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fa421811c09eff295c78d47f8a292e8f1f490e92 | # Dataset Card for "govdocs1-txt-raw"
Somewhere to put the raw txt files before filtering them
Source info/page: https://digitalcorpora.org/corpora/file-corpora/files/
```
@inproceedings{garfinkel2009bringing,
title={Bringing Science to Digital Forensics with Standardized Forensic Corpora},
author={Garfinkel, Simson and Farrell, Paul and Roussev, Vassil and Dinolt, George},
booktitle={Digital Forensic Research Workshop (DFRWS) 2009},
year={2009},
address={Montreal, Canada},
url={https://digitalcorpora.org/corpora/file-corpora/files/}
}
``` | BEE-spoke-data/govdocs1-txt-raw | [
"task_categories:text-generation",
"size_categories:10K<n<100K",
"license:odc-by",
"region:us"
]
| 2023-11-12T23:28:17+00:00 | {"license": "odc-by", "size_categories": ["10K<n<100K"], "task_categories": ["text-generation"], "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "validation", "path": "data/validation-*"}, {"split": "test", "path": "data/test-*"}]}], "dataset_info": {"features": [{"name": "section", "dtype": "string"}, {"name": "filename", "dtype": "string"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 16101385278.039026, "num_examples": 67984}, {"name": "validation", "num_bytes": 894547719.9804866, "num_examples": 3777}, {"name": "test", "num_bytes": 894547719.9804866, "num_examples": 3777}], "download_size": 7656656755, "dataset_size": 17890480718}} | 2023-11-19T20:03:52+00:00 | []
| []
| TAGS
#task_categories-text-generation #size_categories-10K<n<100K #license-odc-by #region-us
| # Dataset Card for "govdocs1-txt-raw"
Somewhere to put the raw txt files before filtering them
Source info/page: URL
| [
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]
|
2824c94d559b5fb54730ddb4eefe43838cd100f2 | # Dataset Card for "banking"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | benayas/banking | [
"region:us"
]
| 2023-11-12T23:31:49+00:00 | {"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "test", "path": "data/test-*"}]}], "dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "category", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 886311, "num_examples": 10003}, {"name": "test", "num_bytes": 254570, "num_examples": 3080}], "download_size": 386242, "dataset_size": 1140881}} | 2023-11-28T21:23:20+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "banking"
More Information needed | [
"# Dataset Card for \"banking\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"banking\"\n\nMore Information needed"
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|
b14a11be631da9c65dcada87ead1e1d4d76f0024 | # Dataset Card for "banking_llm_v1"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | benayas/banking_llm_v1 | [
"region:us"
]
| 2023-11-12T23:33:38+00:00 | {"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "test", "path": "data/test-*"}]}], "dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "category", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 4388589, "num_examples": 10003}, {"name": "test", "num_bytes": 1330770, "num_examples": 3080}], "download_size": 718094, "dataset_size": 5719359}} | 2023-11-26T22:30:00+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "banking_llm_v1"
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|
d5956d76ebda243043638d4d82c36aa5cee12433 | # Dataset Card for "atis"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | benayas/atis | [
"region:us"
]
| 2023-11-12T23:34:25+00:00 | {"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "test", "path": "data/test-*"}]}], "dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "category", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 371856, "num_examples": 4455}, {"name": "test", "num_bytes": 109115, "num_examples": 1373}], "download_size": 160084, "dataset_size": 480971}} | 2023-11-28T21:20:18+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "atis"
More Information needed | [
"# Dataset Card for \"atis\"\n\nMore Information needed"
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"# Dataset Card for \"atis\"\n\nMore Information needed"
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|
f8653fe6c20d1ee98254d8b4469b2f877147e03f | # Dataset Card for "atis_llm_v1"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | benayas/atis_llm_v1 | [
"region:us"
]
| 2023-11-12T23:34:40+00:00 | {"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "test", "path": "data/test-*"}]}], "dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "category", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1890318, "num_examples": 4455}, {"name": "test", "num_bytes": 577125, "num_examples": 1373}], "download_size": 318756, "dataset_size": 2467443}} | 2023-11-26T22:28:29+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "atis_llm_v1"
More Information needed | [
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"TAGS\n#region-us \n",
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|
4152073679ed035c141984fca31be072a910e4c5 |
These are the LLM responses generated by Falcon 7b using the eval dataset of 500 Deep Learning + Machine Learning Questions found here:
https://huggingface.co/datasets/mjphayes/machine_learning_questions
| domnasrabadi/falcon7bRAG_eval | [
"license:apache-2.0",
"region:us"
]
| 2023-11-12T23:38:43+00:00 | {"license": "apache-2.0"} | 2023-11-13T05:57:03+00:00 | []
| []
| TAGS
#license-apache-2.0 #region-us
|
These are the LLM responses generated by Falcon 7b using the eval dataset of 500 Deep Learning + Machine Learning Questions found here:
URL
| []
| [
"TAGS\n#license-apache-2.0 #region-us \n"
]
| [
14
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| [
"passage: TAGS\n#license-apache-2.0 #region-us \n"
]
|
64f723fbd1256dc76e44f29a6e823e1aa4fc41b8 | [
{
"instruction": "What emotional appeal do Innocent archetype brands typically use in their advertising?",
"input": "",
"output": "Innocent archetype brands typically use emotional appeals that focus on happiness, comfort, and a return to simpler times. Their advertising often evokes feelings of warmth, safety, and nostalgia, aiming to create a connection through positive, wholesome imagery."
},
{
"instruction": "How do Sage archetype brands position themselves in terms of expertise and knowledge?",
"input": "",
"output": "Sage archetype brands position themselves as sources of wisdom and expertise. They often highlight their depth of knowledge, commitment to truth, and intellectual prowess, portraying themselves as trusted authorities in their field."
},
{
"instruction": "What is the key messaging strategy of Explorer archetype brands regarding adventure and discovery?",
"input": "",
"output": "The key messaging strategy of Explorer archetype brands is to emphasize the thrill of adventure and the value of discovery. They inspire their audience to embark on journeys, both literal and metaphorical, that promise excitement, freedom, and self-discovery."
},
{
"instruction": "In what ways do Innocent archetype brands promote simplicity and clarity?",
"input": "",
"output": "Innocent archetype brands promote simplicity and clarity by using straightforward messaging, clean and uncomplicated design, and focusing on the essential qualities of their products or services. They aim to provide clear, simple solutions that resonate with a desire for purity and ease."
},
{
"instruction": "How do Sage archetype brands use educational content to engage their audience?",
"input": "",
"output": "Sage archetype brands use educational content such as tutorials, informative articles, expert opinions, and insightful research to engage their audience. They focus on providing valuable knowledge and understanding, thus positioning themselves as go-to sources for information and education."
},
{
"instruction": "What kind of imagery do Explorer archetype brands often use to appeal to their target audience?",
"input": "",
"output": "Explorer archetype brands often use imagery that conveys vast, open landscapes, uncharted territories, and scenes of adventure and exploration. This imagery is designed to invoke feelings of wanderlust, curiosity, and the allure of the unknown."
},
{
"instruction": "How do Innocent archetype brands convey a message of trust and reliability?",
"input": "",
"output": "Innocent archetype brands convey trust and reliability by maintaining a consistent, honest, and straightforward approach in their messaging. They often emphasize traditional values, quality, and a commitment to delivering on their promises without complications or hidden agendas."
},
{
"instruction": "In what ways do Sage archetype brands demonstrate their commitment to truth and accuracy?",
"input": "",
"output": "Sage archetype brands demonstrate their commitment to truth and accuracy by backing their claims with research, data, and expert validation. They prioritize factual information, logical reasoning, and a rational approach to problems and solutions."
},
{
"instruction": "What strategies do Explorer archetype brands use to create a sense of individualism and freedom in their products?",
"input": "",
"output": "Explorer archetype brands create a sense of individualism and freedom by offering customizable options, encouraging personal expression, and promoting the idea that their products are tools for self-exploration and unique experiences."
},
{
"instruction": "How do Innocent archetype brands create a sense of comfort and security in their marketing?",
"input": "",
"output": "Innocent archetype brands create a sense of comfort and security by using warm, reassuring tones in their marketing. They focus on creating a friendly, approachable image and often highlight the safety, reliability, and simplicity of their products."
},
{
"instruction": "What role does nostalgia play in the marketing of Innocent archetype brands?",
"input": "",
"output": "Nostalgia plays a significant role in the marketing of Innocent archetype brands, as they often evoke memories of a simpler, more idyllic past. This approach aims to create an emotional connection with consumers who yearn for a sense of simplicity and purity in their lives."
},
{
"instruction": "How do Sage archetype brands position themselves as thought leaders in their industry?",
"input": "",
"output": "Sage archetype brands position themselves as thought leaders by consistently providing innovative ideas, insightful analysis, and authoritative content. They lead conversations in their industry and are often at the forefront of research and development."
},
{
"instruction": "In what ways do Explorer archetype brands encourage a sense of adventure in their consumers?",
"input": "",
"output": "Explorer archetype brands encourage a sense of adventure by showcasing the thrill and excitement of new experiences. They inspire consumers to step out of their comfort zones, explore new horizons, and embrace the journey of self-discovery."
},
{
"instruction": "How do Innocent archetype brands use simplicity in their product design and functionality?",
"input": "",
"output": "Innocent archetype brands use simplicity in product design and functionality by focusing on user-friendliness, minimalism, and ease of use. Their products often feature straightforward designs that are intuitive and uncomplicated, appealing to consumers' desire for clarity and efficiency."
},
{
"instruction": "What methods do Sage archetype brands use to validate their expertise and knowledge?",
"input": "",
"output": "Sage archetype brands validate their expertise and knowledge by citing scientific studies, showcasing certifications, and highlighting their history of expertise. They often use expert endorsements and factual evidence to reinforce their authority and credibility."
},
{
"instruction": "What messaging themes do Explorer archetype brands typically use to resonate with their target audience?",
"input": "",
"output": "Explorer archetype brands typically use messaging themes centered around freedom, exploration, and personal growth. They emphasize the idea of breaking free from the ordinary, embarking on new journeys, and discovering one's true self through adventure."
},
{
"instruction": "How do Innocent archetype brands communicate their values of purity and wholesomeness?",
"input": "",
"output": "Innocent archetype brands communicate their values of purity and wholesomeness by using clean, natural imagery, focusing on simplicity in their messaging, and often highlighting organic or natural ingredients in their products."
},
{
"instruction": "In what ways do Sage archetype brands use their knowledge to solve consumer problems?",
"input": "",
"output": "Sage archetype brands use their knowledge to solve consumer problems by offering expert advice, providing detailed information, and presenting well-researched solutions. They often position themselves as reliable sources of information that consumers can turn to for solving specific issues."
},
{
"instruction": "What type of product features do Explorer archetype brands highlight to appeal to their audience's sense of discovery?",
"input": "",
"output": "Explorer archetype brands highlight product features that enhance the experience of discovery, such as durability in various environments, adaptability to different situations, and innovative design that encourages exploration and adventure."
},
{
"instruction": "How do Innocent archetype brands create an emotional connection with their audience through their branding?",
"input": "",
"output": "Innocent archetype brands create an emotional connection with their audience by evoking feelings of warmth, safety, and nostalgia. They often use storytelling, familiar imagery, and a comforting tone in their branding to resonate emotionally with consumers."
},
{
"instruction": "What is the core desire of the Explorer archetype in branding?",
"input": "",
"output": "The core desire of the Explorer archetype in branding is the freedom to find out who you are through exploring the world."
},
{
"instruction": "What is the greatest fear of the Explorer archetype according to Chapter 5?",
"input": "",
"output": "The greatest fear of the Explorer archetype is getting trapped, conforming, inner emptiness, and nonbeing."
},
{
"instruction": "How does Starbucks embody the Explorer archetype?",
"input": "",
"output": "Starbucks embodies the Explorer archetype by offering an experience of global discovery and personal freedom, through its diverse range of coffees from different parts of the world, and its store designs and branding that evoke a sense of adventure."
},
{
"instruction": "What does the Explorer archetype's journey represent in branding?",
"input": "",
"output": "In branding, the Explorer archetype's journey represents a search for authenticity and fulfillment, often involving a physical or metaphorical journey to explore new horizons and experiences."
},
{
"instruction": "What type of products and services resonate with the Explorer archetype?",
"input": "",
"output": "Products and services that resonate with the Explorer archetype are those that serve as props on journeys of discovery, such as off-road vehicles, travel services, outdoor gear, and brands that emphasize individuality and freedom."
},
{
"instruction": "Why is 'The Wizard of Oz' an example of the Explorer archetype?",
"input": "",
"output": "'The Wizard of Oz' is an example of the Explorer archetype because it features a journey of self-discovery and exploration in a new, unknown world, embodying the quest for something greater and the exploration of new horizons."
},
{
"instruction": "How does the Explorer archetype view sports and physical activities?",
"input": "",
"output": "The Explorer archetype favors individualistic or solitary sports and physical activities, such as kayaking, skiing, biking, or long-distance running, which offer experiences in nature and opportunities for self-discovery and challenge."
},
{
"instruction": "What is a common marketing strategy for Explorer archetype brands?",
"input": "",
"output": "A common marketing strategy for Explorer archetype brands is to appeal to the sense of adventure and discovery, emphasizing freedom, authenticity, and the journey rather than the destination."
},
{
"instruction": "What is the strategy employed by the Explorer archetype?",
"input": "",
"output": "The strategy of the Explorer archetype involves journeying, seeking out and experiencing new things, and escaping from entrapment and boredom."
},
{
"instruction": "What are some of the narrative genres associated with the Explorer archetype?",
"input": "",
"output": "Narrative genres associated with the Explorer archetype include travelogues, immigrant narratives, science fiction, coming-of-age stories, expatriate literature, and absurdist literature demonstrating human alienation."
},
{
"instruction": "Can you name a few famous Explorer-brand television series?",
"input": "",
"output": "Famous Explorer-brand television series include 'The Lone Ranger' and 'Star Trek', which embody the essence of exploration and adventure."
},
{
"instruction": "What is the typical natural setting for an Explorer's advertisement?",
"input": "",
"output": "The typical natural setting for an Explorer's advertisement includes big open skies, open roads, nature in its various forms, especially mountains, and the night sky symbolizing infinite possibilities."
},
{
"instruction": "How does the Explorer archetype influence clothing brands?",
"input": "",
"output": "Clothing brands influenced by the Explorer archetype offer sturdy clothes that provide freedom of movement, appealing to those who seek adventure and individuality. Brands like Levi’s, REI, Patagonia, and Land’s End are examples."
},
{
"instruction": "What type of politics is prevalent in Silicon Valley and how does it relate to the Explorer archetype?",
"input": "",
"output": "Libertarian politics are prevalent in Silicon Valley, resonating with the Explorer archetype's values of independence, laissez-faire attitudes, and empowerment of the individual."
},
{
"instruction": "How does the Explorer archetype manifest in the consumer market according to 'The Soul of the New Consumer'?",
"input": "",
"output": "According to 'The Soul of the New Consumer', the Explorer archetype manifests in the consumer market as individuals who are better informed, involved, skeptical of advertising, value authenticity, and are restless and time-conscious."
},
{
"instruction": "What is the trap of the Explorer archetype?",
"input": "",
"output": "The trap of the Explorer archetype is aimless wandering and becoming a misfit, losing direction in the pursuit of new experiences and individuality."
},
{
"instruction": "What are the levels of the Explorer archetype?",
"input": "",
"output": "The levels of the Explorer archetype include: Level One - hitting the open road and exploring the world, Level Two - seeking individuality and personal fulfillment, and Level Three - expressing individuality and uniqueness."
},
{
"instruction": "Describe the shadow aspect of the Explorer archetype.",
"input": "",
"output": "The shadow aspect of the Explorer archetype is characterized by being so alienated that one cannot find any way to fit in, leading to extreme detachment and isolation."
},
{
"instruction": "What is the appeal of Explorer archetype in sports?",
"input": "",
"output": "The appeal of the Explorer archetype in sports lies in individualistic or solitary pursuits like kayaking, skiing, or biking, which provide experiences in nature and opportunities for self-discovery."
},
{
"instruction": "How do Explorer brands like Starbucks and Amazon.com cater to their customers' individualism?",
"input": "",
"output": "Explorer brands like Starbucks and Amazon.com cater to individualism by offering unique and personalized experiences, such as customized coffee orders or book recommendations based on previous purchases."
},
{
"instruction": "What makes the United States a quintessential Explorer-brand country?",
"input": "",
"output": "The United States is a quintessential Explorer-brand country due to its immigrant heritage, emphasis on political rights, and historical narrative of exploration and seeking new horizons."
},
{
"instruction": "What challenge do Explorer archetype brands face in Asian and Latin American cultures?",
"input": "",
"output": "Explorer archetype brands face the challenge of adapting to Asian and Latin American cultures that value group relations and community more than individual exploration and independence."
},
{
"instruction": "How does the Explorer archetype manifest in the fashion choices of adolescents?",
"input": "",
"output": "The Explorer archetype manifests in the fashion choices of adolescents through styles that confront conventional norms, expressing their individuality and alignment with outsider or counter-culture trends."
},
{
"instruction": "What role does the Explorer archetype play in midlife transitions?",
"input": "",
"output": "In midlife transitions, the Explorer archetype often becomes reactivated, leading individuals to seek new experiences, change careers, or embark on journeys of self-discovery and personal fulfillment."
},
{
"instruction": "How does The Body Shop brand embody the Explorer archetype?",
"input": "",
"output": "The Body Shop embodies the Explorer archetype by offering products with exotic ingredients, emphasizing global awareness, environmental consciousness, and a spirit of individualistic activism."
},
{
"instruction": "What kind of work environment does the Explorer archetype promote?",
"input": "",
"output": "The Explorer archetype promotes a work environment that values individuality, minimizes hierarchy, and encourages innovation and pioneering spirit, often found in startups and creative industries."
},
{
"instruction": "How do virtual organizations align with the Explorer archetype?",
"input": "",
"output": "Virtual organizations align with the Explorer archetype by offering flexibility, decentralization, and autonomy, enabling individuals to work independently and pursue innovative approaches."
},
{
"instruction": "What type of housing might appeal to someone with an Explorer archetype?",
"input": "",
"output": "Housing that appeals to someone with an Explorer archetype typically includes open, natural spaces with expansive views, like those offered by Timberpeg homes, emphasizing a connection with nature and freedom."
},
{
"instruction": "Why are brands like Starbucks and Amazon.com considered Explorer brands?",
"input": "",
"output": "Brands like Starbucks and Amazon.com are considered Explorer brands because they offer unique and customizable experiences that resonate with the desire for individuality, exploration, and personal freedom."
},
{
"instruction": "What is the relationship between the Explorer archetype and technology products?",
"input": "",
"output": "The relationship between the Explorer archetype and technology products lies in the emphasis on personal empowerment, innovation, and the ability to access a world of information and experiences, aligning with the Explorer's quest for knowledge and freedom."
},
{
"instruction": "How does the Explorer archetype influence customer loyalty?",
"input": "",
"output": "The Explorer archetype influences customer loyalty by appealing to the values of authenticity, individuality, and adventure. Brands that successfully embody these values can foster strong loyalty among Explorer-oriented consumers."
},
{
"instruction": "What is the significance of the name 'Starbucks' in relation to the Explorer archetype?",
"input": "",
"output": "The name 'Starbucks', derived from the novel 'Moby Dick', evokes a sense of adventure and exploration, aligning with the Explorer archetype by suggesting a journey into the unknown and the pursuit of new experiences."
},
{
"instruction": "How does the Explorer archetype manifest in the branding of off-road vehicles?",
"input": "",
"output": "Off-road vehicle brands like Jeep Wrangler embody the Explorer archetype by emphasizing adventure, freedom, and the ability to navigate uncharted territories, appealing to consumers' desire for exploration and discovery."
},
{
"instruction": "What is the core desire of the Explorer archetype in branding?",
"input": "",
"output": "The core desire of the Explorer archetype in branding is the freedom to find out who you are through exploring the world, focusing on experiences that promote self-discovery and authenticity."
},
{
"instruction": "How does Starbucks use the Explorer archetype in its branding?",
"input": "",
"output": "Starbucks utilizes the Explorer archetype by presenting coffee as an exotic, adventurous experience, drawing from diverse global origins, and creating an atmosphere that encourages exploration and individuality."
},
{
"instruction": "In what way does Amazon.com reflect the Explorer archetype?",
"input": "",
"output": "Amazon.com reflects the Explorer archetype by offering a vast and diverse range of products, enabling customers to explore and discover new items and ideas from the comfort of their own home."
},
{
"instruction": "What role does nature play in the Explorer archetype for branding?",
"input": "",
"output": "Nature plays a significant role in the Explorer archetype for branding, as it symbolizes uncharted territory and the joy of discovery. Brands that associate with nature often appeal to the Explorer's desire for adventure and exploration in the natural world."
},
{
"instruction": "How do individual sports relate to the Explorer archetype?",
"input": "",
"output": "Individual sports like kayaking, skiing, and biking resonate with the Explorer archetype by emphasizing personal challenges, exploration of nature, and the journey of self-discovery through solitary or unique physical experiences."
},
{
"instruction": "What is the relationship between the Explorer archetype and immigrant narratives?",
"input": "",
"output": "Immigrant narratives often embody the Explorer archetype by depicting journeys into new lands and cultures, symbolizing the quest for a better life, personal growth, and the exploration of unknown territories."
},
{
"instruction": "How does the branding of boats appeal to the Explorer archetype?",
"input": "",
"output": "Boat branding appeals to the Explorer archetype by emphasizing the freedom and adventure of the open seas, inviting consumers to embark on journeys of discovery and exploration in aquatic environments."
},
{
"instruction": "In what way does the Explorer archetype influence fashion branding?",
"input": "",
"output": "Fashion brands influenced by the Explorer archetype often feature durable, functional, and versatile clothing, appealing to consumers' desire for apparel that supports adventure and individual expression."
},
{
"instruction": "How does The Great Gatsby reflect the Explorer archetype?",
"input": "",
"output": "The Great Gatsby reflects the Explorer archetype through its themes of seeking a new identity, the pursuit of the American Dream, and the exploration of the social landscape of the Jazz Age, representing a journey of self-discovery and aspiration."
},
{
"instruction": "What aspect of the Explorer archetype is captured in the success of travelogues?",
"input": "",
"output": "The success of travelogues captures the Explorer archetype's thirst for discovery, adventure, and the experience of diverse cultures and landscapes, appealing to the inherent desire to explore the unknown."
},
{
"instruction": "How do expatriate literature and the Explorer archetype relate?",
"input": "",
"output": "Expatriate literature relates to the Explorer archetype by depicting the challenges and rewards of living in a foreign culture, reflecting the journey of self-discovery and the quest for a sense of belonging in a new world."
},
{
"instruction": "What is the significance of the 'open road' in Explorer archetype branding?",
"input": "",
"output": "The 'open road' in Explorer archetype branding symbolizes freedom, endless possibilities, and the journey towards self-discovery. It appeals to consumers' longing for adventure and escape from the mundane."
},
{
"instruction": "How does the slogan 'No Boundaries' reflect the Explorer archetype for the Ford Explorer?",
"input": "",
"output": "The slogan 'No Boundaries' for the Ford Explorer reflects the Explorer archetype by emphasizing limitless exploration, the freedom to traverse diverse terrains, and the vehicle's capability to support adventurous lifestyles."
},
{
"instruction": "What is the connection between the Explorer archetype and coming-of-age stories?",
"input": "",
"output": "Coming-of-age stories connect with the Explorer archetype through their focus on personal growth, the journey from innocence to experience, and the exploration of identity and the wider world."
},
{
"instruction": "How does the archetype of the Explorer relate to absurdist literature?",
"input": "",
"output": "Absurdist literature relates to the Explorer archetype by highlighting the quest for meaning in a seemingly chaotic world, reflecting the Explorer's journey towards understanding and navigating human alienation."
},
{
"instruction": "What role does 'The Odyssey' by Homer play in exemplifying the Explorer archetype?",
"input": "",
"output": "Homer's 'The Odyssey' is a classic example of the Explorer archetype, depicting a hero's epic journey full of challenges and discoveries, symbolizing the quest for adventure, knowledge, and homecoming."
},
{
"instruction": "How is the Explorer archetype represented in science fiction?",
"input": "",
"output": "Science fiction often represents the Explorer archetype through stories of space exploration and discovery of new worlds, embodying the human quest for knowledge and the unknown."
},
{
"instruction": "What is the relationship between the Explorer archetype and literature about seeking the promised land?",
"input": "",
"output": "Literature about seeking the promised land resonates with the Explorer archetype by embodying the search for a utopian place of fulfillment and happiness, a journey that mirrors the Explorer's quest for a better world."
},
{
"instruction": "How does the Explorer archetype manifest in the branding of Yamaha motorcycles?",
"input": "",
"output": "Yamaha motorcycles embody the Explorer archetype by promoting independence, adventure, and the thrill of the open road, appealing to consumers' desire for freedom and exploration."
},
{
"instruction": "What is the significance of the slogan 'Take your body where your mind has already wandered' for Jeep Wrangler?",
"input": "",
"output": "Jeep Wrangler's slogan 'Take your body where your mind has already wandered' captures the essence of the Explorer archetype, encouraging adventure, curiosity, and the pursuit of one's desires through exploration."
},
{
"instruction": "How does the Explorer archetype influence the branding of sports equipment?",
"input": "",
"output": "Sports equipment brands influenced by the Explorer archetype focus on enabling adventure, individual challenges, and interaction with nature, appealing to consumers' desire for physical exploration and personal achievement."
},
{
"instruction": "What is the connection between the Explorer archetype and the theme of leaving marriages, jobs, or towns in literature?",
"input": "",
"output": "Themes of leaving marriages, jobs, or towns in literature connect with the Explorer archetype by depicting the pursuit of self-discovery, liberation from constraints, and the journey towards a more authentic life."
},
{
"instruction": "How does the Explorer archetype resonate with the television series 'Star Trek'?",
"input": "",
"output": "The television series 'Star Trek' resonates with the Explorer archetype through its focus on space exploration, encountering new civilizations, and the quest for knowledge and adventure beyond the known universe."
},
{
"instruction": "What characteristics of the Explorer archetype are evident in Jack Kerouac's 'On the Road'?",
"input": "",
"output": "Jack Kerouac's 'On the Road' exemplifies the Explorer archetype with its themes of freedom, self-discovery, and nonconformity, portraying a journey across America that mirrors the internal quest for identity and meaning."
},
{
"instruction": "How does the Explorer archetype influence the marketing of outdoor clothing brands?",
"input": "",
"output": "Outdoor clothing brands influenced by the Explorer archetype focus on durability, functionality, and versatility, catering to consumers' desire for apparel that supports their adventurous and individualistic lifestyles."
},
{
"instruction": "In what way does the slogan 'Your digital images. Your music. Your work. Your ideas.' for Sony's Memory Stick resonate with the Explorer archetype?",
"input": "",
"output": "Sony's Memory Stick slogan 'Your digital images. Your music. Your work. Your ideas.' resonates with the Explorer archetype by emphasizing personal freedom, the storage and expression of individual experiences, and the journey of self-documentation."
},
{
"instruction": "How does folk music embody the Explorer archetype?",
"input": "",
"output": "Folk music embodies the Explorer archetype through its storytelling of journeys, struggles, and discoveries, often reflecting themes of adventure, personal growth, and the quest for freedom."
},
{
"instruction": "What aspect of the Explorer archetype is captured in Mark Twain's 'The Adventures of Huckleberry Finn'?",
"input": "",
"output": "Mark Twain's 'The Adventures of Huckleberry Finn' captures the Explorer archetype through its themes of adventure, freedom, and the protagonist's journey of self-discovery along the Mississippi River."
},
{
"instruction": "How does 'The Waste Land' by T.S. Eliot relate to the Explorer archetype?",
"input": "",
"output": "T.S. Eliot's 'The Waste Land' relates to the Explorer archetype by exploring themes of spiritual emptiness and the search for meaning in a fragmented world, reflecting the Explorer's quest for understanding and wholeness."
}
]
| chunlohk/Archtype | [
"region:us"
]
| 2023-11-12T23:49:01+00:00 | {} | 2023-11-13T05:08:56+00:00 | []
| []
| TAGS
#region-us
| [
{
"instruction": "What emotional appeal do Innocent archetype brands typically use in their advertising?",
"input": "",
"output": "Innocent archetype brands typically use emotional appeals that focus on happiness, comfort, and a return to simpler times. Their advertising often evokes feelings of warmth, safety, and nostalgia, aiming to create a connection through positive, wholesome imagery."
},
{
"instruction": "How do Sage archetype brands position themselves in terms of expertise and knowledge?",
"input": "",
"output": "Sage archetype brands position themselves as sources of wisdom and expertise. They often highlight their depth of knowledge, commitment to truth, and intellectual prowess, portraying themselves as trusted authorities in their field."
},
{
"instruction": "What is the key messaging strategy of Explorer archetype brands regarding adventure and discovery?",
"input": "",
"output": "The key messaging strategy of Explorer archetype brands is to emphasize the thrill of adventure and the value of discovery. They inspire their audience to embark on journeys, both literal and metaphorical, that promise excitement, freedom, and self-discovery."
},
{
"instruction": "In what ways do Innocent archetype brands promote simplicity and clarity?",
"input": "",
"output": "Innocent archetype brands promote simplicity and clarity by using straightforward messaging, clean and uncomplicated design, and focusing on the essential qualities of their products or services. They aim to provide clear, simple solutions that resonate with a desire for purity and ease."
},
{
"instruction": "How do Sage archetype brands use educational content to engage their audience?",
"input": "",
"output": "Sage archetype brands use educational content such as tutorials, informative articles, expert opinions, and insightful research to engage their audience. They focus on providing valuable knowledge and understanding, thus positioning themselves as go-to sources for information and education."
},
{
"instruction": "What kind of imagery do Explorer archetype brands often use to appeal to their target audience?",
"input": "",
"output": "Explorer archetype brands often use imagery that conveys vast, open landscapes, uncharted territories, and scenes of adventure and exploration. This imagery is designed to invoke feelings of wanderlust, curiosity, and the allure of the unknown."
},
{
"instruction": "How do Innocent archetype brands convey a message of trust and reliability?",
"input": "",
"output": "Innocent archetype brands convey trust and reliability by maintaining a consistent, honest, and straightforward approach in their messaging. They often emphasize traditional values, quality, and a commitment to delivering on their promises without complications or hidden agendas."
},
{
"instruction": "In what ways do Sage archetype brands demonstrate their commitment to truth and accuracy?",
"input": "",
"output": "Sage archetype brands demonstrate their commitment to truth and accuracy by backing their claims with research, data, and expert validation. They prioritize factual information, logical reasoning, and a rational approach to problems and solutions."
},
{
"instruction": "What strategies do Explorer archetype brands use to create a sense of individualism and freedom in their products?",
"input": "",
"output": "Explorer archetype brands create a sense of individualism and freedom by offering customizable options, encouraging personal expression, and promoting the idea that their products are tools for self-exploration and unique experiences."
},
{
"instruction": "How do Innocent archetype brands create a sense of comfort and security in their marketing?",
"input": "",
"output": "Innocent archetype brands create a sense of comfort and security by using warm, reassuring tones in their marketing. They focus on creating a friendly, approachable image and often highlight the safety, reliability, and simplicity of their products."
},
{
"instruction": "What role does nostalgia play in the marketing of Innocent archetype brands?",
"input": "",
"output": "Nostalgia plays a significant role in the marketing of Innocent archetype brands, as they often evoke memories of a simpler, more idyllic past. This approach aims to create an emotional connection with consumers who yearn for a sense of simplicity and purity in their lives."
},
{
"instruction": "How do Sage archetype brands position themselves as thought leaders in their industry?",
"input": "",
"output": "Sage archetype brands position themselves as thought leaders by consistently providing innovative ideas, insightful analysis, and authoritative content. They lead conversations in their industry and are often at the forefront of research and development."
},
{
"instruction": "In what ways do Explorer archetype brands encourage a sense of adventure in their consumers?",
"input": "",
"output": "Explorer archetype brands encourage a sense of adventure by showcasing the thrill and excitement of new experiences. They inspire consumers to step out of their comfort zones, explore new horizons, and embrace the journey of self-discovery."
},
{
"instruction": "How do Innocent archetype brands use simplicity in their product design and functionality?",
"input": "",
"output": "Innocent archetype brands use simplicity in product design and functionality by focusing on user-friendliness, minimalism, and ease of use. Their products often feature straightforward designs that are intuitive and uncomplicated, appealing to consumers' desire for clarity and efficiency."
},
{
"instruction": "What methods do Sage archetype brands use to validate their expertise and knowledge?",
"input": "",
"output": "Sage archetype brands validate their expertise and knowledge by citing scientific studies, showcasing certifications, and highlighting their history of expertise. They often use expert endorsements and factual evidence to reinforce their authority and credibility."
},
{
"instruction": "What messaging themes do Explorer archetype brands typically use to resonate with their target audience?",
"input": "",
"output": "Explorer archetype brands typically use messaging themes centered around freedom, exploration, and personal growth. They emphasize the idea of breaking free from the ordinary, embarking on new journeys, and discovering one's true self through adventure."
},
{
"instruction": "How do Innocent archetype brands communicate their values of purity and wholesomeness?",
"input": "",
"output": "Innocent archetype brands communicate their values of purity and wholesomeness by using clean, natural imagery, focusing on simplicity in their messaging, and often highlighting organic or natural ingredients in their products."
},
{
"instruction": "In what ways do Sage archetype brands use their knowledge to solve consumer problems?",
"input": "",
"output": "Sage archetype brands use their knowledge to solve consumer problems by offering expert advice, providing detailed information, and presenting well-researched solutions. They often position themselves as reliable sources of information that consumers can turn to for solving specific issues."
},
{
"instruction": "What type of product features do Explorer archetype brands highlight to appeal to their audience's sense of discovery?",
"input": "",
"output": "Explorer archetype brands highlight product features that enhance the experience of discovery, such as durability in various environments, adaptability to different situations, and innovative design that encourages exploration and adventure."
},
{
"instruction": "How do Innocent archetype brands create an emotional connection with their audience through their branding?",
"input": "",
"output": "Innocent archetype brands create an emotional connection with their audience by evoking feelings of warmth, safety, and nostalgia. They often use storytelling, familiar imagery, and a comforting tone in their branding to resonate emotionally with consumers."
},
{
"instruction": "What is the core desire of the Explorer archetype in branding?",
"input": "",
"output": "The core desire of the Explorer archetype in branding is the freedom to find out who you are through exploring the world."
},
{
"instruction": "What is the greatest fear of the Explorer archetype according to Chapter 5?",
"input": "",
"output": "The greatest fear of the Explorer archetype is getting trapped, conforming, inner emptiness, and nonbeing."
},
{
"instruction": "How does Starbucks embody the Explorer archetype?",
"input": "",
"output": "Starbucks embodies the Explorer archetype by offering an experience of global discovery and personal freedom, through its diverse range of coffees from different parts of the world, and its store designs and branding that evoke a sense of adventure."
},
{
"instruction": "What does the Explorer archetype's journey represent in branding?",
"input": "",
"output": "In branding, the Explorer archetype's journey represents a search for authenticity and fulfillment, often involving a physical or metaphorical journey to explore new horizons and experiences."
},
{
"instruction": "What type of products and services resonate with the Explorer archetype?",
"input": "",
"output": "Products and services that resonate with the Explorer archetype are those that serve as props on journeys of discovery, such as off-road vehicles, travel services, outdoor gear, and brands that emphasize individuality and freedom."
},
{
"instruction": "Why is 'The Wizard of Oz' an example of the Explorer archetype?",
"input": "",
"output": "'The Wizard of Oz' is an example of the Explorer archetype because it features a journey of self-discovery and exploration in a new, unknown world, embodying the quest for something greater and the exploration of new horizons."
},
{
"instruction": "How does the Explorer archetype view sports and physical activities?",
"input": "",
"output": "The Explorer archetype favors individualistic or solitary sports and physical activities, such as kayaking, skiing, biking, or long-distance running, which offer experiences in nature and opportunities for self-discovery and challenge."
},
{
"instruction": "What is a common marketing strategy for Explorer archetype brands?",
"input": "",
"output": "A common marketing strategy for Explorer archetype brands is to appeal to the sense of adventure and discovery, emphasizing freedom, authenticity, and the journey rather than the destination."
},
{
"instruction": "What is the strategy employed by the Explorer archetype?",
"input": "",
"output": "The strategy of the Explorer archetype involves journeying, seeking out and experiencing new things, and escaping from entrapment and boredom."
},
{
"instruction": "What are some of the narrative genres associated with the Explorer archetype?",
"input": "",
"output": "Narrative genres associated with the Explorer archetype include travelogues, immigrant narratives, science fiction, coming-of-age stories, expatriate literature, and absurdist literature demonstrating human alienation."
},
{
"instruction": "Can you name a few famous Explorer-brand television series?",
"input": "",
"output": "Famous Explorer-brand television series include 'The Lone Ranger' and 'Star Trek', which embody the essence of exploration and adventure."
},
{
"instruction": "What is the typical natural setting for an Explorer's advertisement?",
"input": "",
"output": "The typical natural setting for an Explorer's advertisement includes big open skies, open roads, nature in its various forms, especially mountains, and the night sky symbolizing infinite possibilities."
},
{
"instruction": "How does the Explorer archetype influence clothing brands?",
"input": "",
"output": "Clothing brands influenced by the Explorer archetype offer sturdy clothes that provide freedom of movement, appealing to those who seek adventure and individuality. Brands like Levi’s, REI, Patagonia, and Land’s End are examples."
},
{
"instruction": "What type of politics is prevalent in Silicon Valley and how does it relate to the Explorer archetype?",
"input": "",
"output": "Libertarian politics are prevalent in Silicon Valley, resonating with the Explorer archetype's values of independence, laissez-faire attitudes, and empowerment of the individual."
},
{
"instruction": "How does the Explorer archetype manifest in the consumer market according to 'The Soul of the New Consumer'?",
"input": "",
"output": "According to 'The Soul of the New Consumer', the Explorer archetype manifests in the consumer market as individuals who are better informed, involved, skeptical of advertising, value authenticity, and are restless and time-conscious."
},
{
"instruction": "What is the trap of the Explorer archetype?",
"input": "",
"output": "The trap of the Explorer archetype is aimless wandering and becoming a misfit, losing direction in the pursuit of new experiences and individuality."
},
{
"instruction": "What are the levels of the Explorer archetype?",
"input": "",
"output": "The levels of the Explorer archetype include: Level One - hitting the open road and exploring the world, Level Two - seeking individuality and personal fulfillment, and Level Three - expressing individuality and uniqueness."
},
{
"instruction": "Describe the shadow aspect of the Explorer archetype.",
"input": "",
"output": "The shadow aspect of the Explorer archetype is characterized by being so alienated that one cannot find any way to fit in, leading to extreme detachment and isolation."
},
{
"instruction": "What is the appeal of Explorer archetype in sports?",
"input": "",
"output": "The appeal of the Explorer archetype in sports lies in individualistic or solitary pursuits like kayaking, skiing, or biking, which provide experiences in nature and opportunities for self-discovery."
},
{
"instruction": "How do Explorer brands like Starbucks and URL cater to their customers' individualism?",
"input": "",
"output": "Explorer brands like Starbucks and URL cater to individualism by offering unique and personalized experiences, such as customized coffee orders or book recommendations based on previous purchases."
},
{
"instruction": "What makes the United States a quintessential Explorer-brand country?",
"input": "",
"output": "The United States is a quintessential Explorer-brand country due to its immigrant heritage, emphasis on political rights, and historical narrative of exploration and seeking new horizons."
},
{
"instruction": "What challenge do Explorer archetype brands face in Asian and Latin American cultures?",
"input": "",
"output": "Explorer archetype brands face the challenge of adapting to Asian and Latin American cultures that value group relations and community more than individual exploration and independence."
},
{
"instruction": "How does the Explorer archetype manifest in the fashion choices of adolescents?",
"input": "",
"output": "The Explorer archetype manifests in the fashion choices of adolescents through styles that confront conventional norms, expressing their individuality and alignment with outsider or counter-culture trends."
},
{
"instruction": "What role does the Explorer archetype play in midlife transitions?",
"input": "",
"output": "In midlife transitions, the Explorer archetype often becomes reactivated, leading individuals to seek new experiences, change careers, or embark on journeys of self-discovery and personal fulfillment."
},
{
"instruction": "How does The Body Shop brand embody the Explorer archetype?",
"input": "",
"output": "The Body Shop embodies the Explorer archetype by offering products with exotic ingredients, emphasizing global awareness, environmental consciousness, and a spirit of individualistic activism."
},
{
"instruction": "What kind of work environment does the Explorer archetype promote?",
"input": "",
"output": "The Explorer archetype promotes a work environment that values individuality, minimizes hierarchy, and encourages innovation and pioneering spirit, often found in startups and creative industries."
},
{
"instruction": "How do virtual organizations align with the Explorer archetype?",
"input": "",
"output": "Virtual organizations align with the Explorer archetype by offering flexibility, decentralization, and autonomy, enabling individuals to work independently and pursue innovative approaches."
},
{
"instruction": "What type of housing might appeal to someone with an Explorer archetype?",
"input": "",
"output": "Housing that appeals to someone with an Explorer archetype typically includes open, natural spaces with expansive views, like those offered by Timberpeg homes, emphasizing a connection with nature and freedom."
},
{
"instruction": "Why are brands like Starbucks and URL considered Explorer brands?",
"input": "",
"output": "Brands like Starbucks and URL are considered Explorer brands because they offer unique and customizable experiences that resonate with the desire for individuality, exploration, and personal freedom."
},
{
"instruction": "What is the relationship between the Explorer archetype and technology products?",
"input": "",
"output": "The relationship between the Explorer archetype and technology products lies in the emphasis on personal empowerment, innovation, and the ability to access a world of information and experiences, aligning with the Explorer's quest for knowledge and freedom."
