Papers with Code 아카이브

Language Modeling

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

https://github.com/RajasSU/Twitter-Sentiment-Analysis-using-ULMFiT

Universal Language Model Fine-tuning for Text Classification

https://github.com/RajeevReddyIlavala/Low-Shot-Learning-Learning

Matching Networks for One Shot Learning

https://github.com/RamiSketcher/decision-transformer-ammi

Decision Transformer: Reinforcement Learning via Sequence Modeling

https://github.com/Ravoxsg/Word-level-language-modeling

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

https://github.com/Rcrossmeister/Knowledge-to-SQL

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

https://github.com/Rcrossmeister/RLQG

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

https://github.com/RenXiangyuan/tf_bert

Semi-supervised Sequence Learning

https://github.com/RenzKa/simlingo

CarLLaVA: Vision language models for camera-only closed-loop driving

https://github.com/Rishit-dagli/Conformer

Conformer: Convolution-augmented Transformer for Speech Recognition

https://github.com/Rishit-dagli/GLU

Language Modeling with Gated Convolutional Networks

https://github.com/Robotmurlock/Deepwalk-and-Node2vec

DeepWalk: Online Learning of Social Representations

https://github.com/RoganInglis/AudioLM

AudioLM: a Language Modeling Approach to Audio Generation

https://github.com/RoganInglis/AudioLM/tree/main/src/models

W2v-BERT: Combining Contrastive Learning and Masked Language Modeling for Self-Supervised Speech Pre-Training

https://github.com/RookieZB/duma_implementation_by_tf2

DUMA: Reading Comprehension with Transposition Thinking

https://github.com/RossNordby/SoftPromptsForEvaluation

The Pile: An 800GB Dataset of Diverse Text for Language Modeling

https://github.com/RundongChou/elmo-chinese-oversimplified

Deep contextualized word representations

https://github.com/S-Abdelnabi/awt

Regularizing and Optimizing LSTM Language Models

https://github.com/SCismycat/bert_code_view

Semi-supervised Sequence Learning

https://github.com/SDLZY/VCR_Align

UNITER: UNiversal Image-TExt Representation Learning

https://github.com/SNUDerek/multiLSTM

Character-Aware Neural Language Models

https://github.com/SYangDong/bert-with-frozen-code

Semi-supervised Sequence Learning

https://github.com/SachinIchake/KALM

Improving Neural Language Models with a Continuous Cache

https://github.com/SamanJamalAbbasi/DRESS_DeepRL

Semi-supervised Sequence Learning

https://github.com/SamanJamalAbbasi/seq2seq_auto-encoder

Semi-supervised Sequence Learning

https://github.com/SambhawDrag/XLNet.jl

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

https://github.com/SamuelGabriel/R252

Learning Python Code Suggestion with a Sparse Pointer Network

https://github.com/Samyu0304/thought-propagation

Language Models are Few-Shot Learners

https://github.com/SarthakVaswani/ace_bot

CAiRE: An Empathetic Neural Chatbot

https://github.com/SarthakYadav/audax

SpecAugment: A Simple Data Augmentation Method for Automatic Speech Recognition

https://github.com/Satan012/BERT

Semi-supervised Sequence Learning

https://github.com/Saurabh2798/Q-A-bot

End-To-End Memory Networks

https://github.com/ScandEval/ScandEval

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

https://github.com/SdahlSean/RangerOptimizerTensorflow

On the Variance of the Adaptive Learning Rate and Beyond

https://github.com/SeanLee97/AnglE

BeLLM: Backward Dependency Enhanced Large Language Model for Sentence Embeddings

https://github.com/SeonbeomKim/TensorFlow-Matching_Networks_for_One_Shot_Learning

Matching Networks for One Shot Learning

https://github.com/SeonbeomKim/TensorFlow-MemN2N

End-To-End Memory Networks

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