https://academictorrents.com/nolicensespecifiedhttps://academictorrents.com/nolicensespecified
171 million names (100 million unique) This torrent contains: The URL of every searchable Facebook user s profile The name of every searchable Facebook user, both unique and by count (perfect for post-processing, datamining, etc) Processed lists, including first names with count, last names with count, potential usernames with count, etc The programs I used to generate everything So, there you have it: lots of awesome data from Facebook. Now, I just have to find one more problem with Facebook so I can write "Revenge of the Facebook Snatchers" and complete the trilogy. Any suggestions? >:-) Limitations So far, I have only indexed the searchable users, not their friends. Getting their friends will be significantly more data to process, and I don t have those capabilities right now. I d like to tackle that in the future, though, so if anybody has any bandwidth they d like to donate, all I need is an ssh account and Nmap installed. An additional limitation is that these are on
Attribution-ShareAlike 3.0 (CC BY-SA 3.0)https://creativecommons.org/licenses/by-sa/3.0/
License information was derived automatically
Corpus consisting of 10,000 Facebook posts manually annotated on sentiment (2,587 positive, 5,174 neutral, 1,991 negative and 248 bipolar posts). The archive contains data and statistics in an Excel file (FBData.xlsx) and gold data in two text files with posts (gold-posts.txt) and labels (gols-labels.txt) on corresponding lines.
Attribution-NonCommercial 4.0 (CC BY-NC 4.0)https://creativecommons.org/licenses/by-nc/4.0/
License information was derived automatically
PE Video Dataset (PVD)
[📃 Tech Report] [📂 Github] The PE Video Dataset (PVD) is a large-scale collection of 1 million diverse videos, featuring 120,000+ expertly annotated clips. The dataset was introduced in our paper "Perception Encoder".
Overview
PE Video Dataset (PVD) comprises 1M high quality and diverse videos. Among them, 120K videos are accompanied by automated and human-verified annotations. and all videos are accompanied with video description and keywords.… See the full description on the dataset page: https://huggingface.co/datasets/facebook/PE-Video.
This table includes platform data for Facebook participants in the Deactivation experiment. Each row of the dataset corresponds to data from a participant’s Facebook user account. Each column contains a value, or set of values, that aggregates log data for this specific participant over a certain period of time.
MIT Licensehttps://opensource.org/licenses/MIT
License information was derived automatically
## Overview
Facebook is a dataset for instance segmentation tasks - it contains Face T0jN annotations for 1,943 images.
## Getting Started
You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.
## License
This dataset is available under the [MIT license](https://creativecommons.org/licenses/MIT).
Attribution-NonCommercial 4.0 (CC BY-NC 4.0)https://creativecommons.org/licenses/by-nc/4.0/
License information was derived automatically
Data sample for ExploreToM: Program-guided aversarial data generation for theory of mind reasoning
ExploreToM is the first framework to allow large-scale generation of diverse and challenging theory of mind data for robust training and evaluation. Our approach leverages an A* search over a custom domain-specific language to produce complex story structures and novel, diverse, yet plausible scenarios to stress test the limits of LLMs. Our A* search procedure aims to find particularly… See the full description on the dataset page: https://huggingface.co/datasets/facebook/ExploreToM.
Dataset Card for Winoground
Dataset Description
Winoground is a novel task and dataset for evaluating the ability of vision and language models to conduct visio-linguistic compositional reasoning. Given two images and two captions, the goal is to match them correctly—but crucially, both captions contain a completely identical set of words/morphemes, only in a different order. The dataset was carefully hand-curated by expert annotators and is labeled with a rich set of… See the full description on the dataset page: https://huggingface.co/datasets/facebook/winoground.
CC0 1.0 Universal Public Domain Dedicationhttps://creativecommons.org/publicdomain/zero/1.0/
License information was derived automatically
## Overview
Fb Detection is a dataset for object detection tasks - it contains Icons annotations for 1,098 images.
## Getting Started
You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.
## License
This dataset is available under the [Public Domain license](https://creativecommons.org/licenses/Public Domain).
No-reference (NR) perceptual video quality assessment (VQA) is a complex, unsolved, and important problem to social and streaming media applications. Efficient and accurate video quality predictors are needed to monitor and guide the processing of billions of shared, often imperfect, user-generated content (UGC). Unfortunately, current NR models are limited in their prediction capabilities on real-world, "in-the-wild" UGC video data. To advance progress on this problem, we created the largest (by far) subjective video quality dataset, containing 39, 000 real-world distorted videos and 117, 000 space-time localized video patches ("v-patches"), and 5.5M human perceptual quality annotations. Using this, we created two unique NR-VQA models: (a) a local-to-global region-based NR VQA architecture (called PVQ) that learns to predict global video quality and achieves state-of-the-art performance on 3 UGC datasets, and (b) a first-of-a-kind space-time video quality mapping engine (called PVQ Mapper) that helps localize and visualize perceptual distortions in space and time. We will make the new database and prediction models available immediately following the review process.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
## Overview
Facebook Ads PixelPro9 SponsoredTexts is a dataset for object detection tasks - it contains Words LVq8 MDMT annotations for 1,663 images.
## Getting Started
You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.
