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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/floraxinru/NLP_HotelReviews

Universal Language Model Fine-tuning for Text Classification

https://github.com/flowersteam/lamorel

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

https://github.com/floydhub/word-language-model

Recurrent Neural Network Regularization

https://github.com/flyingpumba/interpbench

Interpretability in the Wild: a Circuit for Indirect Object Identification in GPT-2 small

https://github.com/flyliu2017/bert

Semi-supervised Sequence Learning

https://github.com/flyliu2017/bert_modularized

Semi-supervised Sequence Learning

https://github.com/flymin/darts

DARTS: Differentiable Architecture Search

https://github.com/foamliu/Listen-Attend-Spell

Listen, Attend and Spell

https://github.com/foamliu/Listen-Attend-Spell-v2

Listen, Attend and Spell

https://github.com/foamliu/Listen-Attend-and-Spell

Listen, Attend and Spell

https://github.com/for-ai/language-confusion

Aya Dataset: An Open-Access Collection for Multilingual Instruction Tuning

https://github.com/forgi86/lru-reduction

Efficiently Modeling Long Sequences with Structured State Spaces

https://github.com/foundation-model-stack/fms-fsdp

PaLM: Scaling Language Modeling with Pathways

https://github.com/francescodisalvo05/66DaysOfData

Quasi-Recurrent Neural Networks

https://github.com/frankcgq105/BERTCHEN

Semi-supervised Sequence Learning

https://github.com/franknb/Text-Summarization

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

https://github.com/frankxwang/dpo-prefix-sharing

Direct Preference Optimization: Your Language Model is Secretly a Reward Model

https://github.com/freds0/data_augmentation_for_asr

SpecAugment: A Simple Data Augmentation Method for Automatic Speech Recognition

https://github.com/freeflyxiaoma/pycorrector

Neural Language Correction with Character-Based Attention

https://github.com/frgfm/Holocron

On the Variance of the Adaptive Learning Rate and Beyond

https://github.com/fshdnc/enfi-XLM

Cross-lingual Language Model Pretraining

https://github.com/fsoft-ai4code/codecapybara

Evaluating Large Language Models Trained on Code

https://github.com/fstahlberg/tensor2tensor-usr

Simple Fusion: Return of the Language Model

https://github.com/ftramer/lm-extraction-benchmark

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

https://github.com/fudan-zvg/roadnetworktransformer

LaneGraph2Seq: Lane Topology Extraction with Language Model via Vertex-Edge Encoding and Connectivity Enhancement

https://github.com/fudandisc/reform-eval

MME: A Comprehensive Evaluation Benchmark for Multimodal Large Language Models

https://github.com/fujenchu/matchingNet

Matching Networks for One Shot Learning

https://github.com/fuqiang-git-hub/unilmv1-Paddle

Unified Language Model Pre-training for Natural Language Understanding and Generation

https://github.com/fushengwuyu/R-Drop

R-Drop: Regularized Dropout for Neural Networks

https://github.com/fywalter/label-bias

Language Models are Few-Shot Learners

https://github.com/fyyfu/semantic-watermark

A Watermark for Large Language Models

https://github.com/gagan3012/self_rewarding_models

Self-Rewarding Language Models

https://github.com/gair-nlp/abel

Solving Quantitative Reasoning Problems with Language Models

https://github.com/gair-nlp/prox

YuLan-Mini: An Open Data-efficient Language Model

https://github.com/galsang/trees_from_transformers

A Critical Analysis of Biased Parsers in Unsupervised Parsing

https://github.com/gandharvsuri/Conditional-Generative-model-on-BERT

BERT has a Mouth, and It Must Speak: BERT as a Markov Random Field Language Model

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