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--- |
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title: Huggingface Workshop |
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emoji: 😻 |
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colorFrom: yellow |
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colorTo: gray |
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sdk: streamlit |
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sdk_version: 1.41.1 |
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app_file: app.py |
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pinned: false |
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--- |
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# Huggingface Workshop |
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write this as an instructor |
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steps during the workshop: |
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- show huggingface; as a website and as a company |
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- define how huggingface can be valuable in daily business?! |
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should take |
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Huggingface has become an essential hub for the artifical intelligence community to share models, datasets and intelligent applications. |
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Discover it with us. |
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Decide on an AI usecase. |
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- describe what to do |
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e.g. 5 examples |
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Pick a dataset to learn from from [Datasets](https://huggingface.co/datasets). |
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what do I do in ml life-cycle |
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Explore the dataset and prepare it for training. |
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Pick a model to learn from the chosen data from [Models](https://huggingface.co/models). To save time choose a pre-trained model that you want to refine for the specific usecase. |
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Train the model. |
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Use GoogleColab to train a model on GPU for free. |
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Deploy the model with [Spaces](https://huggingface.co/spaces) and build an interface so that the model's behavior can be tested. Using spaces you can run your model on minimal but often sufficient hardware for free. |
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Done :) |
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Stuck? Take a look at the [hints](https://huggingface.co/spaces/till-onethousand/huggingface-workshop/blob/main/HINTS.md). |
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--- |
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Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference |
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