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pre-normalization, and reversible tokenization described therein, with the exception that we use alternating dense and locally banded sparse attention patterns in the layers of the transformer, similar to the Sparse Transformer [CGRS19]. To study the dependence of ML performance on model...
The model learned, NaiveBayesModel, may be used as a Transformer.The workflow of an entire machine learning process - from taking in data, converting it to the required format, and fitting it to a model - is represented in the form of a pipeline in Spark ML. The pipeline consists of a...
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Deep learning has disrupted nearly every field of research, including those of direct importance to drug discovery, such as medicinal chemistry and pharmacology. This revolution has largely been attributed to the unprecedented advances in highly parallel
CodeTF - A One-stop Transformer Library for State-of-the-art Code LLM Table of Contents Introduction Installation Getting Started Inferencing Pipeline Model Zoo Fine-Tuning Your Own Model Evaluate On Well-Known Benchmarks Utilities to Manipulate Source Code Based on AST ...
That being said, it wasn’t until 2018 that NLP had its very own ImageNet moment with the release of large pretrained language models trained using the Transformer architecture; the most notable of these was Google’s BERT, which was launched in November 2018. In 2019, generative models such...
Spatial Transformer Networks Similarity Networks and Functions Conditional Similarity Networks Reasoning Inferring and Executing Programs for Visual Reasoning General NLP nanoGPT, fastest repository for training/finetuning medium-sized GPTs minGPT, Re-implementation of GPT to be small, clean, interpretable an...
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