Whisper is a general-purpose speech recognition model. It is trained on a large dataset of diverse audio and is also a multi-task model that can perform multilingual speech recognition as well as speech translation and language identification.
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We believe that by doing this we will create a revolution in innovation in language. In the same way that stable-diffusion helped the world make art and images in new ways we hope Open Assistant can help improve the world by improving language itself.
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This is a Telegram bot that lets you chat with the chatGPT language model using your local browser. The bot uses Playwright to run chatGPT in Chromium, and can parse code and text, as well as send messages. It also includes a /draw command that allows you to generate pictures using stable diffusion. More features are coming soon.
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Time series forecasting is an essential scientific and business problem and as such has also seen a lot of innovation recently with the use of deep learning based models in addition to the classical methods. An important difference between classical methods like ARIMA and novel deep learning methods is the following.
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Text-to-image has advanced at a breathless pace in 2021 - 2022, starting with DALL·E, then DALL·E 2, Imagen, and now Stable Diffusion. I dug into a couple of papers to learn more about the space and organized my understanding into a few key concepts
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Natural Language Processing remains one of the hottest topics of 2022. By using GitHub stars (albeit certainly not the only measure) as a proxy for popularity, we took a look at what NLP projects are getting the most traction so far this year, just as we recently did with machine learning projects. It’s a list with some familiar names but there are plenty of surprises also!
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At Hugging Face, we are working on tackling various problems in open-source machine learning, including, hosting models securely and openly, enabling reproducibility, explainability and collaboration. We are thrilled to introduce you to our new library: Skops! With Skops, you can host your scikit-learn models on the Hugging Face Hub, create model cards for model documentation and collaborate with others.
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Since their introduction in 2017, transformers have revolutionized Natural Language Processing (NLP). Now, transformers are finding applications all over Deep Learning, be it computer vision (CV), reinforcement learning (RL), Generative Adversarial Networks (GANs), Speech or even Biology. Among other things, transformers have enabled the creation of powerful language models like GPT-3 and were instrumental in DeepMind’s recent AlphaFold2, that tackles protein folding.
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Example Jupyter notebooks that demonstrate how to build, train, and deploy Hugging Face Transformers using Amazon SageMaker and the Amazon SageMaker Python SDK.
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The T5 Transformer frames any NLP task as a text-to-text task enabling pre-trained models to easily learn new tasks. Let’s teach the old dog a new trick!
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