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Language Modeling

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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/ruc-nlpir/flashrag

REPLUG: Retrieval-Augmented Black-Box Language Models

https://github.com/ruckbreasoning/tablellm

TableLLM: Enabling Tabular Data Manipulation by LLMs in Real Office Usage Scenarios

https://github.com/runpeidong/act

ShapeLLM: Universal 3D Object Understanding for Embodied Interaction

https://github.com/runxinxu/childtuning

Raise a Child in Large Language Model: Towards Effective and Generalizable Fine-tuning

https://github.com/ruotianluo/DiscCaptioning

Context-aware Captions from Context-agnostic Supervision

https://github.com/rushhan/VAE-for-Collaborative-Filtering-Pytorch

Variational Autoencoders for Collaborative Filtering

https://github.com/rvlopes/gloria

Scaling Language Models: Methods, Analysis & Insights from Training Gopher

https://github.com/rwbfd/OpenCompetitionV2

ELECTRA: Pre-training Text Encoders as Discriminators Rather Than Generators

https://github.com/rwth-i6/returnn-experiments/tree/master/2019-librispeech-system

RWTH ASR Systems for LibriSpeech: Hybrid vs Attention -- w/o Data Augmentation

https://github.com/rybread1/stack-overflow-question-classification

DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter

https://github.com/ryokamoi/hybrid_textvae

A Hybrid Convolutional Variational Autoencoder for Text Generation

https://github.com/ryokamoi/original_textvae

Generating Sentences from a Continuous Space

https://github.com/s-nlp/russe_detox_2022

Language-agnostic BERT Sentence Embedding

https://github.com/s3prl/End-to-end-ASR-Pytorch

Listen, Attend and Spell

https://github.com/saattrupdan/scandeval

Encoder vs Decoder: Comparative Analysis of Encoder and Decoder Language Models on Multilingual NLU Tasks

https://github.com/saattrupdan/scholarly

Single Headed Attention RNN: Stop Thinking With Your Head

https://github.com/sachin19/mucoco

Controlled Text Generation as Continuous Optimization with Multiple Constraints

https://github.com/sacmehta/espnetv2-coreml

ESPNetv2: A Light-weight, Power Efficient, and General Purpose Convolutional Neural Network

https://github.com/safolab-wisc/dolphins

Dolphins: Multimodal Language Model for Driving

https://github.com/safouaneelg/SRT2I

Self-Rewarding Language Models

https://github.com/sahil00199/KYC

How to Fine-Tune BERT for Text Classification?

https://github.com/sai-prasanna/suggestion-mining-semeval19

Zoho at SemEval-2019 Task 9: Semi-supervised Domain Adaptation using Tri-training for Suggestion Mining

https://github.com/sail-sg/adan

Skywork-MoE: A Deep Dive into Training Techniques for Mixture-of-Experts Language Models

https://github.com/sakura2233565548/Self-Supervised-Representation-Learning-with-Spatial-Temporal-Consistency-for-SLR

Self-Supervised Representation Learning with Spatial-Temporal Consistency for Sign Language Recognition

https://github.com/salesforce/auditnlg

Self-Diagnosis and Self-Debiasing: A Proposal for Reducing Corpus-Based Bias in NLP

https://github.com/salesforce/codet5

RoBERTa: A Robustly Optimized BERT Pretraining Approach

https://github.com/salesforce/pytorch-qrnn

Quasi-Recurrent Neural Networks

https://github.com/salt-nlp/privacylens

CultureBank: An Online Community-Driven Knowledge Base Towards Culturally Aware Language Technologies

https://github.com/sam-ai/BertGrid

BERTgrid: Contextualized Embedding for 2D Document Representation and Understanding

https://github.com/sambanova/lm-evaluation-harness

Language Models are Few-Shot Learners

https://github.com/sambi97/Low-shot-learning_-Deep-learning-project

Matching Networks for One Shot Learning

https://github.com/samueljamesbell/sequence-labeler

Semi-supervised Multitask Learning for Sequence Labeling

https://github.com/samwisegamjeee/pytorch-transformers

Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context

https://github.com/sandai-org/magiattention

Ring Attention with Blockwise Transformers for Near-Infinite Context

https://github.com/sanderwood/clamp3

Unsupervised Cross-lingual Representation Learning at Scale

https://github.com/sanjanamallya/speech_to_text

Listen, Attend and Spell

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.