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Hugging Face: Difference between revisions
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Hugging | ==Introduction== | ||
[[Hugging Face]] is a [[company]] and [[model hub]] that works on the field of [[artificial intelligence]] ([[AI]]), self-described as the “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> Its 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]]. 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 an 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> | The company began by offering a chat platform in 2017. Then, it focused on NLP, creating an 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> | ||
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==Community== | ==Community== | ||
The vast community contribution of [[models]], [[datasets]], and [[spaces]] can be accessed through the platform. Most of the models in this repository are built in [[PyTorch]]. Sometimes, alternatives for the main tasks are also available in [[TensorFlow]] and other [[ML libraries]]. <ref name="”2”"></ref> | |||
A quality of life feature that saves time from exploring through the community models is Tasks. This provides a curated view of the model, dependent on the task that a user wants to accomplish. For each task, there’s an explanation in a visual and intuitive way, with diagrams, videos, and links to a demo that uses the Inference API. To complement this there are also descriptions of use cases and task variants. <ref name="”2”"></ref> | |||
A quality of life feature that saves time from exploring through the community models is Tasks. This provides a curated view of the model, dependent on the task that a user wants to accomplish. For each task there’s an explanation in a visual and intuitive way, with diagrams, | |||
==Inference Endpoints== | ==Inference Endpoints== | ||