Machine Translation Weekly 77: Reference-free Evaluation

This week, I am will comment on a paper by authors from the University of Maryland and Google Research on reference-free evaluation of machine translation, which seems quite disturbing to me and suggests there is a lot about current MT models we still don’t quite understand. The title of the paper is “Assessing Reference-Free Peer Evaluation for Machine Translation” and it will be published at this year’s NAACL conference. The standard evaluation of machine translation uses reference translations: translations that […]

May 1, 2021 Machine Learning

A pytorch reprelication of the model-based reinforcement learning algorithm MBPO

mbpo_pytorch This is a re-implementation of the model-based RL algorithm MBPO in pytorch as described in the following paper: When to Trust Your Model: Model-Based Policy Optimization. This code is based on a previous paper in the NeurIPS reproducibility challenge that reproduces the result with a tensorflow ensemble model but shows a significant drop in performance with a pytorch ensemble model. This code re-implements the ensemble dynamics model with pytorch and closes the gap. Reproduced results The comparison are done […]

May 1, 2021 Text

Text Classification in Turkish Texts with Bert

Text_and_Audio_classification_with_Bert Text Classification in Turkish Texts with Bert. Project Interface Goal= Correctly guessing the classification of texts and audios BERT_Text_Classification It is a text classification task implementation transformers (by HuggingFace) with BERT. It contains several parts: –Data pre-processing –BERT tokenization and input formating –Train with BERT –Evaluation –Save and load saved model Text-classification-transformers Text classification tasks are most easily encountered in the area of natural language processing and can be used in various ways. However, the given data needs to […]

May 1, 2021 Machine Learning

A reinforcement library designed for pytorch

machin Machin is a reinforcement library designed for pytorch. Reinforcement learning library(framework) designed for PyTorch, implements DQN, DDPG, A2C, PPO, SAC, MADDPG, A3C, APEX, IMPALA … Features 1. Automatic Starting from version 0.4.0, Machin now supports automatic config generation, you can get a configurationthrough: python -m machin.auto generate –algo DQN –env openai_gym –output config.json And automatically launch the experiment with pytorch lightning: python -m machin.auto launch –config config.json 2. Readable Compared to other reinforcement learning libraries such as the famous […]

May 1, 2021 Images

A Python library for processing images of image-based spatial transcriptomics

starfish starfish: scalable pipelines for image-based transcriptomics starfish is a Python library for processing images of image-based spatial transcriptomics. It lets you build scalable pipelines that localize and quantify RNA transcripts in image data generated by any FISH method, from simple RNA single-molecule FISH to combinatorial barcoded assays. If you want to give it a try, the quick start tutorial will guide you from installation to running a pipeline in under 10 minutes. For more comprehensive instructions on how to […]

May 1, 2021 Security

A Vulnerability Scanner & Auto Exploiter You can use this tool to check the security

Vulnnr create a target list or select one target, scans then exploits, done!Vulnnr is a Vulnerability Scanner & Auto Exploiter You can use this tool to check the security by finding the vulnerability in your website or you can use this tool to Get Shells Offers LFI Scanners > Coming soon XSS Scanners > Working SQLI injection scanners > Working Domain Scanner > Using hackerone API/finds subdoamins CMS detector > Working Server detector > Working Common vulnerable files Scanner > […]

May 1, 2021 Advanced, Machine Learning, NLP, Python, Text, Topic Modeling, Unstructured Data

Topic Modelling in Natural Language Processing

Introduction Natural language processing is the processing of languages used in the system that exists in the library of nltk where this is processed to cut, extract and transform to new data so that we get good insights into it. It uses only the languages that exist in the library because NLP-related things exist there itself so it cannot understand the things beyond what is present in it. If you do processing on another language then you have to add […]

May 1, 2021 Python

The hidden performance overhead of Python C extensions

Python is slow, and compiled languages like Rust, C, or C++ are fast. So when your application is too slow, rewriting some of your code in a compiled extension can seem like the natural approach to speeding things up. Unfortunately, compiled extensions are sometimes actually slower than the equivalent Python code. And even when they’re faster, the performance improvement might be far less than you’d imagine, due to hidden overhead caused by two factors: Function call overhead. Serialization/deserialization overhead. Let’s […]

April 30, 2021 Data Visualization

A Python library for electronic structure pre/post-processing

PyProcar PyProcar is a robust, open-source Python library used for pre- and post-processing of the electronic structure data coming from DFT calculations. PyProcar provides a set of functions that manage data obtained from the PROCAR format. Basically, the PROCAR format is a projection of the Kohn-Sham states over atomic orbitals. That projection is performed to every k-point in the considered mesh, every energy band and every atom. PyProcar is capable of performing a multitude of tasks including plotting plain and […]

April 30, 2021 Data Analysis

Histogramming for analysis powered by boost-histogram

Hist Hist is a analyst friendly front-end for boost-histogram, designed for Python 3.7+ (3.6 users get version 2.3). Installation You can install this library from PyPI with pip: python3 -m pip install “hist[plot]” If you do not need the plotting features, you can skip the [plot] extra. Features Hist currently provides everything boost-histogram provides, and the following enhancements: Hist augments axes with names: name= is a unique label describing each axis label= is an optional string that is used in […]

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