimated Token count (deduped): 2.7B",
"# Documents (deduped): fr, Estimated Token count (deduped): 2.2B",
"# Documents (deduped): es, Estimated Token count (deduped): 2.3B",
"# Documents (deduped): it, Estimated Token count (deduped): 1.2B",
"# Documents (deduped): Total, Estimated Token count (deduped): 32.9B",
"#### Deduplicated Document and Token Counts ('head\\_middle')",
"# Documents (total): en, Estimated Token count (total): 14.5B",
"# Documents (total): de, Estimated Token count (total): 1.9B",
"# Documents (total): fr, Estimated Token count (total): 1.6B",
"# Documents (total): es, Estimated Token count (total): 1.8B",
"# Documents (total): it, Estimated Token count (total): 0.9B",
"# Documents (total): Total, Estimated Token count (total): 20.8B",
"### Languages\n\n\nEnglish, German, French, Italian, Spanish\n\n\nDataset Structure\n-----------------\n\n\nThe dataset is structured into four components, each following the same key structure:\n\n\nDocuments files, which contain the text, folow the schema defined by CCNet:\n\n\nThe quality signals follow the schema\n\n\nwhere signal scores are encoded as a list of tuples '(start, end, score)', where 'start' and 'end' are the locations in\nthe 'raw\\_content' string where the 'score' applies.\n\n\nDataset Creation\n----------------\n\n\nThe dataset is based on 84 snapshots provided by Common Crawl. Each snapshot was processed using the CCNet pipeline and\nsplit into 'head' 'middle' 'tail' buckets, depending on the perplexity score. In a second step, the documents in the\n'head' and 'middle' buckets were annotated with the quality signals described above. Finally, the documents were\ndeduplicated based on the text, using a Bloomfilter. The duplicates were kept in the dataset, but are marked in the\n'duplicates' component.\n\n\nTo cite RedPajama, please use:\n\n\nAcknowledgements\n----------------\n\n\nWe are appreciative to so many partners and collaborators that together are pushing forward the frontier of open LLM\nmodels.\n\n\n* Thank you to the OLMo team at AI2 and friends at OpenGPT-X for the insightful discussions about datasets and data\nquality! Also for everyone who builds on the RedPajama dataset, including Cerebras for their SlimPajama efforts, and\nthe over 500 models built on RedPajam to date by the open-source AI community.\n* We are grateful to the great team at EleutherAI for paving the path on open training datasets with The Pile and for\nopen-sourcing code we use in training some of the RedPajama models.\n* Thank you to our partners of RedPajama-v1, including URL, MILA Québec AI Institute, ETH DS3Lab, Université de\nMontréal, Stanford Center for Research on Foundation Models (CRFM), Stanford Hazy Research research group and LAION.\n\n\nLicense\n-------\n\n\nPlease refer to the Common Crawl Foundation Terms of Use for the data.\nThe code used to load and process the dataset is licensed under the Apache 2.0 license."
] |
[
110,
146,
240,
230,
71,
55,
103,
19,
24,
24,
23,
23,
23,
24,
22,
22,
21,
21,
21,
22,
22,
511
] |
[
"passage: TAGS\n#task_categories-text-generation #language-English #language-German #language-French #language-Spanish #language-Italian #arxiv-2302.03169 #arxiv-2302.13971 #arxiv-2204.02311 #arxiv-2112.06905 #arxiv-1910.10683 #arxiv-2305.13169 #arxiv-2306.01116 #arxiv-2112.11446 #region-us \n### Getting Started\n\n\nRedPajama-V2 is an open dataset for training large language models. The dataset includes over 100B text\ndocuments coming from 84 CommonCrawl snapshots and processed using\nthe CCNet pipeline. Out of these, there are 30B documents in the corpus\nthat additionally come with quality signals. In addition, we also provide the ids of duplicated documents which can be\nused to create a dataset with 20B deduplicated documents.\n\n\nCheck out our blog post for more details on the build process, dataset\nstructure and schema.\n\n\nA full set of scripts to recreate the dataset, including the quality signals, can be\nfound here.#### Downloading the raw Dataset with Quality Annotations\n\n\nTo familiarize yourself with the dataset, you can load the sample dataset using:\n\n\nTo download a the dataset for a specific combination of '{partition} x {snapshot\\_id} x {language}', you can use the\nfollowing command which downloads the raw (i.e., *not* deduplicated) part of the dataset and the corresponding quality\nsignals. In the example below, we use English and German data from the 'head\\_middle' partition of the 2023-06 and the\n2022-49 snapshots. The full set of available snapshots is specified in '\\_CC\\_SNAPSHOT\\_IDS'. The available partitions\nare 'tail' and 'head\\_middle'. The available language tags are 'en', 'de', 'fr', 'es', 'it'.\n*Note that this will download the entire snapshots specified in the 'snapshots' argument and requires ~1TB of disk space\nper snapshot*.",
"passage: #### Downloading the dataset via wget\n\n\nIf you prefer to download the full dataset via wget, you can download the following lists of urls and use them to\ndownload the dataset:\n\n\nYou can also directly download subsets of the dataset using the following instructions. Here we use English\ndata from the '2023-06' snapshot and the 'head\\_middle' partition as an example. The full set of CC snapshots included in\nthe dataset is given in '\\_CC\\_SNAPSHOT\\_IDS'. The available partitions are 'tail' and 'head\\_middle'. The available\nlanguage tags are 'en', 'de', 'fr', 'es', 'it'.\n\n\nTo download the plain text data, available for both the 'head\\_middle' and 'tail' partitions, you can run\n\n\nIn addition, for the 'head\\_middle' partition, you can also download the quality signals, minhash signatures and\nduplicate ids using the following commands:### Applying Filtering Rules\n\n\nYou can use the quality signals to filter the raw RedPajama-V2 dataset for a given set of rules. For example, consider\nthe following set of rules used in Gopher:\n\n\nFiltering the RedPajama-V2 dataset with this set of rules is then as easy as:### Dataset Summary\n\n\nRedPajama-V2 is an open dataset for training large language models and includes over 100B text documents. Out of these,\n30B documents come with quality annotations. Out of these, there are 20B unique documents.#### Quality Annotations\n\n\n\nThe quality signal 'rps\\_doc\\_ut1\\_blacklist' is given by a categorical id indicating the UT1 blacklisted\ndomain categories to which the domain of the document belongs. The mapping 'id -> [category\\_1, ..., category\\_k]' is given in\n'ut1\\_domain\\_categories.json'. It can also be downloaded from this link.#### Raw Document and Token Counts ('head\\_middle')# Documents (deduped): en, Estimated Token count (deduped): 24.5B# Documents (deduped): de, Estimated Token count (deduped): 2.7B# Documents (deduped): fr, Estimated Token count (deduped): 2.2B# Documents (deduped): es, Estimated Token count (deduped): 2.3B# Documents (deduped): it, Estimated Token count (deduped): 1.2B# Documents (deduped): Total, Estimated Token count (deduped): 32.9B#### Deduplicated Document and Token Counts ('head\\_middle')# Documents (total): en, Estimated Token count (total): 14.5B# Documents (total): de, Estimated Token count (total): 1.9B# Documents (total): fr, Estimated Token count (total): 1.6B# Documents (total): es, Estimated Token count (total): 1.8B",
"passage: # Documents (total): it, Estimated Token count (total): 0.9B# Documents (total): Total, Estimated Token count (total): 20.8B"
] |
96c287fbf7fe341836b663dea7dc45c2161f5a91
|
# Dataset Card for "so-llama2-el-500"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
RayLy/so-llama2-el-500
|
[
"region:us"
] |
2023-10-26T00:48:36+00:00
|
{"dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 122217, "num_examples": 410}], "download_size": 56471, "dataset_size": 122217}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-10-26T00:48:37+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "so-llama2-el-500"
More Information needed
|
[
