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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/HB-Liu/Improved-SemHash-and-Gumbel-Softmax

Discrete Autoencoders for Sequence Models

https://github.com/HIT-SCIR/ELMoForManyLangs

Deep contextualized word representations

https://github.com/HLasse/wav2vec_finetune

SpecAugment: A Simple Data Augmentation Method for Automatic Speech Recognition

https://github.com/HSabbar/Question-Answering

End-To-End Memory Networks

https://github.com/HamadYA/GhostFaceNets

On the Variance of the Adaptive Learning Rate and Beyond

https://github.com/Han-JD/GRU-D

Regularizing and Optimizing LSTM Language Models

https://github.com/Hanlard/Electra_CRF_NER

Improving NER's Performance with Massive financial corpus

https://github.com/Hanlard/M2M-by-fastmoe

FastMoE: A Fast Mixture-of-Expert Training System

https://github.com/HaochenLiu2000/QAP

Deep Bidirectional Language-Knowledge Graph Pretraining

https://github.com/HarunoriKawano/Conformer

Conformer: Convolution-augmented Transformer for Speech Recognition

https://github.com/HazyResearch/m2

Monarch Mixer: A Simple Sub-Quadratic GEMM-Based Architecture

https://github.com/HectorHHZ/Sparse_Matrix_Tuning

Sparse Matrix in Large Language Model Fine-tuning

https://github.com/Hedgehogues/selfie

Selfie: Self-supervised Pretraining for Image Embedding

https://github.com/HeleneFabia/keyword-spotter

SpecAugment: A Simple Data Augmentation Method for Automatic Speech Recognition

https://github.com/HeungChangLee/AutoML

DARTS: Differentiable Architecture Search

https://github.com/Hironsan/anago

Deep contextualized word representations

https://github.com/Holldean/BERT-Pruning

Structured Pruning of Large Language Models

https://github.com/Hritikbansal/RNNs_SVA_OOD

Assessing the Ability of LSTMs to Learn Syntax-Sensitive Dependencies

https://github.com/Hyeongkeun/LAVCap

LAVCap: LLM-based Audio-Visual Captioning using Optimal Transport

https://github.com/HzcIrving/DecisionTransformer_StepbyStep

Decision Transformer: Reinforcement Learning via Sequence Modeling

https://github.com/IDEA-FinAI/Simple-HHEA

Think-on-Graph: Deep and Responsible Reasoning of Large Language Model on Knowledge Graph

https://github.com/IIEKES/cbert_aug

Conditional BERT Contextual Augmentation

https://github.com/IIEKES/cbert_aug_deprecated

Conditional BERT Contextual Augmentation

https://github.com/IMPLabUniPr/BERT-for-ABSA

Adversarial Training for Aspect-Based Sentiment Analysis with BERT

https://github.com/IMvision12/AdEMAMix-Optimizer-Keras

The AdEMAMix Optimizer: Better, Faster, Older

https://github.com/IanTheColder/ONLSTM-analysis

Ordered Neurons: Integrating Tree Structures into Recurrent Neural Networks

https://github.com/IndicoDataSolutions/finetune

RoBERTa: A Robustly Optimized BERT Pretraining Approach

https://github.com/InnerPeace-Wu/im2p-tensorflow

Tying Word Vectors and Word Classifiers: A Loss Framework for Language Modeling

https://github.com/InsaneLife/dssm

On the Sentence Embeddings from Pre-trained Language Models

https://github.com/IsmaelCesar/darts

DARTS: Differentiable Architecture Search

https://github.com/JHKim-snu/GVCCI

OFA: Unifying Architectures, Tasks, and Modalities Through a Simple Sequence-to-Sequence Learning Framework

https://github.com/JHKim-snu/PGA

OFA: Unifying Architectures, Tasks, and Modalities Through a Simple Sequence-to-Sequence Learning Framework

https://github.com/JHart96/keras_elmo_embedding_layer

Deep contextualized word representations

https://github.com/JJoving/SMLAT

Listen, Attend and Spell

https://github.com/JNUpython/bert

Semi-supervised Sequence Learning

https://github.com/JRC1995/Abstractive-Summarization

Long Short-Term Memory-Networks for Machine Reading

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