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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/ammesatyajit/VideoBERT

VideoBERT: A Joint Model for Video and Language Representation Learning

https://github.com/amoramine/Pegasus_Longformer_summarization

Longformer: The Long-Document Transformer

https://github.com/amoramine/Pegasus_with_Longformer_summarization

Longformer: The Long-Document Transformer

https://github.com/amoussawi/recoder

Variational Autoencoders for Collaborative Filtering

https://github.com/amrinkareem/paris3d

PartSLIP: Low-Shot Part Segmentation for 3D Point Clouds via Pretrained Image-Language Models

https://github.com/amritanjali123/NM373_Future_Predicators

Video (language) modeling: a baseline for generative models of natural videos

https://github.com/anaishoareau/french_preprocessing

CamemBERT: a Tasty French Language Model

https://github.com/anandaswarup/phrase_break_prediction

Phrase break prediction with bidirectional encoder representations in Japanese text-to-speech synthesis

https://github.com/andravin/algebranets

AlgebraNets

https://github.com/andreamad8/Universal-Transformer-Pytorch

Universal Transformers

https://github.com/andrewschreiber/hs-math-nlp

AllenNLP Interpret: A Framework for Explaining Predictions of NLP Models

https://github.com/andychinka/dcase-challenge

SpecAugment: A Simple Data Augmentation Method for Automatic Speech Recognition

https://github.com/andymogul/gcrnn_revised

Structured Sequence Modeling with Graph Convolutional Recurrent Networks

https://github.com/anjandeepsahni/automatic_speech_recognition

Listen, Attend and Spell

https://github.com/ankurbanga/Language-Models

Deep contextualized word representations

https://github.com/anlausch/LIBERT

Specializing Unsupervised Pretraining Models for Word-Level Semantic Similarity

https://github.com/annosubmission/GRC-Cache

Cached Transformers: Improving Transformers with Differentiable Memory Cache

https://github.com/anonNo2/MulTeacher-KD

DARTS: Differentiable Architecture Search

https://github.com/anonymouspap/latte_recsys

Variational Autoencoders for Collaborative Filtering

https://github.com/anonymousxzy/compacter

Intrinsic Dimensionality Explains the Effectiveness of Language Model Fine-Tuning

https://github.com/anouck96/parsingfrisian

Massive Choice, Ample Tasks (MaChAmp): A Toolkit for Multi-task Learning in NLP

https://github.com/answerdotai/cold-compress

Efficient Streaming Language Models with Attention Sinks

https://github.com/anthonyckleung/Transfer-Learning-in-Sentiment-Tweets

Universal Language Model Fine-tuning for Text Classification

https://github.com/anti-antares/Deep_Learning_Project_4

Listen, Attend and Spell

https://github.com/antoyang/VidChapters

Vid2Seq: Large-Scale Pretraining of a Visual Language Model for Dense Video Captioning

https://github.com/anubhavmaity/Ag-News-Category-Classifier

Universal Language Model Fine-tuning for Text Classification

https://github.com/anzeyimana/deepkin

Mamba: Linear-Time Sequence Modeling with Selective State Spaces

https://github.com/apmoore1/language-model

Universal Language Model Fine-tuning for Text Classification

https://github.com/apoorvumang/prompt-lookup-decoding

Accelerating Large Language Model Decoding with Speculative Sampling

https://github.com/appcoreopc/berty

Semi-supervised Sequence Learning

https://github.com/apple/ml-mkqa

Unsupervised Cross-lingual Representation Learning at Scale

https://github.com/apple/ml-mobileclip

Data Filtering Networks

https://github.com/apple/ml-veclip

Data Filtering Networks

https://github.com/aragakiyuiii/Gumbel-darts-master

DARTS: Differentiable Architecture Search

https://github.com/arctic-yen/Google_QUEST_Q-A_Labeling

How to Fine-Tune BERT for Text Classification?

https://github.com/ardyh/bert-ada

Adapt or Get Left Behind: Domain Adaptation through BERT Language Model Finetuning for Aspect-Target Sentiment Classification

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