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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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/D-X-Y/NAS-Projects
https://github.com/D-a-r-e-k/Source-Code-Modelling
https://github.com/DIRECT-BIT/SRA-MCTS
CodeGen: An Open Large Language Model for Code with Multi-Turn Program Synthesis
https://github.com/DKhomi/PrefixTuning
https://github.com/DS4SD/docling
SmolDocling: An ultra-compact vision-language model for end-to-end multi-modal document conversion
https://github.com/Dahny/IN4325-Core-IR-Project
https://github.com/DanailKoychev/neural-style
https://github.com/Danqi7/584-final
Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks
https://github.com/DasLab/superfolder-covid-mrna-vaccines
Algorithm for Optimized mRNA Design Improves Stability and Immunogenicity
https://github.com/DataCanvasIO/Hypernets
https://github.com/Davidnh8/artAI
https://github.com/Davidzhangyuanhan/NOAH
https://github.com/Dawars/SqueezeNet-tf
https://github.com/Dawn-Of-Eve/nadir
AdaBelief Optimizer: Adapting Stepsizes by the Belief in Observed Gradients
https://github.com/DeepMark/deepmark
https://github.com/Deepayan137/Adapting-OCR
Universal Language Model Fine-tuning for Text Classification
https://github.com/Delavari-Alireza/Decision_Transformer_Sequence-Modeling_Deep_Reinforcement_Learning
Decision Transformer: Reinforcement Learning via Sequence Modeling
https://github.com/DeligientSloth/QQsim
https://github.com/DeligientSloth/bert-tensorflow
https://github.com/DemisEom/SpecAugment
SpecAugment: A Simple Data Augmentation Method for Automatic Speech Recognition
https://github.com/DeokO/bert-excercise-ongoing
https://github.com/Derposoft/ai-educator
https://github.com/DevSinghSachan/ssl_text_classification
Revisiting LSTM Networks for Semi-Supervised Text Classification via Mixed Objective Function
https://github.com/Divs1159/STING-BEE
STING-BEE: Towards Vision-Language Model for Real-World X-ray Baggage Security Inspection
https://github.com/DmitryUlyanov/fast-neural-doodle
https://github.com/DoctorLiQ/AutoML
https://github.com/Doffery/BERT-Sentiment-Analysis-Amazon-Review
https://github.com/Domminique/Deploy-BERT-for-Sentiment-Analysis-with-FastAPI-
https://github.com/EMBEDDIA/event-detection
Event Detection as Question Answering with Entity Information
https://github.com/EdinburghNLP/code-docstring-corpus
Learning Python Code Suggestion with a Sparse Pointer Network
https://github.com/Edresson/VoiceSplit
https://github.com/EduardoTrevino/fail-talms
https://github.com/Efficient-Scalable-Machine-Learning/event-ssm
https://github.com/EightRice/atn_GPT-3
https://github.com/EkaterinaXie/LiteVAR
Visual Autoregressive Modeling: Scalable Image Generation via Next-Scale Prediction
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.