Popular GitHub Repositories for Spinal Cord

CNN accelerator implemented with Spinal HDL

Code for the paper Biomedical Event Extraction with Hierarchical Knowledge Graphs

Spinal cord gray matter segmentation using deep dilated convolutions.

ANN Search through the COVID CORD-19 Dataset using SBERT.

A Discord bot that generates FULL powerpoints about a given subject thanks to openai's gpt3

DiscordNPC lets you interact with ChatGPT through a Discord voice channel, enabling a natural conversation.

Builds a knowledge graph from the [COVID-19 Open Research Dataset (CORD-19)](https://pages.semanticscholar.org/coronavirus-research) dataset.

Preprocessing and analysis for training SNOMED-CT concept embeddings from CORD-19 corpus

COVID-19 Open Research Dataset Challenge (CORD-19)

A cute personal assistant in Discord using ChatGPT and Vector Databases

??? Developed a document retrieval system to return titles of scientific papers containing the answer to a given user question based on the first version of the COVID-19 Open Research Dataset (CORD-19) ???

Generate Brainrot Content with Python for TikTok, Instagram and YouTube

BioMedical Language Processing with ELECTRA

This repository explores the use of advanced sequence-to-sequence networks and transformer models, such as BERT, BART, PEGASUS, and T5, for summarizing multi-text documents in the medical domain. It leverages extensive datasets like CORD-19 and a Biomedical Abstracts dataset from Hugging Face to fine-tune these models.

? Discord meetings redefined with Memo Mate: Transcribe, summarize, and automate minutes seamlessly! ?

这个代码库包含一个研究项目的代码，该项目在TREC-Covid数据集上，使用PyTerrier实现了基于合著和引文的局部词嵌入方法，并对该方法进行了评估，用于查询扩展。

Semantic-based search using word embedding to help the medical community develop answers to high priority scientific questions using Kaggle's CORD-19 dataset. This repository is part of Kaggle's CORD-19 challenge: https://www.kaggle.com/allen-institute-for-ai/CORD-19-research-challenge

This project is about developing a document retrieval system to return titles of scientific papers containing the answer to a given user question. Two different sentence embedding approaches have been implemented and compared.