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(upbeat music)

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- [Instructor] In this
video, we're going to go over

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the HuggingFace Model Hub navigation.

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This is the huggingface.co landing page.

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To access the model hub,
click on the models tab

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in the upper right corner.

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You should be facing this web interface,

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which can be split into several parts.

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On the left, you'll find categories,

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which you can use to
tailor your model search.

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The first category is the tasks.

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Models on the hub may be used
for a wide variety of tasks.

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These include natural
language processing tasks,

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such as question answering
or text classification,

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but it isn't only limited to NLP.

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Other tasks from other
fields are also available,

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such as image classification
for computer vision,

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or automatic speech
recognition for speech.

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The second category is the libraries.

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Models on the hub usually
share one of three backbones,

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PyTorch, TensorFlow, or JAX.

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However, other backbones, such
as rust or ONNX also exist.

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Finally, this tab can also be used

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to specify from which high-level
framework the models comes.

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This includes Transformers,
but it isn't limited to it.

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The model hub is used to host

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a lot of different frameworks models,

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and we're actively looking to host

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other frameworks models.

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The third category is the datasets tab.

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Selecting a dataset from this
tab means filtering the models

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so that they were trained
on that specific dataset.

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The fourth category is the languages tab.

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Selecting a language from this tab

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means filtering the
models so that they handle

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the language selected.

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Finally, the last category
allows to choose the license

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with which the model is shared.

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On the right, you'll
find the models available

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on the model hub.

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The models are ordered
by downloads by default.

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When clicking on a model,

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you should be facing its model card.

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The model card contains
information about the model,

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its description, intended
use, limitations and biases.

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It can also show code snippets
on how to use the model,

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as well as any relevant information;

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training procedure, data processing,

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evaluation results or copyrights.

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This information is crucial
for the model to be used.

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The better crafted a model card is,

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the easier it will be for other
users to leverage your model

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in their applications.

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On the right of the model
card is the inference API.

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This inference API can be used

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to play with the model directly.

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Feel free to modify the
text and click on compute

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to see how would the model
behave to your inputs.

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At the top of your screen
lies the model tags.

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These include the model task,
as well as any other tag

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that is relevant to the
categories we have just seen.

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The files & versions tab
displays the architecture

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of the repository of that model.

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Here, we can see all the
files that define this model.

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You'll see all usual
features of a Git repository:

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the branches available,

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the commit history

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as well as the commit diff.

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Three different buttons are available

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at the top of the model card.

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The first one shows how to use

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the inference API programmatically.

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The second one shows how to
train this model in SageMaker.

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And the last one shows
how to load that model

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within the appropriate library.

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For BERT, this is transformers.

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(upbeat music)

