Displaying 1 to 20 from 40 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.

spago - Self-contained Machine Learning and Natural Language Processing library in Go

  •    Go

A Machine Learning library written in pure Go designed to support relevant neural architectures in Natural Language Processing. spaGO is self-contained, in that it uses its own lightweight computational graph framework for both training and inference, easy to understand from start to finish.

Haystack - Build a natural language interface for your data

  •    Python

Haystack is an end-to-end framework that enables you to build powerful and production-ready pipelines for different search use cases. Whether you want to perform Question Answering or semantic document search, you can use the State-of-the-Art NLP models in Haystack to provide unique search experiences and allow your users to query in natural language. Haystack is built in a modular fashion so that you can combine the best technology from other open-source projects like Huggingface's Transformers, Elasticsearch, or Milvus.

tapas - End-to-end neural table-text understanding models.

  •    Python

Code and checkpoints for training the transformer-based Table QA models introduced in the paper TAPAS: Weakly Supervised Table Parsing via Pre-training. The easiest way to try out TAPAS with free GPU/TPU is in our Colab, which shows how to do predictions on SQA.

bootcamp - Dealing with all unstructured data, such as reverse image search, audio search, molecular search, video analysis, question and answer systems, NLP, etc

  •    Python

Embed everything, thanks to AI, we can use neural networks to extract feature vectors from unstructured data, such as image, audio and vide etc. Then analyse the unstructured data by calculating the feature vectors, for example calculating the Euclidean or Cosine distance of the vectors to get the similarity. Milvus Bootcamp is designed to expose users to both the simplicity and depth of the Milvus vector database. Discover how to run benchmark tests as well as build similarity search applications like chatbots, recommender systems, reverse image search, molecular search, video search, audio search, and more.

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.

node-question-answering - Fast and production-ready question answering in Node.js

  •    TypeScript

It can run models in SavedModel and TFJS formats locally, as well as remote models thanks to TensorFlow Serving. The following example will automatically download the default DistilBERT model in SavedModel format if not already present, along with the required vocabulary / tokenizer files. It will then run the model and return the answer to the question.

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.

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