IEEEXtreme15.0 Questions And Answers

Last update: Oct 24, 2022

Overview

IEEEXtreme15.0 Questions And Answers

IEEEXtreme is a global challenge in which teams of IEEE Student members – advised and proctored by an IEEE member, and often supported by an IEEE Student Branch – compete in a 24-hour time span against each other to solve a set of programming problems. See the full IEEEXtreme 15.0 rules here.Make a PR if you have a better answer.

This Reposatary Consits Of All Questions We Get In IEEEXtreme 15 And Some Answers With Scores

Owner

Dilan Perera

Computer Science Undergraduate at the University Of Colombo.

GitHub Repository

Composed Image Retrieval using Pretrained LANguage Transformers (CIRPLANT)

CIRPLANT This repository contains the code and pre-trained models for Composed Image Retrieval using Pretrained LANguage Transformers (CIRPLANT) For d

29 Nov 17, 2022

Takes a string and puts it through different languages in Google Translate a requested amount of times, returning nonsense.

PythonTextObfuscator Takes a string and puts it through different languages in Google Translate a requested amount of times, returning nonsense. Requi

2 Aug 29, 2022

Train BPE with fastBPE, and load to Huggingface Tokenizer.

BPEer Train BPE with fastBPE, and load to Huggingface Tokenizer. Description The BPETrainer of Huggingface consumes a lot of memory when I am training

1 Dec 23, 2021

TextAttack 🐙 is a Python framework for adversarial attacks, data augmentation, and model training in NLP

TextAttack 🐙 Generating adversarial examples for NLP models [TextAttack Documentation on ReadTheDocs] About • Setup • Usage • Design About TextAttack

2.2k Jan 03, 2023

Google's Meena transformer chatbot implementation

Here's my attempt at recreating Meena, a state of the art chatbot developed by Google Research and described in the paper Towards a Human-like Open-Domain Chatbot.

94 Dec 25, 2022

Pipelines de datos, 2021.

Este repo ilustra un proceso sencillo de automatización de transformación y modelado de datos, a través de un pipeline utilizando Luigi. Stack princip

8 May 19, 2022

Vad-sli-asr - A Python scripts for a speech processing pipeline with Voice Activity Detection (VAD)

VAD-SLI-ASR Python scripts for a speech processing pipeline with Voice Activity

14 Dec 09, 2022

A collection of Korean Text Datasets ready to use using Tensorflow-Datasets.

tfds-korean A collection of Korean Text Datasets ready to use using Tensorflow-Datasets. TensorFlow-Datasets를 이용한 한국어/한글 데이터셋 모음입니다. Dataset Catalog |

20 Jul 11, 2022

HuggingTweets - Train a model to generate tweets

HuggingTweets - Train a model to generate tweets Create in 5 minutes a tweet generator based on your favorite Tweeter Make my own model with the demo

318 Jan 04, 2023

AI and Machine Learning workflows on Anthos Bare Metal.

Hybrid and Sovereign AI on Anthos Bare Metal Table of Contents Overview Terraform as IaC Substrate ABM Cluster on GCE using Terraform TensorFlow ResNe

8 Nov 26, 2022

中文生成式预训练模型

T5 PEGASUS 中文生成式预训练模型，以mT5为基础架构和初始权重，通过类似PEGASUS的方式进行预训练。详情可见：https://kexue.fm/archives/8209 Tokenizer 我们将T5 PEGASUS的Tokenizer换成了BERT的Tokenizer，它对中文更

410 Jan 03, 2023

Simple and efficient RevNet-Library with DeepSpeed support

RevLib Simple and efficient RevNet-Library with DeepSpeed support Features Half the constant memory usage and faster than RevNet libraries Less memory

112 Dec 05, 2022

Guide: Finetune GPT2-XL (1.5 Billion Parameters) and GPT-NEO (2.7 B) on a single 16 GB VRAM V100 Google Cloud instance with Huggingface Transformers using DeepSpeed

Guide: Finetune GPT2-XL (1.5 Billion Parameters) and GPT-NEO (2.7 Billion Parameters) on a single 16 GB VRAM V100 Google Cloud instance with Huggingfa

289 Jan 06, 2023

Code for the Findings of NAACL 2022(Long Paper): AdapterBias: Parameter-efficient Token-dependent Representation Shift for Adapters in NLP Tasks

AdapterBias: Parameter-efficient Token-dependent Representation Shift for Adapters in NLP Tasks arXiv link: upcoming To be published in Findings of NA

16 Nov 12, 2022

In this workshop we will be exploring NLP state of the art transformers, with SOTA models like T5 and BERT, then build a model using HugginFace transformers framework.

Transformers are all you need In this workshop we will be exploring NLP state of the art transformers, with SOTA models like T5 and BERT, then build a

8 Apr 13, 2022

IEEEXtreme15.0 Questions And Answers

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Overview

IEEEXtreme15.0 Questions And Answers

This Reposatary Consits Of All Questions We Get In IEEEXtreme 15 And Some Answers With Scores

Owner

Dilan Perera

Composed Image Retrieval using Pretrained LANguage Transformers (CIRPLANT)

Takes a string and puts it through different languages in Google Translate a requested amount of times, returning nonsense.

Train BPE with fastBPE, and load to Huggingface Tokenizer.

TextAttack 🐙 is a Python framework for adversarial attacks, data augmentation, and model training in NLP

Google's Meena transformer chatbot implementation

Pipelines de datos, 2021.

Vad-sli-asr - A Python scripts for a speech processing pipeline with Voice Activity Detection (VAD)

A collection of Korean Text Datasets ready to use using Tensorflow-Datasets.

HuggingTweets - Train a model to generate tweets

AI and Machine Learning workflows on Anthos Bare Metal.

中文生成式预训练模型

Simple and efficient RevNet-Library with DeepSpeed support

Guide: Finetune GPT2-XL (1.5 Billion Parameters) and GPT-NEO (2.7 B) on a single 16 GB VRAM V100 Google Cloud instance with Huggingface Transformers using DeepSpeed

Code for the Findings of NAACL 2022(Long Paper): AdapterBias: Parameter-efficient Token-dependent Representation Shift for Adapters in NLP Tasks

ALBERT: A Lite BERT for Self-supervised Learning of Language Representations

Sentiment Analysis Project using Count Vectorizer and TF-IDF Vectorizer

This repository structures data in title, summary, tags, sentiment given a fragment of a conversation

Twewy-discord-chatbot - Build a Discord AI Chatbot that Speaks like Your Favorite Character

结巴中文分词

In this workshop we will be exploring NLP state of the art transformers, with SOTA models like T5 and BERT, then build a model using HugginFace transformers framework.