Deep Learning, NLP, NMT, AI, ML
Image Colorization API Machine Learning Model used- https://github.com/richzhang/colorization/tree/caffe Paper – Steps to run the api locally- Clone the repository Run pip install -r requirements.txt After successful installation run uvicorn main:app –reload to start the server Go to 127.0.0.1:8000 to check endpoints and 127.0.0.1:8000/docs#/ to test the endpoints GitHub View Github
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This repository is a copy of jddeal/python_cmr which is no longer maintained. It has been copied here with the permission of the original author for the purpose of continuing to develop a python library that can be used for CMR access. Python CMR is an easy to use wrapper to the NASA EOSDIS Common Metadata Repository API. This package aims to make querying the API intuitive and less error-prone by providing methods that will preemptively check for invalid input and […]
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Information-Theoretic Multi-Objective Bayesian Optimization with Continuous Approximations Requirements The code is implemented in Python and requires the following packages: sobol_seq platypus sklearn.gaussian_process Citation If you use this code in your academic work please cite our JAIR paper: “A General Output Space Entropy Search Framework for Multi-Objective Bayesian Optimization ” and our workshop paper “Information-Theoretic Multi-Objective Bayesian Optimization with Continuous Approximations” , Syrine Belakaria, Aryan Deshwal, Janardhan Rao Doppa. GitHub – belakaria/iMOCA at pythonawesome.com Contribute to belakaria/iMOCA development by creating an […]
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Python implementation of image filters Use Python to adjust brightness and contrast, add blur, and detect edges! Follow along tutorial: https://youtu.be/4ifdUQmZqhM In order to download this code, either click the green button at the top right and download as ZIP, or use git clone https://github.com/kying18/pyphotoshop.git. You will need to pip install -r requirements.txt (or use pip3 if you are getting a module not found error). In the folder, you will find these files: image.py: contains the Image class that will […]
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We have a DEMO website (http://opennre.thunlp.ai/). Try it out! OpenNRE is an open-source and extensible toolkit that provides a unified framework to implement relation extraction models. This package is designed for the following groups: New to relation extraction: We have hand-by-hand tutorials and detailed documents that can not only enable you to use relation extraction tools, but also help you better understand the research progress in this field. Developers: Our easy-to-use interface and high-performance implementation can acclerate your deployment in […]
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YouTokenToMe is an unsupervised text tokenizer focused on computational efficiency. It currently implements fast Byte Pair Encoding (BPE) [Sennrich et al.]. Our implementation is much faster in training and tokenization than Hugging Face, fastBPE and SentencePiece. In some test cases, it is 90 times faster. Check out our benchmark results. Key advantages: Multithreading for training and tokenization The algorithm has O(N) complexity, where N is the length of training data Highly efficient implementation in C++ Python wrapper and command-line interface […]
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What is DELTA? DELTA is a deep learning based end-to-end natural language and speech processing platform. DELTA aims to provide easy and fast experiences for using, deploying, and developing natural language processing and speech models for both academia and industry use cases. DELTA is mainly implemented using TensorFlow and Python 3. For details of DELTA, please refer to this paper. What can DELTA do? DELTA has been used for developing several state-of-the-art algorithms for publications and
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If you’re using Python in the world of data science or scientific computing, you will soon discover that Python has two different packaging systems: pip and Conda. Which raises some questions: How are they different? What are the tradeoffs between the two? Which should you use? While it’s not possible to answer this question for every situation, in this article you will learn the basic differences, constrained to: Python only; Conda has support for other languages but I won’t go […]
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Preprocess file of the dataset used in implicit sub-populations:(Demographic groups: race and gender) The following code will pre-process the jigsaw dataset and return train/test dataset files including demographic groups information. Step-1: Download the jigsaw dataset: identity_individual_annotations.csv from https://www.kaggle.com/c/jigsaw-unintended-bias-in-toxicity-classification/data. Step-2: python preprocecss_jiasaw_toxicity_gender_and_race_balanced.py Implementation of SSL methods Please follow the official implementations of MixMatch, MixText, and UDA. [1] https://github.com/google-research/mixmatch [2] https://github.com/GT-SALT/MixText [3] https://github.com/google-research/uda GitHub – UCSC-REAL/Disparate-SSL at pythonawesome.com Contribute to UCSC-REAL/Disparate-SSL development by creating an account on GitHub. GitHubUCSC-REAL
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