Hugging Face

From
Revision as of 18:22, 19 April 2023 by BPeat (talk | contribs) (→‎Wolfram Plugin)
Jump to: navigation, search

YouTube search... ...Google search



Hugging Face is an American company that develops tools for building applications using machine learning. It is most notable for its transformers library built for natural language processing applications and its platform that allows users to share machine learning models and datasets. Hugging Face is a community and a platform for artificial intelligence and data science that aims to democratize AI knowledge and assets used in AI models. The platform allows users to build, train and deploy state of the art models powered by open source machine learning. It also provides a place where a broad community of data scientists, researchers, and ML engineers can come together and share ideas, get support and contribute to open source projects. Is there anything else you would like to know? - Wikipedia


Source: Conversation with Bing, 4/14/2023 (1) What is Hugging Face - A Beginner's Guide - ByteXD. https://bytexd.com/what-is-hugging-face-beginners-guide/. (2) Hugging Face – The AI community building the future.. https://huggingface.co/. (3) What's Hugging Face? An AI community for sharing ML models and datasets .... https://towardsdatascience.com/whats-hugging-face-122f4e7eb11a. (4) Private Hub - Hugging Face. https://huggingface.co/platform. (5) Hugging Face Hub documentation. https://huggingface.co/docs/hub/main. (6) What is Hugging Face - A Beginner's Guide - ByteXD. https://bytexd.com/what-is-hugging-face-beginners-guide/. allows users to share machine learning models and datasets.

Wolfram ChatGPT

YouTube ... Quora ...Google search ...Google News ...Bing News

demonstrates a conversational agent implemented with OpenAI GPT-3.5 and LangChain. When necessary, it leverages tools for complex math, searching the internet, and accessing news and weather. Uses talking heads from Ex-Human.

Wolfram Plugin

Wolfram plugin that will give them access to “computation, math, curated knowledge and real-time data” from Wolfram Alpha and Wolfram Language. When you ask the AI bot a question, it will automatically draw information from Wolfram. Wolfram showed ChatGPT being able to do mathematical integration, graph plotting, question-answering and even visual Jupiter’s moons. Firstly, when you send ChatGPT a query, it will reword it and send it over to Wolfram to compute. Wolfram will then respond, giving ChatGPT the answer. Finally, it is now down to ChatGPT’s advanced processing techniques to reiterate the answer back to you.

HuggingGPT

YouTube ... Quora ...Google search ...Google News ...Bing News


HuggingGPT is a framework that leverages Large Language Model (LLM) such as ChatGPT to connect various AI models in machine learning communities like Hugging Face to solve AI tasks. Solving complicated AI tasks with different domains and modalities is a key step toward advanced artificial intelligence. While there are abundant AI models available for different domains and modalities, they cannot handle complicated AI tasks. Considering Large Language Model (LLM) have exhibited exceptional ability in language understanding, generation, interaction, and reasoning, we advocate that LLMs could act as a controller to manage existing AI models to solve complicated AI tasks and language could be a generic interface to empower this. Based on this philosophy, we present HuggingGPT, a framework that leverages LLMs (e.g., ChatGPT) to connect various AI models in machine learning communities (e.g., Hugging Face) to solve AI tasks. Specifically, we use ChatGPT to conduct task planning when receiving a user request, select models according to their function descriptions available in Hugging Face, execute each subtask with the selected AI model, and summarize the response according to the execution results. The workflow of this system consists of four stages:

  1. Task Planning: Using ChatGPT to analyze the requests of users to understand their intention, and disassemble them into possible solvable tasks via prompts.
  2. Model Selection: To solve the planned tasks, ChatGPT selects expert models that are hosted on Hugging Face based on model descriptions.
  3. Task Execution: Invoke and execute each selected model, and return the results to ChatGPT.
  4. Response Generation: Finally, using ChatGPT to integrate the prediction of all models, and generate answers for users.


Hugging Face NLP Library - Open Parallel Corpus (OPUS)

Hugging Face

OPUS is a growing collection of translated texts from the web. In the OPUS project we try to convert and align free online data, to add linguistic annotation, and to provide the community with a publicly available parallel corpus. OPUS is a project undertaken by the University of Helsinki and global partners to gather and open-source a wide variety of language data sets. OPUS is based on open source products and the corpus is also delivered as an open content package. We used several tools to compile the current collection. All pre-processing is done automatically. No manual corrections have been carried out. The OPUS collection is growing! ... OPUS the open parallel corpus

Retrieved from "https://primo.ai/index.php?title=Hugging_Face&oldid=25840"