},
{
"instruction": "How does the Explorer archetype influence customer loyalty?",
"input": "",
"output": "The Explorer archetype influences customer loyalty by appealing to the values of authenticity, individuality, and adventure. Brands that successfully embody these values can foster strong loyalty among Explorer-oriented consumers."
},
{
"instruction": "What is the significance of the name 'Starbucks' in relation to the Explorer archetype?",
"input": "",
"output": "The name 'Starbucks', derived from the novel 'Moby Dick', evokes a sense of adventure and exploration, aligning with the Explorer archetype by suggesting a journey into the unknown and the pursuit of new experiences."
},
{
"instruction": "How does the Explorer archetype manifest in the branding of off-road vehicles?",
"input": "",
"output": "Off-road vehicle brands like Jeep Wrangler embody the Explorer archetype by emphasizing adventure, freedom, and the ability to navigate uncharted territories, appealing to consumers' desire for exploration and discovery."
},
{
"instruction": "What is the core desire of the Explorer archetype in branding?",
"input": "",
"output": "The core desire of the Explorer archetype in branding is the freedom to find out who you are through exploring the world, focusing on experiences that promote self-discovery and authenticity."
},
{
"instruction": "How does Starbucks use the Explorer archetype in its branding?",
"input": "",
"output": "Starbucks utilizes the Explorer archetype by presenting coffee as an exotic, adventurous experience, drawing from diverse global origins, and creating an atmosphere that encourages exploration and individuality."
},
{
"instruction": "In what way does URL reflect the Explorer archetype?",
"input": "",
"output": "URL reflects the Explorer archetype by offering a vast and diverse range of products, enabling customers to explore and discover new items and ideas from the comfort of their own home."
},
{
"instruction": "What role does nature play in the Explorer archetype for branding?",
"input": "",
"output": "Nature plays a significant role in the Explorer archetype for branding, as it symbolizes uncharted territory and the joy of discovery. Brands that associate with nature often appeal to the Explorer's desire for adventure and exploration in the natural world."
},
{
"instruction": "How do individual sports relate to the Explorer archetype?",
"input": "",
"output": "Individual sports like kayaking, skiing, and biking resonate with the Explorer archetype by emphasizing personal challenges, exploration of nature, and the journey of self-discovery through solitary or unique physical experiences."
},
{
"instruction": "What is the relationship between the Explorer archetype and immigrant narratives?",
"input": "",
"output": "Immigrant narratives often embody the Explorer archetype by depicting journeys into new lands and cultures, symbolizing the quest for a better life, personal growth, and the exploration of unknown territories."
},
{
"instruction": "How does the branding of boats appeal to the Explorer archetype?",
"input": "",
"output": "Boat branding appeals to the Explorer archetype by emphasizing the freedom and adventure of the open seas, inviting consumers to embark on journeys of discovery and exploration in aquatic environments."
},
{
"instruction": "In what way does the Explorer archetype influence fashion branding?",
"input": "",
"output": "Fashion brands influenced by the Explorer archetype often feature durable, functional, and versatile clothing, appealing to consumers' desire for apparel that supports adventure and individual expression."
},
{
"instruction": "How does The Great Gatsby reflect the Explorer archetype?",
"input": "",
"output": "The Great Gatsby reflects the Explorer archetype through its themes of seeking a new identity, the pursuit of the American Dream, and the exploration of the social landscape of the Jazz Age, representing a journey of self-discovery and aspiration."
},
{
"instruction": "What aspect of the Explorer archetype is captured in the success of travelogues?",
"input": "",
"output": "The success of travelogues captures the Explorer archetype's thirst for discovery, adventure, and the experience of diverse cultures and landscapes, appealing to the inherent desire to explore the unknown."
},
{
"instruction": "How do expatriate literature and the Explorer archetype relate?",
"input": "",
"output": "Expatriate literature relates to the Explorer archetype by depicting the challenges and rewards of living in a foreign culture, reflecting the journey of self-discovery and the quest for a sense of belonging in a new world."
},
{
"instruction": "What is the significance of the 'open road' in Explorer archetype branding?",
"input": "",
"output": "The 'open road' in Explorer archetype branding symbolizes freedom, endless possibilities, and the journey towards self-discovery. It appeals to consumers' longing for adventure and escape from the mundane."
},
{
"instruction": "How does the slogan 'No Boundaries' reflect the Explorer archetype for the Ford Explorer?",
"input": "",
"output": "The slogan 'No Boundaries' for the Ford Explorer reflects the Explorer archetype by emphasizing limitless exploration, the freedom to traverse diverse terrains, and the vehicle's capability to support adventurous lifestyles."
},
{
"instruction": "What is the connection between the Explorer archetype and coming-of-age stories?",
"input": "",
"output": "Coming-of-age stories connect with the Explorer archetype through their focus on personal growth, the journey from innocence to experience, and the exploration of identity and the wider world."
},
{
"instruction": "How does the archetype of the Explorer relate to absurdist literature?",
"input": "",
"output": "Absurdist literature relates to the Explorer archetype by highlighting the quest for meaning in a seemingly chaotic world, reflecting the Explorer's journey towards understanding and navigating human alienation."
},
{
"instruction": "What role does 'The Odyssey' by Homer play in exemplifying the Explorer archetype?",
"input": "",
"output": "Homer's 'The Odyssey' is a classic example of the Explorer archetype, depicting a hero's epic journey full of challenges and discoveries, symbolizing the quest for adventure, knowledge, and homecoming."
},
{
"instruction": "How is the Explorer archetype represented in science fiction?",
"input": "",
"output": "Science fiction often represents the Explorer archetype through stories of space exploration and discovery of new worlds, embodying the human quest for knowledge and the unknown."
},
{
"instruction": "What is the relationship between the Explorer archetype and literature about seeking the promised land?",
"input": "",
"output": "Literature about seeking the promised land resonates with the Explorer archetype by embodying the search for a utopian place of fulfillment and happiness, a journey that mirrors the Explorer's quest for a better world."
},
{
"instruction": "How does the Explorer archetype manifest in the branding of Yamaha motorcycles?",
"input": "",
"output": "Yamaha motorcycles embody the Explorer archetype by promoting independence, adventure, and the thrill of the open road, appealing to consumers' desire for freedom and exploration."
},
{
"instruction": "What is the significance of the slogan 'Take your body where your mind has already wandered' for Jeep Wrangler?",
"input": "",
"output": "Jeep Wrangler's slogan 'Take your body where your mind has already wandered' captures the essence of the Explorer archetype, encouraging adventure, curiosity, and the pursuit of one's desires through exploration."
},
{
"instruction": "How does the Explorer archetype influence the branding of sports equipment?",
"input": "",
"output": "Sports equipment brands influenced by the Explorer archetype focus on enabling adventure, individual challenges, and interaction with nature, appealing to consumers' desire for physical exploration and personal achievement."
},
{
"instruction": "What is the connection between the Explorer archetype and the theme of leaving marriages, jobs, or towns in literature?",
"input": "",
"output": "Themes of leaving marriages, jobs, or towns in literature connect with the Explorer archetype by depicting the pursuit of self-discovery, liberation from constraints, and the journey towards a more authentic life."
},
{
"instruction": "How does the Explorer archetype resonate with the television series 'Star Trek'?",
"input": "",
"output": "The television series 'Star Trek' resonates with the Explorer archetype through its focus on space exploration, encountering new civilizations, and the quest for knowledge and adventure beyond the known universe."
},
{
"instruction": "What characteristics of the Explorer archetype are evident in Jack Kerouac's 'On the Road'?",
"input": "",
"output": "Jack Kerouac's 'On the Road' exemplifies the Explorer archetype with its themes of freedom, self-discovery, and nonconformity, portraying a journey across America that mirrors the internal quest for identity and meaning."
},
{
"instruction": "How does the Explorer archetype influence the marketing of outdoor clothing brands?",
"input": "",
"output": "Outdoor clothing brands influenced by the Explorer archetype focus on durability, functionality, and versatility, catering to consumers' desire for apparel that supports their adventurous and individualistic lifestyles."
},
{
"instruction": "In what way does the slogan 'Your digital images. Your music. Your work. Your ideas.' for Sony's Memory Stick resonate with the Explorer archetype?",
"input": "",
"output": "Sony's Memory Stick slogan 'Your digital images. Your music. Your work. Your ideas.' resonates with the Explorer archetype by emphasizing personal freedom, the storage and expression of individual experiences, and the journey of self-documentation."
},
{
"instruction": "How does folk music embody the Explorer archetype?",
"input": "",
"output": "Folk music embodies the Explorer archetype through its storytelling of journeys, struggles, and discoveries, often reflecting themes of adventure, personal growth, and the quest for freedom."
},
{
"instruction": "What aspect of the Explorer archetype is captured in Mark Twain's 'The Adventures of Huckleberry Finn'?",
"input": "",
"output": "Mark Twain's 'The Adventures of Huckleberry Finn' captures the Explorer archetype through its themes of adventure, freedom, and the protagonist's journey of self-discovery along the Mississippi River."
},
{
"instruction": "How does 'The Waste Land' by T.S. Eliot relate to the Explorer archetype?",
"input": "",
"output": "T.S. Eliot's 'The Waste Land' relates to the Explorer archetype by exploring themes of spiritual emptiness and the search for meaning in a fragmented world, reflecting the Explorer's quest for understanding and wholeness."
}
]
| []
| [
"TAGS\n#region-us \n"
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| [
6
]
| [
"passage: TAGS\n#region-us \n"
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|
114aae5abd432904fee2d56ad75939dcd31b56e0 | # Dataset Card for "trainset1_2000_cutoff_llama_formatted"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | Rewcifer/trainset1_2000_cutoff_llama_formatted | [
"region:us"
]
| 2023-11-13T00:20:18+00:00 | {"dataset_info": {"features": [{"name": "labels_and_findings", "dtype": "string"}, {"name": "prompts", "dtype": "string"}, {"name": "true_findings", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 390822366, "num_examples": 50000}], "download_size": 90553322, "dataset_size": 390822366}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]} | 2023-11-13T00:20:27+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "trainset1_2000_cutoff_llama_formatted"
More Information needed | [
"# Dataset Card for \"trainset1_2000_cutoff_llama_formatted\"\n\nMore Information needed"
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| [
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|
505a580360d652bcbfc0bd5a806a9321c91924e3 | # Dataset Card for "trainset2_2000_cutoff_llama_formatted"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | Rewcifer/trainset2_2000_cutoff_llama_formatted | [
"region:us"
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| 2023-11-13T00:20:45+00:00 | {"dataset_info": {"features": [{"name": "labels_and_findings", "dtype": "string"}, {"name": "prompts", "dtype": "string"}, {"name": "true_findings", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 391704166, "num_examples": 50000}], "download_size": 90507200, "dataset_size": 391704166}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]} | 2023-11-13T00:21:07+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "trainset2_2000_cutoff_llama_formatted"
More Information needed | [
"# Dataset Card for \"trainset2_2000_cutoff_llama_formatted\"\n\nMore Information needed"
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|
8cdef6ad16536f8f7581b285fc3fc02b323029e2 | # Dataset Card for "trainset3_2000_cutoff_llama_formatted"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | Rewcifer/trainset3_2000_cutoff_llama_formatted | [
"region:us"
]
| 2023-11-13T00:21:55+00:00 | {"dataset_info": {"features": [{"name": "labels_and_findings", "dtype": "string"}, {"name": "prompts", "dtype": "string"}, {"name": "true_findings", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 392417706, "num_examples": 50000}], "download_size": 90834107, "dataset_size": 392417706}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]} | 2023-11-13T00:22:02+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "trainset3_2000_cutoff_llama_formatted"
More Information needed | [
"# Dataset Card for \"trainset3_2000_cutoff_llama_formatted\"\n\nMore Information needed"
]
| [
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|
90aaa13bac3066a7e59f439f5c7bd41e32628b27 | # AI2 Blog / Model Images
Add blog / model images to this dataset.
For us by anyone at AI2.
Please create a new folder by project.
Must be public so people outside of hf/ai2 can see it! | allenai/blog-images | [
"region:us"
]
| 2023-11-13T00:41:19+00:00 | {} | 2024-01-22T00:57:08+00:00 | []
| []
| TAGS
#region-us
| # AI2 Blog / Model Images
Add blog / model images to this dataset.
For us by anyone at AI2.
Please create a new folder by project.
Must be public so people outside of hf/ai2 can see it! | [
"# AI2 Blog / Model Images\n\nAdd blog / model images to this dataset. \n\nFor us by anyone at AI2.\n\nPlease create a new folder by project.\n\nMust be public so people outside of hf/ai2 can see it!"
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]
|
87fbd736c8291b5971a65b5807070b6d06aa02cb | # Dataset Card for "clean_trainset_2000_cutoff_llama"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | Rewcifer/clean_trainset_2000_cutoff_llama | [
"region:us"
]
| 2023-11-13T00:49:41+00:00 | {"dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 394450486.10182, "num_examples": 100767}], "download_size": 90442844, "dataset_size": 394450486.10182}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]} | 2023-11-13T00:49:48+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "clean_trainset_2000_cutoff_llama"
More Information needed | [
"# Dataset Card for \"clean_trainset_2000_cutoff_llama\"\n\nMore Information needed"
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| [
"TAGS\n#region-us \n",
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|
882e0842b1f86a5d428eb0a72ac02beee792be4e |
# 数据集
## 介绍
## 特点
## 如何使用
## 参考资料
1. https://github.com/QwenLM/Qwen-7B
## 联系我们
网站：https://www.linlan.io
邮箱：[email protected] | linlanio/lldataset-zhishi-v1 | [
"task_categories:summarization",
"size_categories:10K<n<100K",
"language:zh",
"license:apache-2.0",
"biology",
"region:us"
]
| 2023-11-13T00:51:02+00:00 | {"language": ["zh"], "license": "apache-2.0", "size_categories": ["10K<n<100K"], "task_categories": ["summarization"], "tags": ["biology"]} | 2023-11-13T01:20:00+00:00 | []
| [
"zh"
]
| TAGS
#task_categories-summarization #size_categories-10K<n<100K #language-Chinese #license-apache-2.0 #biology #region-us
|
# 数据集
## 介绍
## 特点
## 如何使用
## 参考资料
1. URL
## 联系我们
网站：URL
邮箱：contact@URL | [
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]
|
7803a7d58c2d31a90a14e3776811f176a2e5878a | # Dataset Card for "PhoNer_Covid19"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | yuufong/PhoNer_Covid19 | [
"region:us"
]
| 2023-11-13T01:18:11+00:00 | {"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "validation", "path": "data/validation-*"}, {"split": "test", "path": "data/test-*"}]}], "dataset_info": {"features": [{"name": "words", "sequence": "string"}, {"name": "tags", "sequence": "string"}], "splits": [{"name": "train", "num_bytes": 2638301, "num_examples": 5027}, {"name": "validation", "num_bytes": 1158651, "num_examples": 2000}, {"name": "test", "num_bytes": 1158651, "num_examples": 2000}], "download_size": 684199, "dataset_size": 4955603}} | 2023-11-13T01:18:14+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "PhoNer_Covid19"
More Information needed | [
"# Dataset Card for \"PhoNer_Covid19\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
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| [
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|
c002cdee013c8f456227587844b44cf535b3f159 | # Dataset Card for "PhoNer_Covid19_Word"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | yuufong/PhoNer_Covid19_Word | [
"region:us"
]
| 2023-11-13T01:20:13+00:00 | {"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "validation", "path": "data/validation-*"}, {"split": "test", "path": "data/test-*"}]}], "dataset_info": {"features": [{"name": "words", "sequence": "string"}, {"name": "tags", "sequence": "string"}], "splits": [{"name": "train", "num_bytes": 2249018, "num_examples": 5027}, {"name": "validation", "num_bytes": 979915, "num_examples": 2000}, {"name": "test", "num_bytes": 979915, "num_examples": 2000}], "download_size": 685192, "dataset_size": 4208848}} | 2023-11-13T01:20:16+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "PhoNer_Covid19_Word"
More Information needed | [
"# Dataset Card for \"PhoNer_Covid19_Word\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"PhoNer_Covid19_Word\"\n\nMore Information needed"
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| [
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"passage: TAGS\n#region-us \n# Dataset Card for \"PhoNer_Covid19_Word\"\n\nMore Information needed"
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|
d2eb76a526a7868505848fde260ec55ef7fbea07 | # Dataset Card for "chimsuja_dataset"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | potatoSeop/chimsuja_dataset | [
"region:us"
]
| 2023-11-13T01:33:28+00:00 | {"dataset_info": {"features": [{"name": "audio", "dtype": "audio"}, {"name": "script", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 2614465503.562, "num_examples": 2521}], "download_size": 3076362475, "dataset_size": 2614465503.562}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]} | 2023-11-13T10:14:56+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "chimsuja_dataset"
More Information needed | [
"# Dataset Card for \"chimsuja_dataset\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"chimsuja_dataset\"\n\nMore Information needed"
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| [
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"passage: TAGS\n#region-us \n# Dataset Card for \"chimsuja_dataset\"\n\nMore Information needed"
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|
ead98c45ef056ee4caa3f78b38ebf2eff812dfbd | # Dataset Card for "fire-risk"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | danielz01/fire-risk | [
"region:us"
]
| 2023-11-13T01:36:58+00:00 | {"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "label", "dtype": "string"}, {"name": "path", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 11882545100.302, "num_examples": 70331}, {"name": "val", "num_bytes": 3793822470.676, "num_examples": 21541}], "download_size": 15132678783, "dataset_size": 15676367570.978}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "val", "path": "data/val-*"}]}]} | 2023-11-13T01:59:11+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "fire-risk"
More Information needed | [
"# Dataset Card for \"fire-risk\"\n\nMore Information needed"
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| [
"TAGS\n#region-us \n",
"# Dataset Card for \"fire-risk\"\n\nMore Information needed"
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| [
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|
0c6c2bd0b21fdbe37db398a7254a5a9f302bbfa6 | # Dataset Card for "semeval-task-8-a-mono-v2-mistral-7b"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | kpriyanshu256/semeval-task-8-a-mono-v2-mistral-7b | [
"region:us"
]
| 2023-11-13T02:01:58+00:00 | {"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "val", "path": "data/val-*"}, {"split": "test", "path": "data/test-*"}]}], "dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "label", "dtype": "int64"}, {"name": "model", "dtype": "string"}, {"name": "source", "dtype": "string"}, {"name": "id", "dtype": "int64"}, {"name": "mistral-7b_estimated_loss", "dtype": "float64"}, {"name": "mistral-7b_mean_lowest25", "dtype": "float64"}, {"name": "mistral-7b_mean_highest25", "dtype": "float64"}, {"name": "mistral-7b_max", "dtype": "float64"}, {"name": "mistral-7b_min", "dtype": "float64"}, {"name": "mistral-7b_range", "dtype": "float64"}, {"name": "mistral-7b_mean", "dtype": "float64"}, {"name": "mistral-7b_std", "dtype": "float64"}, {"name": "mistral-7b_entropy", "dtype": "float64"}, {"name": "mistral-7b_kurtosis", "dtype": "float64"}, {"name": "mistral-7b_skewness", "dtype": "float64"}, {"name": "mistral-7b_perplexity", "dtype": "float64"}], "splits": [{"name": "train", "num_bytes": 281584304, "num_examples": 95805}, {"name": "val", "num_bytes": 69152233, "num_examples": 23952}, {"name": "test", "num_bytes": 11023757, "num_examples": 5000}], "download_size": 215512867, "dataset_size": 361760294}} | 2023-11-13T02:02:06+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "semeval-task-8-a-mono-v2-mistral-7b"
More Information needed | [
"# Dataset Card for \"semeval-task-8-a-mono-v2-mistral-7b\"\n\nMore Information needed"
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"passage: TAGS\n#region-us \n# Dataset Card for \"semeval-task-8-a-mono-v2-mistral-7b\"\n\nMore Information needed"
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|
7906a2c330f62a3a115800a61d7b33de1f92a9d8 | # Dataset Card for "qa-temp-v2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | Back-up/qa-temp-v2 | [
"region:us"
]
| 2023-11-13T02:19:40+00:00 | {"dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "context", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "response", "struct": [{"name": "response", "dtype": "string"}]}, {"name": "answers", "struct": [{"name": "answer_start", "sequence": "int64"}, {"name": "text", "sequence": "string"}]}, {"name": "instruction", "dtype": "string"}, {"name": "prompt_name", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 37455, "num_examples": 11}], "download_size": 42609, "dataset_size": 37455}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]} | 2023-11-13T02:19:44+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "qa-temp-v2"
More Information needed | [
"# Dataset Card for \"qa-temp-v2\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
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|
600b663cf370072f5592c3d4e8580ef6148de8fe | # Dataset Card for "techdebt_label"
This dataset was generated from [The Technical Debt Dataset](https://github.com/clowee/The-Technical-Debt-Dataset) created by Lenarduzzi, et al. and the citation is down below.
## Dataset Details and Structure
The labels for the dataset were provided by the SonarQube software cited by the paper and matched to the diff in the commit where the message was raised. This diff was then cleaned to only include the lines of code added.
## Bias, Risks, and Limitations
Beware of the limited sample size and label variety in the dataset. Also, the queries used to extract this data are still being checked over to ensure correctness.
## Recommendations
Changes are constantly being made to this dataset to make it better. Please be aware when you use it.
## References
Valentina Lenarduzzi, Nyyti Saarimäki, Davide Taibi. The Technical Debt Dataset. Proceedings for the 15th Conference on Predictive Models and Data Analytics in Software Engineering. Brazil. 2019. | davidgaofc/techdebt_label | [
"region:us"
]
| 2023-11-13T02:30:05+00:00 | {"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "test", "path": "data/test-*"}]}], "dataset_info": {"features": [{"name": "Diff", "dtype": "string"}, {"name": "Message", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 5042681, "num_examples": 8793}, {"name": "test", "num_bytes": 1259287, "num_examples": 2199}], "download_size": 1824024, "dataset_size": 6301968}} | 2023-12-04T05:11:52+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "techdebt_label"
This dataset was generated from The Technical Debt Dataset created by Lenarduzzi, et al. and the citation is down below.
## Dataset Details and Structure
The labels for the dataset were provided by the SonarQube software cited by the paper and matched to the diff in the commit where the message was raised. This diff was then cleaned to only include the lines of code added.
## Bias, Risks, and Limitations
Beware of the limited sample size and label variety in the dataset. Also, the queries used to extract this data are still being checked over to ensure correctness.
## Recommendations
Changes are constantly being made to this dataset to make it better. Please be aware when you use it.
## References
Valentina Lenarduzzi, Nyyti Saarimäki, Davide Taibi. The Technical Debt Dataset. Proceedings for the 15th Conference on Predictive Models and Data Analytics in Software Engineering. Brazil. 2019. | [
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"## References\n\nValentina Lenarduzzi, Nyyti Saarimäki, Davide Taibi. The Technical Debt Dataset. Proceedings for the 15th Conference on Predictive Models and Data Analytics in Software Engineering. Brazil. 2019."
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"## Bias, Risks, and Limitations\n\nBeware of the limited sample size and label variety in the dataset. Also, the queries used to extract this data are still being checked over to ensure correctness.",
"## Recommendations\n\nChanges are constantly being made to this dataset to make it better. Please be aware when you use it.",
"## References\n\nValentina Lenarduzzi, Nyyti Saarimäki, Davide Taibi. The Technical Debt Dataset. Proceedings for the 15th Conference on Predictive Models and Data Analytics in Software Engineering. Brazil. 2019."
]
| [
6,
44,
60,
46,
28,
53
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"techdebt_label\"\n\nThis dataset was generated from The Technical Debt Dataset created by Lenarduzzi, et al. and the citation is down below.## Dataset Details and Structure\n\nThe labels for the dataset were provided by the SonarQube software cited by the paper and matched to the diff in the commit where the message was raised. This diff was then cleaned to only include the lines of code added.## Bias, Risks, and Limitations\n\nBeware of the limited sample size and label variety in the dataset. Also, the queries used to extract this data are still being checked over to ensure correctness.## Recommendations\n\nChanges are constantly being made to this dataset to make it better. Please be aware when you use it.## References\n\nValentina Lenarduzzi, Nyyti Saarimäki, Davide Taibi. The Technical Debt Dataset. Proceedings for the 15th Conference on Predictive Models and Data Analytics in Software Engineering. Brazil. 2019."
]
|
05db270674fa58b4bc34a676fe6b9497f8d41dae | # Dataset Card for "temp-qa-context-no-keyword"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | Back-up/temp-qa-context-no-keyword | [
"region:us"
]
| 2023-11-13T02:32:26+00:00 | {"dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "context", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "response", "struct": [{"name": "response", "dtype": "string"}]}, {"name": "answers", "struct": [{"name": "answer_start", "sequence": "int64"}, {"name": "text", "sequence": "string"}]}, {"name": "instruction", "dtype": "string"}, {"name": "prompt_name", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 10315892, "num_examples": 3802}], "download_size": 3940839, "dataset_size": 10315892}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]} | 2023-11-13T06:43:43+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "temp-qa-context-no-keyword"
More Information needed | [
"# Dataset Card for \"temp-qa-context-no-keyword\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"temp-qa-context-no-keyword\"\n\nMore Information needed"
]
| [
6,
21
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"temp-qa-context-no-keyword\"\n\nMore Information needed"
]
|
c8361f7a4780b48cbe6b7c7fb49e5e6960d3bcd7 | This dataset is used for finetuning protGPT2. The features are ['attention_mask', 'input_ids'], no 'labels'.
After using DataCollatorForLanguageModeling and DataLoader, the features will be ['attention_mask', 'input_ids', 'labels']. | Lifan-Z/tox-antitox-proteins | [
"task_categories:text-generation",
"size_categories:n<1K",
"language:en",
"license:apache-2.0",
"biology",
"medical",
"region:us"
]
| 2023-11-13T02:52:31+00:00 | {"language": ["en"], "license": "apache-2.0", "size_categories": ["n<1K"], "task_categories": ["text-generation"], "pretty_name": "tox-antitox-proteins", "tags": ["biology", "medical"]} | 2023-11-13T09:00:37+00:00 | []
| [
"en"
]
| TAGS
#task_categories-text-generation #size_categories-n<1K #language-English #license-apache-2.0 #biology #medical #region-us
| This dataset is used for finetuning protGPT2. The features are ['attention_mask', 'input_ids'], no 'labels'.
After using DataCollatorForLanguageModeling and DataLoader, the features will be ['attention_mask', 'input_ids', 'labels']. | []
| [
"TAGS\n#task_categories-text-generation #size_categories-n<1K #language-English #license-apache-2.0 #biology #medical #region-us \n"
]
| [
45
]
| [
"passage: TAGS\n#task_categories-text-generation #size_categories-n<1K #language-English #license-apache-2.0 #biology #medical #region-us \n"
]
|
8415b38a9bb76801192b74bcf957c6ba45c2b617 |
# Dataset Card for Evaluation run of maywell/Synatra-11B-Testbench
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/maywell/Synatra-11B-Testbench
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** [email protected]
### Dataset Summary
Dataset automatically created during the evaluation run of model [maywell/Synatra-11B-Testbench](https://huggingface.co/maywell/Synatra-11B-Testbench) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_maywell__Synatra-11B-Testbench_public",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-11-13T02:58:44.682029](https://huggingface.co/datasets/open-llm-leaderboard/details_maywell__Synatra-11B-Testbench_public/blob/main/results_2023-11-13T02-58-44.682029.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
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"acc_norm": 0.5597882201139855,
"acc_norm_stderr": 0.03472937395706884,
"mc1": 0.3733170134638923,
"mc1_stderr": 0.016932370557570634,
"mc2": 0.5196984500314547,
"mc2_stderr": 0.015094792312219791,
"em": 0.012688758389261746,
"em_stderr": 0.0011462418380586376,
"f1": 0.07765100671140933,
"f1_stderr": 0.0018276339186485593
},
"harness|arc:challenge|25": {
"acc": 0.5273037542662116,
"acc_stderr": 0.014589589101985996,
"acc_norm": 0.5733788395904437,
"acc_norm_stderr": 0.014453185592920293
},
"harness|hellaswag|10": {
"acc": 0.5855407289384584,
"acc_stderr": 0.004916216503770336,
"acc_norm": 0.7865962955586536,
"acc_norm_stderr": 0.004088730085367332
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.38,
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"acc_norm": 0.38,
"acc_norm_stderr": 0.048783173121456316
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.48148148148148145,
"acc_stderr": 0.043163785995113245,
"acc_norm": 0.48148148148148145,
"acc_norm_stderr": 0.043163785995113245
},
"harness|hendrycksTest-astronomy|5": {
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"acc_norm": 0.5526315789473685,
"acc_norm_stderr": 0.04046336883978252
},
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"acc_norm": 0.54,
"acc_norm_stderr": 0.05009082659620332
},
"harness|hendrycksTest-clinical_knowledge|5": {
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},
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"acc_norm": 0.6041666666666666,
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},
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"acc_norm": 0.4,
"acc_norm_stderr": 0.049236596391733084
},
"harness|hendrycksTest-college_computer_science|5": {
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"acc_norm": 0.51,
"acc_norm_stderr": 0.05024183937956912
},
"harness|hendrycksTest-college_mathematics|5": {
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},
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},
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},
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"harness|hendrycksTest-sociology|5": {
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},
"harness|hendrycksTest-us_foreign_policy|5": {
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"harness|hendrycksTest-virology|5": {
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},
"harness|hendrycksTest-world_religions|5": {
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"harness|truthfulqa:mc|0": {
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},
"harness|winogrande|5": {
"acc": 0.7576953433307024,
"acc_stderr": 0.01204235252617479
},
"harness|drop|3": {
"em": 0.012688758389261746,
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"f1": 0.07765100671140933,
"f1_stderr": 0.0018276339186485593
},
"harness|gsm8k|5": {
"acc": 0.177407126611069,
"acc_stderr": 0.010522533016890793
}
}
```
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed] | open-llm-leaderboard/details_maywell__Synatra-11B-Testbench | [
"region:us"
]
| 2023-11-13T03:01:43+00:00 | {"pretty_name": "Evaluation run of maywell/Synatra-11B-Testbench", "dataset_summary": "Dataset automatically created during the evaluation run of model [maywell/Synatra-11B-Testbench](https://huggingface.co/maywell/Synatra-11B-Testbench) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\nTo load the details from a run, you can for instance do the following:\n```python\nfrom datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_maywell__Synatra-11B-Testbench_public\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-11-13T02:58:44.682029](https://huggingface.co/datasets/open-llm-leaderboard/details_maywell__Synatra-11B-Testbench_public/blob/main/results_2023-11-13T02-58-44.682029.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):\n\n```python\n{\n \"all\": {\n \"acc\": 0.5527127345036476,\n \"acc_stderr\": 0.033976423589140915,\n \"acc_norm\": 0.5597882201139855,\n \"acc_norm_stderr\": 0.03472937395706884,\n \"mc1\": 0.3733170134638923,\n \"mc1_stderr\": 0.016932370557570634,\n \"mc2\": 0.5196984500314547,\n \"mc2_stderr\": 0.015094792312219791,\n \"em\": 0.012688758389261746,\n \"em_stderr\": 0.0011462418380586376,\n \"f1\": 0.07765100671140933,\n \"f1_stderr\": 0.0018276339186485593\n },\n \"harness|arc:challenge|25\": {\n \"acc\": 0.5273037542662116,\n \"acc_stderr\": 0.014589589101985996,\n \"acc_norm\": 0.5733788395904437,\n \"acc_norm_stderr\": 0.014453185592920293\n },\n \"harness|hellaswag|10\": {\n \"acc\": 0.5855407289384584,\n \"acc_stderr\": 0.004916216503770336,\n \"acc_norm\": 0.7865962955586536,\n \"acc_norm_stderr\": 0.004088730085367332\n },\n \"harness|hendrycksTest-abstract_algebra|5\": {\n \"acc\": 0.38,\n \"acc_stderr\": 0.048783173121456316,\n \"acc_norm\": 0.38,\n \"acc_norm_stderr\": 0.048783173121456316\n },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.48148148148148145,\n \"acc_stderr\": 0.043163785995113245,\n \"acc_norm\": 0.48148148148148145,\n \"acc_norm_stderr\": 0.043163785995113245\n },\n \"harness|hendrycksTest-astronomy|5\": {\n \"acc\": 0.5526315789473685,\n \"acc_stderr\": 0.04046336883978252,\n \"acc_norm\": 0.5526315789473685,\n \"acc_norm_stderr\": 0.04046336883978252\n },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.54,\n \"acc_stderr\": 0.05009082659620332,\n \"acc_norm\": 0.54,\n \"acc_norm_stderr\": 0.05009082659620332\n },\n \"harness|hendrycksTest-clinical_knowledge|5\": {\n \"acc\": 0.6113207547169811,\n \"acc_stderr\": 0.030000485448675986,\n \"acc_norm\": 0.6113207547169811,\n \"acc_norm_stderr\": 0.030000485448675986\n },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.6041666666666666,\n \"acc_stderr\": 0.04089465449325582,\n \"acc_norm\": 0.6041666666666666,\n \"acc_norm_stderr\": 0.04089465449325582\n },\n \"harness|hendrycksTest-college_chemistry|5\": {\n \"acc\": 0.4,\n \"acc_stderr\": 0.049236596391733084,\n \"acc_norm\": 0.4,\n \"acc_norm_stderr\": 0.049236596391733084\n },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\": 0.51,\n \"acc_stderr\": 0.05024183937956912,\n \"acc_norm\": 0.51,\n \"acc_norm_stderr\": 0.05024183937956912\n },\n \"harness|hendrycksTest-college_mathematics|5\": {\n \"acc\": 0.4,\n \"acc_stderr\": 0.04923659639173309,\n \"acc_norm\": 0.4,\n \"acc_norm_stderr\": 0.04923659639173309\n },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.5895953757225434,\n \"acc_stderr\": 0.03750757044895536,\n \"acc_norm\": 0.5895953757225434,\n \"acc_norm_stderr\": 0.03750757044895536\n },\n \"harness|hendrycksTest-college_physics|5\": {\n \"acc\": 0.28431372549019607,\n \"acc_stderr\": 0.04488482852329017,\n \"acc_norm\": 0.28431372549019607,\n \"acc_norm_stderr\": 0.04488482852329017\n },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\": 0.74,\n \"acc_stderr\": 0.04408440022768079,\n \"acc_norm\": 0.74,\n \"acc_norm_stderr\": 0.04408440022768079\n },\n \"harness|hendrycksTest-conceptual_physics|5\": {\n \"acc\": 0.5148936170212766,\n \"acc_stderr\": 0.03267151848924777,\n \"acc_norm\": 0.5148936170212766,\n \"acc_norm_stderr\": 0.03267151848924777\n },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.37719298245614036,\n \"acc_stderr\": 0.04559522141958216,\n \"acc_norm\": 0.37719298245614036,\n \"acc_norm_stderr\": 0.04559522141958216\n },\n \"harness|hendrycksTest-electrical_engineering|5\": {\n \"acc\": 0.5379310344827586,\n \"acc_stderr\": 0.04154659671707548,\n \"acc_norm\": 0.5379310344827586,\n \"acc_norm_stderr\": 0.04154659671707548\n },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\": 0.34656084656084657,\n \"acc_stderr\": 0.024508777521028424,\n \"acc_norm\": 0.34656084656084657,\n \"acc_norm_stderr\": 0.024508777521028424\n },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.3333333333333333,\n \"acc_stderr\": 0.04216370213557835,\n \"acc_norm\": 0.3333333333333333,\n \"acc_norm_stderr\": 0.04216370213557835\n },\n \"harness|hendrycksTest-global_facts|5\": {\n \"acc\": 0.37,\n \"acc_stderr\": 0.048523658709391,\n \"acc_norm\": 0.37,\n \"acc_norm_stderr\": 0.048523658709391\n },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.6483870967741936,\n \"acc_stderr\": 0.027162537826948458,\n \"acc_norm\": 0.6483870967741936,\n \"acc_norm_stderr\": 0.027162537826948458\n },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\": 0.4088669950738916,\n \"acc_stderr\": 0.034590588158832314,\n \"acc_norm\": 0.4088669950738916,\n \"acc_norm_stderr\": 0.034590588158832314\n },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \"acc\": 0.63,\n \"acc_stderr\": 0.04852365870939099,\n \"acc_norm\": 0.63,\n \"acc_norm_stderr\": 0.04852365870939099\n },\n \"harness|hendrycksTest-high_school_european_history|5\": {\n \"acc\": 0.6787878787878788,\n \"acc_stderr\": 0.0364620496325381,\n \"acc_norm\": 0.6787878787878788,\n \"acc_norm_stderr\": 0.0364620496325381\n },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\": 0.7070707070707071,\n \"acc_stderr\": 0.03242497958178815,\n \"acc_norm\": 0.7070707070707071,\n \"acc_norm_stderr\": 0.03242497958178815\n },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n \"acc\": 0.7357512953367875,\n \"acc_stderr\": 0.03182155050916647,\n \"acc_norm\": 0.7357512953367875,\n \"acc_norm_stderr\": 0.03182155050916647\n },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \"acc\": 0.5333333333333333,\n \"acc_stderr\": 0.02529460802398647,\n \"acc_norm\": 0.5333333333333333,\n \"acc_norm_stderr\": 0.02529460802398647\n },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"acc\": 0.27037037037037037,\n \"acc_stderr\": 0.02708037281514565,\n \"acc_norm\": 0.27037037037037037,\n \"acc_norm_stderr\": 0.02708037281514565\n },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \"acc\": 0.5168067226890757,\n \"acc_stderr\": 0.03246013680375308,\n \"acc_norm\": 0.5168067226890757,\n \"acc_norm_stderr\": 0.03246013680375308\n },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\": 0.33112582781456956,\n \"acc_stderr\": 0.038425817186598696,\n \"acc_norm\": 0.33112582781456956,\n \"acc_norm_stderr\": 0.038425817186598696\n },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\": 0.7376146788990826,\n \"acc_stderr\": 0.018861885021534734,\n \"acc_norm\": 0.7376146788990826,\n \"acc_norm_stderr\": 0.018861885021534734\n },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\": 0.4398148148148148,\n \"acc_stderr\": 0.03385177976044811,\n \"acc_norm\": 0.4398148148148148,\n \"acc_norm_stderr\": 0.03385177976044811\n },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\": 0.7058823529411765,\n \"acc_stderr\": 0.03198001660115071,\n \"acc_norm\": 0.7058823529411765,\n \"acc_norm_stderr\": 0.03198001660115071\n },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"acc\": 0.6919831223628692,\n \"acc_stderr\": 0.030052389335605702,\n \"acc_norm\": 0.6919831223628692,\n \"acc_norm_stderr\": 0.030052389335605702\n },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.6143497757847534,\n \"acc_stderr\": 0.03266842214289201,\n \"acc_norm\": 0.6143497757847534,\n \"acc_norm_stderr\": 0.03266842214289201\n },\n \"harness|hendrycksTest-human_sexuality|5\": {\n \"acc\": 0.6641221374045801,\n \"acc_stderr\": 0.041423137719966634,\n \"acc_norm\": 0.6641221374045801,\n \"acc_norm_stderr\": 0.041423137719966634\n },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\": 0.7024793388429752,\n \"acc_stderr\": 0.04173349148083499,\n \"acc_norm\": 0.7024793388429752,\n \"acc_norm_stderr\": 0.04173349148083499\n },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.6759259259259259,\n \"acc_stderr\": 0.045245960070300476,\n \"acc_norm\": 0.6759259259259259,\n \"acc_norm_stderr\": 0.045245960070300476\n },\n \"harness|hendrycksTest-logical_fallacies|5\": {\n \"acc\": 0.6196319018404908,\n \"acc_stderr\": 0.038142698932618374,\n \"acc_norm\": 0.6196319018404908,\n \"acc_norm_stderr\": 0.038142698932618374\n },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.38392857142857145,\n \"acc_stderr\": 0.04616143075028547,\n \"acc_norm\": 0.38392857142857145,\n \"acc_norm_stderr\": 0.04616143075028547\n },\n \"harness|hendrycksTest-management|5\": {\n \"acc\": 0.6893203883495146,\n \"acc_stderr\": 0.04582124160161551,\n \"acc_norm\": 0.6893203883495146,\n \"acc_norm_stderr\": 0.04582124160161551\n },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8205128205128205,\n \"acc_stderr\": 0.025140935950335445,\n \"acc_norm\": 0.8205128205128205,\n \"acc_norm_stderr\": 0.025140935950335445\n },\n \"harness|hendrycksTest-medical_genetics|5\": {\n \"acc\": 0.64,\n \"acc_stderr\": 0.04824181513244218,\n \"acc_norm\": 0.64,\n \"acc_norm_stderr\": 0.04824181513244218\n },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.7484035759897829,\n \"acc_stderr\": 0.015517322365529631,\n \"acc_norm\": 0.7484035759897829,\n \"acc_norm_stderr\": 0.015517322365529631\n },\n \"harness|hendrycksTest-moral_disputes|5\": {\n \"acc\": 0.6358381502890174,\n \"acc_stderr\": 0.025906632631016124,\n \"acc_norm\": 0.6358381502890174,\n \"acc_norm_stderr\": 0.025906632631016124\n },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.32737430167597764,\n \"acc_stderr\": 0.015694238967737386,\n \"acc_norm\": 0.32737430167597764,\n \"acc_norm_stderr\": 0.015694238967737386\n },\n \"harness|hendrycksTest-nutrition|5\": {\n \"acc\": 0.5588235294117647,\n \"acc_stderr\": 0.028431095444176643,\n \"acc_norm\": 0.5588235294117647,\n \"acc_norm_stderr\": 0.028431095444176643\n },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.6430868167202572,\n \"acc_stderr\": 0.027210420375934026,\n \"acc_norm\": 0.6430868167202572,\n \"acc_norm_stderr\": 0.027210420375934026\n },\n \"harness|hendrycksTest-prehistory|5\": {\n \"acc\": 0.5925925925925926,\n \"acc_stderr\": 0.027339546640662734,\n \"acc_norm\": 0.5925925925925926,\n \"acc_norm_stderr\": 0.027339546640662734\n },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"acc\": 0.39361702127659576,\n \"acc_stderr\": 0.029144544781596147,\n \"acc_norm\": 0.39361702127659576,\n \"acc_norm_stderr\": 0.029144544781596147\n },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.38852672750977835,\n \"acc_stderr\": 0.012448817838292355,\n \"acc_norm\": 0.38852672750977835,\n \"acc_norm_stderr\": 0.012448817838292355\n },\n \"harness|hendrycksTest-professional_medicine|5\": {\n \"acc\": 0.5,\n \"acc_stderr\": 0.030372836961539352,\n \"acc_norm\": 0.5,\n \"acc_norm_stderr\": 0.030372836961539352\n },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"acc\": 0.5326797385620915,\n \"acc_stderr\": 0.0201845833591022,\n \"acc_norm\": 0.5326797385620915,\n \"acc_norm_stderr\": 0.0201845833591022\n },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.6,\n \"acc_stderr\": 0.0469237132203465,\n \"acc_norm\": 0.6,\n \"acc_norm_stderr\": 0.0469237132203465\n },\n \"harness|hendrycksTest-security_studies|5\": {\n \"acc\": 0.6285714285714286,\n \"acc_stderr\": 0.030932858792789855,\n \"acc_norm\": 0.6285714285714286,\n \"acc_norm_stderr\": 0.030932858792789855\n },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.7313432835820896,\n \"acc_stderr\": 0.03134328358208954,\n \"acc_norm\": 0.7313432835820896,\n \"acc_norm_stderr\": 0.03134328358208954\n },\n \"harness|hendrycksTest-us_foreign_policy|5\": {\n \"acc\": 0.82,\n \"acc_stderr\": 0.03861229196653694,\n \"acc_norm\": 0.82,\n \"acc_norm_stderr\": 0.03861229196653694\n },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.43373493975903615,\n \"acc_stderr\": 0.038581589406855174,\n \"acc_norm\": 0.43373493975903615,\n \"acc_norm_stderr\": 0.038581589406855174\n },\n \"harness|hendrycksTest-world_religions|5\": {\n \"acc\": 0.7134502923976608,\n \"acc_stderr\": 0.03467826685703826,\n \"acc_norm\": 0.7134502923976608,\n \"acc_norm_stderr\": 0.03467826685703826\n },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.3733170134638923,\n \"mc1_stderr\": 0.016932370557570634,\n \"mc2\": 0.5196984500314547,\n \"mc2_stderr\": 0.015094792312219791\n },\n \"harness|winogrande|5\": {\n \"acc\": 0.7576953433307024,\n \"acc_stderr\": 0.01204235252617479\n },\n \"harness|drop|3\": {\n \"em\": 0.012688758389261746,\n \"em_stderr\": 0.0011462418380586376,\n \"f1\": 0.07765100671140933,\n \"f1_stderr\": 0.0018276339186485593\n },\n \"harness|gsm8k|5\": {\n \"acc\": 0.177407126611069,\n \"acc_stderr\": 0.010522533016890793\n }\n}\n```", "repo_url": "https://huggingface.co/maywell/Synatra-11B-Testbench", "leaderboard_url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard", "point_of_contact": "[email protected]", "configs": [{"config_name": "harness_arc_challenge_25", "data_files": [{"split": "2023_11_13T02_58_44.682029", "path": ["**/details_harness|arc:challenge|25_2023-11-13T02-58-44.682029.parquet"]}, {"split": "latest", "path": ["**/details_harness|arc:challenge|25_2023-11-13T02-58-44.682029.parquet"]}]}, {"config_name": "harness_drop_3", "data_files": [{"split": "2023_11_13T02_58_44.682029", "path": ["**/details_harness|drop|3_2023-11-13T02-58-44.682029.parquet"]}, {"split": "latest", "path": ["**/details_harness|drop|3_2023-11-13T02-58-44.682029.parquet"]}]}, {"config_name": "harness_gsm8k_5", "data_files": [{"split": "2023_11_13T02_58_44.682029", "path": ["**/details_harness|gsm8k|5_2023-11-13T02-58-44.682029.parquet"]}, {"split": "latest", "path": ["**/details_harness|gsm8k|5_2023-11-13T02-58-44.682029.parquet"]}]}, {"config_name": "harness_hellaswag_10", "data_files": [{"split": "2023_11_13T02_58_44.682029", "path": ["**/details_harness|hellaswag|10_2023-11-13T02-58-44.682029.parquet"]}, {"split": "latest", "path": ["**/details_harness|hellaswag|10_2023-11-13T02-58-44.682029.parquet"]}]}, {"config_name": "harness_hendrycksTest_5", "data_files": [{"split": "2023_11_13T02_58_44.682029", "path": ["**/details_harness|hendrycksTest-abstract_algebra|5_2023-11-13T02-58-44.682029.parquet", "**/details_harness|hendrycksTest-anatomy|5_2023-11-13T02-58-44.682029.parquet", "**/details_harness|hendrycksTest-astronomy|5_2023-11-13T02-58-44.682029.parquet", "**/details_harness|hendrycksTest-business_ethics|5_2023-11-13T02-58-44.682029.parquet", "**/details_harness|hendrycksTest-clinical_knowledge|5_2023-11-13T02-58-44.682029.parquet", "**/details_harness|hendrycksTest-college_biology|5_2023-11-13T02-58-44.682029.parquet", "**/details_harness|hendrycksTest-college_chemistry|5_2023-11-13T02-58-44.682029.parquet", "**/details_harness|hendrycksTest-college_computer_science|5_2023-11-13T02-58-44.682029.parquet", "**/details_harness|hendrycksTest-college_mathematics|5_2023-11-13T02-58-44.682029.parquet", "**/details_harness|hendrycksTest-college_medicine|5_2023-11-13T02-58-44.682029.parquet", "**/details_harness|hendrycksTest-college_physics|5_2023-11-13T02-58-44.682029.parquet", "**/details_harness|hendrycksTest-computer_security|5_2023-11-13T02-58-44.682029.parquet", "**/details_harness|hendrycksTest-conceptual_physics|5_2023-11-13T02-58-44.682029.parquet", "**/details_harness|hendrycksTest-econometrics|5_2023-11-13T02-58-44.682029.parquet", "**/details_harness|hendrycksTest-electrical_engineering|5_2023-11-13T02-58-44.682029.parquet", "**/details_harness|hendrycksTest-elementary_mathematics|5_2023-11-13T02-58-44.682029.parquet", 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{"config_name": "results", "data_files": [{"split": "2023_11_13T02_58_44.682029", "path": ["results_2023-11-13T02-58-44.682029.parquet"]}, {"split": "latest", "path": ["results_2023-11-13T02-58-44.682029.parquet"]}]}]} | 2023-11-13T03:02:45+00:00 | []
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| TAGS
#region-us
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# Dataset Card for Evaluation run of maywell/Synatra-11B-Testbench
## Dataset Description
- Homepage:
- Repository: URL
- Paper:
- Leaderboard: URL
- Point of Contact: clementine@URL
### Dataset Summary
Dataset automatically created during the evaluation run of model maywell/Synatra-11B-Testbench on the Open LLM Leaderboard.