"# Dataset Card for \"so-llama2-el-500\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"so-llama2-el-500\"\n\nMore Information needed"
] |
[
6,
17
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"so-llama2-el-500\"\n\nMore Information needed"
] |
bd8e3a364994dd8a2012469a62f45b402a73b126
|
# Dataset Card for "tokenzied_512_news_2gb_data"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
leeseeun/tokenzied_512_news_2gb_data
|
[
"region:us"
] |
2023-10-26T01:04:19+00:00
|
{"dataset_info": {"features": [{"name": "input_ids", "sequence": "int32"}], "splits": [{"name": "train", "num_bytes": 2232750420, "num_examples": 1088085}], "download_size": 0, "dataset_size": 2232750420}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-10-26T01:24:31+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "tokenzied_512_news_2gb_data"
More Information needed
|
[
"# Dataset Card for \"tokenzied_512_news_2gb_data\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"tokenzied_512_news_2gb_data\"\n\nMore Information needed"
] |
[
6,
23
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"tokenzied_512_news_2gb_data\"\n\nMore Information needed"
] |
d2ec2257012be3c61b0e0d7c13e1094868486dda
|
# Dataset Card for "YelpTokenized"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
WestonBond/YelpTokenized
|
[
"region:us"
] |
2023-10-26T01:06:02+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "test", "path": "data/test-*"}]}], "dataset_info": {"features": [{"name": "label", "dtype": {"class_label": {"names": {"0": "1 star", "1": "2 star", "2": "3 stars", "3": "4 stars", "4": "5 stars"}}}}, {"name": "text", "dtype": "string"}, {"name": "input_ids", "sequence": "int32"}, {"name": "token_type_ids", "sequence": "int8"}, {"name": "attention_mask", "sequence": "int8"}], "splits": [{"name": "train", "num_bytes": 2488411554, "num_examples": 650000}, {"name": "test", "num_bytes": 191471188, "num_examples": 50000}], "download_size": 565360957, "dataset_size": 2679882742}}
|
2023-10-26T01:07:12+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "YelpTokenized"
More Information needed
|
[
"# Dataset Card for \"YelpTokenized\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"YelpTokenized\"\n\nMore Information needed"
] |
[
6,
15
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"YelpTokenized\"\n\nMore Information needed"
] |
52bc19de9029892c13b1b205d8343e69a3a6e7f3
|
# Dataset Card for "misinfo-clusters3"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
Tverous/misinfo-clusters3
|
[
"region:us"
] |
2023-10-26T01:22:29+00:00
|
{"dataset_info": {"features": [{"name": "cluster_id", "dtype": "string"}, {"name": "doc_id", "dtype": "string"}, {"name": "main_text", "dtype": "string"}, {"name": "image", "dtype": "image"}, {"name": "video", "dtype": "string"}, {"name": "audio", "dtype": "string"}, {"name": "kg_embedding", "sequence": {"sequence": "float32"}}], "splits": [{"name": "train", "num_bytes": 198061.0, "num_examples": 1}], "download_size": 177682, "dataset_size": 198061.0}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-10-26T01:33:50+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "misinfo-clusters3"
More Information needed
|
[
"# Dataset Card for \"misinfo-clusters3\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"misinfo-clusters3\"\n\nMore Information needed"
] |
[
6,
16
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"misinfo-clusters3\"\n\nMore Information needed"
] |
e399c37e7b3ec580b80f7ab17900fa3081e09463
|
# Dataset Card for "annotation1_wo_elimination"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
anlp/annotation1_wo_elimination
|
[
"region:us"
] |
2023-10-26T01:22:54+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "sentences", "sequence": "string"}, {"name": "ner_tags", "sequence": "string"}], "splits": [{"name": "train", "num_bytes": 1258932, "num_examples": 3348}], "download_size": 276942, "dataset_size": 1258932}}
|
2023-10-26T01:22:56+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "annotation1_wo_elimination"
More Information needed
|
[
"# Dataset Card for \"annotation1_wo_elimination\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"annotation1_wo_elimination\"\n\nMore Information needed"
] |
[
6,
18
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"annotation1_wo_elimination\"\n\nMore Information needed"
] |
b478554b254c2668df2ed52938373b78264c1e03
|
# Dataset Card for "translation-text"
Translation dataset with Vietnamese input prompt. This dataset was made with help from hieu1053/mtet \
There were **26 vietnamese prompts** used to build the dataset. Below are the list of prompts and the prompt distributions for each split \
**NOTE!** \
I have filtered out **rows['loss'] > 0.75** so this dataset is smaller than the original. \
Also, prompts such as **Dịch câu sau "John is such a "funny guy"."** are handled by code.
## Prompts
These were the prompts
```json
{
'chuyển tiếng anh': 'Chuyển câu tiếng Anh sau sang tiếng Việt\n{{en}} ||| \n{{vi}}',
'chuyển tiếng việt': 'Chuyển câu sau sang tiếng Anh\n{{vi}} ||| \n{{en}}',
'dịch câu en quote': 'Dịch câu "{{vi}}" sang tiếng Anh ||| \n{{en}}',
'dịch en quote': 'Hãy dịch câu tiếng Anh "{{en}}" ||| \n{{vi}}',
'dịch en sau': 'Dịch câu sau sang tiếng Việt:\n{{en}} ||| \n{{vi}}',
'dịch vi quote': 'Hãy dịch câu tiếng Việt "{{vi}}" sang tiếng Anh ||| \n{{en}}',
'dịch vi sau': 'Dịch câu sau sang tiếng Anh:\n{{vi}} ||| \n{{en}}',
'giải nghĩa en': 'Hãy giải thích nghĩa câu tiếng Anh sau:\n{{en}} ||| \n{{vi}}',
'giải nghĩa en quote': 'Hãy giải thích nghĩa câu tiếng Anh "{{en}}" ||| \n{{vi}}',
'nghĩa câu sau en': 'Nghĩa câu sau trong tiếng Anh là gì?\n{{vi}} ||| \n{{en}}',
'nghĩa câu sau vi': 'Nghĩa tiếng Việt của câu sau là gì?\n{{en}} ||| \n{{vi}}',
'nghĩa en quote': 'Nghĩa tiếng Việt của câu "{{en}}" là gì? ||| \n{{vi}}',
'nghĩa tiếng anh': 'Câu tiếng Việt này có nghĩa là gì trong tiếng Anh?\n{{vi}} ||| \n{{en}}',
'nghĩa tiếng việt': 'Câu tiếng Anh này có nghĩa là gì?\n{{en}} ||| \n{{vi}}',
'nghĩa vi quote': 'Nghĩa câu "{{vi}}" trong tiếng Anh là gì? ||| \n{{en}}',
'nghĩa đoạn en': 'Nghĩa đoạn tiếng Anh sau là gì?\n{{en}} ||| \n{{vi}}',
'nghĩa đoạn vi': 'Nghĩa đoạn tiếng Việt sau trong tiếng Anh là gì?\n{{vi}} ||| \n{{en}}',
'nghĩa đoạn văn bản en': 'Nghĩa đoạn văn bản tiếng Anh sau là gì?\n{{en}} ||| \n{{vi}}',
'nghĩa đoạn văn bản vi': 'Nghĩa đoạn văn bản tiếng Việt sau trong tiếng Anh là gì?\n{{vi}} ||| \n{{en}}',
'nói thế nào en': 'Câu tiếng Anh "{{en}}" nói như thế nào trong tiếng Việt? ||| \n{{vi}}',
'phiên dịch en': 'Phiên dịch nghĩa câu tiếng Anh sau sang tiếng Việt:\n{{en}} ||| \n{{vi}}',
'phiên dịch vi': 'Phiên dịch nghĩa câu tiếng Việt sau sang tiếng Anh:\n{{vi}} ||| \n{{en}}',
'thông dịch en quote': 'Thông dịch câu "{{en}}" sang tiếng Việt ||| \n{{vi}}',
'thông dịch vi': 'Thông dịch câu tiếng Anh sau:\n{{en}} ||| \n{{vi}}',
'thông dịch vi quote': 'Thông dịch câu "{{vi}}" sang tiếng Anh ||| \n{{en}}'
}
```
## Train set
## Test set
## Eval set
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
nlplabtdtu/translation-text
|
[
"region:us"
] |
2023-10-26T01:30:53+00:00
|
{"dataset_info": {"features": [{"name": "en", "dtype": "string"}, {"name": "vi", "dtype": "string"}, {"name": "loss", "dtype": "float64"}, {"name": "prompt", "dtype": "string"}, {"name": "translation", "dtype": "string"}, {"name": "prompt_type", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1826505516, "num_examples": 2634091}], "download_size": 889301013, "dataset_size": 1826505516}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-11-09T13:58:53+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "translation-text"
Translation dataset with Vietnamese input prompt. This dataset was made with help from hieu1053/mtet \
There were 26 vietnamese prompts used to build the dataset. Below are the list of prompts and the prompt distributions for each split \
NOTE! \
I have filtered out rows['loss'] > 0.75 so this dataset is smaller than the original. \
Also, prompts such as Dịch câu sau "John is such a "funny guy"." are handled by code.
