Stars
ArSarcasm-v2 is an extension to the original ArSarcasm dataset. It was used for the shared task on sarcasm detection and sentiment analysis, which is a part of WANLP 2021.
Adversarial Training for Natural Language Understanding
TensorFlow Recommenders is a library for building recommender system models using TensorFlow.
Ranger - a synergistic optimizer using RAdam (Rectified Adam), Gradient Centralization and LookAhead in one codebase
Generating bash command from natural language https://arxiv.org/abs/1802.08979
Automated Machine Learning with scikit-learn
Transformers for Information Retrieval, Text Classification, NER, QA, Language Modelling, Language Generation, T5, Multi-Modal, and Conversational AI
KGAT: Knowledge Graph Attention Network for Recommendation, KDD2019
Real Time Bidding (RTB) - Demand Side Platform framework
Curated list of awesome Real Time Bidding ( RTB) frameworks, libraries, resources and other related to RTB things
RTBkit is an open-source software package that allows you to create and deploy a Real Time Bidder for display advertising.
OneFlow is a deep learning framework designed to be user-friendly, scalable and efficient.
A collection of research and survey papers of real-time bidding (RTB) based display advertising techniques.
Beyond Accuracy: Behavioral Testing of NLP models with CheckList
An open source python library for scalable Bayesian optimisation.
Optimization Examples with SigOpt
🔠Foreign language reading and translation assistant based on copy and translate.
A highly efficient implementation of Gaussian Processes in PyTorch
Sequential model-based optimization with a `scipy.optimize` interface
A Python toolbox for performing gradient-free optimization
A Python implementation of global optimization with gaussian processes.
Open-source implementation of Google Vizier for hyper parameters tuning
Surrogate Optimization Toolbox for Python
Automated Deep Learning without ANY human intervention. 1'st Solution for AutoDL challenge@NeurIPS.
Benchmark framework to easily compare Bayesian optimization methods on real machine learning tasks