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de-emphasize metrics as a repo type (kinda dead)
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docs/hub/api.md

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## Repo listing API
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The following endpoints help get information about models, datasets, Spaces, and metrics stored on the Hub.
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The following endpoints help get information about models, datasets, and Spaces stored on the Hub.
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<Tip>
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When making API calls to retrieve information about repositories, the <code>createdAt</code> attribute indicates the time when the respective repository was created. It's important to note that there is a unique value, <code>2022-03-02T23:29:04.000Z</code> assigned to all repositories that were created before we began storing creation dates.

docs/hub/index.md

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The Hugging Face Hub is a platform with over 900k models, 200k datasets, and 300k demos in which people can easily collaborate in their ML workflows. The Hub works as a central place where anyone can share, explore, discover, and experiment with open-source Machine Learning.
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No single company, including the Tech Titans, will be able to “solve AI” by themselves – the only way we'll achieve this is by sharing knowledge and resources in a community-centric approach. We are building the largest open-source collection of models, datasets, demos and metrics on the Hugging Face Hub to democratize and advance ML for everyone 🚀.
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No single company, including the Tech Titans, will be able to “solve AI” by themselves – the only way we'll achieve this is by sharing knowledge and resources in a community-centric approach. We are building the largest open-source collection of models, datasets, and demos on the Hugging Face Hub to democratize and advance ML for everyone 🚀.
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We encourage you to read the [Code of Conduct](https://huggingface.co/code-of-conduct) and the [Content Guidelines](https://huggingface.co/content-guidelines) to familiarize yourself with the values that we expect our community members to uphold 🤗.
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## Models
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You can discover and use dozens of thousands of open-source ML models shared by the community. To promote responsible model usage and development, model repos are equipped with [Model Cards](./model-cards) to inform users of each model's limitations and biases. Additional [metadata](./model-cards#model-card-metadata) about info such as their tasks, languages, and metrics can be included, with training metrics charts even added if the repository contains [TensorBoard traces](./tensorboard). It's also easy to add an [**inference widget**](./models-widgets) to your model, allowing anyone to play with the model directly in the browser! For programmatic access, a serverless API is provided to [**instantly serve your model**](./models-inference).
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You can discover and use dozens of thousands of open-source ML models shared by the community. To promote responsible model usage and development, model repos are equipped with [Model Cards](./model-cards) to inform users of each model's limitations and biases. Additional [metadata](./model-cards#model-card-metadata) about info such as their tasks, languages, and evaluation results can be included, with training metrics charts even added if the repository contains [TensorBoard traces](./tensorboard). It's also easy to add an [**inference widget**](./models-widgets) to your model, allowing anyone to play with the model directly in the browser! For programmatic access, a serverless API is provided to [**instantly serve your model**](./models-inference).
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To upload models to the Hub, or download models and integrate them into your work, explore the [**Models documentation**](./models). You can also choose from [**over a dozen libraries**](./models-libraries) such as 🤗 Transformers, Asteroid, and ESPnet that support the Hub.
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