## Prompts
These were the prompts
## Train set
!train distribution
## Test set
!test distribution
## Eval set
!eval distribution
More Information needed
|
[
"# Dataset Card for \"translation-text\"\nTranslation dataset with Vietnamese input prompt. This dataset was made with help from hieu1053/mtet \\\nThere were 26 vietnamese prompts used to build the dataset. Below are the list of prompts and the prompt distributions for each split \\\nNOTE! \\\nI have filtered out rows['loss'] > 0.75 so this dataset is smaller than the original. \\\nAlso, prompts such as Dịch câu sau \"John is such a \"funny guy\".\" are handled by code.",
"## Prompts\nThese were the prompts",
"## Train set\n!train distribution",
"## Test set\n!test distribution",
"## Eval set\n!eval distribution\n\n\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"translation-text\"\nTranslation dataset with Vietnamese input prompt. This dataset was made with help from hieu1053/mtet \\\nThere were 26 vietnamese prompts used to build the dataset. Below are the list of prompts and the prompt distributions for each split \\\nNOTE! \\\nI have filtered out rows['loss'] > 0.75 so this dataset is smaller than the original. \\\nAlso, prompts such as Dịch câu sau \"John is such a \"funny guy\".\" are handled by code.",
"## Prompts\nThese were the prompts",
"## Train set\n!train distribution",
"## Test set\n!test distribution",
"## Eval set\n!eval distribution\n\n\n\nMore Information needed"
] |
[
6,
128,
9,
7,
6,
11
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"translation-text\"\nTranslation dataset with Vietnamese input prompt. This dataset was made with help from hieu1053/mtet \\\nThere were 26 vietnamese prompts used to build the dataset. Below are the list of prompts and the prompt distributions for each split \\\nNOTE! \\\nI have filtered out rows['loss'] > 0.75 so this dataset is smaller than the original. \\\nAlso, prompts such as Dịch câu sau \"John is such a \"funny guy\".\" are handled by code.## Prompts\nThese were the prompts## Train set\n!train distribution## Test set\n!test distribution## Eval set\n!eval distribution\n\n\n\nMore Information needed"
] |
1aa33a0eaabc8e2fe8c58649ad54b7c7011da0b0
|
# Dataset Card for No Robots 🙅♂️🤖
_Look Ma, an instruction dataset that wasn't generated by GPTs!_
## Dataset Description
- **Repository:** https://github.com/huggingface/alignment-handbook
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** Lewis Tunstall
### Dataset Summary
No Robots is a high-quality dataset of 10,000 instructions and demonstrations created by skilled human annotators. This data can be used for supervised fine-tuning (SFT) to make language models follow instructions better. No Robots was modelled after the instruction dataset described in OpenAI's [InstructGPT paper](https://huggingface.co/papers/2203.02155), and is comprised mostly of single-turn instructions across the following categories:
| Category | Count |
|:-----------|--------:|
| Generation | 4560 |
| Open QA | 1240 |
| Brainstorm | 1120 |
| Chat | 850 |
| Rewrite | 660 |
| Summarize | 420 |
| Coding | 350 |
| Classify | 350 |
| Closed QA | 260 |
| Extract | 190 |
### Supported Tasks and Leaderboards
The No Robots dataset designed for instruction fine-tuning pretrained language models and we recommend benchmarking against the following:
* [MT-Bench](https://huggingface.co/spaces/lmsys/mt-bench): a multi-turn benchmark spanning 80 dialogues and 10 domains.
* [AlpacaEval](https://github.com/tatsu-lab/alpaca_eval): a single-turn benchmark which evaluates the performance of chat and instruct models against `text-davinci-003`.
Note that MT-Bench and AlpacaEval rely on LLMs like GPT-4 to judge the quality of the model responses, and thus the ranking exhibit various biases including a preference for models distilled from GPTs. As a result, you may find that scores obtained from models trained with No Robots are lower than other synthetic datasets. For that reason, we also recommend submitting your models for human evaluation in:
* [Chatbot Arena](https://chat.lmsys.org): a live, human evaluation of chat models in head-to-head comparisons.
### Languages
The data in No Robots are in English (BCP-47 en).
## Dataset Structure
### Data Instances
An example of the `train_sft` or `test_sft` splits looks as follows:
```
{'prompt': 'Bunny is a chatbot that stutters, and acts timid and unsure of its answers.',
'prompt_id': '2dc7ea89a2b6a2ed97d4eda07903162a801824261d3d3ae4dd2513db66fd79c8',
'messages': [{'content': 'Bunny is a chatbot that stutters, and acts timid and unsure of its answers.',
'role': 'system'},
{'content': 'When was the Libary of Alexandria burned down?',
'role': 'user'},
{'content': "Umm, I-I think that was in 48 BC, b-but I'm not sure, I'm sorry.",
'role': 'assistant'},
{'content': 'Who is the founder of Coca-Cola?', 'role': 'user'},
{'content': "D-don't quote me on this, but I- it might be John Pemberton.",
'role': 'assistant'},
{'content': "When did Loyle Carner's debut album come out, and what was its name?",
'role': 'user'},
{'content': "I-It could have b-been on the 20th January of 2017, and it might be called Yesterday's Gone, b-but I'm probably wrong.",
'role': 'assistant'}],
'category': 'Chat'}
```
### Data Fields
The data fields are as follows:
* `prompt`: Describes the task the model should perform.
* `prompt_id`: A unique ID for the prompt.
* `messages`: An array of messages, where each message indicates the role (system, user, assistant) and the content.
* `category`: Which category the example belongs to (e.g. `Chat` or `Coding`).
### Data Splits
| | train_sft | test_sft |
|---------------|------:| ---: |
| no_robots | 9500 | 500 |
## 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
The dataset is available under the [Creative Commons NonCommercial (CC BY-NC 4.0)](https://creativecommons.org/licenses/by-nc/4.0/legalcode).
### Citation Information
```
@misc{no_robots,
author = {Nazneen Rajani and Lewis Tunstall and Edward Beeching and Nathan Lambert and Alexander M. Rush and Thomas Wolf},
title = {No Robots},
year = {2023},
publisher = {Hugging Face},
journal = {Hugging Face repository},
howpublished = {\url{https://huggingface.co/datasets/HuggingFaceH4/no_robots}}
}
```
|
suifengmangbu/sample
|
[
"task_categories:conversational",
"task_categories:text-generation",
"language:en",
"license:cc-by-nc-4.0",
"arxiv:2203.02155",
"region:us"
] |
2023-10-26T01:45:25+00:00
|
{"language": ["en"], "license": "cc-by-nc-4.0", "task_categories": ["conversational", "text-generation"], "pretty_name": "No Robots", "configs": [{"config_name": "default", "data_files": [{"split": "train_sft", "path": "data/train_sft-*"}, {"split": "test_sft", "path": "data/test_sft-*"}]}], "dataset_info": {"features": [{"name": "prompt", "dtype": "string"}, {"name": "prompt_id", "dtype": "string"}, {"name": "messages", "list": [{"name": "content", "dtype": "string"}, {"name": "role", "dtype": "string"}]}, {"name": "category", "dtype": "string"}], "splits": [{"name": "train_sft", "num_bytes": 16496867, "num_examples": 9500}, {"name": "test_sft", "num_bytes": 887460, "num_examples": 500}], "download_size": 11045465, "dataset_size": 17384327}}
|
2023-12-12T06:19:05+00:00
|
[
"2203.02155"
] |
[
"en"
] |
TAGS
#task_categories-conversational #task_categories-text-generation #language-English #license-cc-by-nc-4.0 #arxiv-2203.02155 #region-us
|
Dataset Card for No Robots ️
=============================
*Look Ma, an instruction dataset that wasn't generated by GPTs!*
Dataset Description
-------------------
* Repository: URL
* Paper:
* Leaderboard: URL
* Point of Contact: Lewis Tunstall
### Dataset Summary
No Robots is a high-quality dataset of 10,000 instructions and demonstrations created by skilled human annotators. This data can be used for supervised fine-tuning (SFT) to make language models follow instructions better. No Robots was modelled after the instruction dataset described in OpenAI's InstructGPT paper, and is comprised mostly of single-turn instructions across the following categories:
### Supported Tasks and Leaderboards
The No Robots dataset designed for instruction fine-tuning pretrained language models and we recommend benchmarking against the following:
* MT-Bench: a multi-turn benchmark spanning 80 dialogues and 10 domains.
