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/chengzeyi/paraattention

Ring Attention with Blockwise Transformers for Near-Infinite Context

https://github.com/chenhongruixuan/mambacd

VMamba: Visual State Space Model

https://github.com/chenkangyang/paddle_visual_bert

VisualBERT: A Simple and Performant Baseline for Vision and Language

https://github.com/chenxiliu-hnu/timecma

Spatial-Temporal Large Language Model for Traffic Prediction

https://github.com/cheonbok94/Pytorch-Relational-Recurrent-Neural-networks

Relational recurrent neural networks

https://github.com/chesterdu/contrastive_summary

XLNet: Generalized Autoregressive Pretraining for Language Understanding

https://github.com/chia-hsuan-lee/spoken-squad

SPLAT: Speech-Language Joint Pre-Training for Spoken Language Understanding

https://github.com/chiahsuan156/Spoken-SQuAD

SPLAT: Speech-Language Joint Pre-Training for Spoken Language Understanding

https://github.com/chiang9/NLP-Chinese_couplet_generation

AnchiBERT: A Pre-Trained Model for Ancient ChineseLanguage Understanding and Generation

https://github.com/chiayewken/bert-qa

Semi-supervised Sequence Learning

https://github.com/chielingyueh/anaphora_resolution_chemical_patents

Enriching Pre-trained Language Model with Entity Information for Relation Classification

https://github.com/chris-tng/semi-supervised-nlp

Regularizing and Optimizing LSTM Language Models

https://github.com/chris4540/DD2430-ds-proj

Unifying Visual-Semantic Embeddings with Multimodal Neural Language Models

https://github.com/chrisleunglokhin/Capstone-BERT

Semi-supervised Sequence Learning

https://github.com/chrisociepa/allamo

PaLM: Scaling Language Modeling with Pathways

https://github.com/christianyang37/chiwun

How to Protect Copyright Data in Optimization of Large Language Models?

https://github.com/chunhuililili/mt_dnn

A Hybrid Neural Network Model for Commonsense Reasoning

https://github.com/ckiplab/ckiptagger

Why Attention? Analyze BiLSTM Deficiency and Its Remedies in the Case of NER

https://github.com/cle-ros/RoutingNetworks

Routing Networks and the Challenges of Modular and Compositional Computation

https://github.com/clementromac/lamorel

Do As I Can, Not As I Say: Grounding Language in Robotic Affordances

https://github.com/climsocana/tecb-de

TSDAE: Using Transformer-based Sequential Denoising Auto-Encoder for Unsupervised Sentence Embedding Learning

https://github.com/clip-italian/clip-italian

Persistent Anti-Muslim Bias in Large Language Models

https://github.com/cloneofsimo/RealFormer-pytorch

RealFormer: Transformer Likes Residual Attention

https://github.com/cloudmcloudyo/capstone

Alternative structures for character-level RNNs

https://github.com/cloudygoose/fewshot_lama

BitFit: Simple Parameter-efficient Fine-tuning for Transformer-based Masked Language-models

https://github.com/clovaai/textual-kd-slu

RoBERTa: A Robustly Optimized BERT Pretraining Approach

https://github.com/clutr/clutr

Generating Sentences from a Continuous Space

https://github.com/cmdowney88/embeddingstructure

FOCUS: Effective Embedding Initialization for Monolingual Specialization of Multilingual Models

https://github.com/cmunnis/BERT_vs_Transformer-XL

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

https://github.com/cnichkawde/MatchingNetwork

Matching Networks for One Shot Learning

https://github.com/cnielly/prototypical-networks-omniglot

Matching Networks for One Shot Learning

https://github.com/coco60/bert-test

Semi-supervised Sequence Learning

https://github.com/cod3licious/evolvemb

Temporal Analysis of Language through Neural Language Models

https://github.com/code-implementation1/Code4/tree/main/IPT

Is Supervised Syntactic Parsing Beneficial for Language Understanding? An Empirical Investigation

https://github.com/codedecde/Recognizing-Textual-Entailment

Recurrent Batch Normalization

https://github.com/codedotal/gpt-code-clippy

Language Models are Few-Shot Learners

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