Remove Auto-classification Remove BERT Remove ML Remove Natural Language Processing
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Amazon EC2 DL2q instance for cost-efficient, high-performance AI inference is now generally available

AWS Machine Learning Blog

With eight Qualcomm AI 100 Standard accelerators and 128 GiB of total accelerator memory, customers can also use DL2q instances to run popular generative AI applications, such as content generation, text summarization, and virtual assistants, as well as classic AI applications for natural language processing and computer vision.

BERT 95
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What are the Different Types of Transformers in AI

Mlearning.ai

While factors like the number of parameters, activation functions, architectural nuances, context sizes, pretraining data corpus, and languages used in training differentiate these models, one often overlooked aspect that can significantly impact their performance is the training process. That is it for this piece.

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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

Sentiment analysis and other natural language programming (NLP) tasks often start out with pre-trained NLP models and implement fine-tuning of the hyperparameters to adjust the model to changes in the environment. script will create the VPC, subnets, auto scaling groups, the EKS cluster, its nodes, and any other necessary resources.

BERT 73
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Adapting language-based models beyond English

Snorkel AI

While a majority of Natural Language Processing (NLP) models focus on English, the real world requires solutions that work with languages across the globe. Labeling data from scratch for every new language would not scale, even if the final architecture remained the same.

BERT 52
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Simplify Deployment and Monitoring of Foundation Models with DataRobot MLOps

DataRobot Blog

These developments have allowed researchers to create models that can perform a wide range of natural language processing tasks, such as machine translation, summarization, question answering and even dialogue generation. Then you can use the model to perform tasks such as text generation, classification, and translation.

BERT 52
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Deploy thousands of model ensembles with Amazon SageMaker multi-model endpoints on GPU to minimize your hosting costs

AWS Machine Learning Blog

In cases where the MME receives many invocation requests, and additional instances (or an auto-scaling policy) are in place, SageMaker routes some requests to other instances in the inference cluster to accommodate for the high traffic. The second ensemble transforms raw natural language sentences into embeddings and consists of three models.

BERT 75
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Dialogue-guided visual language processing with Amazon SageMaker JumpStart

AWS Machine Learning Blog

The system is further refined with DistilBERT , optimizing our dialogue-guided multi-class classification process. Additionally, you benefit from advanced features like auto scaling of inference endpoints, enhanced security, and built-in model monitoring.