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aws

bdc103_ir

This repository is practice for Big Data and Information Retrieval class.

class2019fall-1

class2019spring

commongen

A Constrained Text Generation Challenge Towards Generative Commonsense Reasoning

csat_data_analysis

Pi_ville_project

csat_text_generation

Pi_ville_project, creating the text with RNN, LSTM, and Transformer

csat_vocab

Pi_ville_project

demo-self-driving

Streamlit app demonstrating an image browser for the Udacity self-driving-car dataset with realtime object detection using YOLO.

emotionx-2019

info

Code for the paper "You Truly Understand What I Need : Intellectual and Friendly Dialogue Agents grounding Knowledge and Persona" which is accepted to EMNLP 2022 (Findings)

j-seo.github.io

ko-gemma

kocommongen-v2

KoCommonGEN v2: A Benchmark for Navigating Korean Commonsense Reasoning Challenges in Large Language Models

kogpt2_finetuning_for_classification

Finetuning for Korean Sentiment analysis 'NSMC'

kommongen

한국어 생성 모델의 상식 추론을 위한 KommonGen 데이터셋입니다.

korean-commongen

[Findings of NAACL2022] A Dog Is Passing Over The Jet? A Text-Generation Dataset for Korean Commonsense Reasoning and Evaluation

kullm

구름(KULLM): 고려대학교에서 개발한, 한국어에 특화된 LLM

news_recommendation

nlg_metrics

NLG Evaluation Metrics

peep-talk

A Situational Conversation-Based English Education Platform

predict_bicycle_with_r

Predicting public bicycle usage 2019 based on time and weather variables with R

pytorch_nlg

renew_csat_project

improve data quality, learning model, and others for preparing report

sgg

Train Scene Graph Generation in PyTorch 1.2 or newer with improved zero and few-shot generalization. Paper: "Graph Density-Aware Losses for Novel Compositions in Scene Graph Generation"

srlev-bih

SRLev-BIH: 한국어 일반 상식 추론 및 생성 능력 평가 지표

subword-nmt

Subword Neural Machine Translation based on rsennrich, but include feature to deal with a sentence already tokenized.

text_summarization

find a middle point 'chat bot' <-> 'x' <-> 'translation'

towards-diverse-qag

udacity_image_classifier

Image_classifier: Using 102 Flowers data, model classifies the flower images where they belong among 102 groups. train.py & predict.py: Users can train the model selecting some hyper-parameters and activation function etc... and check model performance calling predict.py.