Popular GitHub Repositories for Positive Negative

??High-Performance Face Recognition Library on PaddlePaddle & PyTorch??

:dart: Task-oriented embedding tuning for BERT, CLIP, etc.

基于Pytorch和torchtext的自然语言处理深度学习框架。

OCTIS: Comparing Topic Models is Simple! A python package to optimize and evaluate topic models (accepted at EACL2021 demo track)

A Github scanning tool that identifies hardcoded credentials while filtering the false positive data through machine learning models :lock:

Skipgram Negative Sampling implemented in PyTorch

A word2vec negative sampling implementation with correct CBOW update.

Pipeline for particle picking in cryo-electron microscopy images using convolutional neural networks trained from positive and unlabeled examples. Also featuring micrograph and tomogram denoising with DNNs.

Japanese negative positive classification.日本語文書のネガポジを判定。

Reinforced Negative Sampling over Knowledge Graph for Recommendation, WWW2020

SimXNS is a research project for information retrieval. This repo contains official implementations by MSRA NLC team.

[ECCV 2024] Ray Denoising (RayDN): Depth-aware Hard Negative Sampling for Multi-view 3D Object Detection

Jupyter Notebooks for experimenting with negative prompting with Stable Diffusion 2.0.

This repository contains a DistilBERT model fine-tuned using the Hugging Face Transformers library on the IMDb movie review dataset. The model is trained for sentiment analysis, enabling the determination of sentiment polarity (positive or negative) within text reviews.

Regression Graph Neural Network (regGNN) for cognitive score prediction.

⚡️ Implementation of TRON: Transformer Recommender using Optimized Negative-sampling, accepted at ACM RecSys 2023.

The official implementation of CVPR 24' Paper "Learning Transferable Negative Prompts for Out-of-Distribution Detection"

This project walks you on how to create a twitter sentiment analysis model using python. Twitter sentiment analysis is performed to identify the sentiments of the people towards various topics. For this project, we will be analysing the sentiment of people towards Pfizer vaccines. We will be using the data available on Kaggle to create this machine learning model. The collected tweets from Twitter will be analysed using machine learning to identify the different sentiments present in the tweets. The different sentiments identified in this project include positive sentiment, negative sentiment and neutral sentiment. We will also be using different classifiers to see which classifier gives the best model accuracy.

Rethinking Negative Instances for Generative Named Entity Recognition [ACL 2024 Findings]

PyTorch Implementation of Monitored Distillation for Positive Congruent Depth Completion (ECCV 2022)