Frustratingly Simple Pretraining Alternatives to Masked Language Modeling

This is the official implementation for “Frustratingly Simple Pretraining Alternatives to Masked Language Modeling” (EMNLP 2021). Requirements torch transformers datasets scikit-learn tensorflow spacy How to pre-train 1. Clone this repository git clone https://github.com/gucci-j/light-transformer-emnlp2021.git 2. Install required packages cd ./light-transformer-emnlp2021 pip install -r requirements.txt requirements.txt is located just under light-transformer-emnlp2021. We also need spaCy’s en_core_web_sm for preprocessing. If you have not installed this model, please run python -m spacy download en_core_web_sm. 3. Preprocess datasets cd ./src/utils python preprocess_roberta.py –path=/path/to/save/data/ You need […]

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Fully asynchronous trace.moe API wrapper

Fully asynchronous trace.moe API wrapper Installation You can install the stable version from PyPI: $ pip install aiomoe Or get it from github: $ pip install https://github.com/FeeeeK/aiomoe/archive/refs/heads/master.zip Usage Get info about your account import asyncio from aiomoe import AioMoe tm = AioMoe() # or AioMoe(token=”xxxxxxxx”) async def main(): me = await tm.me() print(me) print(f”Used quota: {me.quota_used}/{me.quota}”) asyncio.run(main()) The output will be like this: User(error=None, id=’your ip’, priority=0, concurrency=1, quota=1000, quota_used=0) Used quota: 0/1000 Search anime import asyncio from aiomoe import […]

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This solves the autonomous driving issue which is supported by deep learning technology

This solves the autonomous driving issue which is supported by deep learning technology. Given a video, it splits into images and predicts the angle of turning for each frame. An autonomous car (also known as a driverless car, self-driving car, and robotic car) is a vehicle that is capable of sensing its environment and navigating without human input. Autonomous cars combine a variety of techniques to perceive their surroundings, including radar, laser light, GPS, odometry, and computer vision. Advanced control […]

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GitLab CI security tools runner

Описание проекта: Данный проект является вариантом реализации DevSecOps практик, на базе: Используйте данный репозиторий чтобы построить безопасность в цикле CI/CD. Quick Start Склонировать к себе Common Security Pipeline Исправить все места где встречается комментарий CHECK IT или FIX IT Изменить в ./pipeline/security_tools.yml путь до контейнеров с Security Tools Поднять у себя DefectDojo Прописать у себя в GitLab необходимые переменные: API-ключ Путь до DefectDojo для доступа к вашему DefectDojo в файле ./dd_prepare/dd_prepare.py. Если у вас используется Vault или аналогичное решение, то […]

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TFLearn: Deep learning library featuring a higher-level API for TensorFlow

TFlearn is a modular and transparent deep learning library built on top of Tensorflow. It was designed to provide a higher-level API to TensorFlow in order to facilitate and speed-up experimentations, while remaining fully transparent and compatible with it. TFLearn features include: Easy-to-use and understand high-level API for implementing deep neural networks, with tutorial and examples. Fast prototyping through highly modular built-in neural network layers, regularizers, optimizers, metrics… Full transparency over Tensorflow. All functions are built over tensors and can […]

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ipython-based environment for conducting data-driven research in a consistent and reproducible way

REP is ipython-based environment for conducting data-driven research in a consistent and reproducible way. Main features: unified python wrapper for different ML libraries (wrappers follow extended scikit-learn interface) Sklearn TMVA XGBoost uBoost Theanets Pybrain Neurolab MatrixNet service(available to CERN) parallel training of classifiers on cluster classification/regression reports with plots interactive plots supported smart grid-search algorithms with parallel execution research versioning using git pluggable quality metrics for classification meta-algorithm design (aka ‘rep-lego’) REP is not trying to substitute scikit-learn, but extends […]

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