Displaying 1 to 20 from 30 results

amas - Awesome & Marvelous Amas


Fun: AMAs is a recursive acronym.To the extent possible under law, Sindre Sorhus has waived all copyright and related or neighboring rights to this work.

bi-att-flow - Bi-directional Attention Flow (BiDAF) network is a multi-stage hierarchical process that represents context at different levels of granularity and uses a bi-directional attention flow mechanism to achieve a query-aware context representation without early summarization

  •    Python

The model has ~2.5M parameters. The model was trained with NVidia Titan X (Pascal Architecture, 2016). The model requires at least 12GB of GPU RAM. If your GPU RAM is smaller than 12GB, you can either decrease batch size (performance might degrade), or you can use multi GPU (see below). The training converges at ~18k steps, and it took ~4s per step (i.e. ~20 hours). You can still omit them, but training will be much slower.

NeuronBlocks - NLP DNN Toolkit - Building Your NLP DNN Models Like Playing Lego

  •    Python

NeuronBlocks is a NLP deep learning modeling toolkit that helps engineers/researchers to build end-to-end pipelines for neural network model training for NLP tasks. The main goal of this toolkit is to minimize developing cost for NLP deep neural network model building, including both training and inference stages. NeuronBlocks consists of two major components: Block Zoo and Model Zoo.

deep_qa - A deep NLP library, based on Keras / tf, focused on question answering (but useful for other NLP too)

  •    Python

DeepQA is a library for doing high-level NLP tasks with deep learning, particularly focused on various kinds of question answering. DeepQA is built on top of Keras and TensorFlow, and can be thought of as an interface to these systems that makes NLP easier. DeepQA is built using Python 3. The easiest way to set up a compatible environment is to use Conda. This will set up a virtual environment with the exact version of Python used for development along with all the dependencies needed to run DeepQA.

icebreaker - Web app that allows students to ask real-time, anonymous questions during class

  •    Go

Ice Breaker is a web application designed to allow students to ask real-time, anonymous questions during class. Once started, it allows the creation of rooms, each with its own set of keys: a private instructor key, and a public student key. Each key exposes a different view of the room; students see only a form that allows them to submit questions (optionally including a name), whereas instructors see a stream of incoming questions from students. The envisioned use of this application is that one of the instructors (most likely a TA) monitors the instructor view during class, and asks any incoming questions by proxy. The hope is that this will lower the barrier for shyer students to ask questions during class.

knn4qa - k-nearest neighbor search for question answering (QA) and information retrieval (IR)

  •    Java

This is a learning-to-rank pipeline, which is a part of the project where we study applicability of k-nearest neighbor search methods in IR and QA applications. This project is supported primarily by the NSF grant #1618159 : "Matching and Ranking via Proximity Graphs: Applications to Question Answering and Beyond". For more details, please, check the Wiki page.

squadgym - Environment that can be used to evaluate reasoning capabilities of artificial agents

  •    Python

Recently, the Question Answering dataset Stanford Question Answering Dataset (SQuAD) has gained a lot of attention from practitioners and researchers due its appealing properties for evaluating the capabilities of agents able to answer open domain questions. In this dataset, given a reference context and a question, the agent should be able to generate an answer which may be composed by multiple tokens which are present in the given context. Due to its high quality, it represents a relevant benchmark for intelligent agents able to grasp, from a given context, relevant evidences that are required to generate the answer. The SQuAD dataset contains questions extracted from Wikipedia and related to specific entities. If the agent is able to extract from the related context text the sequence of tokens which compose the answer we may legitimately state that the system demonstrate sound reasoning capabilities. Obviously, the system should be able to generate an answer without exploiting supplementary features associated to the question or to the context but it should be able to "read" from the context text the correct answer.

forum - Ama Laravel? Torne se um Jedi e Ajude outros Padawans


Ama o Laravel? Torne se um Jedi e ajude outros Padawans. Você pode querer receber atualizações do fórum em seu email ou via notificações do GitHub.

jack - Jack the Reader

  •    Python

Jack the Reader - or jack, for short - is a framework for building and using models on a variety of tasks that require reading comprehension. For more informations about the overall architecture, we refer to Jack the Reader – A Machine Reading Framework (ACL 2018). To install Jack, install requirements and TensorFlow. In case you want to use PyTorch for writing models, please install PyTorch as well.

nlu_sim - all kinds of baseline models for sentence similarity

  •    Python

all kinds of baseline models for modeling tasks with pair of sentences: semantic text similarity(STS), natural language inference(NLI), paraphrase identification(PI), question answering(QA). this repository contain models that learn to detect sentence similarity for natural language understanding tasks.

chatbot - DBpedia Chatbot

  •    Java

When running locally or in development include the following configuration as a properties file in the src/main/resources folder. In case you do not have a proper CouchDB instance or API keys please use the following dummy configuration file.

semanticilp - Question Answering as Global Reasoning over Semantic Abstractions (AAAI-18)

  •    Scala

Our system relies on a couple of annotators that are not publicly available. As a result you (if outside CogComp) cannot run our full system. However, we have created a smaller system which works with public annotators. The system is tested with v3.1.22 of CogCompNLP. Download the package and run the annotator servers, on two different ports PORT_NUMBER1 and PORT_NUMBER2.

essence - AutoDiff DAG constructor, built on numpy and Cython

  •    Python

A directed acyclic computational graph builder, built from scratch on numpy and C, with auto-differentiation supported. This was not just another deep learning library, its clean code base was supposed to be read. Great for any one who want to learn about Backprop design in deep learning libraries.

golang-interview-questions - golang 面试集锦


This file contains a number of interview questions that can be used when vetting potential candidates. It is by no means recommended to use every single question here on the same candidate (that would take hours). Choosing a few items from this list should help you vet the intended skills you require. Note: Keep in mind that many of these questions are open-ended and could lead to interesting discussions that tell you more about the person's capabilities than a straight answer would.