AI orchestration framework to build customizable, production-ready LLM applications. Connect components (models, vector DBs, file converters) to pipelines or agents that can interact with your data. With advanced retrieval methods, it's best suited for building RAG, question answering, semantic search or conversational agent chatbots.

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.

A Tensorflow implementation of QANet for machine reading comprehension

机器阅读理解 冠军/亚军代码及中文预训练MRC模型

The prime repository for state-of-the-art Multilingual Question Answering research and development.

Tensorflow Implementation of R-Net

An implementation of the BERT model and its related downstream tasks based on the PyTorch framework

The official implementation of ICLR 2020, "Learning to Retrieve Reasoning Paths over Wikipedia Graph for Question Answering".

A pytorch implementation of Reading Wikipedia to Answer Open-Domain Questions.

A Tensorflow Implementation of R-net: Machine reading comprehension with self matching networks

DAWNBench: An End-to-End Deep Learning Benchmark and Competition

ALBERT model Pretraining and Fine Tuning using TF2.0

R-net in PyTorch, with ELMo

Open R-NET (hy` առնետ 🐁) implementation and detailed analysis: https://git.io/vd8dx

A PyTorch implementation of Mnemonic Reader for the Machine Comprehension task

A PyTorch implemention of Match-LSTM, R-NET and M-Reader for Machine Reading Comprehension

A Pytorch implementation of QANet

An Unofficial Pytorch Implementation of Multi-Granularity Hierarchical Attention Fusion Networks for Reading Comprehension and Question Answering

R-NET implementation in TensorFlow.

TensorFlow Models for the Stanford Question Answering Dataset