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Tag cloud of a mailing list [1] A tag cloud with terms related to Web 2.0. A tag cloud (also known as a word cloud or weighted list in visual design) is a visual representation of text data which is often used to depict keyword metadata on websites, or to visualize free form text. Tags are usually single words, and the importance of each tag is ...
Similarly, an image model prompted with the text "a photo of a CEO" might disproportionately generate images of white male CEOs, [112] if trained on a racially biased data set. A number of methods for mitigating bias have been attempted, such as altering input prompts [ 113 ] and reweighting training data.
Gen-2 is a multimodal AI system that can generate novel videos with text, images or video clips. The model is a continuation of Gen-1 and includes a modality to generate video conditioned to text. Gen-2 is one of the first commercially available text-to-video models .
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Interactive word clouds and word frequency tables can now be obtained directly on keyword retrieval and keyword-in-context (KWIC) results allowing one to quickly identify words associated with specific content categories, or those appearing, before, after a specific target item.
There are over 200 community made themes to use with the app. Obsidian operates on a folder of text documents; [ 18 ] each new note in Obsidian generates a new text document, and all the documents can be searched from within the app. [ 9 ] [ 4 ] Obsidian allows internal linking between notes and creates an interactive graph that visualizes the ...
Voyant "was conceived to enhance reading through lightweight text analytics such as word frequency lists, frequency distribution plots, and KWIC displays." [3] Its interface is composed of panels which perform these varied analytical tasks. These panels can also be embedded in external web texts (e.g. a web article could include a Voyant panel ...
GPT-J was designed to generate English text from a prompt. It was not designed for translating or generating text in other languages or for performance without first fine-tuning the model for a specific task. [2] Nonetheless, GPT-J performs reasonably well even without fine-tuning, even in translation (at least from English to French). [8]