The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).
To load the details from a run, you can for instance do the following:
## Latest results
These are the latest results from run 2023-11-13T02:58:44.682029(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
### Supported Tasks and Leaderboards
### Languages
## Dataset Structure
### Data Instances
### Data Fields
### Data Splits
## Dataset Creation
### Curation Rationale
### Source Data
#### Initial Data Collection and Normalization
#### Who are the source language producers?
### Annotations
#### Annotation process
#### Who are the annotators?
### Personal and Sensitive Information
## Considerations for Using the Data
### Social Impact of Dataset
### Discussion of Biases
### Other Known Limitations
## Additional Information
### Dataset Curators
### Licensing Information
### Contributions
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"### Dataset Summary\n\nDataset automatically created during the evaluation run of model maywell/Synatra-11B-Testbench on the Open LLM Leaderboard.\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
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"### Social Impact of Dataset",
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"passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of maywell/Synatra-11B-Testbench## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL### Dataset Summary\n\nDataset automatically created during the evaluation run of model maywell/Synatra-11B-Testbench on the Open LLM Leaderboard.\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2023-11-13T02:58:44.682029(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):### Supported Tasks and Leaderboards### Languages## Dataset Structure### Data Instances### Data Fields### Data Splits## Dataset Creation### Curation Rationale### Source Data#### Initial Data Collection and Normalization#### Who are the source language producers?### Annotations#### Annotation process#### Who are the annotators?### Personal and Sensitive Information## Considerations for Using the Data### Social Impact of Dataset### Discussion of Biases### Other Known Limitations## Additional Information### Dataset Curators### Licensing Information### Contributions"
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|
a3cf96a6fd58f2fbdab78053ae26df96885bb49d | This is a full view of the data from the MongoDB community (exclude all posts without any responses) | wangzhang/mongoDB_community | [
"region:us"
]
| 2023-11-13T03:33:14+00:00 | {} | 2023-11-13T03:36:32+00:00 | []
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| TAGS
#region-us
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|
e1240237f9196408471e63f7ed34c755adae991d | # Dataset Card for "100_harmless_harmful_behaviors_llama2_chat"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | justinphan3110/100_harmless_harmful_behaviors_llama2_chat | [
"region:us"
]
| 2023-11-13T03:34:43+00:00 | {"dataset_info": {"features": [{"name": "sentence", "sequence": "string"}, {"name": "label", "sequence": "bool"}], "splits": [{"name": "train", "num_bytes": 123896, "num_examples": 100}], "download_size": 22714, "dataset_size": 123896}} | 2023-11-13T03:34:44+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "100_harmless_harmful_behaviors_llama2_chat"
More Information needed | [
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"# Dataset Card for \"100_harmless_harmful_behaviors_llama2_chat\"\n\nMore Information needed"
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| [
6,
29
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"100_harmless_harmful_behaviors_llama2_chat\"\n\nMore Information needed"
]
|
c0b48be8d76fb20de0e8f6fe03adff67bbd4756e | # The Fusion Dataset
<center><img src="logo.jpg" alt="Fusion logo" width="300"/></center>
A massive collection of text from many datasets, the intenet, code, etc.
Currently only text based, may be multimodal in the future
Stages
- raw text files in folders
- deduplicated text
- synthetic grading, quality filters
- final dataset
Split into 3 parts
- Social - Includes Data from roleplay and social media, probably the noisest data*
- Pretrain - Large amounts of text for model pretrains, textbooks, webscrapes, etc
- Instruct - Instruction data, meant to a single format, for a model finetune
*Also the most uncensored/unfiltered, there is 4chan in here, probably a good idea to filter this | VatsaDev/fusion | [
"task_categories:question-answering",
"task_categories:text-generation",
"task_categories:text-classification",
"language:en",
"language:fr",
"language:it",
"language:hi",
"license:mit",
"region:us"
]
| 2023-11-13T03:38:33+00:00 | {"language": ["en", "fr", "it", "hi"], "license": "mit", "task_categories": ["question-answering", "text-generation", "text-classification"]} | 2024-02-17T08:13:02+00:00 | []
| [
"en",
"fr",
"it",
"hi"
]
| TAGS
#task_categories-question-answering #task_categories-text-generation #task_categories-text-classification #language-English #language-French #language-Italian #language-Hindi #license-mit #region-us
| # The Fusion Dataset
<center><img src="URL" alt="Fusion logo" width="300"/></center>
A massive collection of text from many datasets, the intenet, code, etc.
Currently only text based, may be multimodal in the future
Stages
- raw text files in folders
- deduplicated text
- synthetic grading, quality filters
- final dataset
Split into 3 parts
- Social - Includes Data from roleplay and social media, probably the noisest data*
- Pretrain - Large amounts of text for model pretrains, textbooks, webscrapes, etc
- Instruct - Instruction data, meant to a single format, for a model finetune
*Also the most uncensored/unfiltered, there is 4chan in here, probably a good idea to filter this | [
"# The Fusion Dataset\n\n<center><img src=\"URL\" alt=\"Fusion logo\" width=\"300\"/></center>\n\nA massive collection of text from many datasets, the intenet, code, etc.\nCurrently only text based, may be multimodal in the future\n\nStages\n - raw text files in folders\n - deduplicated text\n - synthetic grading, quality filters\n - final dataset\n\nSplit into 3 parts\n - Social - Includes Data from roleplay and social media, probably the noisest data*\n - Pretrain - Large amounts of text for model pretrains, textbooks, webscrapes, etc\n - Instruct - Instruction data, meant to a single format, for a model finetune\n\n*Also the most uncensored/unfiltered, there is 4chan in here, probably a good idea to filter this"
]
| [
"TAGS\n#task_categories-question-answering #task_categories-text-generation #task_categories-text-classification #language-English #language-French #language-Italian #language-Hindi #license-mit #region-us \n",
"# The Fusion Dataset\n\n<center><img src=\"URL\" alt=\"Fusion logo\" width=\"300\"/></center>\n\nA massive collection of text from many datasets, the intenet, code, etc.\nCurrently only text based, may be multimodal in the future\n\nStages\n - raw text files in folders\n - deduplicated text\n - synthetic grading, quality filters\n - final dataset\n\nSplit into 3 parts\n - Social - Includes Data from roleplay and social media, probably the noisest data*\n - Pretrain - Large amounts of text for model pretrains, textbooks, webscrapes, etc\n - Instruct - Instruction data, meant to a single format, for a model finetune\n\n*Also the most uncensored/unfiltered, there is 4chan in here, probably a good idea to filter this"
]
| [
64,
187
]
| [
"passage: TAGS\n#task_categories-question-answering #task_categories-text-generation #task_categories-text-classification #language-English #language-French #language-Italian #language-Hindi #license-mit #region-us \n# The Fusion Dataset\n\n<center><img src=\"URL\" alt=\"Fusion logo\" width=\"300\"/></center>\n\nA massive collection of text from many datasets, the intenet, code, etc.\nCurrently only text based, may be multimodal in the future\n\nStages\n - raw text files in folders\n - deduplicated text\n - synthetic grading, quality filters\n - final dataset\n\nSplit into 3 parts\n - Social - Includes Data from roleplay and social media, probably the noisest data*\n - Pretrain - Large amounts of text for model pretrains, textbooks, webscrapes, etc\n - Instruct - Instruction data, meant to a single format, for a model finetune\n\n*Also the most uncensored/unfiltered, there is 4chan in here, probably a good idea to filter this"
]
|
0b0c01c491b61cd80bd967dc256d712a5a5c44e4 | # Dataset Card for "prm800k-llama-generator-inference"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | parksimon0808/prm800k-llama-generator-inference | [
"region:us"
]
| 2023-11-13T03:51:58+00:00 | {"dataset_info": {"features": [{"name": "texts", "dtype": "string"}, {"name": "answers", "dtype": "string"}], "splits": [{"name": "test", "num_bytes": 506185, "num_examples": 2868}], "download_size": 76891, "dataset_size": 506185}} | 2023-11-13T03:57:56+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "prm800k-llama-generator-inference"
More Information needed | [
"# Dataset Card for \"prm800k-llama-generator-inference\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"prm800k-llama-generator-inference\"\n\nMore Information needed"
]
| [
6,
24
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"prm800k-llama-generator-inference\"\n\nMore Information needed"
]
|
3e4dd4ffbceca8b99dfeaddc3af6c1f03178ec77 | # HEx-PHI: **H**uman-**Ex**tended **P**olicy-Oriented **H**armful **I**nstruction Benchmark
This dataset contains 330 harmful instructions (30 examples x 11 prohibited categories) for LLM harmfulness evaluation.
In our work "[Fine-tuning Aligned Language Models Compromises Safety, Even When Users Do Not Intend To!](https://arxiv.org/pdf/2310.03693.pdf)", to comprehensively cover as many harmfulness categories as possible,
we develop this new safety evaluation benchmark directly based on the exhaustive lists of prohibited use cases found in **Meta**’s Llama-2 usage policy and **OpenAI**’s usage policy.
Specifically, we gather 30 examples of harmful instructions for each of the 11 prohibited use case categories from various sources, including:
* Anthropic [Red Teaming Data](https://github.com/anthropics/hh-rlhf/tree/master/red-team-attempts)
* *AdvBench* from "[Universal and Transferable Adversarial Attacks on Aligned Language Models](https://arxiv.org/abs/2307.15043)"
* The harmful requests are further categorized & refined by human annotators, GPT-4, Claude, jailbroken GPT-3.5, jailbroken Llama2, etc.
For more details, please refer to our project website: [http://llm-tuning-safety.github.io](http://llm-tuning-safety.github.io).
## Dataset Format
The 11 categories of harmful instructions are stored separately in 11 independent files in plain CSV format:
* [category_1.csv](category_1.csv): Illegal Activity
* [category_2.csv](category_2.csv): Child Abuse Content
* [category_3.csv](category_3.csv): Hate / Harass /Violence
* [category_4.csv](category_4.csv): Malware
* [category_5.csv](category_5.csv): Physical Harm
* [category_6.csv](category_6.csv): Economic Harm
* [category_8.csv](category_7.csv): Fraud Deception
* [category_7.csv](category_8.csv): Adult Content
* [category_9.csv](category_9.csv): Political Campaigning
* [category_10.csv](category_10.csv): Privacy Violation Activity
* [category_11.csv](category_11.csv): Tailored Financial Advice
## Dataset Usage
With our 11 harmful categories, we hope HEx-PHI can help researchers comprehensively evaluate fine-grained safety risks and harmfulness underlying LLMs.
Practically, one should use HEx-PHI harmful instructions as (part of) input prompts, and *inspect*👀 whether the responses generated by the LLM satisfy the harmful intentions of the instructions.
👀In our [paper](https://arxiv.org/pdf/2310.03693.pdf), we use GPT-4 as the harmfulness judge to assign each `<user instruction, model response>` pair a harmfulness score from 1 to 5. Refer to Appendix B for details.
## HEx-PHI Dataset License Agreement
This Agreement contains the terms and conditions that govern your access and use of the HEx-PHI Dataset (as defined above). You may not use the HEx-PHI Dataset if you do not accept this Agreement. By clicking to accept, accessing the HEx-PHI Dataset, or both, you hereby agree to the terms of the Agreement. If you are agreeing to be bound by the Agreement on behalf of your employer or another entity, you represent and warrant that you have full legal authority to bind your employer or such entity to this Agreement. If you do not have the requisite authority, you may not accept the Agreement or access the HEx-PHI Dataset on behalf of your employer or another entity.
* Safety and Moderation: **This dataset contains unsafe conversations or prompts that may be perceived as offensive or unsettling.** Users may not use this dataset for training machine learning models for any harmful purpose. The dataset may not be used to generate content in violation of any law. These prompts should not be used as inputs to models that can generate modalities outside of text (including, but not limited to, images, audio, video, or 3D models)
* Non-Endorsement: The views and opinions depicted in this dataset **do not reflect** the perspectives of the researchers or affiliated institutions engaged in the data collection process.
* Legal Compliance: You are mandated to use it in adherence with all pertinent laws and regulations.
* Model Specific Terms: When leveraging direct outputs of a specific model, users must adhere to its **corresponding terms of use and relevant legal standards**.
* Non-Identification: You **must not** attempt to identify the identities of individuals or infer any sensitive personal data encompassed in this dataset.
* Prohibited Transfers: You **should not** distribute, copy, disclose, assign, sublicense, embed, host, or otherwise transfer the dataset to any third party.
* Right to Request Deletion: At any time, we may require you to delete all copies of this instruction dataset (in whole or in part) in your possession and control. You will promptly comply with any and all such requests. Upon our request, you shall provide us with written confirmation of your compliance with such requirement.
* Termination: We may, at any time, for any reason or for no reason, terminate this Agreement, effective immediately upon notice to you. Upon termination, the license granted to you hereunder will immediately terminate, and you will immediately stop using the HEx-PHI Dataset and destroy all copies of the HEx-PHI Dataset and related materials in your possession or control.
* Limitation of Liability: IN NO EVENT WILL WE BE LIABLE FOR ANY CONSEQUENTIAL, INCIDENTAL, EXEMPLARY, PUNITIVE, SPECIAL, OR INDIRECT DAMAGES (INCLUDING DAMAGES FOR LOSS OF PROFITS, BUSINESS INTERRUPTION, OR LOSS OF INFORMATION) ARISING OUT OF OR RELATING TO THIS AGREEMENT OR ITS SUBJECT MATTER, EVEN IF WE HAVE BEEN ADVISED OF THE POSSIBILITY OF SUCH DAMAGES.
Subject to your compliance with the terms and conditions of this Agreement, we grant to you, a limited, non-exclusive, non-transferable, non-sublicensable license to use the HEx-PHI Dataset, including the conversation data and annotations, to research, and evaluate software, algorithms, machine learning models, techniques, and technologies for both research and commercial purposes.
## Citation
```
@inproceedings{
anonymous2024finetuning,
title={Fine-tuning Aligned Language Models Compromises Safety, Even When Users Do Not Intend To!},
author={Xiangyu Qi and Yi Zeng and Tinghao Xie and Pin-Yu Chen and Ruoxi Jia and Prateek Mittal and Peter Henderson},
booktitle={The Twelfth International Conference on Learning Representations},
year={2024},
url={https://openreview.net/forum?id=hTEGyKf0dZ}
}
``` | LLM-Tuning-Safety/HEx-PHI | [
"task_categories:text-generation",
"task_categories:conversational",
"size_categories:n<1K",
"language:en",
"license:other",
"harmfulness",
"benchmark",
"arxiv:2310.03693",
"arxiv:2307.15043",
"region:us"
]
| 2023-11-13T04:19:21+00:00 | {"language": ["en"], "license": "other", "size_categories": ["n<1K"], "task_categories": ["text-generation", "conversational"], "pretty_name": "Human-Extended Policy-Oriented Harmful Instruction Benchmark", "license_name": "hex-phi", "license_link": "https://huggingface.co/datasets/LLM-Tuning-Safety/HEx-PHI/#hex-phi-dataset-license-agreement", "extra_gated_prompt": "You agree to the [HEx-PHI Dataset License Agreement](https://huggingface.co/datasets/LLM-Tuning-Safety/HEx-PHI/#hex-phi-dataset-license-agreement). Also, please specify the following fields in detail (we suggest you fill in your affiliation email), based on which we will inspect and manually grant access to approved users. If you have not been granted access, please email us (see email contact from our paper) and specify more details.", "extra_gated_fields": {"Name": "text", "Email": "text", "Affiliation": "text", "Country": "text", "Purpose": "text"}, "configs": [{"config_name": "default", "data_files": [{"split": "Category_1_Illegal_Activity", "path": "category_1.csv"}, {"split": "Category_2_Child_Abuse_Content", "path": "category_2.csv"}, {"split": "Category_3_Hate_Harass_Violence", "path": "category_3.csv"}, {"split": "Category_4_Malware", "path": "category_4.csv"}, {"split": "Category_5_Physical_Harm", "path": "category_5.csv"}, {"split": "Category_6_Economic_Harm", "path": "category_6.csv"}, {"split": "Category_7_Fraud_Deception", "path": "category_7.csv"}, {"split": "Category_8_Adult_Content", "path": "category_8.csv"}, {"split": "Category_9_Political_Campaigning", "path": "category_9.csv"}, {"split": "Category_10_Privacy_Violation_Activity", "path": "category_10.csv"}, {"split": "Category_11_Tailored_Financial_Advice", "path": "category_11.csv"}]}], "tags": ["harmfulness", "benchmark"]} | 2024-01-24T21:37:41+00:00 | [
"2310.03693",
"2307.15043"
]
| [
"en"
]
| TAGS
#task_categories-text-generation #task_categories-conversational #size_categories-n<1K #language-English #license-other #harmfulness #benchmark #arxiv-2310.03693 #arxiv-2307.15043 #region-us
| # HEx-PHI: Human-Extended Policy-Oriented Harmful Instruction Benchmark
This dataset contains 330 harmful instructions (30 examples x 11 prohibited categories) for LLM harmfulness evaluation.
In our work "Fine-tuning Aligned Language Models Compromises Safety, Even When Users Do Not Intend To!", to comprehensively cover as many harmfulness categories as possible,
we develop this new safety evaluation benchmark directly based on the exhaustive lists of prohibited use cases found in Meta’s Llama-2 usage policy and OpenAI’s usage policy.
Specifically, we gather 30 examples of harmful instructions for each of the 11 prohibited use case categories from various sources, including:
* Anthropic Red Teaming Data
* *AdvBench* from "Universal and Transferable Adversarial Attacks on Aligned Language Models"
* The harmful requests are further categorized & refined by human annotators, GPT-4, Claude, jailbroken GPT-3.5, jailbroken Llama2, etc.
For more details, please refer to our project website: URL.
## Dataset Format
The 11 categories of harmful instructions are stored separately in 11 independent files in plain CSV format:
* category_1.csv: Illegal Activity
* category_2.csv: Child Abuse Content
* category_3.csv: Hate / Harass /Violence
* category_4.csv: Malware
* category_5.csv: Physical Harm
* category_6.csv: Economic Harm
* category_8.csv: Fraud Deception
* category_7.csv: Adult Content
* category_9.csv: Political Campaigning
* category_10.csv: Privacy Violation Activity
* category_11.csv: Tailored Financial Advice
## Dataset Usage
With our 11 harmful categories, we hope HEx-PHI can help researchers comprehensively evaluate fine-grained safety risks and harmfulness underlying LLMs.
Practically, one should use HEx-PHI harmful instructions as (part of) input prompts, and *inspect* whether the responses generated by the LLM satisfy the harmful intentions of the instructions.
In our paper, we use GPT-4 as the harmfulness judge to assign each '<user instruction, model response>' pair a harmfulness score from 1 to 5. Refer to Appendix B for details.
## HEx-PHI Dataset License Agreement
This Agreement contains the terms and conditions that govern your access and use of the HEx-PHI Dataset (as defined above). You may not use the HEx-PHI Dataset if you do not accept this Agreement. By clicking to accept, accessing the HEx-PHI Dataset, or both, you hereby agree to the terms of the Agreement. If you are agreeing to be bound by the Agreement on behalf of your employer or another entity, you represent and warrant that you have full legal authority to bind your employer or such entity to this Agreement. If you do not have the requisite authority, you may not accept the Agreement or access the HEx-PHI Dataset on behalf of your employer or another entity.
* Safety and Moderation: This dataset contains unsafe conversations or prompts that may be perceived as offensive or unsettling. Users may not use this dataset for training machine learning models for any harmful purpose. The dataset may not be used to generate content in violation of any law. These prompts should not be used as inputs to models that can generate modalities outside of text (including, but not limited to, images, audio, video, or 3D models)
* Non-Endorsement: The views and opinions depicted in this dataset do not reflect the perspectives of the researchers or affiliated institutions engaged in the data collection process.
* Legal Compliance: You are mandated to use it in adherence with all pertinent laws and regulations.
* Model Specific Terms: When leveraging direct outputs of a specific model, users must adhere to its corresponding terms of use and relevant legal standards.
* Non-Identification: You must not attempt to identify the identities of individuals or infer any sensitive personal data encompassed in this dataset.
* Prohibited Transfers: You should not distribute, copy, disclose, assign, sublicense, embed, host, or otherwise transfer the dataset to any third party.
* Right to Request Deletion: At any time, we may require you to delete all copies of this instruction dataset (in whole or in part) in your possession and control. You will promptly comply with any and all such requests. Upon our request, you shall provide us with written confirmation of your compliance with such requirement.
* Termination: We may, at any time, for any reason or for no reason, terminate this Agreement, effective immediately upon notice to you. Upon termination, the license granted to you hereunder will immediately terminate, and you will immediately stop using the HEx-PHI Dataset and destroy all copies of the HEx-PHI Dataset and related materials in your possession or control.
* Limitation of Liability: IN NO EVENT WILL WE BE LIABLE FOR ANY CONSEQUENTIAL, INCIDENTAL, EXEMPLARY, PUNITIVE, SPECIAL, OR INDIRECT DAMAGES (INCLUDING DAMAGES FOR LOSS OF PROFITS, BUSINESS INTERRUPTION, OR LOSS OF INFORMATION) ARISING OUT OF OR RELATING TO THIS AGREEMENT OR ITS SUBJECT MATTER, EVEN IF WE HAVE BEEN ADVISED OF THE POSSIBILITY OF SUCH DAMAGES.
Subject to your compliance with the terms and conditions of this Agreement, we grant to you, a limited, non-exclusive, non-transferable, non-sublicensable license to use the HEx-PHI Dataset, including the conversation data and annotations, to research, and evaluate software, algorithms, machine learning models, techniques, and technologies for both research and commercial purposes.
| [
"# HEx-PHI: Human-Extended Policy-Oriented Harmful Instruction Benchmark\n\nThis dataset contains 330 harmful instructions (30 examples x 11 prohibited categories) for LLM harmfulness evaluation.\n\nIn our work \"Fine-tuning Aligned Language Models Compromises Safety, Even When Users Do Not Intend To!\", to comprehensively cover as many harmfulness categories as possible,\nwe develop this new safety evaluation benchmark directly based on the exhaustive lists of prohibited use cases found in Meta’s Llama-2 usage policy and OpenAI’s usage policy.\nSpecifically, we gather 30 examples of harmful instructions for each of the 11 prohibited use case categories from various sources, including:\n* Anthropic Red Teaming Data\n* *AdvBench* from \"Universal and Transferable Adversarial Attacks on Aligned Language Models\"\n* The harmful requests are further categorized & refined by human annotators, GPT-4, Claude, jailbroken GPT-3.5, jailbroken Llama2, etc.\n\n\nFor more details, please refer to our project website: URL.",
"## Dataset Format\n\nThe 11 categories of harmful instructions are stored separately in 11 independent files in plain CSV format:\n* category_1.csv: Illegal Activity\n* category_2.csv: Child Abuse Content\n* category_3.csv: Hate / Harass /Violence\n* category_4.csv: Malware\n* category_5.csv: Physical Harm\n* category_6.csv: Economic Harm\n* category_8.csv: Fraud Deception\n* category_7.csv: Adult Content\n* category_9.csv: Political Campaigning\n* category_10.csv: Privacy Violation Activity\n* category_11.csv: Tailored Financial Advice",
"## Dataset Usage\n\nWith our 11 harmful categories, we hope HEx-PHI can help researchers comprehensively evaluate fine-grained safety risks and harmfulness underlying LLMs.\nPractically, one should use HEx-PHI harmful instructions as (part of) input prompts, and *inspect* whether the responses generated by the LLM satisfy the harmful intentions of the instructions.\n\nIn our paper, we use GPT-4 as the harmfulness judge to assign each '<user instruction, model response>' pair a harmfulness score from 1 to 5. Refer to Appendix B for details.",
"## HEx-PHI Dataset License Agreement\n\nThis Agreement contains the terms and conditions that govern your access and use of the HEx-PHI Dataset (as defined above). You may not use the HEx-PHI Dataset if you do not accept this Agreement. By clicking to accept, accessing the HEx-PHI Dataset, or both, you hereby agree to the terms of the Agreement. If you are agreeing to be bound by the Agreement on behalf of your employer or another entity, you represent and warrant that you have full legal authority to bind your employer or such entity to this Agreement. If you do not have the requisite authority, you may not accept the Agreement or access the HEx-PHI Dataset on behalf of your employer or another entity.\n\n* Safety and Moderation: This dataset contains unsafe conversations or prompts that may be perceived as offensive or unsettling. Users may not use this dataset for training machine learning models for any harmful purpose. The dataset may not be used to generate content in violation of any law. These prompts should not be used as inputs to models that can generate modalities outside of text (including, but not limited to, images, audio, video, or 3D models)\n* Non-Endorsement: The views and opinions depicted in this dataset do not reflect the perspectives of the researchers or affiliated institutions engaged in the data collection process.\n* Legal Compliance: You are mandated to use it in adherence with all pertinent laws and regulations.\n* Model Specific Terms: When leveraging direct outputs of a specific model, users must adhere to its corresponding terms of use and relevant legal standards.\n* Non-Identification: You must not attempt to identify the identities of individuals or infer any sensitive personal data encompassed in this dataset.\n* Prohibited Transfers: You should not distribute, copy, disclose, assign, sublicense, embed, host, or otherwise transfer the dataset to any third party.\n* Right to Request Deletion: At any time, we may require you to delete all copies of this instruction dataset (in whole or in part) in your possession and control. You will promptly comply with any and all such requests. Upon our request, you shall provide us with written confirmation of your compliance with such requirement.\n* Termination: We may, at any time, for any reason or for no reason, terminate this Agreement, effective immediately upon notice to you. Upon termination, the license granted to you hereunder will immediately terminate, and you will immediately stop using the HEx-PHI Dataset and destroy all copies of the HEx-PHI Dataset and related materials in your possession or control.\n* Limitation of Liability: IN NO EVENT WILL WE BE LIABLE FOR ANY CONSEQUENTIAL, INCIDENTAL, EXEMPLARY, PUNITIVE, SPECIAL, OR INDIRECT DAMAGES (INCLUDING DAMAGES FOR LOSS OF PROFITS, BUSINESS INTERRUPTION, OR LOSS OF INFORMATION) ARISING OUT OF OR RELATING TO THIS AGREEMENT OR ITS SUBJECT MATTER, EVEN IF WE HAVE BEEN ADVISED OF THE POSSIBILITY OF SUCH DAMAGES.\n\nSubject to your compliance with the terms and conditions of this Agreement, we grant to you, a limited, non-exclusive, non-transferable, non-sublicensable license to use the HEx-PHI Dataset, including the conversation data and annotations, to research, and evaluate software, algorithms, machine learning models, techniques, and technologies for both research and commercial purposes."
]
| [
"TAGS\n#task_categories-text-generation #task_categories-conversational #size_categories-n<1K #language-English #license-other #harmfulness #benchmark #arxiv-2310.03693 #arxiv-2307.15043 #region-us \n",
"# HEx-PHI: Human-Extended Policy-Oriented Harmful Instruction Benchmark\n\nThis dataset contains 330 harmful instructions (30 examples x 11 prohibited categories) for LLM harmfulness evaluation.\n\nIn our work \"Fine-tuning Aligned Language Models Compromises Safety, Even When Users Do Not Intend To!\", to comprehensively cover as many harmfulness categories as possible,\nwe develop this new safety evaluation benchmark directly based on the exhaustive lists of prohibited use cases found in Meta’s Llama-2 usage policy and OpenAI’s usage policy.\nSpecifically, we gather 30 examples of harmful instructions for each of the 11 prohibited use case categories from various sources, including:\n* Anthropic Red Teaming Data\n* *AdvBench* from \"Universal and Transferable Adversarial Attacks on Aligned Language Models\"\n* The harmful requests are further categorized & refined by human annotators, GPT-4, Claude, jailbroken GPT-3.5, jailbroken Llama2, etc.\n\n\nFor more details, please refer to our project website: URL.",
"## Dataset Format\n\nThe 11 categories of harmful instructions are stored separately in 11 independent files in plain CSV format:\n* category_1.csv: Illegal Activity\n* category_2.csv: Child Abuse Content\n* category_3.csv: Hate / Harass /Violence\n* category_4.csv: Malware\n* category_5.csv: Physical Harm\n* category_6.csv: Economic Harm\n* category_8.csv: Fraud Deception\n* category_7.csv: Adult Content\n* category_9.csv: Political Campaigning\n* category_10.csv: Privacy Violation Activity\n* category_11.csv: Tailored Financial Advice",
"## Dataset Usage\n\nWith our 11 harmful categories, we hope HEx-PHI can help researchers comprehensively evaluate fine-grained safety risks and harmfulness underlying LLMs.\nPractically, one should use HEx-PHI harmful instructions as (part of) input prompts, and *inspect* whether the responses generated by the LLM satisfy the harmful intentions of the instructions.\n\nIn our paper, we use GPT-4 as the harmfulness judge to assign each '<user instruction, model response>' pair a harmfulness score from 1 to 5. Refer to Appendix B for details.",
"## HEx-PHI Dataset License Agreement\n\nThis Agreement contains the terms and conditions that govern your access and use of the HEx-PHI Dataset (as defined above). You may not use the HEx-PHI Dataset if you do not accept this Agreement. By clicking to accept, accessing the HEx-PHI Dataset, or both, you hereby agree to the terms of the Agreement. If you are agreeing to be bound by the Agreement on behalf of your employer or another entity, you represent and warrant that you have full legal authority to bind your employer or such entity to this Agreement. If you do not have the requisite authority, you may not accept the Agreement or access the HEx-PHI Dataset on behalf of your employer or another entity.\n\n* Safety and Moderation: This dataset contains unsafe conversations or prompts that may be perceived as offensive or unsettling. Users may not use this dataset for training machine learning models for any harmful purpose. The dataset may not be used to generate content in violation of any law. These prompts should not be used as inputs to models that can generate modalities outside of text (including, but not limited to, images, audio, video, or 3D models)\n* Non-Endorsement: The views and opinions depicted in this dataset do not reflect the perspectives of the researchers or affiliated institutions engaged in the data collection process.\n* Legal Compliance: You are mandated to use it in adherence with all pertinent laws and regulations.\n* Model Specific Terms: When leveraging direct outputs of a specific model, users must adhere to its corresponding terms of use and relevant legal standards.\n* Non-Identification: You must not attempt to identify the identities of individuals or infer any sensitive personal data encompassed in this dataset.\n* Prohibited Transfers: You should not distribute, copy, disclose, assign, sublicense, embed, host, or otherwise transfer the dataset to any third party.\n* Right to Request Deletion: At any time, we may require you to delete all copies of this instruction dataset (in whole or in part) in your possession and control. You will promptly comply with any and all such requests. Upon our request, you shall provide us with written confirmation of your compliance with such requirement.\n* Termination: We may, at any time, for any reason or for no reason, terminate this Agreement, effective immediately upon notice to you. Upon termination, the license granted to you hereunder will immediately terminate, and you will immediately stop using the HEx-PHI Dataset and destroy all copies of the HEx-PHI Dataset and related materials in your possession or control.\n* Limitation of Liability: IN NO EVENT WILL WE BE LIABLE FOR ANY CONSEQUENTIAL, INCIDENTAL, EXEMPLARY, PUNITIVE, SPECIAL, OR INDIRECT DAMAGES (INCLUDING DAMAGES FOR LOSS OF PROFITS, BUSINESS INTERRUPTION, OR LOSS OF INFORMATION) ARISING OUT OF OR RELATING TO THIS AGREEMENT OR ITS SUBJECT MATTER, EVEN IF WE HAVE BEEN ADVISED OF THE POSSIBILITY OF SUCH DAMAGES.\n\nSubject to your compliance with the terms and conditions of this Agreement, we grant to you, a limited, non-exclusive, non-transferable, non-sublicensable license to use the HEx-PHI Dataset, including the conversation data and annotations, to research, and evaluate software, algorithms, machine learning models, techniques, and technologies for both research and commercial purposes."
]
| [
72,
263,
163,
149,
821
]
| [
"passage: TAGS\n#task_categories-text-generation #task_categories-conversational #size_categories-n<1K #language-English #license-other #harmfulness #benchmark #arxiv-2310.03693 #arxiv-2307.15043 #region-us \n# HEx-PHI: Human-Extended Policy-Oriented Harmful Instruction Benchmark\n\nThis dataset contains 330 harmful instructions (30 examples x 11 prohibited categories) for LLM harmfulness evaluation.\n\nIn our work \"Fine-tuning Aligned Language Models Compromises Safety, Even When Users Do Not Intend To!\", to comprehensively cover as many harmfulness categories as possible,\nwe develop this new safety evaluation benchmark directly based on the exhaustive lists of prohibited use cases found in Meta’s Llama-2 usage policy and OpenAI’s usage policy.\nSpecifically, we gather 30 examples of harmful instructions for each of the 11 prohibited use case categories from various sources, including:\n* Anthropic Red Teaming Data\n* *AdvBench* from \"Universal and Transferable Adversarial Attacks on Aligned Language Models\"\n* The harmful requests are further categorized & refined by human annotators, GPT-4, Claude, jailbroken GPT-3.5, jailbroken Llama2, etc.\n\n\nFor more details, please refer to our project website: URL.## Dataset Format\n\nThe 11 categories of harmful instructions are stored separately in 11 independent files in plain CSV format:\n* category_1.csv: Illegal Activity\n* category_2.csv: Child Abuse Content\n* category_3.csv: Hate / Harass /Violence\n* category_4.csv: Malware\n* category_5.csv: Physical Harm\n* category_6.csv: Economic Harm\n* category_8.csv: Fraud Deception\n* category_7.csv: Adult Content\n* category_9.csv: Political Campaigning\n* category_10.csv: Privacy Violation Activity\n* category_11.csv: Tailored Financial Advice",
"passage: ## Dataset Usage\n\nWith our 11 harmful categories, we hope HEx-PHI can help researchers comprehensively evaluate fine-grained safety risks and harmfulness underlying LLMs.\nPractically, one should use HEx-PHI harmful instructions as (part of) input prompts, and *inspect* whether the responses generated by the LLM satisfy the harmful intentions of the instructions.\n\nIn our paper, we use GPT-4 as the harmfulness judge to assign each '<user instruction, model response>' pair a harmfulness score from 1 to 5. Refer to Appendix B for details."
