Hugging Face: Difference between revisions

Hugging face is a company that works on the field of [[artificial intelligence]] ([[AI]]), self-described as “home of [[machine learning]].” <ref name="”1”">Romano, R (2022).  An introduction to Hugging Face transformers for NLP. Qwak. https://www.qwak.com/post/an-introduction-to-hugging-face-transformers-for-nlp</ref> It’s a community and data science platform that provides both tools that empower users to build, train, and deploy [[machine learning]] ([[ML]]) models that are based on open-source code, and a place where a community of researchers, data scientists, and ML engineers can participate by sharing ideas and contributing to open source projects. <ref name="”2”">Mahmood, O (2022). What’s Hugging Face? Towards Data Science. https://towardsdatascience.com/whats-hugging-face-122f4e7eb11a</ref> It’s open-source hub offers a library of state-of-the-art models for [[Natural Language Processing]] ([[NLP]]), computer vision, and others that are relevant to AI. In August 2022, there were more than 61 thousand pre-trained models. (3) Technological giants like Microsoft, Google, Facebook, Apple, AWS, and others have used Hugging Face’s models, datasets, and libraries. <ref name="”3”">Nabeel, M. What is Hugging Face? Educative. https://www.educative.io/answers/what-is-huggingface</ref> <ref name="”4”">Syal, A (2020). Hugging Face: A Step Towards Democratizing NLP. Towards Data Science. https://towardsdatascience.com/hugging-face-a-step-towards-democratizing-nlp-2c79f258c951</ref>

The company began by offering a chat platform in 2017. Then, it focused on NLP, creating a NLP library that made easily accessible resources like transformers, datasets, tokenizers, etc. Releasing a wide variety of tools made them popular among big tech companies. <ref name="”5”">Sarma, N (2023). Hugging Face pre-trained models: Find the best one for your task. Neptune.ai. https://neptune.ai/blog/hugging-face-pre-trained-models-find-the-best</ref> NLP technologies can help to bridge the communication gap between humans and machines since computers do not process information in the same way. <ref name="”5”"></ref> With these systems, “it is possible for computers to read text, hear speech, interpret it, measure sentiment, and even determine which parts of the text or speech are important”. <ref name="”4”"></ref>

As work practices have become more flexible, there has been an increase in the adoption of tools for remote collaboration between data science teams, experts, and amateurs. Sharing knowledge and resources is gaining relevance in AI in order to advance the field since probably no single company will be able to “solve” it on its own. Hugging Face embraces this community work by providing a community “Hub,” a place where users can share and examine models and datasets, therefore contributing to its goal of democratizing AI for all. <ref name="”3”"></ref>

In 2019, the company raised $15 million to build a comprehensive NLP library. In 2021, it raised another $40 million in a Series B funding round in which existing investors like Lux Capital, A.Capital, and Betaworks participated. <ref name="”1”"></ref> <ref name="”4”"></ref> <ref name="”6”">Dillet, R (2021). Hugging Face raises $40 million for its natural language processing library.  TechCrunch. https://techcrunch.com/2021/03/11/hugging-face-raises-40-million-for-its-natural-language-processing-library/?guce_referrer=aHR0cHM6Ly9lbi53aWtpcGVkaWEub3JnLw&guce_referrer_sig=AQAAACIYR4_aqmp84G_gD8G4LGbxperNQX6g_CtEDFPaIJ9-rf3_yCSbMhn0b4nE-oyzeK0gbOaDYg_ZBF9UVOfhOG58FUzC_cKJFEnF0YaqhE2OsWp5DljgGXCzl-J4NWMV9FrWyYhc0JSUjVvDyYSuwx096p7ABZOPQdsjU0NCJLEn</ref> Besides increasing its funding, Hugging Face has also acquired Gradio, “a platform that enables anyone to demo their ML models through a web-based interface.” <ref name="”1”"></ref>