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Accelerate hyperparameter grid search for sentiment analysis with BERT models using Weights & Biases, Amazon EKS, and TorchElastic

AWS Machine Learning Blog

Transformer-based language models such as BERT ( Bidirectional Transformers for Language Understanding ) have the ability to capture words or sentences within a bigger context of data, and allow for the classification of the news sentiment given the current state of the world. W&B Sweeps will automate this kind of exploration.

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Google’s Dr. Arsanjani on Enterprise Foundation Model Challenges

Snorkel AI

It came to its own with the creation of the transformer architecture: Google’s BERT, OpenAI, GPT2 and then 3, LaMDA for conversation, Mina and Sparrow from Google DeepMind. Others, toward language completion and further downstream tasks. So there’s obviously an evolution. Really quickly, LLMs can do many things.

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Google’s Arsanjani on Enterprise Foundation Model Challenges

Snorkel AI

It came to its own with the creation of the transformer architecture: Google’s BERT, OpenAI, GPT2 and then 3, LaMDA for conversation, Mina and Sparrow from Google DeepMind. Others, toward language completion and further downstream tasks. So there’s obviously an evolution. Really quickly, LLMs can do many things.

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Training large language models on Amazon SageMaker: Best practices

AWS Machine Learning Blog

Large language models (LLMs) are neural network-based language models with hundreds of millions ( BERT ) to over a trillion parameters ( MiCS ), and whose size makes single-GPU training impractical. and above, AWS contributed an automated communication algorithm selection logic for EFA networks ( NCCL_ALGO can be left unset).