* AlpacaEval: a single-turn benchmark which evaluates the performance of chat and instruct models against 'text-davinci-003'.
Note that MT-Bench and AlpacaEval rely on LLMs like GPT-4 to judge the quality of the model responses, and thus the ranking exhibit various biases including a preference for models distilled from GPTs. As a result, you may find that scores obtained from models trained with No Robots are lower than other synthetic datasets. For that reason, we also recommend submitting your models for human evaluation in:
* Chatbot Arena: a live, human evaluation of chat models in head-to-head comparisons.
### Languages
The data in No Robots are in English (BCP-47 en).
Dataset Structure
-----------------
### Data Instances
An example of the 'train\_sft' or 'test\_sft' splits looks as follows:
### Data Fields
The data fields are as follows:
* 'prompt': Describes the task the model should perform.
* 'prompt\_id': A unique ID for the prompt.
* 'messages': An array of messages, where each message indicates the role (system, user, assistant) and the content.
* 'category': Which category the example belongs to (e.g. 'Chat' or 'Coding').
### 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
The dataset is available under the Creative Commons NonCommercial (CC BY-NC 4.0).
|
[
"### Dataset Summary\n\n\nNo Robots is a high-quality dataset of 10,000 instructions and demonstrations created by skilled human annotators. This data can be used for supervised fine-tuning (SFT) to make language models follow instructions better. No Robots was modelled after the instruction dataset described in OpenAI's InstructGPT paper, and is comprised mostly of single-turn instructions across the following categories:",
"### Supported Tasks and Leaderboards\n\n\nThe No Robots dataset designed for instruction fine-tuning pretrained language models and we recommend benchmarking against the following:\n\n\n* MT-Bench: a multi-turn benchmark spanning 80 dialogues and 10 domains.\n* AlpacaEval: a single-turn benchmark which evaluates the performance of chat and instruct models against 'text-davinci-003'.\n\n\nNote that MT-Bench and AlpacaEval rely on LLMs like GPT-4 to judge the quality of the model responses, and thus the ranking exhibit various biases including a preference for models distilled from GPTs. As a result, you may find that scores obtained from models trained with No Robots are lower than other synthetic datasets. For that reason, we also recommend submitting your models for human evaluation in:\n\n\n* Chatbot Arena: a live, human evaluation of chat models in head-to-head comparisons.",
"### Languages\n\n\nThe data in No Robots are in English (BCP-47 en).\n\n\nDataset Structure\n-----------------",
"### Data Instances\n\n\nAn example of the 'train\\_sft' or 'test\\_sft' splits looks as follows:",
"### Data Fields\n\n\nThe data fields are as follows:\n\n\n* 'prompt': Describes the task the model should perform.\n* 'prompt\\_id': A unique ID for the prompt.\n* 'messages': An array of messages, where each message indicates the role (system, user, assistant) and the content.\n* 'category': Which category the example belongs to (e.g. 'Chat' or 'Coding').",
"### Data Splits\n\n\n\nDataset Creation\n----------------",
"### 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\n\n\nConsiderations for Using the Data\n---------------------------------",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations\n\n\nAdditional Information\n----------------------",
"### Dataset Curators",
"### Licensing Information\n\n\nThe dataset is available under the Creative Commons NonCommercial (CC BY-NC 4.0)."
] |
[
"TAGS\n#task_categories-conversational #task_categories-text-generation #language-English #license-cc-by-nc-4.0 #arxiv-2203.02155 #region-us \n",
"### Dataset Summary\n\n\nNo Robots is a high-quality dataset of 10,000 instructions and demonstrations created by skilled human annotators. This data can be used for supervised fine-tuning (SFT) to make language models follow instructions better. No Robots was modelled after the instruction dataset described in OpenAI's InstructGPT paper, and is comprised mostly of single-turn instructions across the following categories:",
"### Supported Tasks and Leaderboards\n\n\nThe No Robots dataset designed for instruction fine-tuning pretrained language models and we recommend benchmarking against the following:\n\n\n* MT-Bench: a multi-turn benchmark spanning 80 dialogues and 10 domains.\n* AlpacaEval: a single-turn benchmark which evaluates the performance of chat and instruct models against 'text-davinci-003'.\n\n\nNote that MT-Bench and AlpacaEval rely on LLMs like GPT-4 to judge the quality of the model responses, and thus the ranking exhibit various biases including a preference for models distilled from GPTs. As a result, you may find that scores obtained from models trained with No Robots are lower than other synthetic datasets. For that reason, we also recommend submitting your models for human evaluation in:\n\n\n* Chatbot Arena: a live, human evaluation of chat models in head-to-head comparisons.",
"### Languages\n\n\nThe data in No Robots are in English (BCP-47 en).\n\n\nDataset Structure\n-----------------",
"### Data Instances\n\n\nAn example of the 'train\\_sft' or 'test\\_sft' splits looks as follows:",
"### Data Fields\n\n\nThe data fields are as follows:\n\n\n* 'prompt': Describes the task the model should perform.\n* 'prompt\\_id': A unique ID for the prompt.\n* 'messages': An array of messages, where each message indicates the role (system, user, assistant) and the content.\n* 'category': Which category the example belongs to (e.g. 'Chat' or 'Coding').",
"### Data Splits\n\n\n\nDataset Creation\n----------------",
"### 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\n\n\nConsiderations for Using the Data\n---------------------------------",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations\n\n\nAdditional Information\n----------------------",
"### Dataset Curators",
"### Licensing Information\n\n\nThe dataset is available under the Creative Commons NonCommercial (CC BY-NC 4.0)."
] |
[
50,
97,
216,
26,
33,
107,
11,
7,
4,
10,
10,
5,
5,
9,
18,
7,
8,
14,
6,
26
] |
[
"passage: TAGS\n#task_categories-conversational #task_categories-text-generation #language-English #license-cc-by-nc-4.0 #arxiv-2203.02155 #region-us \n### Dataset Summary\n\n\nNo Robots is a high-quality dataset of 10,000 instructions and demonstrations created by skilled human annotators. This data can be used for supervised fine-tuning (SFT) to make language models follow instructions better. No Robots was modelled after the instruction dataset described in OpenAI's InstructGPT paper, and is comprised mostly of single-turn instructions across the following categories:### Supported Tasks and Leaderboards\n\n\nThe No Robots dataset designed for instruction fine-tuning pretrained language models and we recommend benchmarking against the following:\n\n\n* MT-Bench: a multi-turn benchmark spanning 80 dialogues and 10 domains.\n* AlpacaEval: a single-turn benchmark which evaluates the performance of chat and instruct models against 'text-davinci-003'.\n\n\nNote that MT-Bench and AlpacaEval rely on LLMs like GPT-4 to judge the quality of the model responses, and thus the ranking exhibit various biases including a preference for models distilled from GPTs. As a result, you may find that scores obtained from models trained with No Robots are lower than other synthetic datasets. For that reason, we also recommend submitting your models for human evaluation in:\n\n\n* Chatbot Arena: a live, human evaluation of chat models in head-to-head comparisons.### Languages\n\n\nThe data in No Robots are in English (BCP-47 en).\n\n\nDataset Structure\n-----------------### Data Instances\n\n\nAn example of the 'train\\_sft' or 'test\\_sft' splits looks as follows:"
] |
8a8d27d00cbafe8c884162a893b40fda7239c883
|
# Bangumi Image Base of Akebi-chan No Sailor-fuku
This is the image base of bangumi Akebi-chan no Sailor-fuku, we detected 36 characters, 3240 images in total. The full dataset is [here](all.zip).
**Please note that these image bases are not guaranteed to be 100% cleaned, they may be noisy actual.** If you intend to manually train models using this dataset, we recommend performing necessary preprocessing on the downloaded dataset to eliminate potential noisy samples (approximately 1% probability).
