In this video, I show you how to manage models and datasets in your own Hugging Face organization:

  • Creating your organization,
  • Creating private repositories with the Hugging Face CLI,
  • Importing models and datasets with git,
  • Editing dataset and model cards,
  • Setting permissions for organization members

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Dataset: https://huggingface.co/datasets/reuters21578

Model: https://huggingface.co/juliensimon/autonlp-reuters-summarization-31447312

New to Transformers? Check out the Hugging Face course at https://huggingface.co/course

In this video, I use AutoNLP, an AutoML product designed by Hugging Face, to fine-tune a model on the Reuters news dataset in order to summarize financial articles.

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Dataset: https://huggingface.co/datasets/reuters21578

Preprocessing notebook and model: https://huggingface.co/juliensimon/autonlp-reuters-summarization-31447312

New to Transformers? Check out the Hugging Face course at https://huggingface.co/course

Transformer models are great. Still, they’re large models, and prediction latency can be a problem. This is the problem that Hugging Face Infinity solves with a single Docker command.

In this video, I start from a pre-trained model hosted on the Hugging Face hub. Using an AWS CPU instance based on the Intel Ice Lake architecture (c6i.xlarge), I optimize my model using the Infinity Multiverse Docker container.

Then, I push the model back to the Hugging Face hub, and I deploy it on a prediction API running in an Infinity container on my AWS instance.

Finally, I predict with the optimized model and get a 5x speedup compared to the original model.

Original model: https://huggingface.co/juliensimon/autonlp-imdb-demo-hf-16622767

Code: https://huggingface.co/juliensimon/imdb-demo-infinity/tree/main/code

New to Transformers? Check out the Hugging Face course at https://huggingface.co/course

In this video, I start from a pre-trained model and a dataset hosted on the Hugging Face hub. Running a Jupyter notebook in SageMaker Studio, I pre-process the data, and I fine-tune a sentiment analysis model on SageMaker infrastructure. Then, I deploy the model on a SageMaker endpoint and predict with it.

Next, I retrieve the trained model in S3 and I use the Hugging Face CLI to push the model to the Hugging Face hub. From there, I use the open source Transformers library to work with the model, just like I would do with any Hugging Face model.

Finally, using the SageMaker SDK, I redeploy the model directly from the Hugging Face hub to a SageMaker endpoint.

Dataset and notebook: https://huggingface.co/juliensimon/reviews-sentiment-analysis/tree/main

New to Transformers? Check out the Hugging Face course at https://huggingface.co/course

The 2021 edition of the State of AI Report came out last week. So did the Kaggle State of Machine Learning and Data Science Survey. There’s much to be learned and discussed in these reports, and a couple of takeaways caught my attention.

“AI is increasingly being applied to mission critical infrastructure like national electric grids and automated supermarket warehousing calculations during pandemics. However, there are questions about whether the maturity of the industry has caught up with the enormity of its growing deployment.”

There’s no denying that Machine Learning-powered applications…

In this video, I use AutoNLP, an AutoML product designed by Hugging Face, to train a model that classifies song lyrics according to their genre.

Then, I use Spaces to build and deploy a test web page, where I paste some lyrics and predict them.

The page is public and you can try it for yourself :)

Dataset and preprocessing notebook: https://huggingface.co/datasets/juliensimon/autonlp-data-song-lyrics-demo

Spaces page: https://huggingface.co/spaces/juliensimon/song-lyrics

Julien Simon

Chief Evangelist, Hugging Face (https://huggingface.co)

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