Civil Rights
Movements, leaders, victories and the continuing fight for equality.
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Explore the people, places, events, achievements, struggles and stories that shaped our journey.
Movements, leaders, victories and the continuing fight for equality.
Innovation, patents, science, technology and world-changing contributions.
Pioneers, champions, Negro Leagues, records, activism and excellence.
Meet the people whose lives, choices and achievements shaped the journey.
Black towns, communities, institutions and places where history happened.
Moments that changed communities, movements, institutions and the nation.
Katherine Johnson’s mathematical calculations helped guide some of America’s most important early space missions while she confronted the racial and gender barriers faced by Black women in twentieth-century America.
MORE →Reflects the personal views, recollections, and perspective of the author, Mike Davis.
This is a personal recollection on the Move fire on May 13, 1985
| fastText | |
|---|---|
 | |
| Developer | Facebook's AI Research (FAIR) lab[1] |
| Release | November 9, 2015 |
| Stable release | 0.9.2[2]
/ April 28, 2020 |
| Written in | C++, Python |
| Platform | Linux, macOS, Windows |
| Type | Machine learning library |
| License | MIT License |
| Website | fasttext |
| Repository | github |
fastText is an open-source library developed by Facebook AI Research (FAIR) for learning word representations and performing text classification. It was publicly released in 2016.[3]
For word representation, fastText incorporates subword information using character n-grams, which enables it to construct representations for words not encountered during training.[4] The library also provides supervised methods designed for efficient text classification.[5]
The GitHub repository was archived on March 19, 2024.
fastText builds on the skip-gram model used in word2vec, but also takes the internal structure of words into account. Instead of learning a representation for each word only as a whole, it represents words using character n-grams. Words that share character sequences can therefore share some of the same learned information.[6]
This use of subword information is particularly useful for rare words and languages with complex word formation, and also allows fastText to construct representations for words that were not seen during training.[6] Facebook AI Research later released pretrained fastText vectors for many languages, including a collection for 157 languages trained on Common Crawl and Wikipedia.[7]
fastText can also be used for supervised text classification. It combines information from the words and word n-grams in a text to predict a class label. The method was designed to be computationally efficient and, in its original evaluation, achieved accuracy comparable to several contemporary deep-learning models while training and making predictions much faster.[8]
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fastText is an open-source library developed by Facebook AI Research (FAIR) for learning word representations and performing text classification. It was publicly released in 2016. For word representation, fastText incorporates subword information using character n-grams, which enables it to construct representations for words not encountered during training. The library also provides supervised methods designed for efficient text classification. The GitHub repository was archived on March 19, 2024.
Tomáš Mikolov is a Czech computer scientist known for his work on neural language models and word representations. He was the lead author of the 2013 paper that introduced the word2vec models, a technique for learning word embeddings from text. He later co-authored research associated with fastText. Mikolov received his PhD from Brno University of Technology and worked at Microsoft Research, Google Brain, and Facebook AI Research. In 2020, he joined the Czech Institute of Informatics, Robotics and Cybernetics at the Czech Technical University in Prague.
Semantic spaces in the natural language domain aim to create representations of natural language that are capable of capturing meaning. The original motivation for semantic spaces stems from two core challenges of natural language: Vocabulary mismatch (the fact that the same meaning can be expressed in many ways) and ambiguity of natural language (the fact that the same term can have several meanings). The application of semantic spaces in natural language processing (NLP) aims at overcoming limitations of rule-based or model-based approaches operating on the keyword level. The main drawback with these approaches is their brittleness, and the large manual effort required to create either rule-based NLP systems or training corpora for model learning. Rule-based and machine learning based models are fixed on the keyword level and break down if the vocabulary differs from that defined in the rules or from the training material used for the statistical models. Research in semantic spaces dates back more than 20 years. In 1996, two papers were published that raised a lot of attention around the general idea of creating semantic spaces: latent semantic analysis and Hyperspace Analogue to Language. However, their adoption was limited by the large computational effort required to construct and use those semantic spaces. A breakthrough with regard to the accuracy of modelling associative relations between words (e.g. "spider-web", "lighter-cigarette", as opposed to synonymous relations such as "whale-dolphin", "astronaut-driver") was achieved by explicit semantic analysis (ESA) in 2007. ESA was a novel (non-machine learning) based approach that represented words in the form of vectors with 100,000 dimensions (where each dimension represents an Article in Wikipedia). However practical applications of the approach are limited due to the large number of required dimensions in the vectors. More recently, advances in neural network techniques in combination with other new approaches (tensors) led to a host of new recent developments: Word2vec from Google, GloVe from Stanford University, and fastText from Facebook AI Research (FAIR) labs.
Qt (/ˈkjuːt/ pronounced "cute") is a cross-platform application development framework for creating graphical user interfaces as well as cross-platform applications that run on various software and hardware platforms such as Linux, Windows, macOS, Android or embedded systems with little or no change in the underlying codebase while still being a native application with native capabilities and speed. Qt is currently being developed by Qt Group, a publicly listed company, and the Qt Project under open-source governance, involving individual developers and organizations working to advance Qt. Qt is available under both commercial licenses and open-source GPL 2.0, GPL 3.0, and LGPL 3.0 licenses.
Before the 1921 destruction of Tulsa’s Greenwood District, Black residents had created a remarkable center of business and community life. The district included stores, professional offices, entertainment venues and homes owned by Black citizens. Understanding Greenwood means learning what was built—not only what was burned.
MORE →Madam C.J. Walker