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Language Modeling

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코드

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

https://github.com/MulukenW/test

Cross-lingual Information Retrieval with BERT

https://github.com/MysteryVaibhav/LAS

Listen, Attend and Spell

https://github.com/NLPLearn/QANet

Character-Aware Neural Language Models

https://github.com/NVIDIA/DeepLearningExamples/tree/master/PyTorch/LanguageModeling/Transformer-XL

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

https://github.com/NVIDIA/DeepLearningExamples/tree/master/TensorFlow/LanguageModeling/Transformer-XL

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

https://github.com/NVIDIA/FasterTransformer

Prefix-Tuning: Optimizing Continuous Prompts for Generation

https://github.com/NVIDIA/NeMo-Curator

Language Models are Few-Shot Learners

https://github.com/NVIDIA/OpenSeq2Seq

Jasper: An End-to-End Convolutional Neural Acoustic Model

https://github.com/NVIDIA/garak

Assessing Language Model Deployment with Risk Cards

https://github.com/NVlabs/unas

DARTS: Differentiable Architecture Search

https://github.com/NamCyan/RecSys-VAE4CF

Variational Autoencoders for Collaborative Filtering

https://github.com/NathanDuran/Sentence-Encoding-for-DA-Classification

XLNet: Generalized Autoregressive Pretraining for Language Understanding

https://github.com/NicGian/text_VAE

Generating Sentences from a Continuous Space

https://github.com/NightmareVoid/LSTM_for_EEG

Regularizing and Optimizing LSTM Language Models

https://github.com/Nimesh-Patel/BERT

Semi-supervised Sequence Learning

https://github.com/NivNayman/XNAS

DARTS: Differentiable Architecture Search

https://github.com/NonameAuPlatal/Dual_Learning

Dual Learning for Machine Translation

https://github.com/Nstats/bert_MRC

Semi-supervised Sequence Learning

https://github.com/Nstats/bert_senti_analysis_ch

Semi-supervised Sequence Learning

https://github.com/Nstats/my_bert

Semi-supervised Sequence Learning

https://github.com/OctopusMind/DPO

Direct Preference Optimization: Your Language Model is Secretly a Reward Model

https://github.com/Oguzhanercan/Vision-Transformers

Relaxed Attention for Transformer Models

https://github.com/OpenMOSS/AnyGPT

AnyGPT: Unified Multimodal LLM with Discrete Sequence Modeling

https://github.com/OrigamiDream/gato

A Generalist Agent

https://github.com/Osobarako/alduswarrensewell

Semi-supervised Sequence Learning

https://github.com/PDas2206/BERT_emotion_analysis

EmotionX-KU: BERT-Max based Contextual Emotion Classifier

https://github.com/PKU-YuanGroup/MoE-LLaVA

Video-LLaVA: Learning United Visual Representation by Alignment Before Projection

https://github.com/PKU-YuanGroup/UniWorld-V1

ImgEdit: A Unified Image Editing Dataset and Benchmark

https://github.com/PaddlePaddle/ERNIE

ERNIE-Gram: Pre-Training with Explicitly N-Gram Masked Language Modeling for Natural Language Understanding

https://github.com/PaddlePaddle/FleetX

ERNIE 3.0 Titan: Exploring Larger-scale Knowledge Enhanced Pre-training for Language Understanding and Generation

https://github.com/PaddlePaddle/FleetX/blob/develop/examples/resnet/train_fleet_static_dgc.py

Deep Gradient Compression: Reducing the Communication Bandwidth for Distributed Training

https://github.com/PaddlePaddle/FleetX/tree/develop/examples/hybrid_parallelism

Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism

https://github.com/PaddlePaddle/PASSL

Tokens-to-Token ViT: Training Vision Transformers from Scratch on ImageNet

https://github.com/PaddlePaddle/PaddleNLP/blob/develop/paddlenlp/transformers/distilbert/modeling.py

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

https://github.com/PaddlePaddle/PaddleNLP/blob/develop/paddlenlp/transformers/roberta/modeling.py

RoBERTa: A Robustly Optimized BERT Pretraining Approach

https://github.com/PaddlePaddle/PaddleNLP/tree/develop/examples/few_shot/p-tuning

GPT Understands, Too

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