]
|
4b752eca248a90df35cdad91b6052f7b88ef4708 | # Dataset Card for "esg1"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | bh8648/esg1 | [
"region:us"
]
| 2023-11-13T04:26:46+00:00 | {"dataset_info": {"features": [{"name": "Major Category", "dtype": "string"}, {"name": "Middle Categoty", "dtype": "string"}, {"name": "Small Category", "dtype": "string"}, {"name": "output", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 291558, "num_examples": 69}], "download_size": 145865, "dataset_size": 291558}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]} | 2023-11-13T04:26:51+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "esg1"
More Information needed | [
"# Dataset Card for \"esg1\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"esg1\"\n\nMore Information needed"
]
| [
6,
13
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"esg1\"\n\nMore Information needed"
]
|
02ca4c95d3eb532e25966e770dfd469b237e6271 | # Dataset Card for "COVID_Vaccine_Tweet_sentiment_analysis_roberta"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | bambadij/COVID_Vaccine_Tweet_sentiment_analysis_roberta | [
"region:us"
]
| 2023-11-13T04:36:10+00:00 | {"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "eval", "path": "data/eval-*"}]}], "dataset_info": {"features": [{"name": "input_ids", "sequence": "int32"}, {"name": "attention_mask", "sequence": "int8"}, {"name": "labels", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 1827789, "num_examples": 7999}, {"name": "eval", "num_bytes": 527000, "num_examples": 2000}], "download_size": 569067, "dataset_size": 2354789}} | 2023-11-13T04:36:13+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "COVID_Vaccine_Tweet_sentiment_analysis_roberta"
More Information needed | [
"# Dataset Card for \"COVID_Vaccine_Tweet_sentiment_analysis_roberta\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"COVID_Vaccine_Tweet_sentiment_analysis_roberta\"\n\nMore Information needed"
]
| [
6,
29
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| [
"passage: TAGS\n#region-us \n# Dataset Card for \"COVID_Vaccine_Tweet_sentiment_analysis_roberta\"\n\nMore Information needed"
]
|
9ff7478da418f2a5f11c5ee940d695a34a992c3e | # Data details
- **274K multimodal feedback and revision data**
- 558K filtered image-text pairs from LAION/CC/SBU, captioned by BLIP.
- 158K GPT-generated multimodal instruction-following data.
- 450K academic-task-oriented VQA data mixture.
- 40K ShareGPT data
# Data collection
Since no multimodal feedback data for training is publicly available as of this writing and human labeling is costly, we used a proprietary LLM to generate feedback data.
As shown in figure, we use an open-source LMM to provide an initial answer to a question about an image. Since current proprietary LLMs cannot process images, we provide object details in text and captions as a proxy for image. For each data instance, we feed the LLM image information consisting of object details and captions, question, initial response, and gold answer as reference answer, allowing the model to evaluate the given inputs and produce feedback. The proprietary LLM might exploit the gold answer to generate the feedback, which can cause potential inaccuracies in feedback during inference time when it is not provided. To avoid this, we give the LLM clear prompts to use text-formatted image details when generating feedback. When constructing the revision data, we set up the system to predict the existing gold answer as the output, using the feedback data, image, question, and initial response obtained from the previous steps as input, without involving any separate model generation process.
Although Volcano is trained using the language modeling objective in a manner consistent with traditional VLMs, it not only follows instructions but also can provide critical feedback based on image information and subsequently self-revise. This enhanced ability is attributed to Volcano's combined training with visual instruction tuning data, feedback, and revision data. | kaist-ai/volcano-train | [
"task_categories:image-to-text",
"size_categories:1M<n<10M",
"language:en",
"image-to-text",
"image-captioning",
"visual-question-answering",
"region:us"
]
| 2023-11-13T04:40:15+00:00 | {"language": ["en"], "size_categories": ["1M<n<10M"], "task_categories": ["image-to-text"], "tags": ["image-to-text", "image-captioning", "visual-question-answering"]} | 2023-11-13T11:37:08+00:00 | []
| [
"en"
]
| TAGS
#task_categories-image-to-text #size_categories-1M<n<10M #language-English #image-to-text #image-captioning #visual-question-answering #region-us
| # Data details
- 274K multimodal feedback and revision data
- 558K filtered image-text pairs from LAION/CC/SBU, captioned by BLIP.
- 158K GPT-generated multimodal instruction-following data.
- 450K academic-task-oriented VQA data mixture.
- 40K ShareGPT data
# Data collection
!image/png
Since no multimodal feedback data for training is publicly available as of this writing and human labeling is costly, we used a proprietary LLM to generate feedback data.
As shown in figure, we use an open-source LMM to provide an initial answer to a question about an image. Since current proprietary LLMs cannot process images, we provide object details in text and captions as a proxy for image. For each data instance, we feed the LLM image information consisting of object details and captions, question, initial response, and gold answer as reference answer, allowing the model to evaluate the given inputs and produce feedback. The proprietary LLM might exploit the gold answer to generate the feedback, which can cause potential inaccuracies in feedback during inference time when it is not provided. To avoid this, we give the LLM clear prompts to use text-formatted image details when generating feedback. When constructing the revision data, we set up the system to predict the existing gold answer as the output, using the feedback data, image, question, and initial response obtained from the previous steps as input, without involving any separate model generation process.
Although Volcano is trained using the language modeling objective in a manner consistent with traditional VLMs, it not only follows instructions but also can provide critical feedback based on image information and subsequently self-revise. This enhanced ability is attributed to Volcano's combined training with visual instruction tuning data, feedback, and revision data. | [
"# Data details\n- 274K multimodal feedback and revision data\n- 558K filtered image-text pairs from LAION/CC/SBU, captioned by BLIP.\n- 158K GPT-generated multimodal instruction-following data.\n- 450K academic-task-oriented VQA data mixture.\n- 40K ShareGPT data",
"# Data collection\n!image/png\nSince no multimodal feedback data for training is publicly available as of this writing and human labeling is costly, we used a proprietary LLM to generate feedback data.\nAs shown in figure, we use an open-source LMM to provide an initial answer to a question about an image. Since current proprietary LLMs cannot process images, we provide object details in text and captions as a proxy for image. For each data instance, we feed the LLM image information consisting of object details and captions, question, initial response, and gold answer as reference answer, allowing the model to evaluate the given inputs and produce feedback. The proprietary LLM might exploit the gold answer to generate the feedback, which can cause potential inaccuracies in feedback during inference time when it is not provided. To avoid this, we give the LLM clear prompts to use text-formatted image details when generating feedback. When constructing the revision data, we set up the system to predict the existing gold answer as the output, using the feedback data, image, question, and initial response obtained from the previous steps as input, without involving any separate model generation process.\nAlthough Volcano is trained using the language modeling objective in a manner consistent with traditional VLMs, it not only follows instructions but also can provide critical feedback based on image information and subsequently self-revise. This enhanced ability is attributed to Volcano's combined training with visual instruction tuning data, feedback, and revision data."
]
| [
"TAGS\n#task_categories-image-to-text #size_categories-1M<n<10M #language-English #image-to-text #image-captioning #visual-question-answering #region-us \n",
"# Data details\n- 274K multimodal feedback and revision data\n- 558K filtered image-text pairs from LAION/CC/SBU, captioned by BLIP.\n- 158K GPT-generated multimodal instruction-following data.\n- 450K academic-task-oriented VQA data mixture.\n- 40K ShareGPT data",
"# Data collection\n!image/png\nSince no multimodal feedback data for training is publicly available as of this writing and human labeling is costly, we used a proprietary LLM to generate feedback data.\nAs shown in figure, we use an open-source LMM to provide an initial answer to a question about an image. Since current proprietary LLMs cannot process images, we provide object details in text and captions as a proxy for image. For each data instance, we feed the LLM image information consisting of object details and captions, question, initial response, and gold answer as reference answer, allowing the model to evaluate the given inputs and produce feedback. The proprietary LLM might exploit the gold answer to generate the feedback, which can cause potential inaccuracies in feedback during inference time when it is not provided. To avoid this, we give the LLM clear prompts to use text-formatted image details when generating feedback. When constructing the revision data, we set up the system to predict the existing gold answer as the output, using the feedback data, image, question, and initial response obtained from the previous steps as input, without involving any separate model generation process.\nAlthough Volcano is trained using the language modeling objective in a manner consistent with traditional VLMs, it not only follows instructions but also can provide critical feedback based on image information and subsequently self-revise. This enhanced ability is attributed to Volcano's combined training with visual instruction tuning data, feedback, and revision data."
]
| [
55,
81,
332
]
| [
"passage: TAGS\n#task_categories-image-to-text #size_categories-1M<n<10M #language-English #image-to-text #image-captioning #visual-question-answering #region-us \n# Data details\n- 274K multimodal feedback and revision data\n- 558K filtered image-text pairs from LAION/CC/SBU, captioned by BLIP.\n- 158K GPT-generated multimodal instruction-following data.\n- 450K academic-task-oriented VQA data mixture.\n- 40K ShareGPT data# Data collection\n!image/png\nSince no multimodal feedback data for training is publicly available as of this writing and human labeling is costly, we used a proprietary LLM to generate feedback data.\nAs shown in figure, we use an open-source LMM to provide an initial answer to a question about an image. Since current proprietary LLMs cannot process images, we provide object details in text and captions as a proxy for image. For each data instance, we feed the LLM image information consisting of object details and captions, question, initial response, and gold answer as reference answer, allowing the model to evaluate the given inputs and produce feedback. The proprietary LLM might exploit the gold answer to generate the feedback, which can cause potential inaccuracies in feedback during inference time when it is not provided. To avoid this, we give the LLM clear prompts to use text-formatted image details when generating feedback. When constructing the revision data, we set up the system to predict the existing gold answer as the output, using the feedback data, image, question, and initial response obtained from the previous steps as input, without involving any separate model generation process.\nAlthough Volcano is trained using the language modeling objective in a manner consistent with traditional VLMs, it not only follows instructions but also can provide critical feedback based on image information and subsequently self-revise. This enhanced ability is attributed to Volcano's combined training with visual instruction tuning data, feedback, and revision data."
]
|
87a2d5b71d31af189a7e170fad131fe27b67c074 | # Dataset Card for "sefaria-hebrew"
this dataset contains jewish texts in hebrew from the sefaria project | sivan22/sefaria-hebrew | [
"region:us"
]
| 2023-11-13T04:59:51+00:00 | {"dataset_info": {"features": [{"name": "language", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "versionSource", "dtype": "string"}, {"name": "versionTitle", "dtype": "string"}, {"name": "status", "dtype": "string"}, {"name": "license", "dtype": "string"}, {"name": "versionTitleInHebrew", "dtype": "string"}, {"name": "actualLanguage", "dtype": "string"}, {"name": "isBaseText", "dtype": "bool"}, {"name": "level_1_index", "dtype": "float64"}, {"name": "level_2_index", "dtype": "float64"}, {"name": "level_3_index", "dtype": "float64"}, {"name": "level_4_index", "dtype": "float64"}, {"name": "level_5_index", "dtype": "float64"}, {"name": "text", "dtype": "string"}, {"name": "versionNotes", "dtype": "string"}, {"name": "versionNotesInHebrew", "dtype": "string"}, {"name": "method", "dtype": "string"}, {"name": "digitizedBySefaria", "dtype": "float64"}, {"name": "heversionSource", "dtype": "string"}, {"name": "priority", "dtype": "float64"}, {"name": "shortVersionTitle", "dtype": "string"}, {"name": "purchaseInformationImage", "dtype": "string"}, {"name": "purchaseInformationURL", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1901352817, "num_examples": 1955969}], "download_size": 544170227, "dataset_size": 1901352817}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]} | 2023-11-13T05:08:42+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "sefaria-hebrew"
this dataset contains jewish texts in hebrew from the sefaria project | [
"# Dataset Card for \"sefaria-hebrew\"\n\nthis dataset contains jewish texts in hebrew from the sefaria project"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"sefaria-hebrew\"\n\nthis dataset contains jewish texts in hebrew from the sefaria project"
]
| [
6,
30
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| [
"passage: TAGS\n#region-us \n# Dataset Card for \"sefaria-hebrew\"\n\nthis dataset contains jewish texts in hebrew from the sefaria project"
]
|
f70ec372a0e9379159dd97c8af6b00c7e2beda73 | # Dataset Card for "law_court_opinion_subset"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | zxvix/law_court_opinion_subset | [
"region:us"
]
| 2023-11-13T05:29:03+00:00 | {"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "test", "path": "data/test-*"}]}], "dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "created_timestamp", "dtype": "string"}, {"name": "downloaded_timestamp", "dtype": "string"}, {"name": "url", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 10473323737.014988, "num_examples": 1000000}, {"name": "test", "num_bytes": 10473323.737014988, "num_examples": 1000}], "download_size": 529670767, "dataset_size": 10483797060.752003}} | 2023-11-13T05:30:57+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "law_court_opinion_subset"
More Information needed | [
"# Dataset Card for \"law_court_opinion_subset\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"law_court_opinion_subset\"\n\nMore Information needed"
]
| [
6,
18
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"passage: TAGS\n#region-us \n# Dataset Card for \"law_court_opinion_subset\"\n\nMore Information needed"
]
|
0f46f2cad604aac86b3da1fdff990d69841a24bd | # Dataset Card for "ethics_legal_concept_llama2_chat"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | justinphan3110/ethics_legal_concept_llama2_chat | [
"region:us"
]
| 2023-11-13T05:50:19+00:00 | {"dataset_info": {"features": [{"name": "sentence", "sequence": "string"}, {"name": "label", "sequence": "bool"}], "splits": [{"name": "train", "num_bytes": 147896, "num_examples": 100}], "download_size": 25209, "dataset_size": 147896}} | 2023-11-13T05:50:20+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "ethics_legal_concept_llama2_chat"
More Information needed | [
"# Dataset Card for \"ethics_legal_concept_llama2_chat\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"ethics_legal_concept_llama2_chat\"\n\nMore Information needed"
]
| [
6,
23
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| [
"passage: TAGS\n#region-us \n# Dataset Card for \"ethics_legal_concept_llama2_chat\"\n\nMore Information needed"
]
|
f70713e265728a386416997e8da16eecdfaef0e7 | # Dataset Card for "Tweet_sentiment_analysis_Distilbert"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | bambadij/Tweet_sentiment_analysis_Distilbert | [
"region:us"
]
| 2023-11-13T05:54:48+00:00 | {"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "eval", "path": "data/eval-*"}]}], "dataset_info": {"features": [{"name": "input_ids", "sequence": "int32"}, {"name": "attention_mask", "sequence": "int8"}, {"name": "labels", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 1712789, "num_examples": 7999}, {"name": "eval", "num_bytes": 472000, "num_examples": 2000}], "download_size": 505986, "dataset_size": 2184789}} | 2023-11-13T05:54:50+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "Tweet_sentiment_analysis_Distilbert"
More Information needed | [
"# Dataset Card for \"Tweet_sentiment_analysis_Distilbert\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"Tweet_sentiment_analysis_Distilbert\"\n\nMore Information needed"
]
| [
6,
23
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"Tweet_sentiment_analysis_Distilbert\"\n\nMore Information needed"
]
|
33482d9b300fd5c0854d85d33adc3529494cdc7c | # Dataset Card for "complexquestion_2WIKIMQA_100"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | presencesw/complexquestion_2WIKIMQA_100 | [
"region:us"
]
| 2023-11-13T05:58:47+00:00 | {"dataset_info": {"features": [{"name": "entities", "sequence": "null"}, {"name": "triplets", "sequence": "null"}, {"name": "answer", "dtype": "string"}, {"name": "complex_question", "dtype": "string"}, {"name": "system_prompt", "dtype": "string"}, {"name": "user_prompt", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 549854, "num_examples": 100}], "download_size": 58343, "dataset_size": 549854}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]} | 2023-11-13T05:58:53+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "complexquestion_2WIKIMQA_100"
More Information needed | [
"# Dataset Card for \"complexquestion_2WIKIMQA_100\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"complexquestion_2WIKIMQA_100\"\n\nMore Information needed"
]
| [
6,
21
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"passage: TAGS\n#region-us \n# Dataset Card for \"complexquestion_2WIKIMQA_100\"\n\nMore Information needed"
]
|
3f204e4b5cb9a66b9cc1d40a88beb560f075c440 | # Dataset Card for "ESC-6K-small"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | DLI-Lab/ESC-6K-small | [
"region:us"
]
| 2023-11-13T07:15:46+00:00 | {"dataset_info": {"features": [{"name": "sample_id", "dtype": "string"}, {"name": "situation", "dtype": "string"}, {"name": "emotion_type", "dtype": "string"}, {"name": "res_pred", "dtype": "string"}, {"name": "problem_type", "dtype": "string"}, {"name": "strg_pred", "dtype": "string"}, {"name": "strg_gold", "dtype": "string"}, {"name": "res_gold", "dtype": "string"}, {"name": "context", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 18568804, "num_examples": 10144}, {"name": "test", "num_bytes": 2294797, "num_examples": 1295}, {"name": "valid", "num_bytes": 2355255, "num_examples": 1271}], "download_size": 4844618, "dataset_size": 23218856}} | 2023-11-13T09:28:47+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "ESC-6K-small"
More Information needed | [
"# Dataset Card for \"ESC-6K-small\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"ESC-6K-small\"\n\nMore Information needed"
]
| [
6,
17
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| [
"passage: TAGS\n#region-us \n# Dataset Card for \"ESC-6K-small\"\n\nMore Information needed"
]
|
2c2ca7b750cb05118e065a7292ee467402a1de58 | # Dataset Card for "temp-qa-no-keyword"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | Back-up/temp-qa-no-keyword | [
"region:us"
]
| 2023-11-13T07:20:57+00:00 | {"dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "context", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "response", "struct": [{"name": "response", "dtype": "string"}]}, {"name": "answers", "struct": [{"name": "answer_start", "sequence": "int64"}, {"name": "text", "sequence": "string"}]}, {"name": "instruction", "dtype": "string"}, {"name": "prompt_name", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 10367194, "num_examples": 3821}], "download_size": 3953684, "dataset_size": 10367194}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]} | 2023-11-13T07:21:04+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "temp-qa-no-keyword"
More Information needed | [
"# Dataset Card for \"temp-qa-no-keyword\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"temp-qa-no-keyword\"\n\nMore Information needed"
]
| [
6,
18
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"passage: TAGS\n#region-us \n# Dataset Card for \"temp-qa-no-keyword\"\n\nMore Information needed"
]
|
8a34e8246db18f2e409801e8e826d9dcc7a54476 | # Dataset Card for "classification_dialogue_search_v0.1"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | Data-Lab/classification_dialogue_search_v0.1 | [
"region:us"
]
| 2023-11-13T07:41:20+00:00 | {"dataset_info": {"features": [{"name": "query", "dtype": "string"}, {"name": "gold", "dtype": "int64"}, {"name": "__index_level_0__", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 312367, "num_examples": 2742}], "download_size": 150463, "dataset_size": 312367}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]} | 2023-11-13T07:41:26+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "classification_dialogue_search_v0.1"
More Information needed | [
"# Dataset Card for \"classification_dialogue_search_v0.1\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"classification_dialogue_search_v0.1\"\n\nMore Information needed"
]
| [
6,
21
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| [
"passage: TAGS\n#region-us \n# Dataset Card for \"classification_dialogue_search_v0.1\"\n\nMore Information needed"
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|
a438662ea895cadfb7317f8538d43eb1a3173f51 | # Dataset Card for "complexquestion_2WIKIMQA_10"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | presencesw/complexquestion_2WIKIMQA_10 | [
"region:us"
]
| 2023-11-13T07:52:08+00:00 | {"dataset_info": {"features": [{"name": "entities", "sequence": "null"}, {"name": "triplets", "sequence": "null"}, {"name": "answer", "dtype": "string"}, {"name": "complex_question", "dtype": "string"}, {"name": "system_prompt", "dtype": "string"}, {"name": "user_prompt", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 54978, "num_examples": 10}], "download_size": 11241, "dataset_size": 54978}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]} | 2023-11-13T07:52:13+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "complexquestion_2WIKIMQA_10"
More Information needed | [
"# Dataset Card for \"complexquestion_2WIKIMQA_10\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"complexquestion_2WIKIMQA_10\"\n\nMore Information needed"
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| [
6,
21
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| [
"passage: TAGS\n#region-us \n# Dataset Card for \"complexquestion_2WIKIMQA_10\"\n\nMore Information needed"
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|
40ecc701219582568fa215e098a5bebe54cfc91e | # Dataset Card for "global_classify_v2_2xtrain"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | sshreyy/global_classify_v2_2xtrain | [
"region:us"
]
| 2023-11-13T07:53:20+00:00 | {"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "labels", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 8135110, "num_examples": 3890}, {"name": "test", "num_bytes": 858889, "num_examples": 405}], "download_size": 2239783, "dataset_size": 8993999}} | 2023-11-13T07:53:39+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "global_classify_v2_2xtrain"
More Information needed | [
"# Dataset Card for \"global_classify_v2_2xtrain\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"global_classify_v2_2xtrain\"\n\nMore Information needed"
]
| [
6,
21
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| [
"passage: TAGS\n#region-us \n# Dataset Card for \"global_classify_v2_2xtrain\"\n\nMore Information needed"
]
|
1f3a51dc07d8b609bf7cd08d6285d79a92c92ba7 | ## Dataset Description
**dCluB** is the collected dataset in the paper: Dynamic Clue Bottlenecks: Interpretable by Design Visual Question Answering.
Given an image and question, it's goal is to create interpretable and faithful by design visual question answering models, through generating the intermediate structures "visual clues".
The dataset was carefully collected from mTurk.
In the paper, we provide our own method to obtain insights into how bottleneck models on VQA can be constructed, and did much analysis to see how future work might try to mitigate blackbox models’ shortcomings.
We aim for dCluB to serve as a useful set for advancing the development of interpretable and faithful VQA models, and driving further progress in the field.
## Download Data
The train, val, and test sets are in separate json files, and the images are from coco images used in vqa v2. You can load the data as follows:
```
from datasets import load_dataset
examples = load_dataset('Fiaa/dCluB')
```
---
license: apache-2.0
---
| Fiaa/dClub | [
"region:us"
]
| 2023-11-13T07:57:01+00:00 | {} | 2023-11-13T08:07:03+00:00 | []
| []
| TAGS
#region-us
| ## Dataset Description
dCluB is the collected dataset in the paper: Dynamic Clue Bottlenecks: Interpretable by Design Visual Question Answering.
Given an image and question, it's goal is to create interpretable and faithful by design visual question answering models, through generating the intermediate structures "visual clues".
The dataset was carefully collected from mTurk.
In the paper, we provide our own method to obtain insights into how bottleneck models on VQA can be constructed, and did much analysis to see how future work might try to mitigate blackbox models’ shortcomings.
We aim for dCluB to serve as a useful set for advancing the development of interpretable and faithful VQA models, and driving further progress in the field.
## Download Data
The train, val, and test sets are in separate json files, and the images are from coco images used in vqa v2. You can load the data as follows:
---
license: apache-2.0
---
| [
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"## Download Data\nThe train, val, and test sets are in separate json files, and the images are from coco images used in vqa v2. You can load the data as follows:\n\n\n---\nlicense: apache-2.0\n---"
]
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"## Download Data\nThe train, val, and test sets are in separate json files, and the images are from coco images used in vqa v2. You can load the data as follows:\n\n\n---\nlicense: apache-2.0\n---"
]
| [
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| [
"passage: TAGS\n#region-us \n## Dataset Description\n\ndCluB is the collected dataset in the paper: Dynamic Clue Bottlenecks: Interpretable by Design Visual Question Answering.\n\n\nGiven an image and question, it's goal is to create interpretable and faithful by design visual question answering models, through generating the intermediate structures \"visual clues\". \nThe dataset was carefully collected from mTurk.\nIn the paper, we provide our own method to obtain insights into how bottleneck models on VQA can be constructed, and did much analysis to see how future work might try to mitigate blackbox models’ shortcomings. \nWe aim for dCluB to serve as a useful set for advancing the development of interpretable and faithful VQA models, and driving further progress in the field.## Download Data\nThe train, val, and test sets are in separate json files, and the images are from coco images used in vqa v2. You can load the data as follows:\n\n\n---\nlicense: apache-2.0\n---"
]
|
28ce79c3b3e4791bb03a8d94cce9e3297a20b4da | # Dataset Card for "temp-qa-context"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | Back-up/temp-qa-context | [
"region:us"
]
| 2023-11-13T07:58:17+00:00 | {"dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "context", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "response", "struct": [{"name": "response", "dtype": "string"}]}, {"name": "answers", "struct": [{"name": "answer_start", "sequence": "int64"}, {"name": "text", "sequence": "string"}]}, {"name": "instruction", "dtype": "string"}, {"name": "prompt_name", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 10011005, "num_examples": 3604}], "download_size": 3752328, "dataset_size": 10011005}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]} | 2023-11-13T14:46:59+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "temp-qa-context"
More Information needed | [
"# Dataset Card for \"temp-qa-context\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"temp-qa-context\"\n\nMore Information needed"
]
| [
6,
16
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"temp-qa-context\"\n\nMore Information needed"
]
|
143c3d2bf83317e60f22cabdb579b29fc564a813 | # Dataset Card for "complexquestion_2WIKIMQA_10_Mistral"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | presencesw/complexquestion_2WIKIMQA_10_Mistral | [
"region:us"
]
| 2023-11-13T07:58:58+00:00 | {"dataset_info": {"features": [{"name": "entities", "sequence": "null"}, {"name": "triplets", "sequence": "null"}, {"name": "answer", "dtype": "string"}, {"name": "complex_question", "dtype": "string"}, {"name": "system_prompt", "dtype": "string"}, {"name": "user_prompt", "dtype": "string"}, {"name": "llm_output", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 73961, "num_examples": 10}], "download_size": 22010, "dataset_size": 73961}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]} | 2023-11-13T08:19:06+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "complexquestion_2WIKIMQA_10_Mistral"
More Information needed | [
"# Dataset Card for \"complexquestion_2WIKIMQA_10_Mistral\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"complexquestion_2WIKIMQA_10_Mistral\"\n\nMore Information needed"
]
| [
6,
24
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| [
"passage: TAGS\n#region-us \n# Dataset Card for \"complexquestion_2WIKIMQA_10_Mistral\"\n\nMore Information needed"
]
|
f279b351f474c9810f55a99c8a484f1ff383559a | # Dataset Card for "AutoBanner"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | npk7264/AutoBanner | [
"region:us"
]
| 2023-11-13T08:02:12+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": 86538555.13, "num_examples": 1362}], "download_size": 83996790, "dataset_size": 86538555.13}} | 2023-11-13T08:02:50+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "AutoBanner"
More Information needed | [
"# Dataset Card for \"AutoBanner\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"AutoBanner\"\n\nMore Information needed"
]
| [
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13
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| [
"passage: TAGS\n#region-us \n# Dataset Card for \"AutoBanner\"\n\nMore Information needed"
]
|
15d1752434b79215fc18093409a31f6e9df4562d | # Dataset Card for "c_x86_O3_exebench_json_cleaned"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | zhangshuoming/c_x86_O3_exebench_json_cleaned | [
"region:us"
]
| 2023-11-13T08:20:48+00:00 | {"dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1268266964.093047, "num_examples": 725290}], "download_size": 200600341, "dataset_size": 1268266964.093047}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]} | 2023-11-13T08:21:34+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "c_x86_O3_exebench_json_cleaned"
More Information needed | [
"# Dataset Card for \"c_x86_O3_exebench_json_cleaned\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"c_x86_O3_exebench_json_cleaned\"\n\nMore Information needed"
]
| [
6,
28
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"c_x86_O3_exebench_json_cleaned\"\n\nMore Information needed"
]
|
cc800f54a0327ddfd5823ed08d1326d7a72e4373 | # Dataset Card for "lima-u2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | TinyPixel/lima-u2 | [
"region:us"
]
| 2023-11-13T08:21:56+00:00 | {"dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1778347, "num_examples": 780}], "download_size": 1041113, "dataset_size": 1778347}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]} | 2024-02-01T05:38:17+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "lima-u2"
More Information needed | [
"# Dataset Card for \"lima-u2\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"lima-u2\"\n\nMore Information needed"
]
| [
6,
14
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"passage: TAGS\n#region-us \n# Dataset Card for \"lima-u2\"\n\nMore Information needed"
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|
964aff8e4784352c3daca304dc9a8d2bf85b6cae | # Dataset Card for "Ryu-AI.datadict"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | MichaelJH/Ryu-AI.datadict | [
"region:us"
]
| 2023-11-13T08:22:21+00:00 | {"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "text", "sequence": "string"}, {"name": "input_ids", "sequence": "int32"}, {"name": "token_type_ids", "sequence": "int8"}, {"name": "attention_mask", "sequence": "int8"}], "splits": [{"name": "train", "num_bytes": 7090578, "num_examples": 21460}], "download_size": 1783799, "dataset_size": 7090578}} | 2023-11-13T08:22:24+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "Ryu-AI.datadict"
More Information needed | [
"# Dataset Card for \"Ryu-AI.datadict\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"Ryu-AI.datadict\"\n\nMore Information needed"
]
| [
6,
17
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| [
"passage: TAGS\n#region-us \n# Dataset Card for \"Ryu-AI.datadict\"\n\nMore Information needed"
]
|
9173a03d2f4639cda8195feb6052ef3083664669 | # Dataset Card for "elm-2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | TinyPixel/elm-2 | [
"region:us"
]
| 2023-11-13T08:32:00+00:00 | {"dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 2577268, "num_examples": 1073}], "download_size": 1393304, "dataset_size": 2577268}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]} | 2024-01-27T09:18:15+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "elm-2"
More Information needed | [
"# Dataset Card for \"elm-2\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"elm-2\"\n\nMore Information needed"
]
| [
6,
12
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| [
"passage: TAGS\n#region-us \n# Dataset Card for \"elm-2\"\n\nMore Information needed"
]
|
5eafe45b28c55024bc5e2b45e404c278d51e6f51 | # Dataset Card for "faq_gu_covid_vac"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | avgalaida/faq_gu_covid_vac | [
"region:us"
]
| 2023-11-13T08:54:44+00:00 | {"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "validation", "path": "data/validation-*"}]}], "dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "context", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "answer", "struct": [{"name": "answer_start", "dtype": "int64"}, {"name": "text", "dtype": "string"}]}], "splits": [{"name": "train", "num_bytes": 6559, "num_examples": 4}, {"name": "validation", "num_bytes": 6459, "num_examples": 4}], "download_size": 34057, "dataset_size": 13018}} | 2023-11-13T08:56:40+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "faq_gu_covid_vac"
More Information needed | [
"# Dataset Card for \"faq_gu_covid_vac\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"faq_gu_covid_vac\"\n\nMore Information needed"
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| [
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"passage: TAGS\n#region-us \n# Dataset Card for \"faq_gu_covid_vac\"\n\nMore Information needed"
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|
b2f3bde41a91cf6fc571eb4628566700775200e0 |
This is a dataset for sentiment analysis created by text-image pairs collected from greek Twitter.
posted: from April 2023 to September 2023
Context: general purpose, mostly politics and athletics
Total pairs: 260
The purpose of the dataset is to be used as a test dataset, not for training/fine-tuning a model.
Labels: Negative, Neutral, Positive
labelling.xlsx contains the labels for texts,images and both modalities. | kostasGRG/greek-twitter-multimodal-dataset | [
"task_categories:text-classification",
"task_categories:image-classification",
"size_categories:n<1K",
"language:el",
"region:us"
]
| 2023-11-13T09:08:02+00:00 | {"language": ["el"], "size_categories": ["n<1K"], "task_categories": ["text-classification", "image-classification"]} | 2023-11-13T09:22:15+00:00 | []
| [
"el"
]
| TAGS
#task_categories-text-classification #task_categories-image-classification #size_categories-n<1K #language-Modern Greek (1453-) #region-us
|
This is a dataset for sentiment analysis created by text-image pairs collected from greek Twitter.
posted: from April 2023 to September 2023
Context: general purpose, mostly politics and athletics
Total pairs: 260
The purpose of the dataset is to be used as a test dataset, not for training/fine-tuning a model.
Labels: Negative, Neutral, Positive
URL contains the labels for texts,images and both modalities. | []
| [
"TAGS\n#task_categories-text-classification #task_categories-image-classification #size_categories-n<1K #language-Modern Greek (1453-) #region-us \n"
]
| [
48
]
| [
"passage: TAGS\n#task_categories-text-classification #task_categories-image-classification #size_categories-n<1K #language-Modern Greek (1453-) #region-us \n"
]
|
7c854a0b6d2fb50f7c2b1479fe25537b69c45b43 | # Dataset Card for "gold_standard_preguntas_suicidio_dataset"
This dataset was extracted from the following documents:
- [Telélefono de la Esperanza. Guía del Suicidio.](https://telefonodelaesperanza.org/assets/Guia%20del%20suicidio.pdf).
- [Telélefono de la Esperanza. 5 preguntas frecuentes](https://telefonodelaesperanza.ch/5-preguntas-comunes-sobre-suicidio/)
- [Organización Mundial de la Salud](https://www.who.int/es/news-room/questions-and-answers/item/suicide)
- [Preguntas frecuentes sobre el suicidio. NIMH.](https://www.nimh.nih.gov/sites/default/files/health/publications/espanol/senales-de-advertencia-sobre-el-suicidio/preguntas-frecuentes-sobre-el-suicidio.pdf)
- [La conducta suicida. Información para pacientes, familiares y allegados. Anexo 1 de la Guía de Práctica Clínica de Prevención y Tratamiento de la Conducta Suicida. Ministerio de Sanidad, 2012.](https://www.fsme.es/centro-de-documentaci%C3%B3n-sobre-conducta-suicida/gu%C3%ADas-sobre-conducta-suicida/la-conducta-suicida-gpc-sns/)
- [Revisión de la Guía de Práctica Clínica de Prevención y Tratamiento de la Conducta Suicida (2012) del Programa de GPC en el SNS GUÍAS DE PRÁCTICA CLÍNICA EN EL SNS MINISTERIO DE SANIDAD](https://www.fsme.es/centro-de-documentaci%C3%B3n-sobre-conducta-suicida/gu%C3%ADas-sobre-conducta-suicida/gpc/) | PrevenIA/gold_standard_preguntas_suicidio_dataset | [
"task_categories:question-answering",
"language:es",
"license:cc-by-3.0",
"suicidio",
"region:us"
]
| 2023-11-13T09:27:13+00:00 | {"language": ["es"], "license": "cc-by-3.0", "task_categories": ["question-answering"], "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "Referencia", "dtype": "string"}, {"name": "Pregunta", "dtype": "string"}, {"name": "Respuesta", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 119693, "num_examples": 118}], "download_size": 53288, "dataset_size": 119693}, "tags": ["suicidio"]} | 2023-11-13T09:32:02+00:00 | []
| [
"es"
]
| TAGS
#task_categories-question-answering #language-Spanish #license-cc-by-3.0 #suicidio #region-us
| # Dataset Card for "gold_standard_preguntas_suicidio_dataset"
This dataset was extracted from the following documents:
- Telélefono de la Esperanza. Guía del Suicidio..
- Telélefono de la Esperanza. 5 preguntas frecuentes
- Organización Mundial de la Salud
- Preguntas frecuentes sobre el suicidio. NIMH.
- La conducta suicida. Información para pacientes, familiares y allegados. Anexo 1 de la Guía de Práctica Clínica de Prevención y Tratamiento de la Conducta Suicida. Ministerio de Sanidad, 2012.
- Revisión de la Guía de Práctica Clínica de Prevención y Tratamiento de la Conducta Suicida (2012) del Programa de GPC en el SNS GUÍAS DE PRÁCTICA CLÍNICA EN EL SNS MINISTERIO DE SANIDAD | [
"# Dataset Card for \"gold_standard_preguntas_suicidio_dataset\"\n\nThis dataset was extracted from the following documents:\n- Telélefono de la Esperanza. Guía del Suicidio..\n- Telélefono de la Esperanza. 5 preguntas frecuentes\n- Organización Mundial de la Salud\n- Preguntas frecuentes sobre el suicidio. NIMH.\n- La conducta suicida. Información para pacientes, familiares y allegados. Anexo 1 de la Guía de Práctica Clínica de Prevención y Tratamiento de la Conducta Suicida. Ministerio de Sanidad, 2012.\n- Revisión de la Guía de Práctica Clínica de Prevención y Tratamiento de la Conducta Suicida (2012) del Programa de GPC en el SNS GUÍAS DE PRÁCTICA CLÍNICA EN EL SNS MINISTERIO DE SANIDAD"
]
| [
"TAGS\n#task_categories-question-answering #language-Spanish #license-cc-by-3.0 #suicidio #region-us \n",
"# Dataset Card for \"gold_standard_preguntas_suicidio_dataset\"\n\nThis dataset was extracted from the following documents:\n- Telélefono de la Esperanza. Guía del Suicidio..\n- Telélefono de la Esperanza. 5 preguntas frecuentes\n- Organización Mundial de la Salud\n- Preguntas frecuentes sobre el suicidio. NIMH.\n- La conducta suicida. Información para pacientes, familiares y allegados. Anexo 1 de la Guía de Práctica Clínica de Prevención y Tratamiento de la Conducta Suicida. Ministerio de Sanidad, 2012.\n- Revisión de la Guía de Práctica Clínica de Prevención y Tratamiento de la Conducta Suicida (2012) del Programa de GPC en el SNS GUÍAS DE PRÁCTICA CLÍNICA EN EL SNS MINISTERIO DE SANIDAD"
]
| [
36,
194
]
| [
"passage: TAGS\n#task_categories-question-answering #language-Spanish #license-cc-by-3.0 #suicidio #region-us \n# Dataset Card for \"gold_standard_preguntas_suicidio_dataset\"\n\nThis dataset was extracted from the following documents:\n- Telélefono de la Esperanza. Guía del Suicidio..\n- Telélefono de la Esperanza. 5 preguntas frecuentes\n- Organización Mundial de la Salud\n- Preguntas frecuentes sobre el suicidio. NIMH.\n- La conducta suicida. Información para pacientes, familiares y allegados. Anexo 1 de la Guía de Práctica Clínica de Prevención y Tratamiento de la Conducta Suicida. Ministerio de Sanidad, 2012.\n- Revisión de la Guía de Práctica Clínica de Prevención y Tratamiento de la Conducta Suicida (2012) del Programa de GPC en el SNS GUÍAS DE PRÁCTICA CLÍNICA EN EL SNS MINISTERIO DE SANIDAD"
]
|
3e76c2881adcf5f42136f312f28b20e605b5bbe2 | # Dataset Card for "ESC-6K-ft-llama2-wo-filtering"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | DLI-Lab/ESC-6K-ft-llama2-wo-filtering | [
"region:us"
]
| 2023-11-13T09:29:53+00:00 | {"dataset_info": {"features": [{"name": "strg_pred", "dtype": "string"}, {"name": "strg_gold", "dtype": "string"}, {"name": "situation", "dtype": "string"}, {"name": "problem_type", "dtype": "string"}, {"name": "emotion_type", "dtype": "string"}, {"name": "context", "dtype": "string"}, {"name": "sample_id", "dtype": "string"}, {"name": "res_gold", "dtype": "string"}, {"name": "res_pred", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 18568804, "num_examples": 10144}, {"name": "test", "num_bytes": 2294797, "num_examples": 1295}, {"name": "valid", "num_bytes": 2355255, "num_examples": 1271}], "download_size": 4844600, "dataset_size": 23218856}} | 2023-11-13T09:30:03+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "ESC-6K-ft-llama2-wo-filtering"
More Information needed | [
"# Dataset Card for \"ESC-6K-ft-llama2-wo-filtering\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"ESC-6K-ft-llama2-wo-filtering\"\n\nMore Information needed"
]
| [
6,
24
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"ESC-6K-ft-llama2-wo-filtering\"\n\nMore Information needed"
]
|
510c084fcd6d37ffa7f58fa8e403f03a2cbb84f1 |
# Bangumi Image Base of Classroom Of The Elite
This is the image base of bangumi Classroom of the Elite, we detected 58 characters, 4577 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 | 131 | [Download](0/dataset.zip) |  |  |  |  |  |  |  |  |
| 1 | 108 | [Download](1/dataset.zip) |  |  |  |  |  |  |  |  |
| 2 | 975 | [Download](2/dataset.zip) |  |  |  |  |  |  |  |  |
| 3 | 10 | [Download](3/dataset.zip) |  |  |  |  |  |  |  |  |
| 4 | 16 | [Download](4/dataset.zip) |  |  |  |  |  |  |  |  |
| 5 | 55 | [Download](5/dataset.zip) |  |  |  |  |  |  |  |  |
| 6 | 138 | [Download](6/dataset.zip) |  |  |  |  |  |  |  |  |
| 7 | 193 | [Download](7/dataset.zip) |  |  |  |  |  |  |  |  |
| 8 | 67 | [Download](8/dataset.zip) |  |  |  |  |  |  |  |  |
| 9 | 88 | [Download](9/dataset.zip) |  |  |  |  |  |  |  |  |
| 10 | 48 | [Download](10/dataset.zip) |  |  |  |  |  |  |  |  |
| 11 | 22 | [Download](11/dataset.zip) |  |  |  |  |  |  |  |  |
| 12 | 18 | [Download](12/dataset.zip) |  |  |  |  |  |  |  |  |
| 13 | 45 | [Download](13/dataset.zip) |  |  |  |  |  |  |  |  |
| 14 | 38 | [Download](14/dataset.zip) |  |  |  |  |  |  |  |  |
| 15 | 23 | [Download](15/dataset.zip) |  |  |  |  |  |  |  |  |
| 16 | 51 | [Download](16/dataset.zip) |  |  |  |  |  |  |  |  |
| 17 | 14 | [Download](17/dataset.zip) |  |  |  |  |  |  |  |  |
| 18 | 12 | [Download](18/dataset.zip) |  |  |  |  |  |  |  |  |
| 19 | 12 | [Download](19/dataset.zip) |  |  |  |  |  |  |  |  |
| 20 | 20 | [Download](20/dataset.zip) |  |  |  |  |  |  |  |  |
| 21 | 190 | [Download](21/dataset.zip) |  |  |  |  |  |  |  |  |
| 22 | 11 | [Download](22/dataset.zip) |  |  |  |  |  |  |  |  |
| 23 | 15 | [Download](23/dataset.zip) |  |  |  |  |  |  |  |  |
| 24 | 18 | [Download](24/dataset.zip) |  |  |  |  |  |  |  |  |
| 25 | 12 | [Download](25/dataset.zip) |  |  |  |  |  |  |  |  |
| 26 | 36 | [Download](26/dataset.zip) |  |  |  |  |  |  |  |  |
| 27 | 558 | [Download](27/dataset.zip) |  |  |  |  |  |  |  |  |
| 28 | 23 | [Download](28/dataset.zip) |  |  |  |  |  |  |  |  |
| 29 | 70 | [Download](29/dataset.zip) |  |  |  |  |  |  |  |  |
| 30 | 12 | [Download](30/dataset.zip) |  |  |  |  |  |  |  |  |
| 31 | 58 | [Download](31/dataset.zip) |  |  |  |  |  |  |  |  |
| 32 | 45 | [Download](32/dataset.zip) |  |  |  |  |  |  |  |  |
| 33 | 54 | [Download](33/dataset.zip) |  |  |  |  |  |  |  |  |
| 34 | 34 | [Download](34/dataset.zip) |  |  |  |  |  |  |  |  |
| 35 | 14 | [Download](35/dataset.zip) |  |  |  |  |  |  |  |  |
| 36 | 293 | [Download](36/dataset.zip) |  |  |  |  |  |  |  |  |
| 37 | 38 | [Download](37/dataset.zip) |  |  |  |  |  |  |  |  |
| 38 | 8 | [Download](38/dataset.zip) |  |  |  |  |  |  |  |  |
| 39 | 284 | [Download](39/dataset.zip) |  |  |  |  |  |  |  |  |
| 40 | 42 | [Download](40/dataset.zip) |  |  |  |  |  |  |  |  |
| 41 | 75 | [Download](41/dataset.zip) |  |  |  |  |  |  |  |  |
| 42 | 26 | [Download](42/dataset.zip) |  |  |  |  |  |  |  |  |
| 43 | 136 | [Download](43/dataset.zip) |  |  |  |  |  |  |  |  |
| 44 | 9 | [Download](44/dataset.zip) |  |  |  |  |  |  |  |  |
| 45 | 26 | [Download](45/dataset.zip) |  |  |  |  |  |  |  |  |
| 46 | 12 | [Download](46/dataset.zip) |  |  |  |  |  |  |  |  |
| 47 | 8 | [Download](47/dataset.zip) |  |  |  |  |  |  |  |  |
| 48 | 9 | [Download](48/dataset.zip) |  |  |  |  |  |  |  |  |
| 49 | 28 | [Download](49/dataset.zip) |  |  |  |  |  |  |  |  |
| 50 | 65 | [Download](50/dataset.zip) |  |  |  |  |  |  |  |  |
| 51 | 21 | [Download](51/dataset.zip) |  |  |  |  |  |  |  |  |
| 52 | 9 | [Download](52/dataset.zip) |  |  |  |  |  |  |  |  |
| 53 | 6 | [Download](53/dataset.zip) |  |  |  |  |  |  | N/A | N/A |
| 54 | 12 | [Download](54/dataset.zip) |  |  |  |  |  |  |  |  |
| 55 | 17 | [Download](55/dataset.zip) |  |  |  |  |  |  |  |  |
| 56 | 18 | [Download](56/dataset.zip) |  |  |  |  |  |  |  |  |
| noise | 201 | [Download](-1/dataset.zip) |  |  |  |  |  |  |  |  |
| BangumiBase/classroomoftheelite | [
"size_categories:1K<n<10K",
"license:mit",
"art",
"region:us"
]
| 2023-11-13T09:29:57+00:00 | {"license": "mit", "size_categories": ["1K<n<10K"], "tags": ["art"]} | 2023-11-13T12:31:01+00:00 | []
| []
| TAGS
#size_categories-1K<n<10K #license-mit #art #region-us
| Bangumi Image Base of Classroom Of The Elite
============================================
This is the image base of bangumi Classroom of the Elite, we detected 58 characters, 4577 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"
]
|
4dbb1a26c6e77680decebda25918c60af3dc6a67 | # Dataset Card for "aerial-animal-population-4tu"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | danielz01/aerial-animal-population-4tu | [
"region:us"
]
| 2023-11-13T09:30:57+00:00 | {"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "objects", "struct": [{"name": "bbox", "sequence": {"sequence": "int64"}}, {"name": "categories", "sequence": "string"}]}, {"name": "path", "dtype": "string"}], "splits": [{"name": "val", "num_bytes": 571961850.0, "num_examples": 56}, {"name": "test", "num_bytes": 1144658117.0, "num_examples": 112}, {"name": "train", "num_bytes": 696798667.8, "num_examples": 1600}], "download_size": 2396042886, "dataset_size": 2413418634.8}, "configs": [{"config_name": "default", "data_files": [{"split": "val", "path": "data/val-*"}, {"split": "test", "path": "data/test-*"}, {"split": "train", "path": "data/train-*"}]}]} | 2023-11-13T10:21:15+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "aerial-animal-population-4tu"
More Information needed | [
"# Dataset Card for \"aerial-animal-population-4tu\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"aerial-animal-population-4tu\"\n\nMore Information needed"
]
| [
6,
20
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| [
"passage: TAGS\n#region-us \n# Dataset Card for \"aerial-animal-population-4tu\"\n\nMore Information needed"
]
|
7cebd238a801a95d380303a0895e34e863c890bf |
This is an **unofficial** copy of the videos in the *LSVQ dataset (Ying et al, CVPR, 2021)*, the largest dataset available for Non-reference Video Quality Assessment (NR-VQA); this is to facilitate research studies on this dataset given that we have received several reports that the original links of the dataset is not available anymore.