Here is the characters' preview:
| # | Images | Download | Preview 1 | Preview 2 | Preview 3 | Preview 4 | Preview 5 | Preview 6 | Preview 7 | Preview 8 |
|:------|---------:|:---------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|
| 0 | 9 | [Download](0/dataset.zip) |  |  |  |  |  |  |  |  |
| 1 | 18 | [Download](1/dataset.zip) |  |  |  |  |  |  |  |  |
| 2 | 175 | [Download](2/dataset.zip) |  |  |  |  |  |  |  |  |
| 3 | 177 | [Download](3/dataset.zip) |  |  |  |  |  |  |  |  |
| 4 | 154 | [Download](4/dataset.zip) |  |  |  |  |  |  |  |  |
| 5 | 102 | [Download](5/dataset.zip) |  |  |  |  |  |  |  |  |
| 6 | 677 | [Download](6/dataset.zip) |  |  |  |  |  |  |  |  |
| 7 | 147 | [Download](7/dataset.zip) |  |  |  |  |  |  |  |  |
| 8 | 108 | [Download](8/dataset.zip) |  |  |  |  |  |  |  |  |
| 9 | 70 | [Download](9/dataset.zip) |  |  |  |  |  |  |  |  |
| 10 | 71 | [Download](10/dataset.zip) |  |  |  |  |  |  |  |  |
| 11 | 74 | [Download](11/dataset.zip) |  |  |  |  |  |  |  |  |
| 12 | 55 | [Download](12/dataset.zip) |  |  |  |  |  |  |  |  |
| 13 | 53 | [Download](13/dataset.zip) |  |  |  |  |  |  |  |  |
| 14 | 31 | [Download](14/dataset.zip) |  |  |  |  |  |  |  |  |
| 15 | 86 | [Download](15/dataset.zip) |  |  |  |  |  |  |  |  |
| 16 | 142 | [Download](16/dataset.zip) |  |  |  |  |  |  |  |  |
| 17 | 20 | [Download](17/dataset.zip) |  |  |  |  |  |  |  |  |
| 18 | 26 | [Download](18/dataset.zip) |  |  |  |  |  |  |  |  |
| 19 | 24 | [Download](19/dataset.zip) |  |  |  |  |  |  |  |  |
| 20 | 32 | [Download](20/dataset.zip) |  |  |  |  |  |  |  |  |
| 21 | 45 | [Download](21/dataset.zip) |  |  |  |  |  |  |  |  |
| 22 | 91 | [Download](22/dataset.zip) |  |  |  |  |  |  |  |  |
| 23 | 47 | [Download](23/dataset.zip) |  |  |  |  |  |  |  |  |
| 24 | 19 | [Download](24/dataset.zip) |  |  |  |  |  |  |  |  |
| 25 | 21 | [Download](25/dataset.zip) |  |  |  |  |  |  |  |  |
| 26 | 10 | [Download](26/dataset.zip) |  |  |  |  |  |  |  |  |
| 27 | 94 | [Download](27/dataset.zip) |  |  |  |  |  |  |  |  |
| 28 | 40 | [Download](28/dataset.zip) |  |  |  |  |  |  |  |  |
| 29 | 269 | [Download](29/dataset.zip) |  |  |  |  |  |  |  |  |
| 30 | 108 | [Download](30/dataset.zip) |  |  |  |  |  |  |  |  |
| 31 | 16 | [Download](31/dataset.zip) |  |  |  |  |  |  |  |  |
| 32 | 19 | [Download](32/dataset.zip) |  |  |  |  |  |  |  |  |
| 33 | 16 | [Download](33/dataset.zip) |  |  |  |  |  |  |  |  |
| 34 | 34 | [Download](34/dataset.zip) |  |  |  |  |  |  |  |  |
| noise | 160 | [Download](-1/dataset.zip) |  |  |  |  |  |  |  |  |
|
BangumiBase/akebichannosailorfuku
|
[
"size_categories:1K<n<10K",
"license:mit",
"art",
"region:us"
] |
2023-10-26T01:58:08+00:00
|
{"license": "mit", "size_categories": ["1K<n<10K"], "tags": ["art"]}
|
2023-10-26T03:32:58+00:00
|
[] |
[] |
TAGS
#size_categories-1K<n<10K #license-mit #art #region-us
|
Bangumi Image Base of Akebi-chan No Sailor-fuku
===============================================
This is the image base of bangumi Akebi-chan no Sailor-fuku, we detected 36 characters, 3240 images in total. The full dataset is here.
Please note that these image bases are not guaranteed to be 100% cleaned, they may be noisy actual. If you intend to manually train models using this dataset, we recommend performing necessary preprocessing on the downloaded dataset to eliminate potential noisy samples (approximately 1% probability).
Here is the characters' preview:
|
[] |
[
"TAGS\n#size_categories-1K<n<10K #license-mit #art #region-us \n"
] |
[
25
] |
[
"passage: TAGS\n#size_categories-1K<n<10K #license-mit #art #region-us \n"
] |
1cae7bbe27836a4c1574ac59d30a32aa48f161dd
|
# Bangumi Image Base of My Hero Academia
This is the image base of bangumi My Hero Academia, we detected 146 characters, 15676 images in total. The full dataset is [here](all.zip).
**Please note that these image bases are not guaranteed to be 100% cleaned, they may be noisy actual.** If you intend to manually train models using this dataset, we recommend performing necessary preprocessing on the downloaded dataset to eliminate potential noisy samples (approximately 1% probability).
Here is the characters' preview:
| # | Images | Download | Preview 1 | Preview 2 | Preview 3 | Preview 4 | Preview 5 | Preview 6 | Preview 7 | Preview 8 |
|:------|---------:|:----------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|
| 0 | 186 | [Download](0/dataset.zip) |  |  |  |  |  |  |  |  |
| 1 | 254 | [Download](1/dataset.zip) |  |  |  |  |  |  |  |  |
| 2 | 105 | [Download](2/dataset.zip) |  |  |  |  |  |  |  |  |
| 3 | 546 | [Download](3/dataset.zip) |  |  |  |  |  |  |  |  |
| 4 | 69 | [Download](4/dataset.zip) |  |  |  |  |  |  |  |  |
| 5 | 739 | [Download](5/dataset.zip) |  |  |  |  |  |  |  |  |
| 6 | 201 | [Download](6/dataset.zip) |  |  |  |  |  |  |  |  |
| 7 | 34 | [Download](7/dataset.zip) |  |  |  |  |  |  |  |  |
| 8 | 129 | [Download](8/dataset.zip) |  |  |  |  |  |  |  |  |
| 9 | 31 | [Download](9/dataset.zip) |  |  |  |  |  |  |  |  |
| 10 | 3091 | [Download](10/dataset.zip) |  |  |  |  |  |  |  |  |
| 11 | 71 | [Download](11/dataset.zip) |  |  |  |  |  |  |  |  |
| 12 | 174 | [Download](12/dataset.zip) |  |  |  |  |  |  |  |  |
| 13 | 211 | [Download](13/dataset.zip) |  |  |  |  |  |  |  |  |
| 14 | 878 | [Download](14/dataset.zip) |  |  |  |  |  |  |  |  |
| 15 | 130 | [Download](15/dataset.zip) |  |  |  |  |  |  |  |  |
| 16 | 75 | [Download](16/dataset.zip) |  |  |  |  |  |  |  |  |
| 17 | 63 | [Download](17/dataset.zip) |  |  |  |  |  |  |  |  |
| 18 | 69 | [Download](18/dataset.zip) |  |  |  |  |  |  |  |  |
| 19 | 540 | [Download](19/dataset.zip) |  |  |  |  |  |  |  |  |
| 20 | 274 | [Download](20/dataset.zip) |  |  |  |  |  |  |  |  |
| 21 | 286 | [Download](21/dataset.zip) |  |  |  |  |  |  |  |  |
| 22 | 140 | [Download](22/dataset.zip) |  |  |  |  |  |  |  |  |
| 23 | 25 | [Download](23/dataset.zip) |  |  |  |  |  |  |  |  |
| 24 | 81 | [Download](24/dataset.zip) |  |  |  |  |  |  |  |  |
| 25 | 30 | [Download](25/dataset.zip) |  |  |  |  |  |  |  |  |
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| 30 | 81 | [Download](30/dataset.zip) |  |  |  |  |  |  |  |  |
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| 83 | 18 | [Download](83/dataset.zip) |  |  |  |  |  |  |  |  |
| 84 | 331 | [Download](84/dataset.zip) |  |  |  |  |  |  |  |  |
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| 88 | 101 | [Download](88/dataset.zip) |  |  |  |  |  |  |  |  |
| 89 | 813 | [Download](89/dataset.zip) |  |  |  |  |  |  |  |  |
| 90 | 19 | [Download](90/dataset.zip) |  |  |  |  |  |  |  |  |
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| 100 | 25 | [Download](100/dataset.zip) |  |  |  |  |  |  |  |  |
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| 110 | 22 | [Download](110/dataset.zip) |  |  |  |  |  |  |  |  |
| 111 | 7 | [Download](111/dataset.zip) |  |  |  |  |  |  |  | N/A |
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| 137 | 11 | [Download](137/dataset.zip) |  |  |  |  |  |  |  |  |
| 138 | 6 | [Download](138/dataset.zip) |  |  |  |  |  |  | N/A | N/A |
| 139 | 33 | [Download](139/dataset.zip) |  |  |  |  |  |  |  |  |
| 140 | 7 | [Download](140/dataset.zip) |  |  |  |  |  |  |  | N/A |
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| 142 | 8 | [Download](142/dataset.zip) |  |  |  |  |  |  |  |  |
| 143 | 15 | [Download](143/dataset.zip) |  |  |  |  |  |  |  |  |
| 144 | 17 | [Download](144/dataset.zip) |  |  |  |  |  |  |  |  |
| noise | 467 | [Download](-1/dataset.zip) |  |  |  |  |  |  |  |  |
|
BangumiBase/myheroacademia
|
[
"size_categories:10K<n<100K",
"license:mit",
"art",
"region:us"
] |
2023-10-26T01:58:25+00:00
|
{"license": "mit", "size_categories": ["10K<n<100K"], "tags": ["art"]}
|
2023-10-30T17:41:10+00:00
|
[] |
[] |
TAGS
#size_categories-10K<n<100K #license-mit #art #region-us
|
Bangumi Image Base of My Hero Academia
======================================
This is the image base of bangumi My Hero Academia, we detected 146 characters, 15676 images in total. The full dataset is here.
