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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/aleksac99/SQuAD-SR

Unsupervised Cross-lingual Representation Learning at Scale

https://github.com/alexandra-chron/ntua-slp-wassa-iest2018

Regularizing and Optimizing LSTM Language Models

https://github.com/alexandra-chron/wassa-2018

Regularizing and Optimizing LSTM Language Models

https://github.com/algharak/BERTenhance

Semi-supervised Sequence Learning

https://github.com/algoashutosh/sentiment-analysis-project

Universal Language Model Fine-tuning for Text Classification

https://github.com/algolzw/image-restoration-sde

Photo-Realistic Image Restoration in the Wild with Controlled Vision-Language Models

https://github.com/alibaba-damo-academy/FunASR

Conformer: Convolution-augmented Transformer for Speech Recognition

https://github.com/alibaba-nlp/multi-cpr

Retrieval Oriented Masking Pre-training Language Model for Dense Passage Retrieval

https://github.com/alibaba/EasyNLP/tree/master/examples/fewshot_learning

Exploiting Cloze Questions for Few Shot Text Classification and Natural Language Inference

https://github.com/alibaba/EasyTransfer/tree/master/scripts/meta_finetune

Meta Fine-Tuning Neural Language Models for Multi-Domain Text Mining

https://github.com/alibaba/rtp-llm

Large Language Model based Long-tail Query Rewriting in Taobao Search

https://github.com/alinazhiltsova/Replication_arxiv_1803_10769

Network Traffic Anomaly Detection Using Recurrent Neural Networks

https://github.com/alisafaya/SHA-RNN.jl

Single Headed Attention RNN: Stop Thinking With Your Head

https://github.com/allen108108/Model-Optimizer_Implementation

On the Variance of the Adaptive Learning Rate and Beyond

https://github.com/allenai/FineGrainedRLHF

Fine-Grained Human Feedback Gives Better Rewards for Language Model Training

https://github.com/allenai/aspire

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

https://github.com/allenai/beacon

BigBIO: A Framework for Data-Centric Biomedical Natural Language Processing

https://github.com/allenai/bilm-tf

Deep contextualized word representations

https://github.com/allenai/macaw

Language Models are Few-Shot Learners

https://github.com/allenai/scifact

DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter

https://github.com/alon-albalak/online-data-mixing

GPT-NeoX-20B: An Open-Source Autoregressive Language Model

https://github.com/alon-albalak/xor-covid

On the Cross-lingual Transferability of Monolingual Representations

https://github.com/alphadl/darts.pytorch1.1

DARTS: Differentiable Architecture Search

https://github.com/alrope123/prompt-waywardness

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

https://github.com/amagooda/SummaRuNNer_coattention

Universal Language Model Fine-tuning for Text Classification

https://github.com/amaiya/causalnlp

Adapting Text Embeddings for Causal Inference

https://github.com/amano-k-lab/braincodec

Self-Supervised Learning of Brain Dynamics from Broad Neuroimaging Data

https://github.com/amarazad/DSRNet

Meta-Context Transformers for Domain-Specific Response Generation

https://github.com/amazon-science/SDFeedback

Teaching Large Language Models to Self-Debug

https://github.com/amazon-science/alexa-teacher-models

AlexaTM 20B: Few-Shot Learning Using a Large-Scale Multilingual Seq2Seq Model

https://github.com/amazon-science/contraclm

ContraCLM: Contrastive Learning For Causal Language Model

https://github.com/amazon-science/efficient-longdoc-classification

Longformer: The Long-Document Transformer

https://github.com/amazon-science/self_debug

Teaching Large Language Models to Self-Debug

https://github.com/ambrim/debiasing_gpt

Self-Diagnosis and Self-Debiasing: A Proposal for Reducing Corpus-Based Bias in NLP

https://github.com/amirabbasasadi/PersianOCR

Implicit Language Model in LSTM for OCR

https://github.com/amittai/cynical

Cynical Selection of Language Model Training Data

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