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Ever felt behind in any Machine Learning tasks related to natural language processing, computer vision or audio recognition? You need to try out @HuggingFace (huggingface.co/), it provides thousands of pre-trained models to perform tasks in those different modalities, as well as the pipeline API to quickly download and use thise models.
In this tutorial, I'll show you a quick overview of all the ML tasks you could solve using Hugging Face pipelines, the basic API for straightforward inference. I'll be building a lot of small Streamlit apps to demonstrate each use case. There will also be teasers of all you can accomplish through the HF ecosystem, from finetuning a model on your own dataset, choosing a custom model, breaking down a pipeline to a tokenizer or processor and model, and hosting your demo on Hugging Face.
This video will be inspiring enough for you to actually try the library to supercharge your next ML project!
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🐦Follow my Streamlit/ML/KZbinr updates on Twitter: / andfanilo
🗣️ My landing page: andfanilo.com/
👉 Links
- All the ML tasks: huggingface.co...
- The actual model hub: huggingface.co...
- Write with Transformer demo: transformer.hu...
- Thomas Wolf interview for TWIML about the start of HF: • Big Science and Embodi...
My tools (Affiliate links)
- 🎵 Music (Epidemic Sound) - www.epidemicso...
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🪶 A year after my first Epic video about Streamlit, here's my newest Epic video, this time about HF. Those take a loooot of time to create, I'd be grateful for a supportive comment, share or buy me a coffee donation...it would mean a lot, for real! Thanks for watching and reading so far!
⚠️ Disclaimer: This video was made possible by Hugging Face. All views and opinions expressed are solely those of the creator, and do not reflect those of Hugging Face.
Links included in this description might be affiliate links. If you purchase a product or service with the links that I provide I may receive a small commission. Thank you for supporting my channel so I can continue providing you with free content!
#streamlit #python #datascience #dataapps