Search Results for "transformers" - Page 2

Sparse Attention is OpenAI’s code release for the Sparse Transformer model, introduced in the paper Generating Long Sequences with Sparse Transformers. It explores how modifying the self-attention mechanism with sparse patterns can reduce the quadratic scaling of standard transformers, making it possible to model much longer sequences efficiently. The repository provides implementations of sparse attention layers, training code, and evaluation scripts for benchmark datasets. ...

PyTorch training code and pretrained models for DETR (DEtection TRansformer). We replace the full complex hand-crafted object detection pipeline with a Transformer, and match Faster R-CNN with a ResNet-50, obtaining 42 AP on COCO using half the computation power (FLOPs) and the same number of parameters. Inference in 50 lines of PyTorch. What it is. Unlike traditional computer vision techniques, DETR approaches object detection as a direct set prediction problem. It consists of a set-based...

PEG.js is a simple parser generator for JavaScript that produces fast parsers with excellent error reporting. You can use it to process complex data or computer languages and build transformers, interpreters, compilers and other tools easily. PEG.js is still very much work in progress. There are no compatibility guarantees until version 1.0. Based on parsing expression grammar formalism, more powerful than traditional LL(k) and LR(k) parsers. Usable from your browser, from the command line, or via JavaScript API.

Annotate your POJOs to create a fully-functional application, with table support, collection filters, and persistence hooks.

The Cocoon Adaption Kit is a NetKernel module which enables the use of Cocoon Components (Generators, Transformers, Serializer, Actions) from within the NetKernel XML Application Server.