*See [FAST-VQA](https://github.com/VQAssessment/FAST-VQA-and-FasterVQA) (Wu et al, ECCV, 2022) or [DOVER](https://github.com/VQAssessment/DOVER) (Wu et al, ICCV, 2023) repo on its converted labels (i.e. quality scores for videos).*
The file links to the labels in either of the repositories above are as follows:
```
--- examplar_data_labels
--- --- train_labels.txt (this is the training set labels of LSVQ)
--- --- LSVQ
--- --- --- labels_test.txt (this is the LSVQ_test test subset)
--- --- --- labels_1080p.txt (this is the LSVQ_1080p test subset)
```
It should be noticed that the copyright of this dataset still belongs to the Facebook Research and LIVE Laboratory in UT Austin, and we may delete this unofficial repo at any time if requested by the copyright holders.
Here is the original copyright notice of this dataset, as follows.
-----------COPYRIGHT NOTICE STARTS WITH THIS LINE------------ Copyright (c) 2020 The University of Texas at Austin All rights reserved.
Permission is hereby granted, without written agreement and without license or royalty fees, to use, copy, modify, and distribute this database (the images, the results and the source files) and its documentation for any purpose, provided that the copyright notice in its entirety appear in all copies of this database, and the original source of this database, Laboratory for Image and Video Engineering (LIVE, http://live.ece.utexas.edu ) at the University of Texas at Austin (UT Austin, http://www.utexas.edu ), is acknowledged in any publication that reports research using this database.
The following papers are to be cited in the bibliography whenever the database is used as:
Z. Ying, M. Mandal, D. Ghadiyaram and A.C. Bovik, "Patch-VQ: ‘Patching Up’ the Video Quality Problem," arXiv 2020.[paper]
Z. Ying, M. Mandal, D. Ghadiyaram and A.C. Bovik, "LIVE Large-Scale Social Video Quality (LSVQ) Database", Online:https://github.com/baidut/PatchVQ, 2020.
IN NO EVENT SHALL THE UNIVERSITY OF TEXAS AT AUSTIN BE LIABLE TO ANY PARTY FOR DIRECT, INDIRECT, SPECIAL, INCIDENTAL, OR CONSEQUENTIAL DAMAGES ARISING OUT OF THE USE OF THIS DATABASE AND ITS DOCUMENTATION, EVEN IF THE UNIVERSITY OF TEXAS AT AUSTIN HAS BEEN ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
THE UNIVERSITY OF TEXAS AT AUSTIN SPECIFICALLY DISCLAIMS ANY WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE. THE DATABASE PROVIDED HEREUNDER IS ON AN "AS IS" BASIS, AND THE UNIVERSITY OF TEXAS AT AUSTIN HAS NO OBLIGATION TO PROVIDE MAINTENANCE, SUPPORT, UPDATES, ENHANCEMENTS, OR MODIFICATIONS.
-----------COPYRIGHT NOTICE ENDS WITH THIS LINE------------ | teowu/LSVQ-videos | [
"task_categories:video-classification",
"license:mit",
"video quality assessment",
"region:us"
]
| 2023-11-13T09:31:18+00:00 | {"license": "mit", "task_categories": ["video-classification"], "tags": ["video quality assessment"]} | 2023-11-13T14:23:27+00:00 | []
| []
| TAGS
#task_categories-video-classification #license-mit #video quality assessment #region-us
|
This is an unofficial copy of the videos in the *LSVQ dataset (Ying et al, CVPR, 2021)*, the largest dataset available for Non-reference Video Quality Assessment (NR-VQA); this is to facilitate research studies on this dataset given that we have received several reports that the original links of the dataset is not available anymore.
*See FAST-VQA (Wu et al, ECCV, 2022) or DOVER (Wu et al, ICCV, 2023) repo on its converted labels (i.e. quality scores for videos).*
The file links to the labels in either of the repositories above are as follows:
It should be noticed that the copyright of this dataset still belongs to the Facebook Research and LIVE Laboratory in UT Austin, and we may delete this unofficial repo at any time if requested by the copyright holders.
Here is the original copyright notice of this dataset, as follows.
-----------COPYRIGHT NOTICE STARTS WITH THIS LINE------------ Copyright (c) 2020 The University of Texas at Austin All rights reserved.
Permission is hereby granted, without written agreement and without license or royalty fees, to use, copy, modify, and distribute this database (the images, the results and the source files) and its documentation for any purpose, provided that the copyright notice in its entirety appear in all copies of this database, and the original source of this database, Laboratory for Image and Video Engineering (LIVE, URL ) at the University of Texas at Austin (UT Austin, URL ), is acknowledged in any publication that reports research using this database.
The following papers are to be cited in the bibliography whenever the database is used as:
Z. Ying, M. Mandal, D. Ghadiyaram and A.C. Bovik, "Patch-VQ: ‘Patching Up’ the Video Quality Problem," arXiv 2020.[paper]
Z. Ying, M. Mandal, D. Ghadiyaram and A.C. Bovik, "LIVE Large-Scale Social Video Quality (LSVQ) Database", Online:URL 2020.
IN NO EVENT SHALL THE UNIVERSITY OF TEXAS AT AUSTIN BE LIABLE TO ANY PARTY FOR DIRECT, INDIRECT, SPECIAL, INCIDENTAL, OR CONSEQUENTIAL DAMAGES ARISING OUT OF THE USE OF THIS DATABASE AND ITS DOCUMENTATION, EVEN IF THE UNIVERSITY OF TEXAS AT AUSTIN HAS BEEN ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
THE UNIVERSITY OF TEXAS AT AUSTIN SPECIFICALLY DISCLAIMS ANY WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE. THE DATABASE PROVIDED HEREUNDER IS ON AN "AS IS" BASIS, AND THE UNIVERSITY OF TEXAS AT AUSTIN HAS NO OBLIGATION TO PROVIDE MAINTENANCE, SUPPORT, UPDATES, ENHANCEMENTS, OR MODIFICATIONS.
-----------COPYRIGHT NOTICE ENDS WITH THIS LINE------------ | []
| [
"TAGS\n#task_categories-video-classification #license-mit #video quality assessment #region-us \n"
]
| [
26
]
| [
"passage: TAGS\n#task_categories-video-classification #license-mit #video quality assessment #region-us \n"
]
|
6149a58adb8657565d10e612b32947405cbdb42c | # Dataset Card for "law_court_opinion_nonlegal"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | zxvix/law_court_opinion_nonlegal | [
"region:us"
]
| 2023-11-13T09:31:29+00:00 | {"configs": [{"config_name": "default", "data_files": [{"split": "test", "path": "data/test-*"}]}], "dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "original_text", "dtype": "string"}], "splits": [{"name": "test", "num_bytes": 200912.0, "num_examples": 100}], "download_size": 131220, "dataset_size": 200912.0}} | 2023-11-14T02:04:23+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "law_court_opinion_nonlegal"
More Information needed | [
"# Dataset Card for \"law_court_opinion_nonlegal\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"law_court_opinion_nonlegal\"\n\nMore Information needed"
]
| [
6,
18
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| [
"passage: TAGS\n#region-us \n# Dataset Card for \"law_court_opinion_nonlegal\"\n\nMore Information needed"
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|
ca4c0a61fb4f601531a903d3a6b55fdb6a8e3527 | # Dataset Card for "llama-intent_data"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | pankajemplay/llama-intent_data | [
"region:us"
]
| 2023-11-13T09:40:18+00:00 | {"dataset_info": {"features": [{"name": "User Query", "dtype": "string"}, {"name": "Intent", "dtype": "string"}, {"name": "id type", "dtype": "string"}, {"name": "id value", "dtype": "string"}, {"name": "id slot filled", "dtype": "bool"}, {"name": "Task", "dtype": "string"}, {"name": "task slot filled", "dtype": "bool"}, {"name": "Bot Response", "dtype": "string"}, {"name": "text", "dtype": "string"}, {"name": "__index_level_0__", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 780758, "num_examples": 1672}], "download_size": 226285, "dataset_size": 780758}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]} | 2023-11-13T09:40:22+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "llama-intent_data"
More Information needed | [
"# Dataset Card for \"llama-intent_data\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"llama-intent_data\"\n\nMore Information needed"
]
| [
6,
17
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"llama-intent_data\"\n\nMore Information needed"
]
|
2cfac64e6b0a4eb5c6b3adde2a97d152abd89e90 |
# Dataset Card for ag_news_tutorial
This dataset has been created with [Argilla](https://docs.argilla.io).
As shown in the sections below, this dataset can be loaded into Argilla as explained in [Load with Argilla](#load-with-argilla), or used directly with the `datasets` library in [Load with `datasets`](#load-with-datasets).
## Dataset Description
- **Homepage:** https://argilla.io
- **Repository:** https://github.com/argilla-io/argilla
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
This dataset contains:
* A dataset configuration file conforming to the Argilla dataset format named `argilla.yaml`. This configuration file will be used to configure the dataset when using the `FeedbackDataset.from_huggingface` method in Argilla.
* Dataset records in a format compatible with HuggingFace `datasets`. These records will be loaded automatically when using `FeedbackDataset.from_huggingface` and can be loaded independently using the `datasets` library via `load_dataset`.
* The [annotation guidelines](#annotation-guidelines) that have been used for building and curating the dataset, if they've been defined in Argilla.
### Load with Argilla
To load with Argilla, you'll just need to install Argilla as `pip install argilla --upgrade` and then use the following code:
```python
import argilla as rg
ds = rg.FeedbackDataset.from_huggingface("plaguss/ag_news_tutorial")
```
### Load with `datasets`
To load this dataset with `datasets`, you'll just need to install `datasets` as `pip install datasets --upgrade` and then use the following code:
```python
from datasets import load_dataset
ds = load_dataset("plaguss/ag_news_tutorial")
```
### Supported Tasks and Leaderboards
This dataset can contain [multiple fields, questions and responses](https://docs.argilla.io/en/latest/conceptual_guides/data_model.html#feedback-dataset) so it can be used for different NLP tasks, depending on the configuration. The dataset structure is described in the [Dataset Structure section](#dataset-structure).
There are no leaderboards associated with this dataset.
### Languages
[More Information Needed]
## Dataset Structure
### Data in Argilla
The dataset is created in Argilla with: **fields**, **questions**, **suggestions**, **metadata**, and **guidelines**.
The **fields** are the dataset records themselves, for the moment just text fields are supported. These are the ones that will be used to provide responses to the questions.
| Field Name | Title | Type | Required | Markdown |
| ---------- | ----- | ---- | -------- | -------- |
| text | Text from the article | text | True | False |
The **questions** are the questions that will be asked to the annotators. They can be of different types, such as rating, text, label_selection, multi_label_selection, or ranking.
| Question Name | Title | Type | Required | Description | Values/Labels |
| ------------- | ----- | ---- | -------- | ----------- | ------------- |
| label | In which category does this article fit? | label_selection | True | N/A | ['0', '1', '2', '3'] |
The **suggestions** are human or machine generated recommendations for each question to assist the annotator during the annotation process, so those are always linked to the existing questions, and named appending "-suggestion" and "-suggestion-metadata" to those, containing the value/s of the suggestion and its metadata, respectively. So on, the possible values are the same as in the table above, but the column name is appended with "-suggestion" and the metadata is appended with "-suggestion-metadata".
**✨ NEW** The **metadata** is a dictionary that can be used to provide additional information about the dataset record. This can be useful to provide additional context to the annotators, or to provide additional information about the dataset record itself. For example, you can use this to provide a link to the original source of the dataset record, or to provide additional information about the dataset record itself, such as the author, the date, or the source. The metadata is always optional, and can be potentially linked to the `metadata_properties` defined in the dataset configuration file in `argilla.yaml`.
The **guidelines**, are optional as well, and are just a plain string that can be used to provide instructions to the annotators. Find those in the [annotation guidelines](#annotation-guidelines) section.
### Data Instances
An example of a dataset instance in Argilla looks as follows:
```json
{
"external_id": "record-0",
"fields": {
"text": "Wall St. Bears Claw Back Into the Black (Reuters) Reuters - Short-sellers, Wall Street\u0027s dwindling\\band of ultra-cynics, are seeing green again."
},
"metadata": {},
"responses": [],
"suggestions": []
}
```
While the same record in HuggingFace `datasets` looks as follows:
```json
{
"external_id": "record-0",
"label": [],
"label-suggestion": null,
"label-suggestion-metadata": {
"agent": null,
"score": null,
"type": null
},
"metadata": "{}",
"text": "Wall St. Bears Claw Back Into the Black (Reuters) Reuters - Short-sellers, Wall Street\u0027s dwindling\\band of ultra-cynics, are seeing green again."
}
```
### Data Fields
Among the dataset fields, we differentiate between the following:
* **Fields:** These are the dataset records themselves, for the moment just text fields are supported. These are the ones that will be used to provide responses to the questions.
* **text** is of type `text`.
* **Questions:** These are the questions that will be asked to the annotators. They can be of different types, such as `RatingQuestion`, `TextQuestion`, `LabelQuestion`, `MultiLabelQuestion`, and `RankingQuestion`.
* **label** is of type `label_selection` with the following allowed values ['0', '1', '2', '3'].
* **Suggestions:** As of Argilla 1.13.0, the suggestions have been included to provide the annotators with suggestions to ease or assist during the annotation process. Suggestions are linked to the existing questions, are always optional, and contain not just the suggestion itself, but also the metadata linked to it, if applicable.
* (optional) **label-suggestion** is of type `label_selection` with the following allowed values ['0', '1', '2', '3'].
Additionally, we also have two more fields that are optional and are the following:
* **✨ NEW** **metadata:** This is an optional field that can be used to provide additional information about the dataset record. This can be useful to provide additional context to the annotators, or to provide additional information about the dataset record itself. For example, you can use this to provide a link to the original source of the dataset record, or to provide additional information about the dataset record itself, such as the author, the date, or the source. The metadata is always optional, and can be potentially linked to the `metadata_properties` defined in the dataset configuration file in `argilla.yaml`.
* **external_id:** This is an optional field that can be used to provide an external ID for the dataset record. This can be useful if you want to link the dataset record to an external resource, such as a database or a file.
### Data Splits
The dataset contains a single split, which is `train`.
## 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 guidelines
This dataset contains a collection of news articles. Please label them on the category they belong.
#### 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] | plaguss/ag_news_tutorial | [
"size_categories:1K<n<10K",
"rlfh",
"argilla",
"human-feedback",
"region:us"
]
| 2023-11-13T09:43:58+00:00 | {"size_categories": "1K<n<10K", "tags": ["rlfh", "argilla", "human-feedback"]} | 2023-11-13T09:44:04+00:00 | []
| []
| TAGS
#size_categories-1K<n<10K #rlfh #argilla #human-feedback #region-us
| Dataset Card for ag\_news\_tutorial
===================================
This dataset has been created with Argilla.
As shown in the sections below, this dataset can be loaded into Argilla as explained in Load with Argilla, or used directly with the 'datasets' library in Load with 'datasets'.
Dataset Description
-------------------
* Homepage: URL
* Repository: URL
* Paper:
* Leaderboard:
* Point of Contact:
### Dataset Summary
This dataset contains:
* A dataset configuration file conforming to the Argilla dataset format named 'URL'. This configuration file will be used to configure the dataset when using the 'FeedbackDataset.from\_huggingface' method in Argilla.
* Dataset records in a format compatible with HuggingFace 'datasets'. These records will be loaded automatically when using 'FeedbackDataset.from\_huggingface' and can be loaded independently using the 'datasets' library via 'load\_dataset'.
* The annotation guidelines that have been used for building and curating the dataset, if they've been defined in Argilla.
### Load with Argilla
To load with Argilla, you'll just need to install Argilla as 'pip install argilla --upgrade' and then use the following code:
### Load with 'datasets'
To load this dataset with 'datasets', you'll just need to install 'datasets' as 'pip install datasets --upgrade' and then use the following code:
### Supported Tasks and Leaderboards
This dataset can contain multiple fields, questions and responses so it can be used for different NLP tasks, depending on the configuration. The dataset structure is described in the Dataset Structure section.
There are no leaderboards associated with this dataset.
### Languages
Dataset Structure
-----------------
### Data in Argilla
The dataset is created in Argilla with: fields, questions, suggestions, metadata, and guidelines.
The fields are the dataset records themselves, for the moment just text fields are supported. These are the ones that will be used to provide responses to the questions.
The questions are the questions that will be asked to the annotators. They can be of different types, such as rating, text, label\_selection, multi\_label\_selection, or ranking.
The suggestions are human or machine generated recommendations for each question to assist the annotator during the annotation process, so those are always linked to the existing questions, and named appending "-suggestion" and "-suggestion-metadata" to those, containing the value/s of the suggestion and its metadata, respectively. So on, the possible values are the same as in the table above, but the column name is appended with "-suggestion" and the metadata is appended with "-suggestion-metadata".
NEW The metadata is a dictionary that can be used to provide additional information about the dataset record. This can be useful to provide additional context to the annotators, or to provide additional information about the dataset record itself. For example, you can use this to provide a link to the original source of the dataset record, or to provide additional information about the dataset record itself, such as the author, the date, or the source. The metadata is always optional, and can be potentially linked to the 'metadata\_properties' defined in the dataset configuration file in 'URL'.
The guidelines, are optional as well, and are just a plain string that can be used to provide instructions to the annotators. Find those in the annotation guidelines section.
### Data Instances
An example of a dataset instance in Argilla looks as follows:
While the same record in HuggingFace 'datasets' looks as follows:
### Data Fields
Among the dataset fields, we differentiate between the following:
* Fields: These are the dataset records themselves, for the moment just text fields are supported. These are the ones that will be used to provide responses to the questions.
+ text is of type 'text'.
* Questions: These are the questions that will be asked to the annotators. They can be of different types, such as 'RatingQuestion', 'TextQuestion', 'LabelQuestion', 'MultiLabelQuestion', and 'RankingQuestion'.
+ label is of type 'label\_selection' with the following allowed values ['0', '1', '2', '3'].
* Suggestions: As of Argilla 1.13.0, the suggestions have been included to provide the annotators with suggestions to ease or assist during the annotation process. Suggestions are linked to the existing questions, are always optional, and contain not just the suggestion itself, but also the metadata linked to it, if applicable.
+ (optional) label-suggestion is of type 'label\_selection' with the following allowed values ['0', '1', '2', '3'].
Additionally, we also have two more fields that are optional and are the following:
* NEW metadata: This is an optional field that can be used to provide additional information about the dataset record. This can be useful to provide additional context to the annotators, or to provide additional information about the dataset record itself. For example, you can use this to provide a link to the original source of the dataset record, or to provide additional information about the dataset record itself, such as the author, the date, or the source. The metadata is always optional, and can be potentially linked to the 'metadata\_properties' defined in the dataset configuration file in 'URL'.
* external\_id: This is an optional field that can be used to provide an external ID for the dataset record. This can be useful if you want to link the dataset record to an external resource, such as a database or a file.
### Data Splits
The dataset contains a single split, which is 'train'.
Dataset Creation
----------------
### Curation Rationale
### Source Data
#### Initial Data Collection and Normalization
#### Who are the source language producers?
### Annotations
#### Annotation guidelines
This dataset contains a collection of news articles. Please label them on the category they belong.
#### Annotation process
#### Who are the annotators?
### Personal and Sensitive Information
Considerations for Using the Data
---------------------------------
### Social Impact of Dataset
### Discussion of Biases
### Other Known Limitations
Additional Information
----------------------
### Dataset Curators
### Licensing Information
### Contributions
| [
"### Dataset Summary\n\n\nThis dataset contains:\n\n\n* A dataset configuration file conforming to the Argilla dataset format named 'URL'. This configuration file will be used to configure the dataset when using the 'FeedbackDataset.from\\_huggingface' method in Argilla.\n* Dataset records in a format compatible with HuggingFace 'datasets'. These records will be loaded automatically when using 'FeedbackDataset.from\\_huggingface' and can be loaded independently using the 'datasets' library via 'load\\_dataset'.\n* The annotation guidelines that have been used for building and curating the dataset, if they've been defined in Argilla.",
"### Load with Argilla\n\n\nTo load with Argilla, you'll just need to install Argilla as 'pip install argilla --upgrade' and then use the following code:",
"### Load with 'datasets'\n\n\nTo load this dataset with 'datasets', you'll just need to install 'datasets' as 'pip install datasets --upgrade' and then use the following code:",
"### Supported Tasks and Leaderboards\n\n\nThis dataset can contain multiple fields, questions and responses so it can be used for different NLP tasks, depending on the configuration. The dataset structure is described in the Dataset Structure section.\n\n\nThere are no leaderboards associated with this dataset.",
"### Languages\n\n\nDataset Structure\n-----------------",
"### Data in Argilla\n\n\nThe dataset is created in Argilla with: fields, questions, suggestions, metadata, and guidelines.\n\n\nThe fields are the dataset records themselves, for the moment just text fields are supported. These are the ones that will be used to provide responses to the questions.\n\n\n\nThe questions are the questions that will be asked to the annotators. They can be of different types, such as rating, text, label\\_selection, multi\\_label\\_selection, or ranking.\n\n\n\nThe suggestions are human or machine generated recommendations for each question to assist the annotator during the annotation process, so those are always linked to the existing questions, and named appending \"-suggestion\" and \"-suggestion-metadata\" to those, containing the value/s of the suggestion and its metadata, respectively. So on, the possible values are the same as in the table above, but the column name is appended with \"-suggestion\" and the metadata is appended with \"-suggestion-metadata\".\n\n\nNEW The metadata is a dictionary that can be used to provide additional information about the dataset record. This can be useful to provide additional context to the annotators, or to provide additional information about the dataset record itself. For example, you can use this to provide a link to the original source of the dataset record, or to provide additional information about the dataset record itself, such as the author, the date, or the source. The metadata is always optional, and can be potentially linked to the 'metadata\\_properties' defined in the dataset configuration file in 'URL'.\n\n\nThe guidelines, are optional as well, and are just a plain string that can be used to provide instructions to the annotators. Find those in the annotation guidelines section.",
"### Data Instances\n\n\nAn example of a dataset instance in Argilla looks as follows:\n\n\nWhile the same record in HuggingFace 'datasets' looks as follows:",
"### Data Fields\n\n\nAmong the dataset fields, we differentiate between the following:\n\n\n* Fields: These are the dataset records themselves, for the moment just text fields are supported. These are the ones that will be used to provide responses to the questions.\n\n\n\t+ text is of type 'text'.\n* Questions: These are the questions that will be asked to the annotators. They can be of different types, such as 'RatingQuestion', 'TextQuestion', 'LabelQuestion', 'MultiLabelQuestion', and 'RankingQuestion'.\n\n\n\t+ label is of type 'label\\_selection' with the following allowed values ['0', '1', '2', '3'].\n* Suggestions: As of Argilla 1.13.0, the suggestions have been included to provide the annotators with suggestions to ease or assist during the annotation process. Suggestions are linked to the existing questions, are always optional, and contain not just the suggestion itself, but also the metadata linked to it, if applicable.\n\n\n\t+ (optional) label-suggestion is of type 'label\\_selection' with the following allowed values ['0', '1', '2', '3'].\n\n\nAdditionally, we also have two more fields that are optional and are the following:\n\n\n* NEW metadata: This is an optional field that can be used to provide additional information about the dataset record. This can be useful to provide additional context to the annotators, or to provide additional information about the dataset record itself. For example, you can use this to provide a link to the original source of the dataset record, or to provide additional information about the dataset record itself, such as the author, the date, or the source. The metadata is always optional, and can be potentially linked to the 'metadata\\_properties' defined in the dataset configuration file in 'URL'.\n* external\\_id: This is an optional field that can be used to provide an external ID for the dataset record. This can be useful if you want to link the dataset record to an external resource, such as a database or a file.",
"### Data Splits\n\n\nThe dataset contains a single split, which is 'train'.\n\n\nDataset Creation\n----------------",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?",
"### Annotations",
"#### Annotation guidelines\n\n\nThis dataset contains a collection of news articles. Please label them on the category they belong.",
"#### 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",
"### Contributions"
]
| [
"TAGS\n#size_categories-1K<n<10K #rlfh #argilla #human-feedback #region-us \n",
"### Dataset Summary\n\n\nThis dataset contains:\n\n\n* A dataset configuration file conforming to the Argilla dataset format named 'URL'. This configuration file will be used to configure the dataset when using the 'FeedbackDataset.from\\_huggingface' method in Argilla.\n* Dataset records in a format compatible with HuggingFace 'datasets'. These records will be loaded automatically when using 'FeedbackDataset.from\\_huggingface' and can be loaded independently using the 'datasets' library via 'load\\_dataset'.\n* The annotation guidelines that have been used for building and curating the dataset, if they've been defined in Argilla.",
"### Load with Argilla\n\n\nTo load with Argilla, you'll just need to install Argilla as 'pip install argilla --upgrade' and then use the following code:",
"### Load with 'datasets'\n\n\nTo load this dataset with 'datasets', you'll just need to install 'datasets' as 'pip install datasets --upgrade' and then use the following code:",
"### Supported Tasks and Leaderboards\n\n\nThis dataset can contain multiple fields, questions and responses so it can be used for different NLP tasks, depending on the configuration. The dataset structure is described in the Dataset Structure section.\n\n\nThere are no leaderboards associated with this dataset.",
"### Languages\n\n\nDataset Structure\n-----------------",
"### Data in Argilla\n\n\nThe dataset is created in Argilla with: fields, questions, suggestions, metadata, and guidelines.\n\n\nThe fields are the dataset records themselves, for the moment just text fields are supported. These are the ones that will be used to provide responses to the questions.\n\n\n\nThe questions are the questions that will be asked to the annotators. They can be of different types, such as rating, text, label\\_selection, multi\\_label\\_selection, or ranking.\n\n\n\nThe suggestions are human or machine generated recommendations for each question to assist the annotator during the annotation process, so those are always linked to the existing questions, and named appending \"-suggestion\" and \"-suggestion-metadata\" to those, containing the value/s of the suggestion and its metadata, respectively. So on, the possible values are the same as in the table above, but the column name is appended with \"-suggestion\" and the metadata is appended with \"-suggestion-metadata\".\n\n\nNEW The metadata is a dictionary that can be used to provide additional information about the dataset record. This can be useful to provide additional context to the annotators, or to provide additional information about the dataset record itself. For example, you can use this to provide a link to the original source of the dataset record, or to provide additional information about the dataset record itself, such as the author, the date, or the source. The metadata is always optional, and can be potentially linked to the 'metadata\\_properties' defined in the dataset configuration file in 'URL'.\n\n\nThe guidelines, are optional as well, and are just a plain string that can be used to provide instructions to the annotators. Find those in the annotation guidelines section.",
"### Data Instances\n\n\nAn example of a dataset instance in Argilla looks as follows:\n\n\nWhile the same record in HuggingFace 'datasets' looks as follows:",
"### Data Fields\n\n\nAmong the dataset fields, we differentiate between the following:\n\n\n* Fields: These are the dataset records themselves, for the moment just text fields are supported. These are the ones that will be used to provide responses to the questions.\n\n\n\t+ text is of type 'text'.\n* Questions: These are the questions that will be asked to the annotators. They can be of different types, such as 'RatingQuestion', 'TextQuestion', 'LabelQuestion', 'MultiLabelQuestion', and 'RankingQuestion'.\n\n\n\t+ label is of type 'label\\_selection' with the following allowed values ['0', '1', '2', '3'].\n* Suggestions: As of Argilla 1.13.0, the suggestions have been included to provide the annotators with suggestions to ease or assist during the annotation process. Suggestions are linked to the existing questions, are always optional, and contain not just the suggestion itself, but also the metadata linked to it, if applicable.\n\n\n\t+ (optional) label-suggestion is of type 'label\\_selection' with the following allowed values ['0', '1', '2', '3'].\n\n\nAdditionally, we also have two more fields that are optional and are the following:\n\n\n* NEW metadata: This is an optional field that can be used to provide additional information about the dataset record. This can be useful to provide additional context to the annotators, or to provide additional information about the dataset record itself. For example, you can use this to provide a link to the original source of the dataset record, or to provide additional information about the dataset record itself, such as the author, the date, or the source. The metadata is always optional, and can be potentially linked to the 'metadata\\_properties' defined in the dataset configuration file in 'URL'.\n* external\\_id: This is an optional field that can be used to provide an external ID for the dataset record. This can be useful if you want to link the dataset record to an external resource, such as a database or a file.",
"### Data Splits\n\n\nThe dataset contains a single split, which is 'train'.\n\n\nDataset Creation\n----------------",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?",
"### Annotations",
"#### Annotation guidelines\n\n\nThis dataset contains a collection of news articles. Please label them on the category they belong.",
"#### 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",
"### Contributions"
]
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"passage: TAGS\n#size_categories-1K<n<10K #rlfh #argilla #human-feedback #region-us \n### Dataset Summary\n\n\nThis dataset contains:\n\n\n* A dataset configuration file conforming to the Argilla dataset format named 'URL'. This configuration file will be used to configure the dataset when using the 'FeedbackDataset.from\\_huggingface' method in Argilla.\n* Dataset records in a format compatible with HuggingFace 'datasets'. These records will be loaded automatically when using 'FeedbackDataset.from\\_huggingface' and can be loaded independently using the 'datasets' library via 'load\\_dataset'.\n* The annotation guidelines that have been used for building and curating the dataset, if they've been defined in Argilla.### Load with Argilla\n\n\nTo load with Argilla, you'll just need to install Argilla as 'pip install argilla --upgrade' and then use the following code:### Load with 'datasets'\n\n\nTo load this dataset with 'datasets', you'll just need to install 'datasets' as 'pip install datasets --upgrade' and then use the following code:### Supported Tasks and Leaderboards\n\n\nThis dataset can contain multiple fields, questions and responses so it can be used for different NLP tasks, depending on the configuration. The dataset structure is described in the Dataset Structure section.\n\n\nThere are no leaderboards associated with this dataset.### Languages\n\n\nDataset Structure\n-----------------",
"passage: ### Data in Argilla\n\n\nThe dataset is created in Argilla with: fields, questions, suggestions, metadata, and guidelines.\n\n\nThe fields are the dataset records themselves, for the moment just text fields are supported. These are the ones that will be used to provide responses to the questions.\n\n\n\nThe questions are the questions that will be asked to the annotators. They can be of different types, such as rating, text, label\\_selection, multi\\_label\\_selection, or ranking.\n\n\n\nThe suggestions are human or machine generated recommendations for each question to assist the annotator during the annotation process, so those are always linked to the existing questions, and named appending \"-suggestion\" and \"-suggestion-metadata\" to those, containing the value/s of the suggestion and its metadata, respectively. So on, the possible values are the same as in the table above, but the column name is appended with \"-suggestion\" and the metadata is appended with \"-suggestion-metadata\".\n\n\nNEW The metadata is a dictionary that can be used to provide additional information about the dataset record. This can be useful to provide additional context to the annotators, or to provide additional information about the dataset record itself. For example, you can use this to provide a link to the original source of the dataset record, or to provide additional information about the dataset record itself, such as the author, the date, or the source. The metadata is always optional, and can be potentially linked to the 'metadata\\_properties' defined in the dataset configuration file in 'URL'.\n\n\nThe guidelines, are optional as well, and are just a plain string that can be used to provide instructions to the annotators. Find those in the annotation guidelines section.### Data Instances\n\n\nAn example of a dataset instance in Argilla looks as follows:\n\n\nWhile the same record in HuggingFace 'datasets' looks as follows:"
]
|
49b7d3f2462c675aa6da3b4ed59bc5b93a241e6e | **LVIS-Instruct4V**:
We introduce a fine-grained visual instruction dataset, LVIS-Instruct4V, which contains *220K* visually aligned and context-aware instructions produced by prompting the powerful GPT-4V with images from LVIS. | X2FD/LVIS-Instruct4V | [
"region:us"
]
| 2023-11-13T09:48:38+00:00 | {} | 2023-11-13T17:13:29+00:00 | []
| []
| TAGS
#region-us
| LVIS-Instruct4V:
We introduce a fine-grained visual instruction dataset, LVIS-Instruct4V, which contains *220K* visually aligned and context-aware instructions produced by prompting the powerful GPT-4V with images from LVIS. | []
| [
"TAGS\n#region-us \n"
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| [
6
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|
28a35cac78ae202334fff14fce5f4eb61583f474 | # Dataset Card for "pokemon-llava-captions"
This dataset is just [lambdalabs/pokemon-blip-captions](https://huggingface.co/datasets/lambdalabs/pokemon-blip-captions) but the captions come
from the [LLaVA model](https://github.com/haotian-liu/LLaVA).
Refer to [the notebook](https://colab.research.google.com/drive/1RlBcCHyLRcr3y-anInlpheqDJWK7Axth?usp=sharing) that generated this dataset. | diffusers/pokemon-llava-captions | [
"region:us"
]
| 2023-11-13T09:58:49+00:00 | {"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 340870512.0, "num_examples": 833}], "download_size": 340816661, "dataset_size": 340870512.0}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]} | 2023-11-25T06:24:28+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "pokemon-llava-captions"
This dataset is just lambdalabs/pokemon-blip-captions but the captions come
from the LLaVA model.
Refer to the notebook that generated this dataset. | [
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|
f516648970618aa93e3c39cd3ecba2c7f12e586b |
# Dataset Card for Dataset Name
<!-- Provide a quick summary of the dataset. -->
## 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] | kinit-tomassako/ver_claimdetection_demo2 | [
"region:us"
]
| 2023-11-13T10:00:51+00:00 | {"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data.csv"}]}]} | 2023-11-30T18:49:26+00:00 | []
| []
| TAGS
#region-us
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# Dataset Card for Dataset Name
## Dataset Details
### Dataset Description
- Curated by:
- Funded by [optional]:
- Shared by [optional]:
- Language(s) (NLP):
- License:
### Dataset Sources [optional]
- Repository:
- Paper [optional]:
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#### 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]
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APA:
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0b8866cfc8aa425787d53a214dea48d010d8ec6f | original dataset = [squarelike/sharegpt_deepl_ko_translation](https://huggingface.co/datasets/squarelike/sharegpt_deepl_ko_translation)
| jhflow/share_gpt_translation_with_instruction | [
"region:us"
]
| 2023-11-13T10:03:25+00:00 | {} | 2023-11-13T10:08:14+00:00 | []
| []
| TAGS
#region-us
| original dataset = squarelike/sharegpt_deepl_ko_translation
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507ce20023a1e9ff642d52dc95ec1d16cc639b10 | # Dataset Card for "alpaca-gpt4-1k"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | wt-golf/alpaca-gpt4-1k | [
"region:us"
]
| 2023-11-13T10:15:53+00:00 | {"configs": [{"config_name": "default", "data_files": [{"split": "data", "path": "data/data-*"}]}], "dataset_info": {"features": [{"name": "instruction", "dtype": "string"}, {"name": "input", "dtype": "string"}, {"name": "output", "dtype": "string"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "data", "num_bytes": 1723332, "num_examples": 1000}], "download_size": 942428, "dataset_size": 1723332}} | 2023-11-13T14:21:42+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "alpaca-gpt4-1k"
More Information needed | [
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63f2fc5c51ba5044cf65fbc81587cf4feb1b7396 | # Dataset Card for "f1_dataset"
This dataset includes race telemetry data from the Formula1 Montreail 2023 GP. It was obtained from the Ergast API using the fastf1 library.
We built an [interactive demo](https://huggingface.co/spaces/renumics/f1_montreal_gp) for this dataset on Hugging Face spaces.
