Language Modelling
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A language model is a model of natural language. Language models are useful for a variety of tasks, including speech recognition, machine translation, natural language generation (generating more human-like text), optical character recognition, route optimization, handwriting recognition, grammar induction, and information retrieval.
Large language models (LLMs), currently their most advanced form, are predominantly based on transformers trained on larger datasets (frequently using words scraped from the public internet). They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.
Source: Wikipedia
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/AdarshKumar712/PPLM.jl
Plug and Play Language Models: A Simple Approach to Controlled Text Generation
https://github.com/Adi-iitd/AI-Art
https://github.com/Adrianceja01/Neural-Transfer
https://github.com/Advancing-Machine-Human-Reasoning-Lab/apt
https://github.com/Ago3/Adversifier
A BERT-Based Transfer Learning Approach for Hate Speech Detection in Online Social Media
https://github.com/Akella17/Handwriting_Synthesis
https://github.com/Akella17/Voice_Style_Transfer
https://github.com/AlbertFeng-0405/SceneTransformer-pytorch
Scene Transformer: A unified architecture for predicting multiple agent trajectories
https://github.com/AleksSol/mmp_activity_diary
https://github.com/Alex-Norden/NST
https://github.com/AlexWan0/infini-gram
Infini-gram: Scaling Unbounded n-gram Language Models to a Trillion Tokens
https://github.com/Alexander-H-Liu/End-to-end-ASR-Pytorch
https://github.com/Alexey-Borisov/3_course_diary
Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks
https://github.com/Alina9/Neural-Style-Transfer-
https://github.com/AlvinIsonomia/darts.pytorch1.3_MultiGPU
https://github.com/Amadeus979/decision-transformer
Decision Transformer: Reinforcement Learning via Sequence Modeling
https://github.com/AmazingDay1/TAME
Mamba: Linear-Time Sequence Modeling with Selective State Spaces
https://github.com/Amir-Hofo/Language_Modeling_with_LSTM_models
https://github.com/AmirmohammadRostami/KeywordsSpotting-EfficientNet-A0
SpecAugment: A Simple Data Augmentation Method for Automatic Speech Recognition
https://github.com/AndreasInk/Quiz-APIv2
Simplifying Paragraph-level Question Generation via Transformer Language Models
https://github.com/AndreiMoraru123/ContextCollector
https://github.com/AndyDodss/iicnn
https://github.com/AndySECP/Papers-Implementation
https://github.com/AnesBenmerzoug/Handwriting-Model
https://github.com/AniketBajpai/deep-handwriting-generation
https://github.com/AnirudhMaiya/RAdam
https://github.com/Ankur3107/awesome-daily-blog
TextKD-GAN: Text Generation using KnowledgeDistillation and Generative Adversarial Networks
https://github.com/Anna996/Neural-Style-Transfer-Project
https://github.com/AntheaLi/cs224nProject
Meta Fine-Tuning Neural Language Models for Multi-Domain Text Mining
https://github.com/AntreasAntoniou/MatchingNetworks
https://github.com/AnttiKarlsson/finnish_ulmfit
Universal Language Model Fine-tuning for Text Classification
https://github.com/AnzorGozalishvili/sentence_transformers_serving
Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks
https://github.com/Aradhye2002/selective-peft-toolkit
BitFit: Simple Parameter-efficient Fine-tuning for Transformer-based Masked Language-models
https://github.com/AranKomat/adapinp
https://github.com/ArionDas/InfiniGPT
Leave No Context Behind: Efficient Infinite Context Transformers with Infini-attention
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.