Popular GitHub Repositories for Squad

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.

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.

An implementation of the BERT model and its related downstream tasks based on the PyTorch framework. @月来客栈

SQuAD Question Answering Using BERT, PyTorch

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

Important paper implementations for Question Answering using PyTorch

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

Persian (Farsi) Question Answering Dataset (+ Models)

ALBERT model Pretraining and Fine Tuning using TF2.0

Pytorch implementation of "Dynamic Coattention Networks For Question Answering"

BERT which stands for Bidirectional Encoder Representations from Transformations is the SOTA in Transfer Learning in NLP.

Train Llama 2 & 3 on the SQuAD v2 task as an example of how to specialize a generalized (foundation) model.

A style transfer testing workflow for ComfyUI. Made with ? by the CozyMantis squad.

Implement Question Generator with SOTA pre-trained Language Models (RoBERTa, BERT, GPT, BART, T5, etc.)

Guide KorQuAD upload to leaderboard (EM 68.947 / F1 88.468) model which only use BERT-multilingual(single)

Code and datasets of "Multilingual Extractive Reading Comprehension by Runtime Machine Translation"

BERT based pretrained model using SQuAD 2.0 Dataset for Question-Answering

Fine-tuned BERT on SQuAd 2.0 Dataset. Applied Knowledge Distillation (KD) and fine-tuned DistilBERT (student) using BERT as the teacher model. Reduced the size of the original BERT by 40%.