You can explore the dataset on your machine with [Spotlight](https://github.com/Renumics/spotlight):
```bash
pip install renumics-spotlight
```
```Python
import datasets
from renumics import spotlight
ds = datasets.load_dataset('renumics/f1_dataset', split='train')
dtypes = {"DistanceToDriverAhead": spotlight.Sequence1D, "RPM": spotlight.Sequence1D, "Speed": spotlight.Sequence1D, "nGear": spotlight.Sequence1D,
"Throttle": spotlight.Sequence1D, "Brake": spotlight.Sequence1D, "DRS": spotlight.Sequence1D, "X": spotlight.Sequence1D, "Y": spotlight.Sequence1D, "Z": spotlight.Sequence1D,
'RPM_emb': spotlight.Embedding, 'Speed_emb': spotlight.Embedding, 'nGear_emb': spotlight.Embedding, 'Throttle_emb': spotlight.Embedding, 'Brake_emb': spotlight.Embedding,
'X_emb': spotlight.Embedding, 'Y_emb': spotlight.Embedding, 'Z_emb': spotlight.Embedding}
spotlight.show(ds, dtype=dtypes)
``` | renumics/f1_dataset | [
"region:us"
]
| 2023-11-13T10:33:33+00:00 | {"dataset_info": {"features": [{"name": "Time", "dtype": "duration[ns]"}, {"name": "Driver", "dtype": "string"}, {"name": "DriverNumber", "dtype": "string"}, {"name": "LapTime", "dtype": "duration[ns]"}, {"name": "LapNumber", "dtype": "float64"}, {"name": "Stint", "dtype": "float64"}, {"name": "PitOutTime", "dtype": "duration[ns]"}, {"name": "PitInTime", "dtype": "duration[ns]"}, {"name": "Sector1Time", "dtype": "duration[ns]"}, {"name": "Sector2Time", "dtype": "duration[ns]"}, {"name": "Sector3Time", "dtype": "duration[ns]"}, {"name": "Sector1SessionTime", "dtype": "duration[ns]"}, {"name": "Sector2SessionTime", "dtype": "duration[ns]"}, {"name": "Sector3SessionTime", "dtype": "duration[ns]"}, {"name": "SpeedI1", "dtype": "float64"}, {"name": "SpeedI2", "dtype": "float64"}, {"name": "SpeedFL", "dtype": "float64"}, {"name": "SpeedST", "dtype": "float64"}, {"name": "IsPersonalBest", "dtype": "bool"}, {"name": "Compound", "dtype": "string"}, {"name": "TyreLife", "dtype": "float64"}, {"name": "FreshTyre", "dtype": "bool"}, {"name": "Team", "dtype": "string"}, {"name": "LapStartTime", "dtype": "duration[ns]"}, {"name": "LapStartDate", "dtype": "timestamp[ns]"}, {"name": "TrackStatus", "dtype": "string"}, {"name": "Position", "dtype": "float64"}, {"name": "Deleted", "dtype": "bool"}, {"name": "DeletedReason", "dtype": "string"}, {"name": "FastF1Generated", "dtype": "bool"}, {"name": "IsAccurate", "dtype": "bool"}, {"name": "DistanceToDriverAhead", "sequence": {"sequence": "float64"}}, {"name": "RPM", "sequence": {"sequence": "float64"}}, {"name": "Speed", "sequence": {"sequence": "float64"}}, {"name": "nGear", "sequence": {"sequence": "float64"}}, {"name": "Throttle", "sequence": {"sequence": "float64"}}, {"name": "Brake", "sequence": {"sequence": "float64"}}, {"name": "DRS", "sequence": {"sequence": "float64"}}, {"name": "X", "sequence": {"sequence": "float64"}}, {"name": "Y", "sequence": {"sequence": "float64"}}, {"name": "Z", "sequence": {"sequence": "float64"}}, {"name": "gear_vis", "dtype": "image"}, {"name": "speed_vis", "dtype": "image"}, {"name": "RPM_emb", "sequence": "float64"}, {"name": "Speed_emb", "sequence": "float64"}, {"name": "nGear_emb", "sequence": "float64"}, {"name": "Throttle_emb", "sequence": "float64"}, {"name": "Brake_emb", "sequence": "float64"}, {"name": "X_emb", "sequence": "float64"}, {"name": "Y_emb", "sequence": "float64"}, {"name": "Z_emb", "sequence": "float64"}, {"name": "portrait", "dtype": "image"}], "splits": [{"name": "train", "num_bytes": 561415487.5469999, "num_examples": 1317}], "download_size": 300522146, "dataset_size": 561415487.5469999}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]} | 2023-11-13T18:15:08+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "f1_dataset"
This dataset includes race telemetry data from the Formula1 Montreail 2023 GP. It was obtained from the Ergast API using the fastf1 library.
We built an interactive demo for this dataset on Hugging Face spaces.
!image/png
You can explore the dataset on your machine with Spotlight:
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|
ae0e60f007fcb26f635fe4936a31d4ac3f022e32 | # Dataset Card for "validation_2000_cutoff_llama_2k_batch_train_2_results_test"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | Rewcifer/validation_2000_cutoff_llama_2k_batch_train_2_results_test | [
"region:us"
]
| 2023-11-13T10:34:21+00:00 | {"dataset_info": {"features": [{"name": "labels_and_findings", "dtype": "string"}, {"name": "prompts", "dtype": "string"}, {"name": "true_findings", "dtype": "string"}, {"name": "generated_texts", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 17471501, "num_examples": 2000}], "download_size": 4257850, "dataset_size": 17471501}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]} | 2023-11-13T10:34:28+00:00 | []
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| TAGS
#region-us
| # Dataset Card for "validation_2000_cutoff_llama_2k_batch_train_2_results_test"
More Information needed | [
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|
6d9fce6875319273443c135d4622e755a12f0402 | # Dataset Card for "diet_type"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | Thefoodprocessor/diet_type | [
"region:us"
]
| 2023-11-13T10:53:12+00:00 | {"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "recipe", "dtype": "string"}, {"name": "diet_type", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 112976459, "num_examples": 74465}], "download_size": 55147763, "dataset_size": 112976459}} | 2023-11-13T10:53:16+00:00 | []
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#region-us
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0183ed3b1eb35cbd46deff4de663c0d12cb40acf | # DPO formatted Helpful and Harmless RLHF Dataset
This dataset is built from [Anthropic's hh-rlhf dataset](https://huggingface.co/datasets/Anthropic/hh-rlhf).
It is modified as follows:
- The prompt formatting is switched to the Llama 2 format with [INST] and [/INST]
- The data is split into prompt, chosen and rejected rows, as required by [HuggingFace's DPO trainer](https://huggingface.co/docs/trl/dpo_trainer).
Purchase access to this dataset [here](https://buy.stripe.com/dR614QcWh95VdZ68xb). Purchase entitles the user to make use of the dataset for training large language models.
---
- The original dataset card follows below:
# Dataset Card for HH-RLHF
## Dataset Summary
This repository provides access to two different kinds of data:
1. Human preference data about helpfulness and harmlessness from [Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback](https://arxiv.org/abs/2204.05862). These data are meant to train preference (or reward) models for subsequent RLHF training. These data are *not* meant for supervised training of dialogue agents. Training dialogue agents on these data is likely to lead to harmful models and this shold be avoided.
2. Human-generated and annotated red teaming dialogues from [Red Teaming Language Models to Reduce Harms: Methods, Scaling Behaviors, and Lessons Learned](https://www.anthropic.com/red_teaming.pdf). These data are meant to understand how crowdworkers red team models and what types of red team attacks are succesful or not. The data are *not* meant for fine-tuning or preference modeling (use the data above for preference modeling). These data are entire transcripts of conversations that are derived from the harmlessness preference modeling data described above, where only the chosen response is incorporated into the overall transcript. Furthermore, the transcripts are annotated with human and automated measurements of how harmful the overall dialogues are.
**Disclaimer**: The data (especially the harmlessness preference data and the red team data) contain content that may be offensive or upsetting. Topics include, but are not limited to, discriminatory language and discussions of abuse, violence, self-harm, exploitation, and other potentially upsetting subject matter. Please only engage with the data in accordance with your own personal risk tolerance. The data are intended for research purposes, especially research that can make models *less* harmful. The views expressed in the data do not reflect the views of Anthropic or any of its employees. As mentioned above, these data are *not* intended for training dialogue agents as this will likely lead to harmful model behavior.
Each of these datasets are described further below.
## Human preference data about helpfulness and harmlessness (PM Data)
The data are described in the paper: [Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback](https://arxiv.org/abs/2204.05862). If you find the data useful, please cite the paper. The data format is very simple -- each line of the jsonl files contains a pair of texts, one "chosen" and one "rejected".
For **helpfulness**, the data are grouped into train/test splits in three tranches: from our base models (context-distilled 52B language models), via rejection sampling (mostly with best-of-16 sampling) against an early preference model, and a dataset sampled during our iterated "online" process.
For **harmlessness**, the data are only collected for our base models, but otherwise formatted in the same way.
Details about the data collection process and crowdworker population can be found in the paper, specifically in section 2 and appendix D.
## Red teaming data (not PM Data)
The data are described in the paper: [Red Teaming Language Models to Reduce Harms: Methods, Scaling Behaviors, and Lessons Learned](https://www.anthropic.com/red_teaming.pdf). If you find the data useful, please cite the paper. At a high level these data can be examined to understand what succesful red team attempts look like.
Details about the data and data collection procedures can be found in the Datasheet in the appendix of the paper.
Each line of the jsonl file contains a dictionary with the following fields:
- `transcript` a text transcript of a conversation between a human adversary (red team member) and an AI assistant
- `min_harmlessness_score_transcript` a real value score of the harmlessness of the AI assistant (lower is more harmful) as obtained from a preference model
- `num_params` number of parameters in the language model powering the AI assistant
- `model_type` type of model powering the AI assistant
- `rating` the red team member's rating of how successful they were at breaking the AI assistant (Likert scale, higher is more successful)
- `task_description` a short text description written by the red team member about how they tried to red team the AI assistant
- `task_description_harmlessness_score` a real value score of the harmlessness of the task description (lower is more harmful) as obtained from a preference model
- `red_team_member_id` an arbitrary identifier of the red team member. one red team member can generate multiple red team attacks
- `is_upworker` a binary indicator that is true if the red team member was from the crowd platform Upwork or false if they were from MTurk
- `tags` a list of up to 6 tags per transcript. tags are short descriptions of the red team attempts generated by crowdworkers who reviewed red team data post-hoc. tags were only provided for a random sample of 1000 red team attempts for two of four model types.
## Usage
Each of the above datasets is located in a separate sub-directory. To load an individual subset, use the `data_dir` argument of the `load_dataset()` function as follows:
```python
from datasets import load_dataset
# Load all helpfulness/harmless subsets (share the same schema)
dataset = load_dataset("Anthropic/hh-rlhf")
# Load one of the harmless subsets
dataset = load_dataset("Anthropic/hh-rlhf", data_dir="harmless-base")
# Load the red teaming subset
dataset = load_dataset("Anthropic/hh-rlhf", data_dir="red-team-attempts")
```
## Contact
The original authors host this dataset on GitHub here: https://github.com/anthropics/hh-rlhf
You can submit inquiries to: [email protected] | Trelis/hh-rlhf-dpo | [
"license:other",
"human-feedback",
"DPO",
"rlhf",
"Llama 2",
"arxiv:2204.05862",
"region:us"
]
| 2023-11-13T10:54:53+00:00 | {"license": "other", "tags": ["human-feedback", "DPO", "rlhf", "Llama 2"], "extra_gated_prompt": "Access to this repo requires the purchase of a license (see link on model card below)", "extra_gated_fields": {"Name": "text", "Affiliation": "text", "Email": "text", "I have purchased access (access will be granted once your payment clears)": "checkbox", "I agree to the terms of the license described on the dataset card": "checkbox"}} | 2023-11-13T14:03:24+00:00 | [
"2204.05862"
]
| []
| TAGS
#license-other #human-feedback #DPO #rlhf #Llama 2 #arxiv-2204.05862 #region-us
| # DPO formatted Helpful and Harmless RLHF Dataset
This dataset is built from Anthropic's hh-rlhf dataset.
It is modified as follows:
- The prompt formatting is switched to the Llama 2 format with [INST] and [/INST]
- The data is split into prompt, chosen and rejected rows, as required by HuggingFace's DPO trainer.
Purchase access to this dataset here. Purchase entitles the user to make use of the dataset for training large language models.
---
- The original dataset card follows below:
# Dataset Card for HH-RLHF
## Dataset Summary
This repository provides access to two different kinds of data:
1. Human preference data about helpfulness and harmlessness from Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback. These data are meant to train preference (or reward) models for subsequent RLHF training. These data are *not* meant for supervised training of dialogue agents. Training dialogue agents on these data is likely to lead to harmful models and this shold be avoided.
2. Human-generated and annotated red teaming dialogues from Red Teaming Language Models to Reduce Harms: Methods, Scaling Behaviors, and Lessons Learned. These data are meant to understand how crowdworkers red team models and what types of red team attacks are succesful or not. The data are *not* meant for fine-tuning or preference modeling (use the data above for preference modeling). These data are entire transcripts of conversations that are derived from the harmlessness preference modeling data described above, where only the chosen response is incorporated into the overall transcript. Furthermore, the transcripts are annotated with human and automated measurements of how harmful the overall dialogues are.
Disclaimer: The data (especially the harmlessness preference data and the red team data) contain content that may be offensive or upsetting. Topics include, but are not limited to, discriminatory language and discussions of abuse, violence, self-harm, exploitation, and other potentially upsetting subject matter. Please only engage with the data in accordance with your own personal risk tolerance. The data are intended for research purposes, especially research that can make models *less* harmful. The views expressed in the data do not reflect the views of Anthropic or any of its employees. As mentioned above, these data are *not* intended for training dialogue agents as this will likely lead to harmful model behavior.
Each of these datasets are described further below.
## Human preference data about helpfulness and harmlessness (PM Data)
The data are described in the paper: Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback. If you find the data useful, please cite the paper. The data format is very simple -- each line of the jsonl files contains a pair of texts, one "chosen" and one "rejected".
For helpfulness, the data are grouped into train/test splits in three tranches: from our base models (context-distilled 52B language models), via rejection sampling (mostly with best-of-16 sampling) against an early preference model, and a dataset sampled during our iterated "online" process.
For harmlessness, the data are only collected for our base models, but otherwise formatted in the same way.
Details about the data collection process and crowdworker population can be found in the paper, specifically in section 2 and appendix D.
## Red teaming data (not PM Data)
The data are described in the paper: Red Teaming Language Models to Reduce Harms: Methods, Scaling Behaviors, and Lessons Learned. If you find the data useful, please cite the paper. At a high level these data can be examined to understand what succesful red team attempts look like.
Details about the data and data collection procedures can be found in the Datasheet in the appendix of the paper.
Each line of the jsonl file contains a dictionary with the following fields:
- 'transcript' a text transcript of a conversation between a human adversary (red team member) and an AI assistant
- 'min_harmlessness_score_transcript' a real value score of the harmlessness of the AI assistant (lower is more harmful) as obtained from a preference model
- 'num_params' number of parameters in the language model powering the AI assistant
- 'model_type' type of model powering the AI assistant
- 'rating' the red team member's rating of how successful they were at breaking the AI assistant (Likert scale, higher is more successful)
- 'task_description' a short text description written by the red team member about how they tried to red team the AI assistant
- 'task_description_harmlessness_score' a real value score of the harmlessness of the task description (lower is more harmful) as obtained from a preference model
- 'red_team_member_id' an arbitrary identifier of the red team member. one red team member can generate multiple red team attacks
- 'is_upworker' a binary indicator that is true if the red team member was from the crowd platform Upwork or false if they were from MTurk
- 'tags' a list of up to 6 tags per transcript. tags are short descriptions of the red team attempts generated by crowdworkers who reviewed red team data post-hoc. tags were only provided for a random sample of 1000 red team attempts for two of four model types.
## Usage
Each of the above datasets is located in a separate sub-directory. To load an individual subset, use the 'data_dir' argument of the 'load_dataset()' function as follows:
## Contact
The original authors host this dataset on GitHub here: URL
You can submit inquiries to: redteam@URL | [
"# DPO formatted Helpful and Harmless RLHF Dataset\nThis dataset is built from Anthropic's hh-rlhf dataset.\n\nIt is modified as follows:\n- The prompt formatting is switched to the Llama 2 format with [INST] and [/INST]\n- The data is split into prompt, chosen and rejected rows, as required by HuggingFace's DPO trainer.\n\nPurchase access to this dataset here. Purchase entitles the user to make use of the dataset for training large language models.\n\n---\n\n- The original dataset card follows below:",
"# Dataset Card for HH-RLHF",
"## Dataset Summary\n\n\nThis repository provides access to two different kinds of data:\n1. Human preference data about helpfulness and harmlessness from Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback. These data are meant to train preference (or reward) models for subsequent RLHF training. These data are *not* meant for supervised training of dialogue agents. Training dialogue agents on these data is likely to lead to harmful models and this shold be avoided.\n2. Human-generated and annotated red teaming dialogues from Red Teaming Language Models to Reduce Harms: Methods, Scaling Behaviors, and Lessons Learned. These data are meant to understand how crowdworkers red team models and what types of red team attacks are succesful or not. The data are *not* meant for fine-tuning or preference modeling (use the data above for preference modeling). These data are entire transcripts of conversations that are derived from the harmlessness preference modeling data described above, where only the chosen response is incorporated into the overall transcript. Furthermore, the transcripts are annotated with human and automated measurements of how harmful the overall dialogues are.\n\nDisclaimer: The data (especially the harmlessness preference data and the red team data) contain content that may be offensive or upsetting. Topics include, but are not limited to, discriminatory language and discussions of abuse, violence, self-harm, exploitation, and other potentially upsetting subject matter. Please only engage with the data in accordance with your own personal risk tolerance. The data are intended for research purposes, especially research that can make models *less* harmful. The views expressed in the data do not reflect the views of Anthropic or any of its employees. As mentioned above, these data are *not* intended for training dialogue agents as this will likely lead to harmful model behavior. \n\nEach of these datasets are described further below.",
"## Human preference data about helpfulness and harmlessness (PM Data)\n\nThe data are described in the paper: Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback. If you find the data useful, please cite the paper. The data format is very simple -- each line of the jsonl files contains a pair of texts, one \"chosen\" and one \"rejected\".\n\nFor helpfulness, the data are grouped into train/test splits in three tranches: from our base models (context-distilled 52B language models), via rejection sampling (mostly with best-of-16 sampling) against an early preference model, and a dataset sampled during our iterated \"online\" process. \n\nFor harmlessness, the data are only collected for our base models, but otherwise formatted in the same way.\n\nDetails about the data collection process and crowdworker population can be found in the paper, specifically in section 2 and appendix D.",
"## Red teaming data (not PM Data)\n\nThe data are described in the paper: Red Teaming Language Models to Reduce Harms: Methods, Scaling Behaviors, and Lessons Learned. If you find the data useful, please cite the paper. At a high level these data can be examined to understand what succesful red team attempts look like. \n\nDetails about the data and data collection procedures can be found in the Datasheet in the appendix of the paper. \n\nEach line of the jsonl file contains a dictionary with the following fields:\n- 'transcript' a text transcript of a conversation between a human adversary (red team member) and an AI assistant\n- 'min_harmlessness_score_transcript' a real value score of the harmlessness of the AI assistant (lower is more harmful) as obtained from a preference model\n- 'num_params' number of parameters in the language model powering the AI assistant\n- 'model_type' type of model powering the AI assistant\n- 'rating' the red team member's rating of how successful they were at breaking the AI assistant (Likert scale, higher is more successful) \n- 'task_description' a short text description written by the red team member about how they tried to red team the AI assistant\n- 'task_description_harmlessness_score' a real value score of the harmlessness of the task description (lower is more harmful) as obtained from a preference model\n- 'red_team_member_id' an arbitrary identifier of the red team member. one red team member can generate multiple red team attacks\n- 'is_upworker' a binary indicator that is true if the red team member was from the crowd platform Upwork or false if they were from MTurk\n- 'tags' a list of up to 6 tags per transcript. tags are short descriptions of the red team attempts generated by crowdworkers who reviewed red team data post-hoc. tags were only provided for a random sample of 1000 red team attempts for two of four model types.",
"## Usage\n\nEach of the above datasets is located in a separate sub-directory. To load an individual subset, use the 'data_dir' argument of the 'load_dataset()' function as follows:",
"## Contact\n\nThe original authors host this dataset on GitHub here: URL\nYou can submit inquiries to: redteam@URL"
]
| [
"TAGS\n#license-other #human-feedback #DPO #rlhf #Llama 2 #arxiv-2204.05862 #region-us \n",
"# DPO formatted Helpful and Harmless RLHF Dataset\nThis dataset is built from Anthropic's hh-rlhf dataset.\n\nIt is modified as follows:\n- The prompt formatting is switched to the Llama 2 format with [INST] and [/INST]\n- The data is split into prompt, chosen and rejected rows, as required by HuggingFace's DPO trainer.\n\nPurchase access to this dataset here. Purchase entitles the user to make use of the dataset for training large language models.\n\n---\n\n- The original dataset card follows below:",
"# Dataset Card for HH-RLHF",
"## Dataset Summary\n\n\nThis repository provides access to two different kinds of data:\n1. Human preference data about helpfulness and harmlessness from Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback. These data are meant to train preference (or reward) models for subsequent RLHF training. These data are *not* meant for supervised training of dialogue agents. Training dialogue agents on these data is likely to lead to harmful models and this shold be avoided.\n2. Human-generated and annotated red teaming dialogues from Red Teaming Language Models to Reduce Harms: Methods, Scaling Behaviors, and Lessons Learned. These data are meant to understand how crowdworkers red team models and what types of red team attacks are succesful or not. The data are *not* meant for fine-tuning or preference modeling (use the data above for preference modeling). These data are entire transcripts of conversations that are derived from the harmlessness preference modeling data described above, where only the chosen response is incorporated into the overall transcript. Furthermore, the transcripts are annotated with human and automated measurements of how harmful the overall dialogues are.\n\nDisclaimer: The data (especially the harmlessness preference data and the red team data) contain content that may be offensive or upsetting. Topics include, but are not limited to, discriminatory language and discussions of abuse, violence, self-harm, exploitation, and other potentially upsetting subject matter. Please only engage with the data in accordance with your own personal risk tolerance. The data are intended for research purposes, especially research that can make models *less* harmful. The views expressed in the data do not reflect the views of Anthropic or any of its employees. As mentioned above, these data are *not* intended for training dialogue agents as this will likely lead to harmful model behavior. \n\nEach of these datasets are described further below.",
"## Human preference data about helpfulness and harmlessness (PM Data)\n\nThe data are described in the paper: Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback. If you find the data useful, please cite the paper. The data format is very simple -- each line of the jsonl files contains a pair of texts, one \"chosen\" and one \"rejected\".\n\nFor helpfulness, the data are grouped into train/test splits in three tranches: from our base models (context-distilled 52B language models), via rejection sampling (mostly with best-of-16 sampling) against an early preference model, and a dataset sampled during our iterated \"online\" process. \n\nFor harmlessness, the data are only collected for our base models, but otherwise formatted in the same way.\n\nDetails about the data collection process and crowdworker population can be found in the paper, specifically in section 2 and appendix D.",
"## Red teaming data (not PM Data)\n\nThe data are described in the paper: Red Teaming Language Models to Reduce Harms: Methods, Scaling Behaviors, and Lessons Learned. If you find the data useful, please cite the paper. At a high level these data can be examined to understand what succesful red team attempts look like. \n\nDetails about the data and data collection procedures can be found in the Datasheet in the appendix of the paper. \n\nEach line of the jsonl file contains a dictionary with the following fields:\n- 'transcript' a text transcript of a conversation between a human adversary (red team member) and an AI assistant\n- 'min_harmlessness_score_transcript' a real value score of the harmlessness of the AI assistant (lower is more harmful) as obtained from a preference model\n- 'num_params' number of parameters in the language model powering the AI assistant\n- 'model_type' type of model powering the AI assistant\n- 'rating' the red team member's rating of how successful they were at breaking the AI assistant (Likert scale, higher is more successful) \n- 'task_description' a short text description written by the red team member about how they tried to red team the AI assistant\n- 'task_description_harmlessness_score' a real value score of the harmlessness of the task description (lower is more harmful) as obtained from a preference model\n- 'red_team_member_id' an arbitrary identifier of the red team member. one red team member can generate multiple red team attacks\n- 'is_upworker' a binary indicator that is true if the red team member was from the crowd platform Upwork or false if they were from MTurk\n- 'tags' a list of up to 6 tags per transcript. tags are short descriptions of the red team attempts generated by crowdworkers who reviewed red team data post-hoc. tags were only provided for a random sample of 1000 red team attempts for two of four model types.",
"## Usage\n\nEach of the above datasets is located in a separate sub-directory. To load an individual subset, use the 'data_dir' argument of the 'load_dataset()' function as follows:",
"## Contact\n\nThe original authors host this dataset on GitHub here: URL\nYou can submit inquiries to: redteam@URL"
]
| [
36,
142,
10,
442,
220,
469,
50,
29
]
| [
"passage: TAGS\n#license-other #human-feedback #DPO #rlhf #Llama 2 #arxiv-2204.05862 #region-us \n# DPO formatted Helpful and Harmless RLHF Dataset\nThis dataset is built from Anthropic's hh-rlhf dataset.\n\nIt is modified as follows:\n- The prompt formatting is switched to the Llama 2 format with [INST] and [/INST]\n- The data is split into prompt, chosen and rejected rows, as required by HuggingFace's DPO trainer.\n\nPurchase access to this dataset here. Purchase entitles the user to make use of the dataset for training large language models.\n\n---\n\n- The original dataset card follows below:# Dataset Card for HH-RLHF",
"passage: ## Dataset Summary\n\n\nThis repository provides access to two different kinds of data:\n1. Human preference data about helpfulness and harmlessness from Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback. These data are meant to train preference (or reward) models for subsequent RLHF training. These data are *not* meant for supervised training of dialogue agents. Training dialogue agents on these data is likely to lead to harmful models and this shold be avoided.\n2. Human-generated and annotated red teaming dialogues from Red Teaming Language Models to Reduce Harms: Methods, Scaling Behaviors, and Lessons Learned. These data are meant to understand how crowdworkers red team models and what types of red team attacks are succesful or not. The data are *not* meant for fine-tuning or preference modeling (use the data above for preference modeling). These data are entire transcripts of conversations that are derived from the harmlessness preference modeling data described above, where only the chosen response is incorporated into the overall transcript. Furthermore, the transcripts are annotated with human and automated measurements of how harmful the overall dialogues are.\n\nDisclaimer: The data (especially the harmlessness preference data and the red team data) contain content that may be offensive or upsetting. Topics include, but are not limited to, discriminatory language and discussions of abuse, violence, self-harm, exploitation, and other potentially upsetting subject matter. Please only engage with the data in accordance with your own personal risk tolerance. The data are intended for research purposes, especially research that can make models *less* harmful. The views expressed in the data do not reflect the views of Anthropic or any of its employees. As mentioned above, these data are *not* intended for training dialogue agents as this will likely lead to harmful model behavior. \n\nEach of these datasets are described further below.## Human preference data about helpfulness and harmlessness (PM Data)\n\nThe data are described in the paper: Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback. If you find the data useful, please cite the paper. The data format is very simple -- each line of the jsonl files contains a pair of texts, one \"chosen\" and one \"rejected\".\n\nFor helpfulness, the data are grouped into train/test splits in three tranches: from our base models (context-distilled 52B language models), via rejection sampling (mostly with best-of-16 sampling) against an early preference model, and a dataset sampled during our iterated \"online\" process. \n\nFor harmlessness, the data are only collected for our base models, but otherwise formatted in the same way.\n\nDetails about the data collection process and crowdworker population can be found in the paper, specifically in section 2 and appendix D."
]
|
0de0559e25ceb7317b2ef8874550b0ccb198e7ad | # Dataset Card for "GVLM_Cropped_256"
This is an official release of the GVLM-CD dataset. In this version, we cropped the images into patches of size 256*256.
If you use GVLM-CD in a scientific publication, we would appreciate using the following citations:
```
@article{zhang2023cross,
title={Cross-domain landslide mapping from large-scale remote sensing images using prototype-guided domain-aware progressive representation learning},
author={Zhang, Xiaokang and Yu, Weikang and Pun, Man-On and Shi, Wenzhong},
journal={ISPRS Journal of Photogrammetry and Remote Sensing},
volume={197},
pages={1--17},
year={2023},
publisher={Elsevier}
}
```
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | ericyu/GVLM_Cropped_256 | [
"region:us"
]
| 2023-11-13T11:01:52+00:00 | {"dataset_info": {"features": [{"name": "imageA", "dtype": "image"}, {"name": "imageB", "dtype": "image"}, {"name": "label", "dtype": "image"}], "splits": [{"name": "train", "num_bytes": 112278146.48, "num_examples": 4558}, {"name": "test", "num_bytes": 37388998.684, "num_examples": 1519}, {"name": "val", "num_bytes": 37425501.773, "num_examples": 1519}], "download_size": 186554180, "dataset_size": 187092646.937}} | 2023-11-14T15:05:40+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "GVLM_Cropped_256"
This is an official release of the GVLM-CD dataset. In this version, we cropped the images into patches of size 256*256.
If you use GVLM-CD in a scientific publication, we would appreciate using the following citations:
More Information needed | [
"# Dataset Card for \"GVLM_Cropped_256\"\n\nThis is an official release of the GVLM-CD dataset. In this version, we cropped the images into patches of size 256*256.\n\nIf you use GVLM-CD in a scientific publication, we would appreciate using the following citations:\n\n\n\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"GVLM_Cropped_256\"\n\nThis is an official release of the GVLM-CD dataset. In this version, we cropped the images into patches of size 256*256.\n\nIf you use GVLM-CD in a scientific publication, we would appreciate using the following citations:\n\n\n\n\nMore Information needed"
]
| [
6,
73
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"GVLM_Cropped_256\"\n\nThis is an official release of the GVLM-CD dataset. In this version, we cropped the images into patches of size 256*256.\n\nIf you use GVLM-CD in a scientific publication, we would appreciate using the following citations:\n\n\n\n\nMore Information needed"
]
|
187b040413331b0271a25643d4a3048d52d31b70 | # Dataset Card for "allergy_type"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | Thefoodprocessor/allergy_type | [
"region:us"
]
| 2023-11-13T11:04:35+00:00 | {"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "recipe", "dtype": "string"}, {"name": "allergy_type", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 108603536, "num_examples": 74465}], "download_size": 55013888, "dataset_size": 108603536}} | 2023-11-13T11:04:38+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "allergy_type"
More Information needed | [
"# Dataset Card for \"allergy_type\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"allergy_type\"\n\nMore Information needed"
]
| [
6,
14
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"allergy_type\"\n\nMore Information needed"
]
|
0292d99962ea4c5b3d57ce8579286556efb94261 | # Dataset Card for "fm-updates-llama-7b"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | coastalcph/fm-updates-llama-7b | [
"region:us"
]
| 2023-11-13T11:04:59+00:00 | {"configs": [{"config_name": "default", "data_files": [{"split": "test", "path": "data/test-*"}, {"split": "validation", "path": "data/validation-*"}]}], "dataset_info": {"features": [{"name": "query", "struct": [{"name": "label", "dtype": "string"}, {"name": "objects", "list": [{"name": "aliases", "sequence": "string"}, {"name": "label", "dtype": "string"}, {"name": "qid", "dtype": "string"}]}, {"name": "qid", "dtype": "string"}, {"name": "rel_id", "dtype": "string"}, {"name": "relation", "dtype": "string"}]}, {"name": "prediction", "struct": [{"name": "predictions", "list": [{"name": "answer", "dtype": "string"}, {"name": "first_token_probability", "dtype": "float64"}, {"name": "per_token_probability", "sequence": "float64"}, {"name": "perplexity", "dtype": "float64"}]}, {"name": "query", "dtype": "string"}]}, {"name": "f1", "dtype": "float64"}, {"name": "relation", "dtype": "string"}, {"name": "type", "dtype": "string"}, {"name": "original_answer", "dtype": "string"}, {"name": "updates", "sequence": "string"}], "splits": [{"name": "test", "num_bytes": 440727.6757775683, "num_examples": 480}, {"name": "validation", "num_bytes": 46827.315551366635, "num_examples": 51}], "download_size": 380771, "dataset_size": 487554.99132893496}} | 2023-11-21T16:56:04+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "fm-updates-llama-7b"
More Information needed | [
"# Dataset Card for \"fm-updates-llama-7b\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"fm-updates-llama-7b\"\n\nMore Information needed"
]
| [
6,
20
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| [
"passage: TAGS\n#region-us \n# Dataset Card for \"fm-updates-llama-7b\"\n\nMore Information needed"
]
|
f467940a55f779f22b4c0843687770196ba6c830 | # Dataset Card for "wine_type"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | Eitanli/wine_type | [
"region:us"
]
| 2023-11-13T11:07:40+00:00 | {"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "recipe", "dtype": "string"}, {"name": "wine_type", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 110426494, "num_examples": 74465}], "download_size": 54694496, "dataset_size": 110426494}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]} | 2023-11-17T08:29:21+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "wine_type"
More Information needed | [
"# Dataset Card for \"wine_type\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"wine_type\"\n\nMore Information needed"
]
| [
6,
14
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"wine_type\"\n\nMore Information needed"
]
|
7f628f3e707513e73eb4031586f1f821c1214b57 | # Dataset Card for "fm-updates-llama2-7b"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | coastalcph/fm-updates-llama2-7b | [
"region:us"
]
| 2023-11-13T11:08:09+00:00 | {"configs": [{"config_name": "default", "data_files": [{"split": "test", "path": "data/test-*"}, {"split": "validation", "path": "data/validation-*"}]}], "dataset_info": {"features": [{"name": "query", "struct": [{"name": "label", "dtype": "string"}, {"name": "objects", "list": [{"name": "aliases", "sequence": "string"}, {"name": "label", "dtype": "string"}, {"name": "qid", "dtype": "string"}]}, {"name": "qid", "dtype": "string"}, {"name": "rel_id", "dtype": "string"}, {"name": "relation", "dtype": "string"}]}, {"name": "prediction", "struct": [{"name": "predictions", "list": [{"name": "answer", "dtype": "string"}, {"name": "first_token_probability", "dtype": "float64"}, {"name": "per_token_probability", "sequence": "float64"}, {"name": "perplexity", "dtype": "float64"}]}, {"name": "query", "dtype": "string"}]}, {"name": "f1", "dtype": "float64"}, {"name": "relation", "dtype": "string"}, {"name": "type", "dtype": "string"}, {"name": "original_answer", "dtype": "string"}, {"name": "updates", "sequence": "string"}], "splits": [{"name": "test", "num_bytes": 442069.6082474227, "num_examples": 492}, {"name": "validation", "num_bytes": 48519.83505154639, "num_examples": 54}], "download_size": 385039, "dataset_size": 490589.44329896907}} | 2023-11-21T16:57:59+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "fm-updates-llama2-7b"
More Information needed | [
"# Dataset Card for \"fm-updates-llama2-7b\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"fm-updates-llama2-7b\"\n\nMore Information needed"
]
| [
6,
21
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"fm-updates-llama2-7b\"\n\nMore Information needed"
]
|
43f3f0a1fc45c1c9a3cdab212694609f5c1396b4 | # Dataset Card for "agent_action_small_34_class"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | Raihan004/agent_action_small_34_class | [
"region:us"
]
| 2023-11-13T11:14:05+00:00 | {"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "test", "path": "data/test-*"}]}], "dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "\u0995\u09c1\u0995\u09c1\u09b0_\u0995\u09ae\u09cd\u09aa\u09bf\u0989\u099f\u09be\u09b0_\u09ac\u09cd\u09af\u09ac\u09b9\u09be\u09b0_\u0995\u09b0\u09be", "1": "\u0995\u09c1\u0995\u09c1\u09b0_\u0996\u09be\u0993\u09af\u09bc\u09be", "2": "\u0995\u09c1\u0995\u09c1\u09b0_\u0996\u09c7\u09b2\u09be_\u0995\u09b0\u09be", "3": "\u0995\u09c1\u0995\u09c1\u09b0_\u0998\u09c1\u09ae\u09be\u09a8\u09c7\u09be", "4": "\u0995\u09c1\u0995\u09c1\u09b0_\u09aa\u09a1\u09bc\u09be", "5": "\u0995\u09c1\u0995\u09c1\u09b0_\u09aa\u09be\u09a8_\u0995\u09b0\u09be", "6": "\u0995\u09c1\u0995\u09c1\u09b0_\u09b9\u09be\u0981\u099f\u09be", "7": "\u099b\u09c7\u09b2\u09c7_\u0995\u09a5\u09be_\u09ac\u09b2\u09be", "8": "\u099b\u09c7\u09b2\u09c7_\u0995\u09ae\u09cd\u09aa\u09bf\u0989\u099f\u09be\u09b0_\u09ac\u09cd\u09af\u09ac\u09b9\u09be\u09b0_\u0995\u09b0\u09be", "9": "\u099b\u09c7\u09b2\u09c7_\u0996\u09be\u0993\u09af\u09bc\u09be", "10": "\u099b\u09c7\u09b2\u09c7_\u0996\u09c7\u09b2\u09be_\u0995\u09b0\u09be", "11": "\u099b\u09c7\u09b2\u09c7_\u0998\u09c1\u09ae\u09be\u09a8\u09c7\u09be", "12": "\u099b\u09c7\u09b2\u09c7_\u09aa\u09a1\u09bc\u09be", "13": "\u099b\u09c7\u09b2\u09c7_\u09aa\u09be\u09a8_\u0995\u09b0\u09be", "14": "\u099b\u09c7\u09b2\u09c7_\u09b0\u09be\u09a8\u09cd\u09a8\u09be_\u0995\u09b0\u09be", "15": "\u099b\u09c7\u09b2\u09c7_\u09b2\u09c7\u0996\u09be", "16": "\u099b\u09c7\u09b2\u09c7_\u09b9\u09be\u0981\u099f\u09be", "17": "\u09ac\u09bf\u09a1\u09bc\u09be\u09b2_\u0995\u09ae\u09cd\u09aa\u09bf\u0989\u099f\u09be\u09b0_\u09ac\u09cd\u09af\u09ac\u09b9\u09be\u09b0_\u0995\u09b0\u09be", "18": "\u09ac\u09bf\u09a1\u09bc\u09be\u09b2_\u0996\u09be\u0993\u09af\u09bc\u09be", "19": "\u09ac\u09bf\u09a1\u09bc\u09be\u09b2_\u0996\u09c7\u09b2\u09be_\u0995\u09b0\u09be", "20": "\u09ac\u09bf\u09a1\u09bc\u09be\u09b2_\u0998\u09c1\u09ae\u09be\u09a8\u09c7\u09be", "21": "\u09ac\u09bf\u09a1\u09bc\u09be\u09b2_\u09aa\u09a1\u09bc\u09be", "22": "\u09ac\u09bf\u09a1\u09bc\u09be\u09b2_\u09aa\u09be\u09a8_\u0995\u09b0\u09be", "23": "\u09ac\u09bf\u09a1\u09bc\u09be\u09b2_\u09b9\u09be\u0981\u099f\u09be", "24": "\u09ae\u09c7\u09af\u09bc\u09c7_\u0995\u09a5\u09be_\u09ac\u09b2\u09be", "25": "\u09ae\u09c7\u09af\u09bc\u09c7_\u0995\u09ae\u09cd\u09aa\u09bf\u0989\u099f\u09be\u09b0_\u09ac\u09cd\u09af\u09ac\u09b9\u09be\u09b0_\u0995\u09b0\u09be", "26": "\u09ae\u09c7\u09af\u09bc\u09c7_\u0996\u09be\u0993\u09af\u09bc\u09be", "27": "\u09ae\u09c7\u09af\u09bc\u09c7_\u0996\u09c7\u09b2\u09be_\u0995\u09b0\u09be", "28": "\u09ae\u09c7\u09af\u09bc\u09c7_\u0998\u09c1\u09ae\u09be\u09a8\u09c7\u09be", "29": "\u09ae\u09c7\u09af\u09bc\u09c7_\u09aa\u09a1\u09bc\u09be", "30": "\u09ae\u09c7\u09af\u09bc\u09c7_\u09aa\u09be\u09a8_\u0995\u09b0\u09be", "31": "\u09ae\u09c7\u09af\u09bc\u09c7_\u09b0\u09be\u09a8\u09cd\u09a8\u09be_\u0995\u09b0\u09be", "32": "\u09ae\u09c7\u09af\u09bc\u09c7_\u09b2\u09c7\u0996\u09be", "33": "\u09ae\u09c7\u09af\u09bc\u09c7_\u09b9\u09be\u0981\u099f\u09be"}}}}], "splits": [{"name": "train", "num_bytes": 424468988.641, "num_examples": 3937}, {"name": "test", "num_bytes": 68710204.0, "num_examples": 695}], "download_size": 492649945, "dataset_size": 493179192.641}} | 2023-11-13T12:50:04+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "agent_action_small_34_class"
More Information needed | [
"# Dataset Card for \"agent_action_small_34_class\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"agent_action_small_34_class\"\n\nMore Information needed"
]
| [
6,
20
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"agent_action_small_34_class\"\n\nMore Information needed"
]
|
4c949943f89c53d4a9773db70dcb751fec0a7ca0 | # Dataset Card for "guanaco-llama2-1k"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | ndubey/guanaco-llama2-1k | [
"region:us"
]
| 2023-11-13T11:16:05+00:00 | {"dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1654448, "num_examples": 1000}], "download_size": 966693, "dataset_size": 1654448}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]} | 2023-11-13T11:16:07+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "guanaco-llama2-1k"
More Information needed | [
"# Dataset Card for \"guanaco-llama2-1k\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"guanaco-llama2-1k\"\n\nMore Information needed"
]
| [
6,
18
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"guanaco-llama2-1k\"\n\nMore Information needed"
]
|
19f51ce28f348024842eed368eb41104ecc21c12 | # Dataset Card for "doc-boundary"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | vigneshgs7/doc-boundary | [
"region:us"
]
| 2023-11-13T11:26:33+00:00 | {"dataset_info": {"features": [{"name": "pixel_values", "dtype": "image"}, {"name": "label", "dtype": "image"}], "splits": [{"name": "train", "num_bytes": 1091849664.0, "num_examples": 22}], "download_size": 69528467, "dataset_size": 1091849664.0}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]} | 2023-11-14T07:09:28+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "doc-boundary"
More Information needed | [
"# Dataset Card for \"doc-boundary\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"doc-boundary\"\n\nMore Information needed"
]
| [
6,
14
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"doc-boundary\"\n\nMore Information needed"
]
|
f1ef0ccffd24b34a73bb500be4a45635e555ea47 | # Dataset Card for "militarywatchmagazine"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | orgcatorg/militarywatchmagazine | [
"region:us"
]
| 2023-11-13T11:28:32+00:00 | {"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "content", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "source_link", "dtype": "string"}, {"name": "description", "dtype": "string"}, {"name": "date", "dtype": "string"}, {"name": "category", "sequence": "string"}], "splits": [{"name": "train", "num_bytes": 792080, "num_examples": 133}], "download_size": 441406, "dataset_size": 792080}} | 2024-02-17T04:20:53+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "militarywatchmagazine"
More Information needed | [
"# Dataset Card for \"militarywatchmagazine\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"militarywatchmagazine\"\n\nMore Information needed"
]
| [
6,
15
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"militarywatchmagazine\"\n\nMore Information needed"
]
|
f06f0439b331dde48e0d4166dd67f89c0854fcad | # Dataset Card for "recipes"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | Thefoodprocessor/recipes | [
"region:us"
]
| 2023-11-13T11:33:32+00:00 | {"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "recipe", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 105767040, "num_examples": 74465}], "download_size": 53711472, "dataset_size": 105767040}} | 2023-11-13T11:33:36+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "recipes"
More Information needed | [
"# Dataset Card for \"recipes\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"recipes\"\n\nMore Information needed"
]
| [
6,
13
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"recipes\"\n\nMore Information needed"
]
|
359414a48268b8d7776381ae4f1121aa33a124e8 | # Dataset Card for "clean_title"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | Thefoodprocessor/clean_title | [
"region:us"
]
| 2023-11-13T11:35:48+00:00 | {"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "recipe", "dtype": "string"}, {"name": "title_cleaned", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 108673191, "num_examples": 74465}], "download_size": 55560085, "dataset_size": 108673191}} | 2023-11-13T11:35:52+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "clean_title"
More Information needed | [
"# Dataset Card for \"clean_title\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"clean_title\"\n\nMore Information needed"
]
| [
6,
14
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"clean_title\"\n\nMore Information needed"
]
|
d7d83224562ff64f6b3f327d533630706edf54d0 | # Dataset Card for "TG_QnA"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | Muthuchancoach/TG_QnA | [
"region:us"
]
| 2023-11-13T11:40:03+00:00 | {"dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 39973, "num_examples": 158}], "download_size": 10001, "dataset_size": 39973}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]} | 2023-11-15T07:07:10+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "TG_QnA"
More Information needed | [
"# Dataset Card for \"TG_QnA\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"TG_QnA\"\n\nMore Information needed"
]
| [
6,
15
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"TG_QnA\"\n\nMore Information needed"
]
|
a183460797b562a89e44e2172390ae975e8f706e | train data for dreambooth
| DaisiesX/dreambooth_pic | [
"region:us"
]
| 2023-11-13T11:46:47+00:00 | {} | 2023-11-13T12:02:45+00:00 | []
| []
| TAGS
#region-us
| train data for dreambooth
| []
| [
"TAGS\n#region-us \n"
]
| [
6
]
| [
"passage: TAGS\n#region-us \n"
]
|
bc1d4f67c4de40455457c23b838f82946f8ec151 | # Dataset Card for "army-technology"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | orgcatorg/army-technology | [
"region:us"
]
| 2023-11-13T11:56:54+00:00 | {"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "content", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "source_link", "dtype": "string"}, {"name": "description", "dtype": "string"}, {"name": "date", "dtype": "string"}, {"name": "image", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 481578, "num_examples": 108}], "download_size": 273264, "dataset_size": 481578}} | 2024-02-17T04:20:56+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "army-technology"
More Information needed | [
"# Dataset Card for \"army-technology\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"army-technology\"\n\nMore Information needed"
]
| [
6,
16
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"army-technology\"\n\nMore Information needed"
]
|
b943d0a5c7a236418e7c768bb0923bc968e73c5b |
# Dataset Card for Evaluation run of blueapple8259/TinyStories-Alpaca
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/blueapple8259/TinyStories-Alpaca
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** [email protected]
### Dataset Summary
Dataset automatically created during the evaluation run of model [blueapple8259/TinyStories-Alpaca](https://huggingface.co/blueapple8259/TinyStories-Alpaca) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_blueapple8259__TinyStories-Alpaca_public",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-11-13T12:08:32.889015](https://huggingface.co/datasets/open-llm-leaderboard/details_blueapple8259__TinyStories-Alpaca_public/blob/main/results_2023-11-13T12-08-32.889015.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"acc": 0.2343052459270292,
"acc_stderr": 0.030014283954142254,
"acc_norm": 0.2339194036543238,
"acc_norm_stderr": 0.030804772038430715,
"mc1": 0.23745410036719705,
"mc1_stderr": 0.014896277441041834,
"mc2": 0.46675301460809676,
"mc2_stderr": 0.016264340534335325,
"em": 0.0012583892617449664,
"em_stderr": 0.00036305608931191567,
"f1": 0.008077810402684559,
"f1_stderr": 0.000561047245736677
},
"harness|arc:challenge|25": {
"acc": 0.20392491467576793,
"acc_stderr": 0.011774262478702259,
"acc_norm": 0.23976109215017063,
"acc_norm_stderr": 0.012476304127453961
},
"harness|hellaswag|10": {
"acc": 0.25781716789484166,
"acc_stderr": 0.004365388351563101,
"acc_norm": 0.24915355506871142,
"acc_norm_stderr": 0.004316389476434519
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.22,
"acc_stderr": 0.04163331998932268,
"acc_norm": 0.22,
"acc_norm_stderr": 0.04163331998932268
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.2962962962962963,
"acc_stderr": 0.03944624162501116,
"acc_norm": 0.2962962962962963,
"acc_norm_stderr": 0.03944624162501116
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.17763157894736842,
"acc_stderr": 0.031103182383123398,
"acc_norm": 0.17763157894736842,
"acc_norm_stderr": 0.031103182383123398
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.29,
"acc_stderr": 0.04560480215720683,
"acc_norm": 0.29,
"acc_norm_stderr": 0.04560480215720683
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.22264150943396227,
"acc_stderr": 0.0256042334708991,
"acc_norm": 0.22264150943396227,
"acc_norm_stderr": 0.0256042334708991
},
"harness|hendrycksTest-college_biology|5": {
"acc": 0.2152777777777778,
"acc_stderr": 0.034370793441061344,
"acc_norm": 0.2152777777777778,
"acc_norm_stderr": 0.034370793441061344
},
"harness|hendrycksTest-college_chemistry|5": {
"acc": 0.2,
"acc_stderr": 0.04020151261036845,
"acc_norm": 0.2,
"acc_norm_stderr": 0.04020151261036845
},
"harness|hendrycksTest-college_computer_science|5": {
"acc": 0.17,
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}
```
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed] | open-llm-leaderboard/details_blueapple8259__TinyStories-Alpaca | [
"region:us"
]
| 2023-11-13T12:10:48+00:00 | {"pretty_name": "Evaluation run of blueapple8259/TinyStories-Alpaca", "dataset_summary": "Dataset automatically created during the evaluation run of model [blueapple8259/TinyStories-Alpaca](https://huggingface.co/blueapple8259/TinyStories-Alpaca) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\nTo load the details from a run, you can for instance do the following:\n```python\nfrom datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_blueapple8259__TinyStories-Alpaca_public\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-11-13T12:08:32.889015](https://huggingface.co/datasets/open-llm-leaderboard/details_blueapple8259__TinyStories-Alpaca_public/blob/main/results_2023-11-13T12-08-32.889015.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):\n\n```python\n{\n \"all\": {\n \"acc\": 0.2343052459270292,\n \"acc_stderr\": 0.030014283954142254,\n \"acc_norm\": 0.2339194036543238,\n \"acc_norm_stderr\": 0.030804772038430715,\n \"mc1\": 0.23745410036719705,\n \"mc1_stderr\": 0.014896277441041834,\n \"mc2\": 0.46675301460809676,\n \"mc2_stderr\": 0.016264340534335325,\n \"em\": 0.0012583892617449664,\n \"em_stderr\": 0.00036305608931191567,\n \"f1\": 0.008077810402684559,\n \"f1_stderr\": 0.000561047245736677\n },\n \"harness|arc:challenge|25\": {\n \"acc\": 0.20392491467576793,\n \"acc_stderr\": 0.011774262478702259,\n \"acc_norm\": 0.23976109215017063,\n \"acc_norm_stderr\": 0.012476304127453961\n },\n \"harness|hellaswag|10\": {\n \"acc\": 0.25781716789484166,\n \"acc_stderr\": 0.004365388351563101,\n \"acc_norm\": 0.24915355506871142,\n \"acc_norm_stderr\": 0.004316389476434519\n },\n \"harness|hendrycksTest-abstract_algebra|5\": {\n \"acc\": 0.22,\n \"acc_stderr\": 0.04163331998932268,\n \"acc_norm\": 0.22,\n \"acc_norm_stderr\": 0.04163331998932268\n },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.2962962962962963,\n \"acc_stderr\": 0.03944624162501116,\n \"acc_norm\": 0.2962962962962963,\n \"acc_norm_stderr\": 0.03944624162501116\n },\n \"harness|hendrycksTest-astronomy|5\": {\n \"acc\": 0.17763157894736842,\n \"acc_stderr\": 0.031103182383123398,\n \"acc_norm\": 0.17763157894736842,\n \"acc_norm_stderr\": 0.031103182383123398\n },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.29,\n \"acc_stderr\": 0.04560480215720683,\n \"acc_norm\": 0.29,\n \"acc_norm_stderr\": 0.04560480215720683\n },\n \"harness|hendrycksTest-clinical_knowledge|5\": {\n \"acc\": 0.22264150943396227,\n \"acc_stderr\": 0.0256042334708991,\n \"acc_norm\": 0.22264150943396227,\n \"acc_norm_stderr\": 0.0256042334708991\n },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.2152777777777778,\n \"acc_stderr\": 0.034370793441061344,\n \"acc_norm\": 0.2152777777777778,\n \"acc_norm_stderr\": 0.034370793441061344\n },\n \"harness|hendrycksTest-college_chemistry|5\": {\n \"acc\": 0.2,\n \"acc_stderr\": 0.04020151261036845,\n \"acc_norm\": 0.2,\n \"acc_norm_stderr\": 0.04020151261036845\n },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\": 0.17,\n \"acc_stderr\": 0.0377525168068637,\n \"acc_norm\": 0.17,\n \"acc_norm_stderr\": 0.0377525168068637\n },\n \"harness|hendrycksTest-college_mathematics|5\": {\n \"acc\": 0.25,\n \"acc_stderr\": 0.04351941398892446,\n \"acc_norm\": 0.25,\n \"acc_norm_stderr\": 0.04351941398892446\n },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.2543352601156069,\n \"acc_stderr\": 0.0332055644308557,\n \"acc_norm\": 0.2543352601156069,\n \"acc_norm_stderr\": 0.0332055644308557\n },\n \"harness|hendrycksTest-college_physics|5\": {\n \"acc\": 0.22549019607843138,\n \"acc_stderr\": 0.041583075330832865,\n \"acc_norm\": 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# Dataset Card for Evaluation run of blueapple8259/TinyStories-Alpaca
## Dataset Description
- Homepage:
- Repository: URL
- Paper:
- Leaderboard: URL
- Point of Contact: clementine@URL
### Dataset Summary
Dataset automatically created during the evaluation run of model blueapple8259/TinyStories-Alpaca on the Open LLM Leaderboard.
