Machine Learning

A Python library that makes it extremely easy to experiment with state-of-the-art

QuickAI QuickAI is a Python library that makes it extremely easy to experiment with state-of-the-art Machine Learning models. Motivation When I started to get into more advanced Machine Learning, I started to see how these famous neural networkarchitectures(such as EfficientNet), were doing amazing things. However, when I tried to implement these architecturesto problems that I wanted to solve, I realized that it was not super easy to implement and quickly experiment with thesearchitectures. That is where QuickAI came in. It […]

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Machine Learning

Minimal deep learning library written from scratch in Python

SmallPebble Minimal deep learning library written from scratch in Python, using NumPy/CuPy. SmallPebble is a minimal automatic differentiation and deep learning library written from scratch in Python, using NumPy/CuPy. The implementation is relatively small, and mainly in the file: smallpebble.py. To help understand it, check out this introduction to autodiff, which presents an autodiff framework that works in the same way as SmallPebble (except using scalars instead of NumPy arrays). SmallPebble’s raison d’etre is to be a simplified deep learning […]

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RESTful API

Backlog API v2 Client Library for Python

BacklogPy BacklogPy is Backlog API v2 Client Library for Python 2/3 Install You can install the client library with pip: $ pip install BacklogPy Example The client Library has API Call methods for All Backlog v2 API: >>> from BacklogPy import Backlog >>> backlog = Backlog(‘space_name’,’api-key’) >>> response = backlog.get_project_list(all=True, archived=True) >>> print(response.json()[0]) {‘archived’: False, ‘chartEnabled’: True, ‘displayOrder’: 1234563786, ‘id’: 12345, ‘name’: ‘Coffee Project’, ‘projectKey’: ‘COFFEE_PROJECT’, ‘projectLeaderCanEditProjectLeader’: True, ‘subtaskingEnabled’: False, ‘textFormattingRule’: ‘markdown’, ‘useWikiTreeView’: True} Also you can use dict parameters […]

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Imitation learning, NLP

Issue #129 – Simultaneous MT Using Imitation Learning

06 May21 Issue #129 – Simultaneous MT Using Imitation Learning Author: Dr. Karin Sim, Machine Translation Scientist @ Iconic Introduction For the second time in our blog series we look at Simultaneous Machine Translation (SiMT). In SiMT, translation begins before the full source input has necessarily been processed, reducing the delay as much as possible. By necessity this results in a trade off between delay and MT quality. This subject was discussed in a previous blog post. The full pipe […]

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Machine Learning

Pytorch implementation for the Dynamic Concept Learner

DCL-PyTorch Pytorch implementation for the Dynamic Concept Learner (DCL). Prerequisites Python 3 PyTorch 1.0 or higher, with NVIDIA CUDA Support Other required python packages specified by requirements.txt. See the Installation. Installation Install Jacinle: Clone the package, and add the bin path to your global PATH environment variable: git clone https://github.com/vacancy/Jacinle –recursive export PATH=/bin:$PATH Clone this repository: git clone https://github.com/zfchenUnique/DCL-Release.git –recursive Create a conda environment for NS-CL, and install the requirements. This includes the required python packagesfrom both Jacinle NS-CL. Most […]

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Tool

Python and OpenCV-based scene cut/transition detection program & library

PySceneDetect Video Scene Cut Detection and Analysis Tool. Quick Install: To install PySceneDetect via pip with all dependencies: pip install scenedetect[opencv] For servers, you can use the headless (non-GUI) version of OpenCV by installing scenedetect[opencv-headless]. To enable video splitting support, you will also need to have mkvmerge or ffmpeg installed – see the documentation on Video Splitting Support for details. Requires Python modules click, numpy, OpenCV cv2, and (optional) tqdm for displaying progress. For details, see the dependencies on the […]

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NLP

WikiMatrix: Mining 135M Parallel Sentences in 1620 Language Pairs from Wikipedia

Abstract We present an approach based on multilingual sentence embeddings to automatically extract parallel sentences from the content of Wikipedia articles in 96 languages, including several dialects or low-resource languages. We systematically consider all possible language pairs. In total, we are able to extract 135M parallel sentences for 1620 different language pairs, out of which only 34M are aligned with English. This corpus is freely available. To get an indication on the quality of the extracted bitexts, we train neural […]

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NLP

Autoregressive Entity Retrieval

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NLP

Answering Complex Open-Domain Questions with Multi-Hop Dense Retrieval

May 3, 2021 By: Wenhan Xiong, Xiang Lorraine Li, Srinivasan Iyer, Jingfei Du, Patrick Lewis, William Wang, Yashar Mehdad, Wen-tau Yih, Sebastian Riedel, Douwe Kiela, Barlas Oğuz Abstract We propose a simple and efficient multi-hop dense retrieval approach for answering complex open-domain questions, which achieves state-of-the-art performance on two multi-hop datasets, HotpotQA and multi-evidence FEVER. Contrary to previous work, our method does not require access to any corpus-specific information, such as inter-document hyperlinks or human-annotated entity markers, and can be […]

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Python

Natural Language Processing With Python’s NLTK Package

Natural language processing (NLP) is a field that focuses on making natural human language usable by computer programs. NLTK, or Natural Language Toolkit, is a Python package that you can use for NLP. A lot of the data that you could be analyzing is unstructured data and contains human-readable text. Before you can analyze that data programmatically, you first need to preprocess it. In this tutorial, you’ll take your first look at the kinds of text preprocessing tasks you can […]

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