## License
This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).
https://creativecommons.org/publicdomain/zero/1.0/https://creativecommons.org/publicdomain/zero/1.0/
https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F1842206%2Fd4a6033b6bd31af45d5175d02e697934%2FAPPLEAPPS2.png?generation=1700357122842963&alt=media" alt="">
These reviews are from Apple App Store
This dataset should paint a good picture on what is the public's perception of the apps over the years. Using this dataset, we can do the following
(AND MANY MORE!)
Images generated using Bing Image Generator
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
## Overview
Facebook Scrap is a dataset for object detection tasks - it contains Facebook Scrap annotations for 243 images.
## Getting Started
You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.
## License
This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).
https://choosealicense.com/licenses/llama3.2/https://choosealicense.com/licenses/llama3.2/
Dataset Card for PLM-Image Auto
[📃 Tech Report] [📂 Github] Sythetic image captions and QAs used in PLM, please refer to the paper, Section 3, for more details. The sythetic annotations covers: SA1B, Openimages, Obejct365, ArxivQA, UCSF, PDFAcc.
Dataset Structure
Image Captions (SA1B, Openimages, Obejct365)
Data fields are :
image_id: a string feature, unique identifier for the image. image: a string feature, the actual image path in the correspoding data… See the full description on the dataset page: https://huggingface.co/datasets/facebook/PLM-Image-Auto.
MIT Licensehttps://opensource.org/licenses/MIT
License information was derived automatically
Dataset Card for KILT
Dataset Summary
KILT has been built from 11 datasets representing 5 types of tasks:
Fact-checking Entity linking Slot filling Open domain QA Dialog generation
All these datasets have been grounded in a single pre-processed Wikipedia dump, allowing for fairer and more consistent evaluation as well as enabling new task setups such as multitask and transfer learning with minimal effort. KILT also provides tools to analyze and understand the… See the full description on the dataset page: https://huggingface.co/datasets/facebook/kilt_tasks.
Attribution-NonCommercial-NoDerivs 4.0 (CC BY-NC-ND 4.0)https://creativecommons.org/licenses/by-nc-nd/4.0/
License information was derived automatically
Facebook probably needs no introduction; nonetheless, here is a quick history of the company. The world’s biggest and most-famous social network was launched by Mark Zuckerberg while he was a...
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
## Overview
Facebook Ads PixelPro9 Elements is a dataset for object detection tasks - it contains Words LVq8 VzTm annotations for 1,663 images.
## Getting Started
You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.
## License
This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).
https://choosealicense.com/licenses/cc0-1.0/https://choosealicense.com/licenses/cc0-1.0/
A large-scale multilingual speech corpus for representation learning, semi-supervised learning and interpretation.
https://creativecommons.org/publicdomain/zero/1.0/https://creativecommons.org/publicdomain/zero/1.0/
https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F16190527%2F53f929ff911508241fa2b6808c9d885f%2FFacebookAdsBidding-1200x630-1.jpg?generation=1696942877786519&alt=media" alt="">
Facebook recently introduced a new bidding system called "average bidding"(test group) alongside the existing "maximum bidding"(control group) system. These bidding systems determine which ads get displayed to users based on how much advertisers are willing to pay.
With "maximum bidding," advertisers specify the maximum amount they are willing to pay for each impression. For example, an advertiser might say, "I'm willing to pay a maximum of $10 for each impression."
With "average bidding," advertisers specify an average amount they are willing to pay for impressions. For instance, they might say, "On average, I'm willing to pay $6 for each impression."
Here's the key point: In this dataset, we've gathered the results of these two bidding strategies over the last 40 days to see which one is more effective at getting their ads displayed to the target audience.
As a forward-thinking company poised to make waves in the realm of Facebook advertising, we're on a mission to unearth the most advantageous approach for our brand. Our burning questions:
Enter the AB Test: Today, we embark on a journey of data-driven discovery, where the clash of titans—Average Bidding versus Maximum Bidding—will be meticulously dissected and evaluated. We're on a quest for insights that will define our Facebook advertising strategy's future, using data as our compass and innovation as our weapon.
The outcome of this AB Test will not just answer our questions but will be the harbinger of a glorious era in our Facebook advertising endeavors. Stay tuned for a transformation that will leave a mark on the digital advertising landscape.
https://networkrepository.com/policy.phphttps://networkrepository.com/policy.php
Facebook social network - A social friendship network extracted from Facebook consisting of people (nodes) with edges representing friendship ties.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
## Overview
Fb_posting_ui is a dataset for object detection tasks - it contains Objects annotations for 1,960 images.
## Getting Started
You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.
## License
This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).
https://academictorrents.com/nolicensespecifiedhttps://academictorrents.com/nolicensespecified
171 million names (100 million unique) This torrent contains: The URL of every searchable Facebook user s profile The name of every searchable Facebook user, both unique and by count (perfect for post-processing, datamining, etc) Processed lists, including first names with count, last names with count, potential usernames with count, etc The programs I used to generate everything So, there you have it: lots of awesome data from Facebook. Now, I just have to find one more problem with Facebook so I can write "Revenge of the Facebook Snatchers" and complete the trilogy. Any suggestions? >:-) Limitations So far, I have only indexed the searchable users, not their friends. Getting their friends will be significantly more data to process, and I don t have those capabilities right now. I d like to tackle that in the future, though, so if anybody has any bandwidth they d like to donate, all I need is an ssh account and Nmap installed. An additional limitation is that these are on