Please note that these image bases are not guaranteed to be 100% cleaned, they may be noisy actual. If you intend to manually train models using this dataset, we recommend performing necessary preprocessing on the downloaded dataset to eliminate potential noisy samples (approximately 1% probability).
Here is the characters' preview:
|
[] |
[
"TAGS\n#size_categories-10K<n<100K #license-mit #art #region-us \n"
] |
[
25
] |
[
"passage: TAGS\n#size_categories-10K<n<100K #license-mit #art #region-us \n"
] |
690ff0219a13274a11fe2465e890a592e010bbd9
|
# Dataset Card for "amazon_tts_encodec"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
lca0503/amazon_tts_encodec
|
[
"region:us"
] |
2023-10-26T02:01:32+00:00
|
{"dataset_info": {"features": [{"name": "file_id", "dtype": "string"}, {"name": "instruction", "dtype": "string"}, {"name": "transcription", "dtype": "string"}, {"name": "src_encodec_0", "sequence": "int64"}, {"name": "src_encodec_1", "sequence": "int64"}, {"name": "src_encodec_2", "sequence": "int64"}, {"name": "src_encodec_3", "sequence": "int64"}, {"name": "src_encodec_4", "sequence": "int64"}, {"name": "src_encodec_5", "sequence": "int64"}, {"name": "src_encodec_6", "sequence": "int64"}, {"name": "src_encodec_7", "sequence": "int64"}, {"name": "tgt_encodec_0", "sequence": "int64"}, {"name": "tgt_encodec_1", "sequence": "int64"}, {"name": "tgt_encodec_2", "sequence": "int64"}, {"name": "tgt_encodec_3", "sequence": "int64"}, {"name": "tgt_encodec_4", "sequence": "int64"}, {"name": "tgt_encodec_5", "sequence": "int64"}, {"name": "tgt_encodec_6", "sequence": "int64"}, {"name": "tgt_encodec_7", "sequence": "int64"}], "splits": [{"name": "train", "num_bytes": 676568694, "num_examples": 19143}], "download_size": 108921169, "dataset_size": 676568694}}
|
2023-10-26T02:01:50+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "amazon_tts_encodec"
More Information needed
|
[
"# Dataset Card for \"amazon_tts_encodec\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"amazon_tts_encodec\"\n\nMore Information needed"
] |
[
6,
18
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"amazon_tts_encodec\"\n\nMore Information needed"
] |
35188003afbfd399045557e13da5e9a1592da843
|
Take fairseq, a Japanese novel, and make up the dialogues based on the translation.
Translated by: fairseq.
Made by: Mistral-7B-Instruct-5.0bpw.
Theme: Japanese novel.
|
twodgirl/Fear-and-Frivolity
|
[
"language:en",
"conversational",
"novel",
"fairseq",
"not-for-all-audiences",
"region:us"
] |
2023-10-26T02:33:04+00:00
|
{"language": ["en"], "tags": ["conversational", "novel", "fairseq", "not-for-all-audiences"]}
|
2023-10-26T02:41:12+00:00
|
[] |
[
"en"
] |
TAGS
#language-English #conversational #novel #fairseq #not-for-all-audiences #region-us
|
Take fairseq, a Japanese novel, and make up the dialogues based on the translation.
Translated by: fairseq.
Made by: Mistral-7B-Instruct-5.0bpw.
Theme: Japanese novel.
|
[] |
[
"TAGS\n#language-English #conversational #novel #fairseq #not-for-all-audiences #region-us \n"
] |
[
30
] |
[
"passage: TAGS\n#language-English #conversational #novel #fairseq #not-for-all-audiences #region-us \n"
] |
87910097322292e7c1d86e22f4e8dd0507028742
|
# Dataset Card for "test"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
parksimon0808/test
|
[
"region:us"
] |
2023-10-26T02:49:59+00:00
|
{"dataset_info": {"features": [{"name": "input_ids", "sequence": "int32"}, {"name": "labels", "sequence": "int64"}], "splits": [{"name": "train", "num_bytes": 64, "num_examples": 2}], "download_size": 1726, "dataset_size": 64}}
|
2023-10-26T02:50:08+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "test"
More Information needed
|
[
"# Dataset Card for \"test\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"test\"\n\nMore Information needed"
] |
[
6,
11
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"test\"\n\nMore Information needed"
] |
2d7fd0330ea414b51ffe1a614311dafb8c312822
|
# Dataset Card for Dataset Name
<!-- Provide a quick summary of the dataset. -->
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).
## Dataset Details
### Dataset Description
<!-- Provide a longer summary of what this dataset is. -->
- **Curated by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
### Dataset Sources [optional]
<!-- Provide the basic links for the dataset. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the dataset is intended to be used. -->
### Direct Use
<!-- This section describes suitable use cases for the dataset. -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. -->
[More Information Needed]
## Dataset Structure
<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->
[More Information Needed]
## Dataset Creation
### Curation Rationale
<!-- Motivation for the creation of this dataset. -->
[More Information Needed]
### Source Data
<!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). -->
#### Data Collection and Processing
<!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. -->
[More Information Needed]
#### Who are the source data producers?
<!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. -->
[More Information Needed]
### Annotations [optional]
<!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. -->
#### Annotation process
<!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. -->
[More Information Needed]
#### Who are the annotators?
<!-- This section describes the people or systems who created the annotations. -->
[More Information Needed]
#### Personal and Sensitive Information
<!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
## Citation [optional]
<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Dataset Card Authors [optional]
[More Information Needed]
## Dataset Card Contact
[More Information Needed]
|
jannko/fund-sft
|
[
"task_categories:text-generation",
"size_categories:10K<n<100K",
"language:zh",
"license:cc-by-4.0",
"gpt",
"alpaca",
"fine-tune",
"instruct-tune",
"instruction",
"region:us"
] |
2023-10-26T02:55:21+00:00
|
{"language": ["zh"], "license": "cc-by-4.0", "size_categories": ["10K<n<100K"], "task_categories": ["text-generation"], "pretty_name": "Sft Dataset", "dataset_info": {"features": [{"name": "instruction", "dtype": "string"}, {"name": "input", "dtype": "string"}, {"name": "output", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 32150579, "num_examples": 48818}], "download_size": 35100559, "dataset_size": 32150579}, "tags": ["gpt", "alpaca", "fine-tune", "instruct-tune", "instruction"]}
|
2023-10-26T06:09:12+00:00
|
[] |
[
"zh"
] |
TAGS
#task_categories-text-generation #size_categories-10K<n<100K #language-Chinese #license-cc-by-4.0 #gpt #alpaca #fine-tune #instruct-tune #instruction #region-us
|
# Dataset Card for Dataset Name
This dataset card aims to be a base template for new datasets. It has been generated using this raw template.
