Papers with Code 아카이브

Language Modeling

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코드

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

https://github.com/shreyashankar/gpt3-sandbox

Language Models are Few-Shot Learners

https://github.com/shrutimohanty/Hcodeep

Evolving Deep Neural Networks

https://github.com/shuaijiang/Whisper-Finetune

SpecAugment: A Simple Data Augmentation Method for Automatic Speech Recognition

https://github.com/shun601/4th-tellus-satellite-challenge

On the Variance of the Adaptive Learning Rate and Beyond

https://github.com/sicara/easy-few-shot-learning

Matching Networks for One Shot Learning

https://github.com/sidak/SentEval

Evaluation of sentence embeddings in downstream and linguistic probing tasks

https://github.com/siddikui/Fast-DARTS

DARTS: Differentiable Architecture Search

https://github.com/sidney1505/arc_maml_transformer

Universal Transformers

https://github.com/sihengli99/textbind

TextBind: Multi-turn Interleaved Multimodal Instruction-following in the Wild

https://github.com/simecek/Czech-ULMFiT

Universal Language Model Fine-tuning for Text Classification

https://github.com/simnyatsanga/image-caption-generator

Where to put the Image in an Image Caption Generator

https://github.com/simon-benigeri/lstm-language-model

Recurrent Neural Network Regularization

https://github.com/simon-benigeri/narrative-generation

RoBERTa: A Robustly Optimized BERT Pretraining Approach

https://github.com/simonjisu/E2EMN

End-To-End Memory Networks

https://github.com/simonjisu/NMT

Recurrent Memory Networks for Language Modeling

https://github.com/sinamoradian/SNIP-NAS-2

DARTS: Differentiable Architecture Search

https://github.com/sindhusweety/VAE-Generating-Sentences-From-a-Continuous-Space

Generating Sentences from a Continuous Space

https://github.com/sithu31296/image-captioning

ClipCap: CLIP Prefix for Image Captioning

https://github.com/sjoshi804/hierarchical-darts-neural-architecture-search

DARTS: Differentiable Architecture Search

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

Language-agnostic BERT Sentence Embedding

https://github.com/skonor/group_and_shuffle

Group and Shuffle: Efficient Structured Orthogonal Parametrization

https://github.com/skyworkai/moe-plus-plus

Chat-UniVi: Unified Visual Representation Empowers Large Language Models with Image and Video Understanding

https://github.com/skyworkai/moh

Chat-UniVi: Unified Visual Representation Empowers Large Language Models with Image and Video Understanding

https://github.com/sl-93/SUPERVISED-CONTRASTIVE-LEARNING-FOR-PRE-TRAINED-LANGUAGE-MODEL-FINE-TUNING

Supervised Contrastive Learning for Pre-trained Language Model Fine-tuning

https://github.com/slczgwh/REDN

Downstream Model Design of Pre-trained Language Model for Relation Extraction Task

https://github.com/sleepthroughdifficulties/kernelwhitening

On the Sentence Embeddings from Pre-trained Language Models

https://github.com/smallbenchnlp/ELECTRA-DeBERTa

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

https://github.com/smart-lty/parallelspeculativedecoding

Fast Inference from Transformers via Speculative Decoding

https://github.com/smarton-empower/smarton-ai

Language Models are Few-Shot Learners

https://github.com/smelliecat/aaemime

LlamaFactory: Unified Efficient Fine-Tuning of 100+ Language Models

https://github.com/smile-data/smile

Language Models are Few-Shot Learners

https://github.com/smisthzhu/attentionocr

A Feasible Framework for Arbitrary-Shaped Scene Text Recognition

https://github.com/smvorwerk/xlstm-cuda

xLSTM: Extended Long Short-Term Memory

https://github.com/snexus/llm-search

Precise Zero-Shot Dense Retrieval without Relevance Labels

https://github.com/snjstudent/MyREALM

REALM: Retrieval-Augmented Language Model Pre-Training

https://github.com/sntcristian/and-kge

SPECTER: Document-level Representation Learning using Citation-informed Transformers

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