The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).
To load the details from a run, you can for instance do the following:
## Latest results
These are the latest results from run 2023-11-13T12:08:32.889015(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
### Supported Tasks and Leaderboards
### Languages
## Dataset Structure
### Data Instances
### Data Fields
### Data Splits
## Dataset Creation
### Curation Rationale
### Source Data
#### Initial Data Collection and Normalization
#### Who are the source language producers?
### Annotations
#### Annotation process
#### Who are the annotators?
### Personal and Sensitive Information
## Considerations for Using the Data
### Social Impact of Dataset
### Discussion of Biases
### Other Known Limitations
## Additional Information
### Dataset Curators
### Licensing Information
### Contributions
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09159b8747488c2486a4fca46e66851150425ab3 | Use only for Alignment research. NOETI is not responsible for what you might do with it. | NobodyExistsOnTheInternet/ToxicQAv1.5 | [
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| 2023-11-13T12:11:57+00:00 | {"license": "mit", "tags": ["not-for-all-audiences"]} | 2024-01-10T14:28:58+00:00 | []
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#license-mit #not-for-all-audiences #region-us
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7e6fc001a315c7aafd7a64c44bb31b788ae76709 | # Translated STS dataset to Norwegian Bokmål
Machine translated using the *No language left behind* model series, specifically the 1.3B variant: https://huggingface.co/facebook/nllb-200-distilled-1.3B | tollefj/biosses-sts-NOB | [
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| 2023-11-13T12:21:55+00:00 | {"license": "cc-by-4.0"} | 2024-01-06T12:26:49+00:00 | []
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#license-cc-by-4.0 #region-us
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Machine translated using the *No language left behind* model series, specifically the 1.3B variant: URL | [
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8c3f5c5879b50ccaf190e3700421a4c688f2f4a1 | # Translated STS dataset to Norwegian Bokmål
Machine translated using the *No language left behind* model series, specifically the 1.3B variant: https://huggingface.co/facebook/nllb-200-distilled-1.3B | tollefj/sickr-sts-NOB | [
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| 2023-11-13T12:25:18+00:00 | {"license": "cc-by-4.0"} | 2024-01-06T12:27:00+00:00 | []
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#license-cc-by-4.0 #region-us
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Machine translated using the *No language left behind* model series, specifically the 1.3B variant: URL | [
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1fee40e194f3a5f78aa6d832809e98c1d8716e68 | # Translated STS dataset to Norwegian Bokmål
Machine translated using the *No language left behind* model series, specifically the 1.3B variant: https://huggingface.co/facebook/nllb-200-distilled-1.3B | tollefj/sts12-sts-NOB | [
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| 2023-11-13T12:28:29+00:00 | {"license": "cc-by-4.0"} | 2024-01-06T12:26:12+00:00 | []
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Machine translated using the *No language left behind* model series, specifically the 1.3B variant: URL | [
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fc57bddeab3fee984b6895fdc1f731183849da6b | # Translated STS dataset to Norwegian Bokmål
Machine translated using the *No language left behind* model series, specifically the 1.3B variant: https://huggingface.co/facebook/nllb-200-distilled-1.3B | tollefj/sts13-sts-NOB | [
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| 2023-11-13T12:29:18+00:00 | {"license": "cc-by-4.0"} | 2024-01-06T12:26:21+00:00 | []
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Machine translated using the *No language left behind* model series, specifically the 1.3B variant: URL | [
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17dd39d2cc2ccb2a141f6b1e5d886f4c2e2ba9c4 | # Translated STS dataset to Norwegian Bokmål
Machine translated using the *No language left behind* model series, specifically the 1.3B variant: https://huggingface.co/facebook/nllb-200-distilled-1.3B | tollefj/sts14-sts-NOB | [
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| 2023-11-13T12:31:01+00:00 | {"license": "cc-by-4.0"} | 2024-01-06T12:26:30+00:00 | []
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#license-cc-by-4.0 #region-us
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Machine translated using the *No language left behind* model series, specifically the 1.3B variant: URL | [
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2b07d53b5dfcadbe5ac345d6c35c62d61a3b3968 | # Dataset Card for "law_court_opinion_100"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | zxvix/law_court_opinion_100 | [
"region:us"
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| 2023-11-13T12:31:22+00:00 | {"configs": [{"config_name": "default", "data_files": [{"split": "test", "path": "data/test-*"}]}], "dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "created_timestamp", "dtype": "string"}, {"name": "downloaded_timestamp", "dtype": "string"}, {"name": "url", "dtype": "string"}], "splits": [{"name": "test", "num_bytes": 98368, "num_examples": 100}], "download_size": 62128, "dataset_size": 98368}} | 2023-11-13T12:31:26+00:00 | []
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More Information needed | [
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2b0259a15befce46bb30a3fb430de4b68b38eb95 | # Dataset Card for "mistral-intent-data"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | pankajemplay/mistral-intent-data | [
"region:us"
]
| 2023-11-13T12:32:15+00:00 | {"dataset_info": {"features": [{"name": "User Query", "dtype": "string"}, {"name": "Intent", "dtype": "string"}, {"name": "id type", "dtype": "string"}, {"name": "id value", "dtype": "string"}, {"name": "id slot filled", "dtype": "bool"}, {"name": "Task", "dtype": "string"}, {"name": "task slot filled", "dtype": "bool"}, {"name": "Bot Response", "dtype": "string"}, {"name": "text", "dtype": "string"}, {"name": "__index_level_0__", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 853957, "num_examples": 1171}], "download_size": 188944, "dataset_size": 853957}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]} | 2023-11-13T12:32:17+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "mistral-intent-data"
More Information needed | [
"# Dataset Card for \"mistral-intent-data\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"mistral-intent-data\"\n\nMore Information needed"
]
| [
6,
17
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"mistral-intent-data\"\n\nMore Information needed"
]
|
f1db002815d4c7d597fb216df1bc32b86cf304f2 | # Translated STS dataset to Norwegian Bokmål
Machine translated using the *No language left behind* model series, specifically the 1.3B variant: https://huggingface.co/facebook/nllb-200-distilled-1.3B | tollefj/sts15-sts-NOB | [
"license:cc-by-4.0",
"region:us"
]
| 2023-11-13T12:32:32+00:00 | {"license": "cc-by-4.0"} | 2024-01-06T12:26:37+00:00 | []
| []
| TAGS
#license-cc-by-4.0 #region-us
| # Translated STS dataset to Norwegian Bokmål
Machine translated using the *No language left behind* model series, specifically the 1.3B variant: URL | [
"# Translated STS dataset to Norwegian Bokmål\nMachine translated using the *No language left behind* model series, specifically the 1.3B variant: URL"
]
| [
"TAGS\n#license-cc-by-4.0 #region-us \n",
"# Translated STS dataset to Norwegian Bokmål\nMachine translated using the *No language left behind* model series, specifically the 1.3B variant: URL"
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| [
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34
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]
|
97c476294b11109eda9104cdb8b0ab2c95728aa6 | # Translated STS dataset to Norwegian Bokmål
Machine translated using the *No language left behind* model series, specifically the 1.3B variant: https://huggingface.co/facebook/nllb-200-distilled-1.3B | tollefj/sts16-sts-NOB | [
"license:cc-by-4.0",
"region:us"
]
| 2023-11-13T12:33:15+00:00 | {"license": "cc-by-4.0"} | 2024-01-06T12:26:43+00:00 | []
| []
| TAGS
#license-cc-by-4.0 #region-us
| # Translated STS dataset to Norwegian Bokmål
Machine translated using the *No language left behind* model series, specifically the 1.3B variant: URL | [
"# Translated STS dataset to Norwegian Bokmål\nMachine translated using the *No language left behind* model series, specifically the 1.3B variant: URL"
]
| [
"TAGS\n#license-cc-by-4.0 #region-us \n",
"# Translated STS dataset to Norwegian Bokmål\nMachine translated using the *No language left behind* model series, specifically the 1.3B variant: URL"
]
| [
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34
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| [
"passage: TAGS\n#license-cc-by-4.0 #region-us \n# Translated STS dataset to Norwegian Bokmål\nMachine translated using the *No language left behind* model series, specifically the 1.3B variant: URL"
]
|
cd46ae41512a724a59703bd049f8945a367466e7 | # Dataset Card for "bulgarianmilitary"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | orgcatorg/bulgarianmilitary | [
"region:us"
]
| 2023-11-13T12:55:38+00:00 | {"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "content", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "source_link", "dtype": "string"}, {"name": "description", "dtype": "string"}, {"name": "date", "dtype": "string"}, {"name": "image", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1666371, "num_examples": 262}], "download_size": 940004, "dataset_size": 1666371}} | 2024-02-17T04:21:01+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "bulgarianmilitary"
More Information needed | [
"# Dataset Card for \"bulgarianmilitary\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"bulgarianmilitary\"\n\nMore Information needed"
]
| [
6,
15
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"bulgarianmilitary\"\n\nMore Information needed"
]
|
92cc9cb54020326f2567269ab8d9fc66ca7c0ccc | # Dataset Card for "processed_bert_dataset"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | Definite/processed_bert_dataset | [
"region:us"
]
| 2023-11-13T13:15:03+00:00 | {"dataset_info": {"features": [{"name": "input_ids", "sequence": "int32"}, {"name": "token_type_ids", "sequence": "int8"}, {"name": "attention_mask", "sequence": "int8"}, {"name": "special_tokens_mask", "sequence": "int8"}], "splits": [{"name": "train", "num_bytes": 3600000.0, "num_examples": 1000}], "download_size": 1132443, "dataset_size": 3600000.0}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]} | 2023-11-13T13:15:04+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "processed_bert_dataset"
More Information needed | [
"# Dataset Card for \"processed_bert_dataset\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"processed_bert_dataset\"\n\nMore Information needed"
]
| [
6,
17
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| [
"passage: TAGS\n#region-us \n# Dataset Card for \"processed_bert_dataset\"\n\nMore Information needed"
]
|
4be1b9e8cc5779f712d8675a8233113dd8e67bf2 | # Dataset Card for "MIS"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | ali444/MIS | [
"region:us"
]
| 2023-11-13T13:19:15+00:00 | {"dataset_info": {"features": [{"name": "passing_semester", "dtype": "string"}, {"name": "college_name", "dtype": "string"}, {"name": "prog_name", "dtype": "string"}, {"name": "cms_no", "dtype": "int64"}, {"name": "reg_no", "dtype": "string"}, {"name": "name", "dtype": "string"}, {"name": "guardian_name", "dtype": "string"}, {"name": "Intake_semester", "dtype": "string"}, {"name": "earned_cr_hrs", "dtype": "float64"}, {"name": "inter_max_12", "dtype": "float64"}, {"name": "inter_obt_12", "dtype": "float64"}, {"name": "inter_max_11", "dtype": "float64"}, {"name": "inter_obt_11", "dtype": "float64"}], "splits": [{"name": "train", "num_bytes": 625269, "num_examples": 3126}], "download_size": 147966, "dataset_size": 625269}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]} | 2023-11-13T13:19:16+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "MIS"
More Information needed | [
"# Dataset Card for \"MIS\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"MIS\"\n\nMore Information needed"
]
| [
6,
11
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| [
"passage: TAGS\n#region-us \n# Dataset Card for \"MIS\"\n\nMore Information needed"
]
|
69bfddb3a5d32897ee6b224e1c9397857616f556 | # Dataset Card for "guanaco-sharegpt-style"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | philschmid/guanaco-sharegpt-style | [
"region:us"
]
| 2023-11-13T13:23:37+00:00 | {"dataset_info": {"features": [{"name": "conversations", "list": [{"name": "from", "dtype": "string"}, {"name": "value", "dtype": "string"}]}], "splits": [{"name": "train", "num_bytes": 13979844, "num_examples": 9033}], "download_size": 8238076, "dataset_size": 13979844}} | 2023-11-13T13:24:10+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "guanaco-sharegpt-style"
More Information needed | [
"# Dataset Card for \"guanaco-sharegpt-style\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"guanaco-sharegpt-style\"\n\nMore Information needed"
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| [
6,
19
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| [
"passage: TAGS\n#region-us \n# Dataset Card for \"guanaco-sharegpt-style\"\n\nMore Information needed"
]
|
a2fd82da82cd42c31876026ef3c124540984ee16 |
# Dataset Card for Evaluation run of BEE-spoke-data/verysmol_llama-v11-KIx2
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/BEE-spoke-data/verysmol_llama-v11-KIx2
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** [email protected]
### Dataset Summary
Dataset automatically created during the evaluation run of model [BEE-spoke-data/verysmol_llama-v11-KIx2](https://huggingface.co/BEE-spoke-data/verysmol_llama-v11-KIx2) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_BEE-spoke-data__verysmol_llama-v11-KIx2_public",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-11-13T13:21:49.840481](https://huggingface.co/datasets/open-llm-leaderboard/details_BEE-spoke-data__verysmol_llama-v11-KIx2_public/blob/main/results_2023-11-13T13-21-49.840481.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
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"acc_norm": 0.25279484630397214,
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"mc1_stderr": 0.015201522246299962,
"mc2": 0.44749716634136827,
"mc2_stderr": 0.015554683095212777,
"em": 0.001153523489932886,
"em_stderr": 0.0003476179896857093,
"f1": 0.03032822986577186,
"f1_stderr": 0.0010726730256709186
},
"harness|arc:challenge|25": {
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"acc_norm": 0.22696245733788395,
"acc_norm_stderr": 0.012240491536132866
},
"harness|hellaswag|10": {
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"acc_stderr": 0.0044298311529146735,
"acc_norm": 0.27604062935670187,
"acc_norm_stderr": 0.004461235175488315
},
"harness|hendrycksTest-abstract_algebra|5": {
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"acc_norm": 0.17,
"acc_norm_stderr": 0.03775251680686371
},
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},
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},
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},
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},
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"acc_norm": 0.25,
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},
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"acc_norm_stderr": 0.014333522059217889
},
"harness|hendrycksTest-nutrition|5": {
"acc": 0.2777777777777778,
"acc_stderr": 0.02564686309713791,
"acc_norm": 0.2777777777777778,
"acc_norm_stderr": 0.02564686309713791
},
"harness|hendrycksTest-philosophy|5": {
"acc": 0.19935691318327975,
"acc_stderr": 0.022691033780549656,
"acc_norm": 0.19935691318327975,
"acc_norm_stderr": 0.022691033780549656
},
"harness|hendrycksTest-prehistory|5": {
"acc": 0.2191358024691358,
"acc_stderr": 0.023016705640262192,
"acc_norm": 0.2191358024691358,
"acc_norm_stderr": 0.023016705640262192
},
"harness|hendrycksTest-professional_accounting|5": {
"acc": 0.24822695035460993,
"acc_stderr": 0.0257700156442904,
"acc_norm": 0.24822695035460993,
"acc_norm_stderr": 0.0257700156442904
},
"harness|hendrycksTest-professional_law|5": {
"acc": 0.2470664928292047,
"acc_stderr": 0.011015752255279333,
"acc_norm": 0.2470664928292047,
"acc_norm_stderr": 0.011015752255279333
},
"harness|hendrycksTest-professional_medicine|5": {
"acc": 0.4007352941176471,
"acc_stderr": 0.0297682635289331,
"acc_norm": 0.4007352941176471,
"acc_norm_stderr": 0.0297682635289331
},
"harness|hendrycksTest-professional_psychology|5": {
"acc": 0.25163398692810457,
"acc_stderr": 0.017555818091322256,
"acc_norm": 0.25163398692810457,
"acc_norm_stderr": 0.017555818091322256
},
"harness|hendrycksTest-public_relations|5": {
"acc": 0.23636363636363636,
"acc_stderr": 0.04069306319721378,
"acc_norm": 0.23636363636363636,
"acc_norm_stderr": 0.04069306319721378
},
"harness|hendrycksTest-security_studies|5": {
"acc": 0.2163265306122449,
"acc_stderr": 0.026358916334904035,
"acc_norm": 0.2163265306122449,
"acc_norm_stderr": 0.026358916334904035
},
"harness|hendrycksTest-sociology|5": {
"acc": 0.263681592039801,
"acc_stderr": 0.031157150869355575,
"acc_norm": 0.263681592039801,
"acc_norm_stderr": 0.031157150869355575
},
"harness|hendrycksTest-us_foreign_policy|5": {
"acc": 0.3,
"acc_stderr": 0.046056618647183814,
"acc_norm": 0.3,
"acc_norm_stderr": 0.046056618647183814
},
"harness|hendrycksTest-virology|5": {
"acc": 0.25903614457831325,
"acc_stderr": 0.03410646614071857,
"acc_norm": 0.25903614457831325,
"acc_norm_stderr": 0.03410646614071857
},
"harness|hendrycksTest-world_religions|5": {
"acc": 0.2046783625730994,
"acc_stderr": 0.03094445977853321,
"acc_norm": 0.2046783625730994,
"acc_norm_stderr": 0.03094445977853321
},
"harness|truthfulqa:mc|0": {
"mc1": 0.2521419828641371,
"mc1_stderr": 0.015201522246299962,
"mc2": 0.44749716634136827,
"mc2_stderr": 0.015554683095212777
},
"harness|winogrande|5": {
"acc": 0.5153906866614049,
"acc_stderr": 0.014045826789783656
},
"harness|drop|3": {
"em": 0.001153523489932886,
"em_stderr": 0.0003476179896857093,
"f1": 0.03032822986577186,
"f1_stderr": 0.0010726730256709186
},
"harness|gsm8k|5": {
"acc": 0.003032600454890068,
"acc_stderr": 0.0015145735612245434
}
}
```
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed] | open-llm-leaderboard/details_BEE-spoke-data__verysmol_llama-v11-KIx2 | [
"region:us"
]
| 2023-11-13T13:23:44+00:00 | {"pretty_name": "Evaluation run of BEE-spoke-data/verysmol_llama-v11-KIx2", "dataset_summary": "Dataset automatically created during the evaluation run of model [BEE-spoke-data/verysmol_llama-v11-KIx2](https://huggingface.co/BEE-spoke-data/verysmol_llama-v11-KIx2) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\nTo load the details from a run, you can for instance do the following:\n```python\nfrom datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_BEE-spoke-data__verysmol_llama-v11-KIx2_public\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-11-13T13:21:49.840481](https://huggingface.co/datasets/open-llm-leaderboard/details_BEE-spoke-data__verysmol_llama-v11-KIx2_public/blob/main/results_2023-11-13T13-21-49.840481.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):\n\n```python\n{\n \"all\": {\n \"acc\": 0.25242844116774144,\n \"acc_stderr\": 0.030580549886448656,\n \"acc_norm\": 0.25279484630397214,\n \"acc_norm_stderr\": 0.03136408554761852,\n \"mc1\": 0.2521419828641371,\n \"mc1_stderr\": 0.015201522246299962,\n \"mc2\": 0.44749716634136827,\n \"mc2_stderr\": 0.015554683095212777,\n \"em\": 0.001153523489932886,\n \"em_stderr\": 0.0003476179896857093,\n \"f1\": 0.03032822986577186,\n \"f1_stderr\": 0.0010726730256709186\n },\n \"harness|arc:challenge|25\": {\n \"acc\": 0.19795221843003413,\n \"acc_stderr\": 0.011643990971573407,\n \"acc_norm\": 0.22696245733788395,\n \"acc_norm_stderr\": 0.012240491536132866\n },\n \"harness|hellaswag|10\": {\n \"acc\": 0.2698665604461263,\n \"acc_stderr\": 0.0044298311529146735,\n \"acc_norm\": 0.27604062935670187,\n \"acc_norm_stderr\": 0.004461235175488315\n },\n \"harness|hendrycksTest-abstract_algebra|5\": {\n \"acc\": 0.17,\n \"acc_stderr\": 0.03775251680686371,\n \"acc_norm\": 0.17,\n \"acc_norm_stderr\": 0.03775251680686371\n },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.26666666666666666,\n \"acc_stderr\": 0.038201699145179055,\n \"acc_norm\": 0.26666666666666666,\n \"acc_norm_stderr\": 0.038201699145179055\n },\n \"harness|hendrycksTest-astronomy|5\": {\n \"acc\": 0.17763157894736842,\n \"acc_stderr\": 0.031103182383123398,\n \"acc_norm\": 0.17763157894736842,\n \"acc_norm_stderr\": 0.031103182383123398\n },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.29,\n \"acc_stderr\": 0.045604802157206845,\n \"acc_norm\": 0.29,\n \"acc_norm_stderr\": 0.045604802157206845\n },\n \"harness|hendrycksTest-clinical_knowledge|5\": {\n \"acc\": 0.2188679245283019,\n \"acc_stderr\": 0.02544786382510863,\n \"acc_norm\": 0.2188679245283019,\n \"acc_norm_stderr\": 0.02544786382510863\n },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.2361111111111111,\n \"acc_stderr\": 0.03551446610810826,\n \"acc_norm\": 0.2361111111111111,\n \"acc_norm_stderr\": 0.03551446610810826\n },\n \"harness|hendrycksTest-college_chemistry|5\": {\n \"acc\": 0.25,\n \"acc_stderr\": 0.04351941398892446,\n \"acc_norm\": 0.25,\n \"acc_norm_stderr\": 0.04351941398892446\n },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\": 0.23,\n \"acc_stderr\": 0.04229525846816508,\n \"acc_norm\": 0.23,\n \"acc_norm_stderr\": 0.04229525846816508\n },\n \"harness|hendrycksTest-college_mathematics|5\": {\n \"acc\": 0.22,\n \"acc_stderr\": 0.04163331998932268,\n \"acc_norm\": 0.22,\n \"acc_norm_stderr\": 0.04163331998932268\n },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.2023121387283237,\n \"acc_stderr\": 0.03063114553919882,\n \"acc_norm\": 0.2023121387283237,\n \"acc_norm_stderr\": 0.03063114553919882\n },\n \"harness|hendrycksTest-college_physics|5\": {\n \"acc\": 0.21568627450980393,\n \"acc_stderr\": 0.04092563958237654,\n \"acc_norm\": 0.21568627450980393,\n \"acc_norm_stderr\": 0.04092563958237654\n },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\": 0.21,\n \"acc_stderr\": 0.04093601807403326,\n \"acc_norm\": 0.21,\n \"acc_norm_stderr\": 0.04093601807403326\n },\n \"harness|hendrycksTest-conceptual_physics|5\": {\n \"acc\": 0.26382978723404255,\n \"acc_stderr\": 0.028809989854102973,\n \"acc_norm\": 0.26382978723404255,\n \"acc_norm_stderr\": 0.028809989854102973\n },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.2719298245614035,\n \"acc_stderr\": 0.04185774424022057,\n \"acc_norm\": 0.2719298245614035,\n \"acc_norm_stderr\": 0.04185774424022057\n },\n \"harness|hendrycksTest-electrical_engineering|5\": {\n \"acc\": 0.21379310344827587,\n \"acc_stderr\": 0.0341652044774755,\n \"acc_norm\": 0.21379310344827587,\n \"acc_norm_stderr\": 0.0341652044774755\n },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\": 0.25132275132275134,\n \"acc_stderr\": 0.022340482339643898,\n \"acc_norm\": 0.25132275132275134,\n \"acc_norm_stderr\": 0.022340482339643898\n },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.21428571428571427,\n \"acc_stderr\": 0.036700664510471825,\n \"acc_norm\": 0.21428571428571427,\n \"acc_norm_stderr\": 0.036700664510471825\n },\n \"harness|hendrycksTest-global_facts|5\": {\n \"acc\": 0.14,\n \"acc_stderr\": 0.034873508801977704,\n \"acc_norm\": 0.14,\n \"acc_norm_stderr\": 0.034873508801977704\n },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.3096774193548387,\n \"acc_stderr\": 0.026302774983517418,\n \"acc_norm\": 0.3096774193548387,\n \"acc_norm_stderr\": 0.026302774983517418\n },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\": 0.2955665024630542,\n \"acc_stderr\": 0.032104944337514575,\n \"acc_norm\": 0.2955665024630542,\n \"acc_norm_stderr\": 0.032104944337514575\n },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \"acc\": 0.24,\n \"acc_stderr\": 0.04292346959909282,\n \"acc_norm\": 0.24,\n \"acc_norm_stderr\": 0.04292346959909282\n },\n \"harness|hendrycksTest-high_school_european_history|5\": {\n \"acc\": 0.2606060606060606,\n \"acc_stderr\": 0.034277431758165236,\n \"acc_norm\": 0.2606060606060606,\n \"acc_norm_stderr\": 0.034277431758165236\n },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\": 0.3383838383838384,\n \"acc_stderr\": 0.03371124142626304,\n \"acc_norm\": 0.3383838383838384,\n \"acc_norm_stderr\": 0.03371124142626304\n },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n \"acc\": 0.27461139896373055,\n \"acc_stderr\": 0.03221024508041154,\n \"acc_norm\": 0.27461139896373055,\n \"acc_norm_stderr\": 0.03221024508041154\n },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \"acc\": 0.33076923076923076,\n \"acc_stderr\": 0.02385479568097113,\n \"acc_norm\": 0.33076923076923076,\n \"acc_norm_stderr\": 0.02385479568097113\n },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"acc\": 0.2740740740740741,\n \"acc_stderr\": 0.027195934804085626,\n \"acc_norm\": 0.2740740740740741,\n \"acc_norm_stderr\": 0.027195934804085626\n },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \"acc\": 0.29411764705882354,\n \"acc_stderr\": 0.029597329730978093,\n \"acc_norm\": 0.29411764705882354,\n \"acc_norm_stderr\": 0.029597329730978093\n },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\": 0.2185430463576159,\n \"acc_stderr\": 0.03374235550425694,\n \"acc_norm\": 0.2185430463576159,\n \"acc_norm_stderr\": 0.03374235550425694\n },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\": 0.22568807339449543,\n \"acc_stderr\": 0.01792308766780306,\n \"acc_norm\": 0.22568807339449543,\n \"acc_norm_stderr\": 0.01792308766780306\n },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\": 0.46296296296296297,\n \"acc_stderr\": 0.03400603625538272,\n \"acc_norm\": 0.46296296296296297,\n \"acc_norm_stderr\": 0.03400603625538272\n },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\": 0.24019607843137256,\n \"acc_stderr\": 0.02998373305591362,\n \"acc_norm\": 0.24019607843137256,\n \"acc_norm_stderr\": 0.02998373305591362\n },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"acc\": 0.25316455696202533,\n \"acc_stderr\": 0.028304657943035303,\n \"acc_norm\": 0.25316455696202533,\n \"acc_norm_stderr\": 0.028304657943035303\n },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.27802690582959644,\n \"acc_stderr\": 0.030069584874494026,\n \"acc_norm\": 0.27802690582959644,\n \"acc_norm_stderr\": 0.030069584874494026\n },\n \"harness|hendrycksTest-human_sexuality|5\": {\n \"acc\": 0.25190839694656486,\n \"acc_stderr\": 0.03807387116306086,\n \"acc_norm\": 0.25190839694656486,\n \"acc_norm_stderr\": 0.03807387116306086\n },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\": 0.24793388429752067,\n \"acc_stderr\": 0.039418975265163025,\n \"acc_norm\": 0.24793388429752067,\n \"acc_norm_stderr\": 0.039418975265163025\n },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.23148148148148148,\n \"acc_stderr\": 0.04077494709252626,\n \"acc_norm\": 0.23148148148148148,\n \"acc_norm_stderr\": 0.04077494709252626\n },\n \"harness|hendrycksTest-logical_fallacies|5\": {\n \"acc\": 0.22699386503067484,\n \"acc_stderr\": 0.032910995786157686,\n \"acc_norm\": 0.22699386503067484,\n \"acc_norm_stderr\": 0.032910995786157686\n },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.3125,\n \"acc_stderr\": 0.043994650575715215,\n \"acc_norm\": 0.3125,\n \"acc_norm_stderr\": 0.043994650575715215\n },\n \"harness|hendrycksTest-management|5\": {\n \"acc\": 0.17475728155339806,\n \"acc_stderr\": 0.037601780060266224,\n \"acc_norm\": 0.17475728155339806,\n \"acc_norm_stderr\": 0.037601780060266224\n },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.24358974358974358,\n \"acc_stderr\": 0.0281209665039144,\n \"acc_norm\": 0.24358974358974358,\n \"acc_norm_stderr\": 0.0281209665039144\n },\n \"harness|hendrycksTest-medical_genetics|5\": {\n \"acc\": 0.3,\n \"acc_stderr\": 0.046056618647183814,\n \"acc_norm\": 0.3,\n \"acc_norm_stderr\": 0.046056618647183814\n },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.26309067688378035,\n \"acc_stderr\": 0.015745497169049046,\n \"acc_norm\": 0.26309067688378035,\n \"acc_norm_stderr\": 0.015745497169049046\n },\n \"harness|hendrycksTest-moral_disputes|5\": {\n \"acc\": 0.2543352601156069,\n \"acc_stderr\": 0.02344582627654555,\n \"acc_norm\": 0.2543352601156069,\n \"acc_norm_stderr\": 0.02344582627654555\n },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.2424581005586592,\n \"acc_stderr\": 0.014333522059217889,\n \"acc_norm\": 0.2424581005586592,\n \"acc_norm_stderr\": 0.014333522059217889\n },\n \"harness|hendrycksTest-nutrition|5\": {\n \"acc\": 0.2777777777777778,\n \"acc_stderr\": 0.02564686309713791,\n \"acc_norm\": 0.2777777777777778,\n \"acc_norm_stderr\": 0.02564686309713791\n },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.19935691318327975,\n \"acc_stderr\": 0.022691033780549656,\n \"acc_norm\": 0.19935691318327975,\n \"acc_norm_stderr\": 0.022691033780549656\n },\n \"harness|hendrycksTest-prehistory|5\": {\n \"acc\": 0.2191358024691358,\n \"acc_stderr\": 0.023016705640262192,\n \"acc_norm\": 0.2191358024691358,\n \"acc_norm_stderr\": 0.023016705640262192\n },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"acc\": 0.24822695035460993,\n \"acc_stderr\": 0.0257700156442904,\n \"acc_norm\": 0.24822695035460993,\n \"acc_norm_stderr\": 0.0257700156442904\n },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.2470664928292047,\n \"acc_stderr\": 0.011015752255279333,\n \"acc_norm\": 0.2470664928292047,\n \"acc_norm_stderr\": 0.011015752255279333\n },\n \"harness|hendrycksTest-professional_medicine|5\": {\n \"acc\": 0.4007352941176471,\n \"acc_stderr\": 0.0297682635289331,\n \"acc_norm\": 0.4007352941176471,\n \"acc_norm_stderr\": 0.0297682635289331\n },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"acc\": 0.25163398692810457,\n \"acc_stderr\": 0.017555818091322256,\n \"acc_norm\": 0.25163398692810457,\n \"acc_norm_stderr\": 0.017555818091322256\n },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.23636363636363636,\n \"acc_stderr\": 0.04069306319721378,\n \"acc_norm\": 0.23636363636363636,\n \"acc_norm_stderr\": 0.04069306319721378\n },\n \"harness|hendrycksTest-security_studies|5\": {\n \"acc\": 0.2163265306122449,\n \"acc_stderr\": 0.026358916334904035,\n \"acc_norm\": 0.2163265306122449,\n \"acc_norm_stderr\": 0.026358916334904035\n },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.263681592039801,\n \"acc_stderr\": 0.031157150869355575,\n \"acc_norm\": 0.263681592039801,\n \"acc_norm_stderr\": 0.031157150869355575\n },\n \"harness|hendrycksTest-us_foreign_policy|5\": {\n \"acc\": 0.3,\n \"acc_stderr\": 0.046056618647183814,\n \"acc_norm\": 0.3,\n \"acc_norm_stderr\": 0.046056618647183814\n },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.25903614457831325,\n \"acc_stderr\": 0.03410646614071857,\n \"acc_norm\": 0.25903614457831325,\n \"acc_norm_stderr\": 0.03410646614071857\n },\n \"harness|hendrycksTest-world_religions|5\": {\n \"acc\": 0.2046783625730994,\n \"acc_stderr\": 0.03094445977853321,\n \"acc_norm\": 0.2046783625730994,\n \"acc_norm_stderr\": 0.03094445977853321\n },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.2521419828641371,\n \"mc1_stderr\": 0.015201522246299962,\n \"mc2\": 0.44749716634136827,\n \"mc2_stderr\": 0.015554683095212777\n },\n \"harness|winogrande|5\": {\n \"acc\": 0.5153906866614049,\n \"acc_stderr\": 0.014045826789783656\n },\n \"harness|drop|3\": {\n \"em\": 0.001153523489932886,\n \"em_stderr\": 0.0003476179896857093,\n \"f1\": 0.03032822986577186,\n 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| []
| TAGS
#region-us
|
# Dataset Card for Evaluation run of BEE-spoke-data/verysmol_llama-v11-KIx2
## Dataset Description
- Homepage:
- Repository: URL
- Paper:
- Leaderboard: URL
- Point of Contact: clementine@URL
### Dataset Summary
Dataset automatically created during the evaluation run of model BEE-spoke-data/verysmol_llama-v11-KIx2 on the Open LLM Leaderboard.