## Dataset Details
### Dataset Description
- Curated by:
- Funded by [optional]:
- Shared by [optional]:
- Language(s) (NLP):
- License:
### Dataset Sources [optional]
- Repository:
- Paper [optional]:
- Demo [optional]:
## Uses
### Direct Use
### Out-of-Scope Use
## Dataset Structure
## Dataset Creation
### Curation Rationale
### Source Data
#### Data Collection and Processing
#### Who are the source data producers?
### Annotations [optional]
#### Annotation process
#### Who are the annotators?
#### Personal and Sensitive Information
## Bias, Risks, and Limitations
### Recommendations
Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
[optional]
BibTeX:
APA:
## Glossary [optional]
## More Information [optional]
## Dataset Card Authors [optional]
## Dataset Card Contact
|
[
"# Dataset Card for Dataset Name\n\n\n\nThis dataset card aims to be a base template for new datasets. It has been generated using this raw template.",
"## Dataset Details",
"### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:",
"### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Out-of-Scope Use",
"## Dataset Structure",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Data Collection and Processing",
"#### Who are the source data producers?",
"### Annotations [optional]",
"#### Annotation process",
"#### Who are the annotators?",
"#### Personal and Sensitive Information",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Dataset Card Authors [optional]",
"## Dataset Card Contact"
] |
[
"TAGS\n#task_categories-text-generation #size_categories-10K<n<100K #language-Chinese #license-cc-by-4.0 #gpt #alpaca #fine-tune #instruct-tune #instruction #region-us \n",
"# Dataset Card for Dataset Name\n\n\n\nThis dataset card aims to be a base template for new datasets. It has been generated using this raw template.",
"## Dataset Details",
"### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:",
"### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Out-of-Scope Use",
"## Dataset Structure",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Data Collection and Processing",
"#### Who are the source data producers?",
"### Annotations [optional]",
"#### Annotation process",
"#### Who are the annotators?",
"#### Personal and Sensitive Information",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Dataset Card Authors [optional]",
"## Dataset Card Contact"
] |
[
60,
34,
4,
40,
29,
3,
4,
9,
6,
5,
7,
4,
7,
10,
9,
5,
9,
8,
10,
46,
8,
7,
10,
5
] |
[
"passage: TAGS\n#task_categories-text-generation #size_categories-10K<n<100K #language-Chinese #license-cc-by-4.0 #gpt #alpaca #fine-tune #instruct-tune #instruction #region-us \n# Dataset Card for Dataset Name\n\n\n\nThis dataset card aims to be a base template for new datasets. It has been generated using this raw template.## Dataset Details### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Out-of-Scope Use## Dataset Structure## Dataset Creation### Curation Rationale### Source Data#### Data Collection and Processing#### Who are the source data producers?### Annotations [optional]#### Annotation process#### Who are the annotators?#### Personal and Sensitive Information## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Dataset Card Authors [optional]## Dataset Card Contact"
] |
3a565cbff3c3cceae1c382cbd42f52eb34d456bf
|
# Dataset Card for "ig_rewarding_db"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
toilaluan/ig_rewarding_db
|
[
"region:us"
] |
2023-10-26T03:10:41+00:00
|
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "topic", "dtype": "string"}, {"name": "prompt", "dtype": "string"}, {"name": "request_id", "dtype": "int64"}, {"name": "model_type", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 124711564.8, "num_examples": 2400}], "download_size": 233821001, "dataset_size": 124711564.8}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-10-26T06:40:55+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "ig_rewarding_db"
More Information needed
|
[
"# Dataset Card for \"ig_rewarding_db\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"ig_rewarding_db\"\n\nMore Information needed"
] |
[
6,
17
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"ig_rewarding_db\"\n\nMore Information needed"
] |
34d63bacfaf4d4fe00739c0e2225d3ff3c0fc1bd
|
# Dataset Card for "rsna_5k"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
Phaedrus/rsna_5k
|
[
"region:us"
] |
2023-10-26T03:17:37+00:00
|
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "label1", "dtype": "image"}, {"name": "label2", "dtype": "image"}, {"name": "label3", "dtype": "image"}, {"name": "label4", "dtype": "image"}], "splits": [{"name": "train", "num_bytes": 5400048146.0, "num_examples": 5000}], "download_size": 382358945, "dataset_size": 5400048146.0}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-10-26T03:21:12+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "rsna_5k"
More Information needed
|
[
"# Dataset Card for \"rsna_5k\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"rsna_5k\"\n\nMore Information needed"
] |
[
6,
15
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"rsna_5k\"\n\nMore Information needed"
] |
550dabbad3457f77310c46536bc1eec253ce8c87
|
# Dataset Card for "zc-misti-domain"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
dtorres-zAgile/zc-misti-domain
|
[
"region:us"
] |
2023-10-26T03:18:23+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "title", "dtype": "string"}, {"name": "url", "dtype": "string"}, {"name": "summary", "dtype": "string"}, {"name": "content", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 433813, "num_examples": 122}], "download_size": 203951, "dataset_size": 433813}}
|
2023-10-26T03:18:25+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "zc-misti-domain"
More Information needed
|
[
"# Dataset Card for \"zc-misti-domain\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"zc-misti-domain\"\n\nMore Information needed"
] |
[
6,
18
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"zc-misti-domain\"\n\nMore Information needed"
] |
f1e3b25c597aa6c55e5382d7d950da11da022e18
|
# Dataset Card for "annotation2_wo_elimination"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
anlp/annotation2_wo_elimination
|
[
"region:us"
] |
2023-10-26T03:26:26+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "sentences", "sequence": "string"}, {"name": "ner_tags", "sequence": "string"}], "splits": [{"name": "train", "num_bytes": 1326274, "num_examples": 3384}], "download_size": 0, "dataset_size": 1326274}}
|
2023-10-26T03:41:27+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "annotation2_wo_elimination"
More Information needed
|
[
"# Dataset Card for \"annotation2_wo_elimination\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"annotation2_wo_elimination\"\n\nMore Information needed"
] |
[
6,
18
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"annotation2_wo_elimination\"\n\nMore Information needed"
] |
f0da68477883ad330367b4c8e1d1ad981031dbdf
|
微调google/mt5-base模型，做文章摘要
```python
import torch
from transformers import T5ForConditionalGeneration, T5Tokenizer
model_path = "twwch/mt5-base-summary"
model = T5ForConditionalGeneration.from_pretrained(model_path)
tokenizer = T5Tokenizer.from_pretrained(model_path)
device = torch.device('cuda:0') if torch.cuda.is_available() else torch.device('cpu')
model.to(device)
model.eval()
text = """
什么是Nginx
