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Scaling Training of HuggingFace Transformers With Determined
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Scaling Training of HuggingFace Transformers With Determined

Author(s): Towards AI Team

Join Determined AI’s third lunch-and-learn session and learn how to scale the training of HuggingFace Transformers with Determined.

Join an exciting lunch-and-learn event by our friends at Determined AI

Training complex state-of-the-art natural language processing (NLP) models is now a breeze, thanks to HuggingFace — making it an essential open-source go-to for data scientists and machine learning engineers to implement Transformers models and configure them as state-of-the-art NLP models with straightforward library calls. As a result, the library has become crucial for training NLP models, like in Baidu or Alibaba, and has contributed to state-of-the-art results in several NLP tasks.

Our friends at Determined AI are hosting an exciting lunch-and-learn covering training HuggingFace Transformers at scale using Determined! Learn to train Transformers with distributed training, hyperparameter searches, and cheap spot instances — all without modifying code.

Please consider joining on Wednesday, June 30th at 10 AM PT for a hands-on tutorial from Liam Li, a Senior Machine Learning Engineer at Determined AI, and Angela Jiang, a Product Manager at Determined AI (lunch included!).

This fantastic hands-on tutorial will cover the basics of Determined Transformers and walk through how to build a chatbot using a large Transformer language model with distributed training and spot instances.

Join in and come away with an understanding of how to train Transformers at scale with distributed training, experiment and artifact tracking, and resource management, all without needing to modify code.

Please make sure to register for the Meetup event, the zoom meeting, and if you are on slack, please consider joining Determined’s Slack community


Scaling Training of HuggingFace Transformers With Determined was originally published in Towards AI on Medium, where people are continuing the conversation by highlighting and responding to this story.

Published via Towards AI

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