The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).
To load the details from a run, you can for instance do the following:
## Latest results
These are the latest results from run 2023-11-13T13:21:49.840481(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
### Supported Tasks and Leaderboards
### Languages
## Dataset Structure
### Data Instances
### Data Fields
### Data Splits
## Dataset Creation
### Curation Rationale
### Source Data
#### Initial Data Collection and Normalization
#### Who are the source language producers?
### Annotations
#### Annotation process
#### Who are the annotators?
### Personal and Sensitive Information
## Considerations for Using the Data
### Social Impact of Dataset
### Discussion of Biases
### Other Known Limitations
## Additional Information
### Dataset Curators
### Licensing Information
### Contributions
| [
"# Dataset Card for Evaluation run of BEE-spoke-data/verysmol_llama-v11-KIx2",
"## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL",
"### Dataset Summary\n\nDataset automatically created during the evaluation run of model BEE-spoke-data/verysmol_llama-v11-KIx2 on the Open LLM Leaderboard.\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2023-11-13T13:21:49.840481(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"### Supported Tasks and Leaderboards",
"### Languages",
"## Dataset Structure",
"### Data Instances",
"### Data Fields",
"### Data Splits",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?",
"### Annotations",
"#### Annotation process",
"#### Who are the annotators?",
"### Personal and Sensitive Information",
"## Considerations for Using the Data",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations",
"## Additional Information",
"### Dataset Curators",
"### Licensing Information",
"### Contributions"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for Evaluation run of BEE-spoke-data/verysmol_llama-v11-KIx2",
"## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL",
"### Dataset Summary\n\nDataset automatically created during the evaluation run of model BEE-spoke-data/verysmol_llama-v11-KIx2 on the Open LLM Leaderboard.\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2023-11-13T13:21:49.840481(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"### Supported Tasks and Leaderboards",
"### Languages",
"## Dataset Structure",
"### Data Instances",
"### Data Fields",
"### Data Splits",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?",
"### Annotations",
"#### Annotation process",
"#### Who are the annotators?",
"### Personal and Sensitive Information",
"## Considerations for Using the Data",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations",
"## Additional Information",
"### Dataset Curators",
"### Licensing Information",
"### Contributions"
]
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"passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of BEE-spoke-data/verysmol_llama-v11-KIx2## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL### Dataset Summary\n\nDataset automatically created during the evaluation run of model BEE-spoke-data/verysmol_llama-v11-KIx2 on the Open LLM Leaderboard.\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2023-11-13T13:21:49.840481(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):### Supported Tasks and Leaderboards### Languages## Dataset Structure### Data Instances### Data Fields### Data Splits## Dataset Creation### Curation Rationale### Source Data#### Initial Data Collection and Normalization#### Who are the source language producers?### Annotations#### Annotation process#### Who are the annotators?### Personal and Sensitive Information## Considerations for Using the Data### Social Impact of Dataset### Discussion of Biases### Other Known Limitations## Additional Information### Dataset Curators### Licensing Information### Contributions"
]
|
fe29c867ed04a95cc4f070e85128153fc9fd9fe5 |
# Dataset Card for Evaluation run of postbot/distilgpt2-emailgen
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/postbot/distilgpt2-emailgen
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** [email protected]
### Dataset Summary
Dataset automatically created during the evaluation run of model [postbot/distilgpt2-emailgen](https://huggingface.co/postbot/distilgpt2-emailgen) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_postbot__distilgpt2-emailgen_public",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-11-13T13:25:05.974225](https://huggingface.co/datasets/open-llm-leaderboard/details_postbot__distilgpt2-emailgen_public/blob/main/results_2023-11-13T13-25-05.974225.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"acc": 0.2585985031430374,
"acc_stderr": 0.03091312867789808,
"acc_norm": 0.2592605342225761,
"acc_norm_stderr": 0.03173517189546408,
"mc1": 0.24357405140758873,
"mc1_stderr": 0.015026354824910782,
"mc2": 0.46170278335459186,
"mc2_stderr": 0.01541047587026832,
"em": 0.0,
"em_stderr": 0.0,
"f1": 0.011639052013422831,
"f1_stderr": 0.0006056902097790024
},
"harness|arc:challenge|25": {
"acc": 0.18600682593856654,
"acc_stderr": 0.01137094018326675,
"acc_norm": 0.2175767918088737,
"acc_norm_stderr": 0.012057262020972497
},
"harness|hellaswag|10": {
"acc": 0.2687711611232822,
"acc_stderr": 0.004424146562746121,
"acc_norm": 0.27524397530372435,
"acc_norm_stderr": 0.004457243336616497
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.29,
"acc_stderr": 0.04560480215720684,
"acc_norm": 0.29,
"acc_norm_stderr": 0.04560480215720684
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.32592592592592595,
"acc_stderr": 0.040491220417025055,
"acc_norm": 0.32592592592592595,
"acc_norm_stderr": 0.040491220417025055
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.17105263157894737,
"acc_stderr": 0.030643607071677088,
"acc_norm": 0.17105263157894737,
"acc_norm_stderr": 0.030643607071677088
},
"harness|hendrycksTest-business_ethics|5": {
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"acc_norm": 0.21,
"acc_norm_stderr": 0.040936018074033256
},
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"acc_norm": 0.2188679245283019,
"acc_norm_stderr": 0.02544786382510861
},
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"acc_norm_stderr": 0.034370793441061344
},
"harness|hendrycksTest-college_chemistry|5": {
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"acc_norm": 0.25,
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"acc_stderr": 0.04648231987117316,
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},
"harness|hendrycksTest-college_mathematics|5": {
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"acc_norm": 0.23,
"acc_norm_stderr": 0.042295258468165065
},
"harness|hendrycksTest-college_medicine|5": {
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"acc_norm_stderr": 0.04280105837364395
},
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"harness|hendrycksTest-high_school_microeconomics|5": {
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"harness|hendrycksTest-high_school_physics|5": {
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"harness|truthfulqa:mc|0": {
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"em": 0.0,
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"f1_stderr": 0.0006056902097790024
},
"harness|gsm8k|5": {
"acc": 0.0,
"acc_stderr": 0.0
}
}
```
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed] | open-llm-leaderboard/details_postbot__distilgpt2-emailgen | [
"region:us"
]
| 2023-11-13T13:26:53+00:00 | {"pretty_name": "Evaluation run of postbot/distilgpt2-emailgen", "dataset_summary": "Dataset automatically created during the evaluation run of model [postbot/distilgpt2-emailgen](https://huggingface.co/postbot/distilgpt2-emailgen) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\nTo load the details from a run, you can for instance do the following:\n```python\nfrom datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_postbot__distilgpt2-emailgen_public\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-11-13T13:25:05.974225](https://huggingface.co/datasets/open-llm-leaderboard/details_postbot__distilgpt2-emailgen_public/blob/main/results_2023-11-13T13-25-05.974225.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):\n\n```python\n{\n \"all\": {\n \"acc\": 0.2585985031430374,\n \"acc_stderr\": 0.03091312867789808,\n \"acc_norm\": 0.2592605342225761,\n \"acc_norm_stderr\": 0.03173517189546408,\n \"mc1\": 0.24357405140758873,\n \"mc1_stderr\": 0.015026354824910782,\n \"mc2\": 0.46170278335459186,\n \"mc2_stderr\": 0.01541047587026832,\n \"em\": 0.0,\n \"em_stderr\": 0.0,\n \"f1\": 0.011639052013422831,\n \"f1_stderr\": 0.0006056902097790024\n },\n \"harness|arc:challenge|25\": {\n \"acc\": 0.18600682593856654,\n \"acc_stderr\": 0.01137094018326675,\n \"acc_norm\": 0.2175767918088737,\n \"acc_norm_stderr\": 0.012057262020972497\n },\n \"harness|hellaswag|10\": {\n \"acc\": 0.2687711611232822,\n \"acc_stderr\": 0.004424146562746121,\n \"acc_norm\": 0.27524397530372435,\n \"acc_norm_stderr\": 0.004457243336616497\n },\n \"harness|hendrycksTest-abstract_algebra|5\": {\n \"acc\": 0.29,\n \"acc_stderr\": 0.04560480215720684,\n \"acc_norm\": 0.29,\n \"acc_norm_stderr\": 0.04560480215720684\n },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.32592592592592595,\n \"acc_stderr\": 0.040491220417025055,\n \"acc_norm\": 0.32592592592592595,\n \"acc_norm_stderr\": 0.040491220417025055\n },\n \"harness|hendrycksTest-astronomy|5\": {\n \"acc\": 0.17105263157894737,\n \"acc_stderr\": 0.030643607071677088,\n \"acc_norm\": 0.17105263157894737,\n \"acc_norm_stderr\": 0.030643607071677088\n },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.21,\n \"acc_stderr\": 0.040936018074033256,\n \"acc_norm\": 0.21,\n \"acc_norm_stderr\": 0.040936018074033256\n },\n \"harness|hendrycksTest-clinical_knowledge|5\": {\n \"acc\": 0.2188679245283019,\n \"acc_stderr\": 0.02544786382510861,\n \"acc_norm\": 0.2188679245283019,\n \"acc_norm_stderr\": 0.02544786382510861\n },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.2152777777777778,\n \"acc_stderr\": 0.034370793441061344,\n \"acc_norm\": 0.2152777777777778,\n \"acc_norm_stderr\": 0.034370793441061344\n },\n \"harness|hendrycksTest-college_chemistry|5\": {\n \"acc\": 0.25,\n \"acc_stderr\": 0.04351941398892446,\n \"acc_norm\": 0.25,\n \"acc_norm_stderr\": 0.04351941398892446\n },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\": 0.31,\n \"acc_stderr\": 0.04648231987117316,\n \"acc_norm\": 0.31,\n \"acc_norm_stderr\": 0.04648231987117316\n },\n \"harness|hendrycksTest-college_mathematics|5\": {\n \"acc\": 0.23,\n \"acc_stderr\": 0.042295258468165065,\n \"acc_norm\": 0.23,\n \"acc_norm_stderr\": 0.042295258468165065\n },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.23699421965317918,\n \"acc_stderr\": 0.03242414757483098,\n \"acc_norm\": 0.23699421965317918,\n \"acc_norm_stderr\": 0.03242414757483098\n },\n \"harness|hendrycksTest-college_physics|5\": {\n \"acc\": 0.24509803921568626,\n \"acc_stderr\": 0.04280105837364395,\n \"acc_norm\": 0.24509803921568626,\n \"acc_norm_stderr\": 0.04280105837364395\n },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\": 0.26,\n \"acc_stderr\": 0.044084400227680814,\n \"acc_norm\": 0.26,\n \"acc_norm_stderr\": 0.044084400227680814\n },\n \"harness|hendrycksTest-conceptual_physics|5\": {\n \"acc\": 0.225531914893617,\n \"acc_stderr\": 0.027321078417387533,\n \"acc_norm\": 0.225531914893617,\n \"acc_norm_stderr\": 0.027321078417387533\n },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.2543859649122807,\n \"acc_stderr\": 0.04096985139843671,\n \"acc_norm\": 0.2543859649122807,\n \"acc_norm_stderr\": 0.04096985139843671\n },\n \"harness|hendrycksTest-electrical_engineering|5\": {\n \"acc\": 0.2896551724137931,\n \"acc_stderr\": 0.03780019230438014,\n \"acc_norm\": 0.2896551724137931,\n \"acc_norm_stderr\": 0.03780019230438014\n },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\": 0.25396825396825395,\n \"acc_stderr\": 0.022418042891113942,\n \"acc_norm\": 0.25396825396825395,\n \"acc_norm_stderr\": 0.022418042891113942\n },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.15079365079365079,\n \"acc_stderr\": 0.03200686497287392,\n \"acc_norm\": 0.15079365079365079,\n \"acc_norm_stderr\": 0.03200686497287392\n },\n \"harness|hendrycksTest-global_facts|5\": {\n \"acc\": 0.31,\n \"acc_stderr\": 0.04648231987117316,\n \"acc_norm\": 0.31,\n \"acc_norm_stderr\": 0.04648231987117316\n },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.3161290322580645,\n \"acc_stderr\": 0.02645087448904277,\n \"acc_norm\": 0.3161290322580645,\n \"acc_norm_stderr\": 0.02645087448904277\n },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\": 0.30049261083743845,\n \"acc_stderr\": 0.03225799476233484,\n \"acc_norm\": 0.30049261083743845,\n \"acc_norm_stderr\": 0.03225799476233484\n },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \"acc\": 0.23,\n \"acc_stderr\": 0.042295258468165065,\n \"acc_norm\": 0.23,\n \"acc_norm_stderr\": 0.042295258468165065\n },\n \"harness|hendrycksTest-high_school_european_history|5\": {\n \"acc\": 0.24242424242424243,\n \"acc_stderr\": 0.03346409881055953,\n \"acc_norm\": 0.24242424242424243,\n \"acc_norm_stderr\": 0.03346409881055953\n },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\": 0.25252525252525254,\n \"acc_stderr\": 0.030954055470365904,\n \"acc_norm\": 0.25252525252525254,\n \"acc_norm_stderr\": 0.030954055470365904\n },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n \"acc\": 0.23834196891191708,\n \"acc_stderr\": 0.030748905363909902,\n \"acc_norm\": 0.23834196891191708,\n \"acc_norm_stderr\": 0.030748905363909902\n },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \"acc\": 0.22564102564102564,\n \"acc_stderr\": 0.021193632525148543,\n \"acc_norm\": 0.22564102564102564,\n \"acc_norm_stderr\": 0.021193632525148543\n },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"acc\": 0.2518518518518518,\n \"acc_stderr\": 0.02646611753895991,\n \"acc_norm\": 0.2518518518518518,\n \"acc_norm_stderr\": 0.02646611753895991\n },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \"acc\": 0.35294117647058826,\n \"acc_stderr\": 0.031041941304059288,\n \"acc_norm\": 0.35294117647058826,\n \"acc_norm_stderr\": 0.031041941304059288\n },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\": 0.33112582781456956,\n \"acc_stderr\": 0.038425817186598696,\n \"acc_norm\": 0.33112582781456956,\n \"acc_norm_stderr\": 0.038425817186598696\n },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\": 0.21651376146788992,\n \"acc_stderr\": 0.017658710594443128,\n \"acc_norm\": 0.21651376146788992,\n \"acc_norm_stderr\": 0.017658710594443128\n },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\": 0.4722222222222222,\n \"acc_stderr\": 0.0340470532865388,\n \"acc_norm\": 0.4722222222222222,\n \"acc_norm_stderr\": 0.0340470532865388\n },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\": 0.25980392156862747,\n \"acc_stderr\": 0.03077855467869326,\n \"acc_norm\": 0.25980392156862747,\n \"acc_norm_stderr\": 0.03077855467869326\n },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"acc\": 0.25316455696202533,\n \"acc_stderr\": 0.028304657943035307,\n \"acc_norm\": 0.25316455696202533,\n \"acc_norm_stderr\": 0.028304657943035307\n },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.2600896860986547,\n \"acc_stderr\": 0.02944249558585746,\n \"acc_norm\": 0.2600896860986547,\n \"acc_norm_stderr\": 0.02944249558585746\n },\n \"harness|hendrycksTest-human_sexuality|5\": {\n \"acc\": 0.2595419847328244,\n \"acc_stderr\": 0.03844876139785271,\n \"acc_norm\": 0.2595419847328244,\n \"acc_norm_stderr\": 0.03844876139785271\n },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\": 0.256198347107438,\n \"acc_stderr\": 0.03984979653302872,\n \"acc_norm\": 0.256198347107438,\n \"acc_norm_stderr\": 0.03984979653302872\n },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.25,\n \"acc_stderr\": 0.04186091791394607,\n \"acc_norm\": 0.25,\n \"acc_norm_stderr\": 0.04186091791394607\n },\n \"harness|hendrycksTest-logical_fallacies|5\": {\n \"acc\": 0.26380368098159507,\n \"acc_stderr\": 0.03462419931615624,\n \"acc_norm\": 0.26380368098159507,\n \"acc_norm_stderr\": 0.03462419931615624\n },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.26785714285714285,\n \"acc_stderr\": 0.042032772914677614,\n \"acc_norm\": 0.26785714285714285,\n \"acc_norm_stderr\": 0.042032772914677614\n },\n \"harness|hendrycksTest-management|5\": {\n \"acc\": 0.17475728155339806,\n \"acc_stderr\": 0.037601780060266224,\n \"acc_norm\": 0.17475728155339806,\n \"acc_norm_stderr\": 0.037601780060266224\n },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.18803418803418803,\n \"acc_stderr\": 0.02559819368665226,\n \"acc_norm\": 0.18803418803418803,\n \"acc_norm_stderr\": 0.02559819368665226\n },\n \"harness|hendrycksTest-medical_genetics|5\": {\n \"acc\": 0.3,\n \"acc_stderr\": 0.046056618647183814,\n \"acc_norm\": 0.3,\n \"acc_norm_stderr\": 0.046056618647183814\n },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.2554278416347382,\n \"acc_stderr\": 0.015594955384455766,\n \"acc_norm\": 0.2554278416347382,\n \"acc_norm_stderr\": 0.015594955384455766\n },\n \"harness|hendrycksTest-moral_disputes|5\": {\n \"acc\": 0.24566473988439305,\n \"acc_stderr\": 0.02317629820399201,\n \"acc_norm\": 0.24566473988439305,\n \"acc_norm_stderr\": 0.02317629820399201\n },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.2424581005586592,\n \"acc_stderr\": 0.014333522059217889,\n \"acc_norm\": 0.2424581005586592,\n \"acc_norm_stderr\": 0.014333522059217889\n },\n \"harness|hendrycksTest-nutrition|5\": {\n \"acc\": 0.2549019607843137,\n \"acc_stderr\": 0.02495418432487991,\n \"acc_norm\": 0.2549019607843137,\n \"acc_norm_stderr\": 0.02495418432487991\n },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.28938906752411575,\n \"acc_stderr\": 0.025755865922632924,\n \"acc_norm\": 0.28938906752411575,\n \"acc_norm_stderr\": 0.025755865922632924\n },\n \"harness|hendrycksTest-prehistory|5\": {\n \"acc\": 0.28703703703703703,\n \"acc_stderr\": 0.025171041915309684,\n \"acc_norm\": 0.28703703703703703,\n \"acc_norm_stderr\": 0.025171041915309684\n },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"acc\": 0.19858156028368795,\n \"acc_stderr\": 0.023798301637942106,\n \"acc_norm\": 0.19858156028368795,\n \"acc_norm_stderr\": 0.023798301637942106\n },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.24837027379400262,\n \"acc_stderr\": 0.011035212598034501,\n \"acc_norm\": 0.24837027379400262,\n \"acc_norm_stderr\": 0.011035212598034501\n },\n \"harness|hendrycksTest-professional_medicine|5\": {\n \"acc\": 0.4485294117647059,\n \"acc_stderr\": 0.030211479609121593,\n \"acc_norm\": 0.4485294117647059,\n \"acc_norm_stderr\": 0.030211479609121593\n },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"acc\": 0.24836601307189543,\n \"acc_stderr\": 0.017479487001364764,\n \"acc_norm\": 0.24836601307189543,\n \"acc_norm_stderr\": 0.017479487001364764\n },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.24545454545454545,\n \"acc_stderr\": 0.041220665028782834,\n \"acc_norm\": 0.24545454545454545,\n \"acc_norm_stderr\": 0.041220665028782834\n },\n \"harness|hendrycksTest-security_studies|5\": {\n \"acc\": 0.2653061224489796,\n \"acc_stderr\": 0.028263889943784596,\n \"acc_norm\": 0.2653061224489796,\n \"acc_norm_stderr\": 0.028263889943784596\n },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.2537313432835821,\n \"acc_stderr\": 0.03076944496729602,\n \"acc_norm\": 0.2537313432835821,\n \"acc_norm_stderr\": 0.03076944496729602\n },\n \"harness|hendrycksTest-us_foreign_policy|5\": {\n \"acc\": 0.3,\n \"acc_stderr\": 0.046056618647183814,\n \"acc_norm\": 0.3,\n \"acc_norm_stderr\": 0.046056618647183814\n },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.19879518072289157,\n \"acc_stderr\": 0.031069390260789424,\n \"acc_norm\": 0.19879518072289157,\n \"acc_norm_stderr\": 0.031069390260789424\n },\n \"harness|hendrycksTest-world_religions|5\": {\n \"acc\": 0.21052631578947367,\n \"acc_stderr\": 0.0312678171466318,\n \"acc_norm\": 0.21052631578947367,\n \"acc_norm_stderr\": 0.0312678171466318\n },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.24357405140758873,\n \"mc1_stderr\": 0.015026354824910782,\n \"mc2\": 0.46170278335459186,\n \"mc2_stderr\": 0.01541047587026832\n },\n \"harness|winogrande|5\": {\n \"acc\": 0.516179952644041,\n \"acc_stderr\": 0.014045126130978603\n },\n \"harness|drop|3\": {\n \"em\": 0.0,\n \"em_stderr\": 0.0,\n \"f1\": 0.011639052013422831,\n \"f1_stderr\": 0.0006056902097790024\n },\n \"harness|gsm8k|5\": {\n \"acc\": 0.0,\n \"acc_stderr\": 0.0\n }\n}\n```", "repo_url": "https://huggingface.co/postbot/distilgpt2-emailgen", "leaderboard_url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard", "point_of_contact": "[email protected]", "configs": [{"config_name": "harness_arc_challenge_25", "data_files": [{"split": "2023_11_13T13_25_05.974225", "path": ["**/details_harness|arc:challenge|25_2023-11-13T13-25-05.974225.parquet"]}, {"split": "latest", "path": ["**/details_harness|arc:challenge|25_2023-11-13T13-25-05.974225.parquet"]}]}, {"config_name": "harness_drop_3", "data_files": [{"split": "2023_11_13T13_25_05.974225", "path": ["**/details_harness|drop|3_2023-11-13T13-25-05.974225.parquet"]}, {"split": "latest", "path": ["**/details_harness|drop|3_2023-11-13T13-25-05.974225.parquet"]}]}, {"config_name": "harness_gsm8k_5", "data_files": [{"split": "2023_11_13T13_25_05.974225", "path": ["**/details_harness|gsm8k|5_2023-11-13T13-25-05.974225.parquet"]}, {"split": "latest", "path": ["**/details_harness|gsm8k|5_2023-11-13T13-25-05.974225.parquet"]}]}, {"config_name": "harness_hellaswag_10", "data_files": [{"split": "2023_11_13T13_25_05.974225", "path": ["**/details_harness|hellaswag|10_2023-11-13T13-25-05.974225.parquet"]}, {"split": "latest", "path": ["**/details_harness|hellaswag|10_2023-11-13T13-25-05.974225.parquet"]}]}, {"config_name": "harness_hendrycksTest_5", "data_files": [{"split": "2023_11_13T13_25_05.974225", "path": ["**/details_harness|hendrycksTest-abstract_algebra|5_2023-11-13T13-25-05.974225.parquet", "**/details_harness|hendrycksTest-anatomy|5_2023-11-13T13-25-05.974225.parquet", "**/details_harness|hendrycksTest-astronomy|5_2023-11-13T13-25-05.974225.parquet", "**/details_harness|hendrycksTest-business_ethics|5_2023-11-13T13-25-05.974225.parquet", "**/details_harness|hendrycksTest-clinical_knowledge|5_2023-11-13T13-25-05.974225.parquet", "**/details_harness|hendrycksTest-college_biology|5_2023-11-13T13-25-05.974225.parquet", "**/details_harness|hendrycksTest-college_chemistry|5_2023-11-13T13-25-05.974225.parquet", "**/details_harness|hendrycksTest-college_computer_science|5_2023-11-13T13-25-05.974225.parquet", "**/details_harness|hendrycksTest-college_mathematics|5_2023-11-13T13-25-05.974225.parquet", "**/details_harness|hendrycksTest-college_medicine|5_2023-11-13T13-25-05.974225.parquet", "**/details_harness|hendrycksTest-college_physics|5_2023-11-13T13-25-05.974225.parquet", "**/details_harness|hendrycksTest-computer_security|5_2023-11-13T13-25-05.974225.parquet", "**/details_harness|hendrycksTest-conceptual_physics|5_2023-11-13T13-25-05.974225.parquet", "**/details_harness|hendrycksTest-econometrics|5_2023-11-13T13-25-05.974225.parquet", "**/details_harness|hendrycksTest-electrical_engineering|5_2023-11-13T13-25-05.974225.parquet", "**/details_harness|hendrycksTest-elementary_mathematics|5_2023-11-13T13-25-05.974225.parquet", 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{"config_name": "results", "data_files": [{"split": "2023_11_13T13_25_05.974225", "path": ["results_2023-11-13T13-25-05.974225.parquet"]}, {"split": "latest", "path": ["results_2023-11-13T13-25-05.974225.parquet"]}]}]} | 2023-11-13T13:27:40+00:00 | []
| []
| TAGS
#region-us
|
# Dataset Card for Evaluation run of postbot/distilgpt2-emailgen
## Dataset Description
- Homepage:
- Repository: URL
- Paper:
- Leaderboard: URL
- Point of Contact: clementine@URL
### Dataset Summary
Dataset automatically created during the evaluation run of model postbot/distilgpt2-emailgen on the Open LLM Leaderboard.
The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).
To load the details from a run, you can for instance do the following:
## Latest results
These are the latest results from run 2023-11-13T13:25:05.974225(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
### Supported Tasks and Leaderboards
### Languages
## Dataset Structure
### Data Instances
### Data Fields
### Data Splits
## Dataset Creation
### Curation Rationale
### Source Data
#### Initial Data Collection and Normalization
#### Who are the source language producers?
### Annotations
#### Annotation process
#### Who are the annotators?
### Personal and Sensitive Information
## Considerations for Using the Data
### Social Impact of Dataset
### Discussion of Biases
### Other Known Limitations
## Additional Information
### Dataset Curators
### Licensing Information
### Contributions
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"### Dataset Summary\n\nDataset automatically created during the evaluation run of model postbot/distilgpt2-emailgen on the Open LLM Leaderboard.\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
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"### Dataset Summary\n\nDataset automatically created during the evaluation run of model postbot/distilgpt2-emailgen on the Open LLM Leaderboard.\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2023-11-13T13:25:05.974225(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
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"### Social Impact of Dataset",
"### Discussion of Biases",
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"### Dataset Curators",
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"passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of postbot/distilgpt2-emailgen## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL### Dataset Summary\n\nDataset automatically created during the evaluation run of model postbot/distilgpt2-emailgen on the Open LLM Leaderboard.\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2023-11-13T13:25:05.974225(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):### Supported Tasks and Leaderboards### Languages## Dataset Structure### Data Instances### Data Fields### Data Splits## Dataset Creation### Curation Rationale### Source Data#### Initial Data Collection and Normalization#### Who are the source language producers?### Annotations#### Annotation process#### Who are the annotators?### Personal and Sensitive Information## Considerations for Using the Data### Social Impact of Dataset### Discussion of Biases### Other Known Limitations## Additional Information### Dataset Curators### Licensing Information### Contributions"
]
|
09f19a8a98229c26c2c6eaf5cb6419636cc1497d | # Dataset Card for Common Voice Corpus 15.0
<!-- Provide a quick summary of the dataset. -->
This dataset is an unofficial version of the Mozilla Common Voice Corpus 15. It was downloaded and converted from the project's website https://commonvoice.mozilla.org/.
## Languages
```
Abkhaz, Albanian, Amharic, Arabic, Armenian, Assamese, Asturian, Azerbaijani, Basaa, Bashkir, Basque, Belarusian, Bengali, Breton, Bulgarian, Cantonese, Catalan, Central Kurdish, Chinese (China), Chinese (Hong Kong), Chinese (Taiwan), Chuvash, Czech, Danish, Dhivehi, Dioula, Dutch, English, Erzya, Esperanto, Estonian, Finnish, French, Frisian, Galician, Georgian, German, Greek, Guarani, Hakha Chin, Hausa, Hill Mari, Hindi, Hungarian, Icelandic, Igbo, Indonesian, Interlingua, Irish, Italian, Japanese, Kabyle, Kazakh, Kinyarwanda, Korean, Kurmanji Kurdish, Kyrgyz, Lao, Latvian, Lithuanian, Luganda, Macedonian, Malayalam, Maltese, Marathi, Meadow Mari, Moksha, Mongolian, Nepali, Norwegian Nynorsk, Occitan, Odia, Pashto, Persian, Polish, Portuguese, Punjabi, Quechua Chanka, Romanian, Romansh Sursilvan, Romansh Vallader, Russian, Sakha, Santali (Ol Chiki), Saraiki, Sardinian, Serbian, Slovak, Slovenian, Sorbian, Upper, Spanish, Swahili, Swedish, Taiwanese (Minnan), Tamazight, Tamil, Tatar, Thai, Tigre, Tigrinya, Toki Pona, Turkish, Turkmen, Twi, Ukrainian, Urdu, Uyghur, Uzbek, Vietnamese, Votic, Welsh, Yoruba
```
## How to use
The datasets library allows you to load and pre-process your dataset in pure Python, at scale. The dataset can be downloaded and prepared in one call to your local drive by using the load_dataset function.
For example, to download the Portuguese config, simply specify the corresponding language config name (i.e., "pt" for Portuguese):
```
from datasets import load_dataset
cv_15 = load_dataset("fsicoli/common_voice_15_0", "pt", split="train")
```
Using the datasets library, you can also stream the dataset on-the-fly by adding a streaming=True argument to the load_dataset function call. Loading a dataset in streaming mode loads individual samples of the dataset at a time, rather than downloading the entire dataset to disk.
```
from datasets import load_dataset
cv_15 = load_dataset("fsicoli/common_voice_15_0", "pt", split="train", streaming=True)
print(next(iter(cv_15)))
```
Bonus: create a PyTorch dataloader directly with your own datasets (local/streamed).
### Local
```
from datasets import load_dataset
from torch.utils.data.sampler import BatchSampler, RandomSampler
cv_15 = load_dataset("fsicoli/common_voice_15_0", "pt", split="train")
batch_sampler = BatchSampler(RandomSampler(cv_15), batch_size=32, drop_last=False)
dataloader = DataLoader(cv_15, batch_sampler=batch_sampler)
```
### Streaming
```
from datasets import load_dataset
from torch.utils.data import DataLoader
cv_15 = load_dataset("fsicoli/common_voice_15_0", "pt", split="train")
dataloader = DataLoader(cv_15, batch_size=32)
```
To find out more about loading and preparing audio datasets, head over to hf.co/blog/audio-datasets.
### Dataset Structure
Data Instances
A typical data point comprises the path to the audio file and its sentence. Additional fields include accent, age, client_id, up_votes, down_votes, gender, locale and segment.
### Licensing Information
Public Domain, CC-0
### Citation Information
```
@inproceedings{commonvoice:2020,
author = {Ardila, R. and Branson, M. and Davis, K. and Henretty, M. and Kohler, M. and Meyer, J. and Morais, R. and Saunders, L. and Tyers, F. M. and Weber, G.},
title = {Common Voice: A Massively-Multilingual Speech Corpus},
booktitle = {Proceedings of the 12th Conference on Language Resources and Evaluation (LREC 2020)},
pages = {4211--4215},
year = 2020
}
``` | fsicoli/common_voice_15_0 | [
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"language:yue",
"language:zgh",
"language:zh",
"language:yo",
"license:cc",
"mozilla",
"foundation",
"region:us"
]
| 2023-11-13T13:27:04+00:00 | {"language": ["ab", "af", "am", "ar", "as", "ast", "az", "ba", "bas", "be", "bg", "bn", "br", "ca", "ckb", "cnh", "cs", "cv", "cy", "da", "de", "dv", "dyu", "el", "en", "eo", "es", "et", "eu", "fa", "fi", "fr", "gl", "gn", "ha", "he", "hi", "hsb", "hu", "ia", "id", "ig", "is", "it", "ja", "ka", "kab", "kk", "kmr", "ko", "ky", "lg", "lo", "lt", "lv", "mdf", "mhr", "mk", "ml", "mn", "mr", "mrj", "mt", "myv", "nl", "oc", "or", "pl", "ps", "pt", "quy", "ro", "ru", "rw", "sah", "sat", "sc", "sk", "skr", "sl", "sq", "sr", "sw", "ta", "th", "ti", "tig", "tk", "tok", "tr", "tt", "tw", "ug", "uk", "ur", "uz", "vi", "vot", "yue", "zgh", "zh", "yo"], "license": "cc", "size_categories": ["100B<n<1T"], "task_categories": ["automatic-speech-recognition"], "pretty_name": "Common Voice Corpus 15.0", "tags": ["mozilla", "foundation"]} | 2023-12-20T18:55:52+00:00 | []
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]
| TAGS
#task_categories-automatic-speech-recognition #size_categories-100B<n<1T #language-Abkhazian #language-Afrikaans #language-Amharic #language-Arabic #language-Assamese #language-Asturian #language-Azerbaijani #language-Bashkir #language-Basa (Cameroon) #language-Belarusian #language-Bulgarian #language-Bengali #language-Breton #language-Catalan #language-Central Kurdish #language-Hakha Chin #language-Czech #language-Chuvash #language-Welsh #language-Danish #language-German #language-Dhivehi #language-Dyula #language-Modern Greek (1453-) #language-English #language-Esperanto #language-Spanish #language-Estonian #language-Basque #language-Persian #language-Finnish #language-French #language-Galician #language-Guarani #language-Hausa #language-Hebrew #language-Hindi #language-Upper Sorbian #language-Hungarian #language-Interlingua (International Auxiliary Language Association) #language-Indonesian #language-Igbo #language-Icelandic #language-Italian #language-Japanese #language-Georgian #language-Kabyle #language-Kazakh #language-Northern Kurdish #language-Korean #language-Kirghiz #language-Ganda #language-Lao #language-Lithuanian #language-Latvian #language-Moksha #language-Eastern Mari #language-Macedonian #language-Malayalam #language-Mongolian #language-Marathi #language-Western Mari #language-Maltese #language-Erzya #language-Dutch #language-Occitan (post 1500) #language-Oriya (macrolanguage) #language-Polish #language-Pushto #language-Portuguese #language-Ayacucho Quechua #language-Romanian #language-Russian #language-Kinyarwanda #language-Yakut #language-Santali #language-Sardinian #language-Slovak #language-Saraiki #language-Slovenian #language-Albanian #language-Serbian #language-Swahili (macrolanguage) #language-Tamil #language-Thai #language-Tigrinya #language-Tigre #language-Turkmen #language-Toki Pona #language-Turkish #language-Tatar #language-Twi #language-Uighur #language-Ukrainian #language-Urdu #language-Uzbek #language-Vietnamese #language-Votic #language-Yue Chinese #language-Standard Moroccan Tamazight #language-Chinese #language-Yoruba #license-cc #mozilla #foundation #region-us
| # Dataset Card for Common Voice Corpus 15.0
This dataset is an unofficial version of the Mozilla Common Voice Corpus 15. It was downloaded and converted from the project's website URL
## Languages
## How to use
The datasets library allows you to load and pre-process your dataset in pure Python, at scale. The dataset can be downloaded and prepared in one call to your local drive by using the load_dataset function.
For example, to download the Portuguese config, simply specify the corresponding language config name (i.e., "pt" for Portuguese):
Using the datasets library, you can also stream the dataset on-the-fly by adding a streaming=True argument to the load_dataset function call. Loading a dataset in streaming mode loads individual samples of the dataset at a time, rather than downloading the entire dataset to disk.
Bonus: create a PyTorch dataloader directly with your own datasets (local/streamed).
### Local
### Streaming
To find out more about loading and preparing audio datasets, head over to URL
### Dataset Structure
Data Instances
A typical data point comprises the path to the audio file and its sentence. Additional fields include accent, age, client_id, up_votes, down_votes, gender, locale and segment.
### Licensing Information
Public Domain, CC-0
| [
"# Dataset Card for Common Voice Corpus 15.0\n\n\n\nThis dataset is an unofficial version of the Mozilla Common Voice Corpus 15. It was downloaded and converted from the project's website URL",
"## Languages",
"## How to use\nThe datasets library allows you to load and pre-process your dataset in pure Python, at scale. The dataset can be downloaded and prepared in one call to your local drive by using the load_dataset function.\n\nFor example, to download the Portuguese config, simply specify the corresponding language config name (i.e., \"pt\" for Portuguese):\n\nUsing the datasets library, you can also stream the dataset on-the-fly by adding a streaming=True argument to the load_dataset function call. Loading a dataset in streaming mode loads individual samples of the dataset at a time, rather than downloading the entire dataset to disk.\n\n\n\nBonus: create a PyTorch dataloader directly with your own datasets (local/streamed).",
"### Local",
"### Streaming\n\n\nTo find out more about loading and preparing audio datasets, head over to URL",
"### Dataset Structure\nData Instances\nA typical data point comprises the path to the audio file and its sentence. Additional fields include accent, age, client_id, up_votes, down_votes, gender, locale and segment.",
"### Licensing Information\nPublic Domain, CC-0"
]
| [
"TAGS\n#task_categories-automatic-speech-recognition #size_categories-100B<n<1T #language-Abkhazian #language-Afrikaans #language-Amharic #language-Arabic #language-Assamese #language-Asturian #language-Azerbaijani #language-Bashkir #language-Basa (Cameroon) #language-Belarusian #language-Bulgarian #language-Bengali #language-Breton #language-Catalan #language-Central Kurdish #language-Hakha Chin #language-Czech #language-Chuvash #language-Welsh #language-Danish #language-German #language-Dhivehi #language-Dyula #language-Modern Greek (1453-) #language-English #language-Esperanto #language-Spanish #language-Estonian #language-Basque #language-Persian #language-Finnish #language-French #language-Galician #language-Guarani #language-Hausa #language-Hebrew #language-Hindi #language-Upper Sorbian #language-Hungarian #language-Interlingua (International Auxiliary Language Association) #language-Indonesian #language-Igbo #language-Icelandic #language-Italian #language-Japanese #language-Georgian #language-Kabyle #language-Kazakh #language-Northern Kurdish #language-Korean #language-Kirghiz #language-Ganda #language-Lao #language-Lithuanian #language-Latvian #language-Moksha #language-Eastern Mari #language-Macedonian #language-Malayalam #language-Mongolian #language-Marathi #language-Western Mari #language-Maltese #language-Erzya #language-Dutch #language-Occitan (post 1500) #language-Oriya (macrolanguage) #language-Polish #language-Pushto #language-Portuguese #language-Ayacucho Quechua #language-Romanian #language-Russian #language-Kinyarwanda #language-Yakut #language-Santali #language-Sardinian #language-Slovak #language-Saraiki #language-Slovenian #language-Albanian #language-Serbian #language-Swahili (macrolanguage) #language-Tamil #language-Thai #language-Tigrinya #language-Tigre #language-Turkmen #language-Toki Pona #language-Turkish #language-Tatar #language-Twi #language-Uighur #language-Ukrainian #language-Urdu #language-Uzbek #language-Vietnamese #language-Votic #language-Yue Chinese #language-Standard Moroccan Tamazight #language-Chinese #language-Yoruba #license-cc #mozilla #foundation #region-us \n",
"# Dataset Card for Common Voice Corpus 15.0\n\n\n\nThis dataset is an unofficial version of the Mozilla Common Voice Corpus 15. It was downloaded and converted from the project's website URL",
"## Languages",
"## How to use\nThe datasets library allows you to load and pre-process your dataset in pure Python, at scale. The dataset can be downloaded and prepared in one call to your local drive by using the load_dataset function.\n\nFor example, to download the Portuguese config, simply specify the corresponding language config name (i.e., \"pt\" for Portuguese):\n\nUsing the datasets library, you can also stream the dataset on-the-fly by adding a streaming=True argument to the load_dataset function call. Loading a dataset in streaming mode loads individual samples of the dataset at a time, rather than downloading the entire dataset to disk.\n\n\n\nBonus: create a PyTorch dataloader directly with your own datasets (local/streamed).",
"### Local",
"### Streaming\n\n\nTo find out more about loading and preparing audio datasets, head over to URL",
"### Dataset Structure\nData Instances\nA typical data point comprises the path to the audio file and its sentence. Additional fields include accent, age, client_id, up_votes, down_votes, gender, locale and segment.",
"### Licensing Information\nPublic Domain, CC-0"
]
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| [
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|
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