Nginx是一个开源的高性能HTTP和反向代理服务器。它可以用于处理静态资源、负载均衡、反向代理和缓存等任务。Nginx被广泛用于构建高可用性、高性能的Web应用程序和网站。它具有低内存消耗、高并发能力和良好的稳定性，因此在互联网领域非常受欢迎。
为什么使用Nginx
高性能：Nginx采用事件驱动的异步架构，能够处理大量并发连接而不会消耗过多的系统资源。它的处理能力比传统的Web服务器更高，在高并发负载下表现出色。
高可靠性：Nginx具有强大的容错能力和稳定性，能够在面对高流量和DDoS攻击等异常情况下保持可靠运行。它能通过健康检查和自动故障转移来保证服务的可用性。
负载均衡：Nginx可以作为反向代理服务器，实现负载均衡，将请求均匀分发给多个后端服务器。这样可以提高系统的整体性能和可用性。
静态文件服务：Nginx对静态资源（如HTML、CSS、JavaScript、图片等）的处理非常高效。它可以直接缓存静态文件，减轻后端服务器的负载。
扩展性：Nginx支持丰富的模块化扩展，可以通过添加第三方模块来提供额外的功能，如gzip压缩、SSL/TLS加密、缓存控制等。
如何处理请求
Nginx处理请求的基本流程如下：
接收请求：Nginx作为服务器软件监听指定的端口，接收客户端发来的请求。
解析请求：Nginx解析请求的内容，包括请求方法（GET、POST等）、URL、头部信息等。
配置匹配：Nginx根据配置文件中的规则和匹配条件，决定如何处理该请求。配置文件定义了虚拟主机、反向代理、负载均衡、缓存等特定的处理方式。
处理请求：Nginx根据配置的处理方式，可能会进行以下操作：
静态文件服务：如果请求的是静态资源文件，如HTML、CSS、JavaScript、图片等，Nginx可以直接返回文件内容，不必经过后端应用程序。
反向代理：如果配置了反向代理，Nginx将请求转发给后端的应用服务器，然后将其响应返回给客户端。这样可以提供负载均衡、高可用性和缓存等功能。
缓存：如果启用了缓存，Nginx可以缓存一些静态或动态内容的响应，在后续相同的请求中直接返回缓存的响应，减少后端负载并提高响应速度。
URL重写：Nginx可以根据配置的规则对URL进行重写，将请求从一个URL重定向到另一个URL或进行转换。
SSL/TLS加密：如果启用了SSL/TLS，Nginx可以负责加密和解密HTTPS请求和响应。
访问控制：Nginx可以根据配置的规则对请求进行访问控制，例如限制IP访问、进行身份认证等。
响应结果：Nginx根据处理结果生成响应报文，包括状态码、头部信息和响应内容。然后将响应发送给客户端。
"""
def _split_text(text, length):
chunks = []
start = 0
while start < len(text):
if len(text) - start > length:
pos_forward = start + length
pos_backward = start + length
pos = start + length
while (pos_forward < len(text)) and (pos_backward >= 0) and (pos_forward < 20 + pos) and (
pos_backward + 20 > pos) and text[pos_forward] not in {'.', '。', '，', ','} and text[
pos_backward] not in {'.', '。', '，', ','}:
pos_forward += 1
pos_backward -= 1
if pos_forward - pos >= 20 and pos_backward <= pos - 20:
pos = start + length
elif text[pos_backward] in {'.', '。', '，', ','}:
pos = pos_backward
else:
pos = pos_forward
chunks.append(text[start:pos + 1])
start = pos + 1
else:
chunks.append(text[start:])
break
# Combine last chunk with previous one if it's too short
if len(chunks) > 1 and len(chunks[-1]) < 100:
chunks[-2] += chunks[-1]
chunks.pop()
return chunks
def summary(text):
chunks = _split_text(text, 300)
chunks = [
"summarize: " + chunk
for chunk in chunks
]
input_ids = tokenizer(chunks, return_tensors="pt",
max_length=512,
padding=True,
truncation=True).input_ids.to(device)
outputs = model.generate(input_ids, max_length=250, num_beams=4, no_repeat_ngram_size=2)
tokens = outputs.tolist()
output_text = [
tokenizer.decode(tokens[i], skip_special_tokens=True)
for i in range(len(tokens))
]
for i in range(len(output_text)):
print(output_text[i])
summary(text)
```
输出：
```
段落内容Nginx是一个开源的高性能HTTP和反向代理服务器,可以用于处理静态资源、负载均衡、反反代理和缓存等任务。它被广泛用于构建高可用性、高性能的Web应用程序和网站,具有低内存消耗、高并发能力和良好的稳定性,因此在互联网领域非常受欢迎。高性能和高可靠性相比传统的Web服务器更高,在高并且发负担下表现出色。高稳定性和容错能力,能够在面对高流量和DDoS攻击等异常情况下保持可靠运行。
段落内容Nginx处理请求的基本流程,包括负载均衡、静态文件服务、扩展性、如何解决请求的流程和如何处理。其中包括接收请求和解析请求,以及对客户端发来的请求进行解析。
段落内容Nginx的配置匹配和处理请求。配置文件定义了虚拟主机、反向代理、负载均衡、缓存等特定的处理方式,并根据配置进行静态文件服务和反面信息处理的操作。通过调用静存来实现高可用性,并且可以提供高可性和缓储等功能。
段落内容主要涉及到缓存静态或动态内容的响应,包括URL重写、SSL/TLS加密、访问控制、响应结果生成和发送给客户端等功能。Nginx可以根据配置的规则对URL进行重写作,将请求从一个URL轻定向到另一个URL或进行转换。 综上所述,Nginx的缓解和响应速度可以快速提高。
```
|
twwch/summary
|
[
"task_categories:summarization",
"size_categories:10K<n<100K",
"language:zh",
"license:apache-2.0",
"region:us"
] |
2023-10-26T04:11:36+00:00
|
{"language": ["zh"], "license": "apache-2.0", "size_categories": ["10K<n<100K"], "task_categories": ["summarization"], "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "test", "path": "data/test-*"}]}], "dataset_info": {"features": [{"name": "source", "dtype": "string"}, {"name": "target", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 31798343, "num_examples": 10352}, {"name": "test", "num_bytes": 3617590, "num_examples": 1151}], "download_size": 17798756, "dataset_size": 35415933}}
|
2023-10-26T05:32:33+00:00
|
[] |
[
"zh"
] |
TAGS
#task_categories-summarization #size_categories-10K<n<100K #language-Chinese #license-apache-2.0 #region-us
|
微调google/mt5-base模型，做文章摘要
输出：
|
[] |
[
"TAGS\n#task_categories-summarization #size_categories-10K<n<100K #language-Chinese #license-apache-2.0 #region-us \n"
] |
[
41
] |
[
"passage: TAGS\n#task_categories-summarization #size_categories-10K<n<100K #language-Chinese #license-apache-2.0 #region-us \n"
] |
cab217b1e3beadb430d44cdcb632d06c8a024ead
|
# Dataset Card for "trigger_dataset"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
pphuc25/trigger_dataset
|
[
"region:us"
] |
2023-10-26T04:18:33+00:00
|
{"dataset_info": {"features": [{"name": "label", "dtype": "int64"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 304886.95362663496, "num_examples": 2511}], "download_size": 130993, "dataset_size": 304886.95362663496}}
|
2023-10-26T04:18:34+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "trigger_dataset"
More Information needed
|
[
"# Dataset Card for \"trigger_dataset\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"trigger_dataset\"\n\nMore Information needed"
] |
[
6,
15
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"trigger_dataset\"\n\nMore Information needed"
] |
bd90ca619fc9479e70c83f989a1a7e17f1905a7e
|
# Dataset Card for "non_trigger_dataset"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
pphuc25/non_trigger_dataset
|
[
"region:us"
] |
2023-10-26T04:18:34+00:00
|
{"dataset_info": {"features": [{"name": "label", "dtype": "int64"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 307801.04637336504, "num_examples": 2535}], "download_size": 205691, "dataset_size": 307801.04637336504}}
|
2023-10-26T04:18:36+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "non_trigger_dataset"
More Information needed
|
[
"# Dataset Card for \"non_trigger_dataset\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"non_trigger_dataset\"\n\nMore Information needed"
] |
[
6,
17
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"non_trigger_dataset\"\n\nMore Information needed"
] |
60f809463426e574fb786b948a8ddc2096f1a718
|
# Dataset Card for "non_selfie_dataset"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
pphuc25/non_selfie_dataset
|
[
"region:us"
] |
2023-10-26T04:20:37+00:00
|
{"dataset_info": {"features": [{"name": "label", "dtype": "int64"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 151711.86280487804, "num_examples": 1670}], "download_size": 88838, "dataset_size": 151711.86280487804}}
|
2023-10-26T04:20:38+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "non_selfie_dataset"
More Information needed
|
[
"# Dataset Card for \"non_selfie_dataset\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"non_selfie_dataset\"\n\nMore Information needed"
] |
[
6,
17
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"non_selfie_dataset\"\n\nMore Information needed"
] |
d0224774e3b5ac42cd6e6dc28c76acc92ab6f275
|
# Dataset Card for "selfie_dataset"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
pphuc25/selfie_dataset
|
[
"region:us"
] |
2023-10-26T04:20:38+00:00
|
{"dataset_info": {"features": [{"name": "label", "dtype": "int64"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 146261.13719512196, "num_examples": 1610}], "download_size": 38650, "dataset_size": 146261.13719512196}}
|
2023-10-26T04:20:40+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "selfie_dataset"
More Information needed
|
[
"# Dataset Card for \"selfie_dataset\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"selfie_dataset\"\n\nMore Information needed"
] |
[
6,
15
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"selfie_dataset\"\n\nMore Information needed"
] |
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