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/haiyang-w/tokenformer

Mamba: Linear-Time Sequence Modeling with Selective State Spaces

https://github.com/halo090770/bert

Semi-supervised Sequence Learning

https://github.com/han-shi/SparseBERT

Longformer: The Long-Document Transformer

https://github.com/hanguo97/flute

Pushing the Limits of Large Language Model Quantization via the Linearity Theorem

https://github.com/hannamw/eap-ig

Sparse Feature Circuits: Discovering and Editing Interpretable Causal Graphs in Language Models

https://github.com/hannw/nlstm

Nested LSTMs

https://github.com/hanoonaR/object-centric-ovd

PromptDet: Towards Open-vocabulary Detection using Uncurated Images

https://github.com/hansungj/CharCNN_Pytorch

Character-Aware Neural Language Models

https://github.com/haonan-li/cmmlu

BatGPT: A Bidirectional Autoregessive Talker from Generative Pre-trained Transformer

https://github.com/haonan3/anchorcontext

Ring Attention with Blockwise Transformers for Near-Infinite Context

https://github.com/harruff/Senior_Project_Repository

Improving Generalization Performance by Switching from Adam to SGD

https://github.com/harveyThomas4692/llmlex

Symbolic Regression with Multimodal Large Language Models and Kolmogorov Arnold Networks

https://github.com/hasangchun/ContextNet

ContextNet: Improving Convolutional Neural Networks for Automatic Speech Recognition with Global Context

https://github.com/hasteck/EDLAE_NeurIPS2020

Variational Autoencoders for Collaborative Filtering

https://github.com/hasteck/MRF_NeurIPS_2019

Variational Autoencoders for Collaborative Filtering

https://github.com/havenhq/mamba-chat

Mamba: Linear-Time Sequence Modeling with Selective State Spaces

https://github.com/hazyresearch/ama_prompting

Language Models are Few-Shot Learners

https://github.com/hazyresearch/based

Can Mamba Learn How to Learn? A Comparative Study on In-Context Learning Tasks

https://github.com/hazyresearch/flash-attention

Efficient Memory Management for Large Language Model Serving with PagedAttention

https://github.com/hbaflast/bert-sentiment-analysis-pytorch

CamemBERT: a Tasty French Language Model

https://github.com/hbaflast/bert-sentiment-analysis-tensorflow

CamemBERT: a Tasty French Language Model

https://github.com/he1ght/BiBERT_CE

BERT, mBERT, or BiBERT? A Study on Contextualized Embeddings for Neural Machine Translation

https://github.com/helboukkouri/character-bert

Deep contextualized word representations

https://github.com/hellokevin07/elastictrainer

Prefix-Tuning: Optimizing Continuous Prompts for Generation

https://github.com/helmy-elrais/RoBERT_Recurrence_over_BERT

How to Fine-Tune BERT for Text Classification?

https://github.com/helw150/levanter

Sophia: A Scalable Stochastic Second-order Optimizer for Language Model Pre-training

https://github.com/hemingkx/Spec-Bench

Unlocking Efficiency in Large Language Model Inference: A Comprehensive Survey of Speculative Decoding

https://github.com/heraclex12/VLSP2020-Fake-News-Detection

How to Fine-Tune BERT for Text Classification?

https://github.com/heraclex12/vietpunc

Unsupervised Cross-lingual Representation Learning at Scale

https://github.com/heye0507/individualNLPClassifier

Universal Language Model Fine-tuning for Text Classification

https://github.com/heyuan9/medscore

DnDScore: Decontextualization and Decomposition for Factuality Verification in Long-Form Text Generation

https://github.com/hgstudent/las

Listen, Attend and Spell

https://github.com/hikaruya8/lstm_model_py

Recurrent Neural Network Regularization

https://github.com/hilberthit/gpt-3

Language Models are Few-Shot Learners

https://github.com/hit-scir/chinese-mixtral-8x7b

Allocating Large Vocabulary Capacity for Cross-lingual Language Model Pre-training

https://github.com/hit-scir/huozi

Sailor: Open Language Models for South-East Asia

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