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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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/matthewcym/sflm
공식 코드Revisiting Self-Training for Few-Shot Learning of Language Model
https://github.com/matthewcym/voicebench
공식 코드https://github.com/matthewfl/nlp-entity-convnet
공식 코드Capturing Semantic Similarity for Entity Linking with Convolutional Neural Networks
https://github.com/matthewmatero/melt
공식 코드https://github.com/matthieufp/commute
공식 코드https://github.com/matthieufp/zerommt
공식 코드https://github.com/mattroos/null_class_adversarial_defense
공식 코드https://github.com/maudzung/SFA3D
공식 코드https://github.com/mawdoo3/Multi-dialect-Arabic-BERT
공식 코드Multi-Dialect Arabic BERT for Country-Level Dialect Identification
https://github.com/maximecb/gym-miniworld
공식 코드Pre-trained Word Embeddings for Goal-conditional Transfer Learning in Reinforcement Learning
https://github.com/maximedb/full
공식 코드https://github.com/maximek3/e-ViL
공식 코드e-ViL: A Dataset and Benchmark for Natural Language Explanations in Vision-Language Tasks
https://github.com/maxischuh/twinbooster
공식 코드https://github.com/maxwe11y/gaslightingllm
공식 코드https://github.com/maxxu05/relcon
공식 코드RelCon: Relative Contrastive Learning for a Motion Foundation Model for Wearable Data
https://github.com/mayaokawa/opinion_dynamics
공식 코드Predicting Opinion Dynamics via Sociologically-Informed Neural Networks
https://github.com/mayhugotong/videoinsta
공식 코드VideoINSTA: Zero-shot Long Video Understanding via Informative Spatial-Temporal Reasoning with LLMs
https://github.com/mayo-radiology-informatics-lab/conflare
공식 코드https://github.com/mayubo2333/llm-ie
공식 코드https://github.com/mayug/0-shot-llm-vision
공식 코드Do Vision and Language Encoders Represent the World Similarly?
https://github.com/mazumder-lab/comet
공식 코드COMET: Learning Cardinality Constrained Mixture of Experts with Trees and Local Search
https://github.com/mbilalzafar/fair-classification
공식 코드https://github.com/mbzuai-nlp/arabicmmlu
공식 코드ArabicMMLU: Assessing Massive Multitask Language Understanding in Arabic
https://github.com/mbzuai-nlp/fire
공식 코드FIRE: Fact-checking with Iterative Retrieval and Verification
https://github.com/mbzuai-nlp/lamini-lm
공식 코드LaMini-LM: A Diverse Herd of Distilled Models from Large-Scale Instructions
https://github.com/mbzuai-nlp/x-claim
공식 코드Lost in Translation, Found in Spans: Identifying Claims in Multilingual Social Media
https://github.com/mbzuai-oryx/geochat
공식 코드GeoChat: Grounded Large Vision-Language Model for Remote Sensing
https://github.com/mbzuai-oryx/palo
공식 코드https://github.com/mbzuai-oryx/xraygpt
공식 코드XrayGPT: Chest Radiographs Summarization using Medical Vision-Language Models
https://github.com/mcgill-nlp/bias-bench
공식 코드An Empirical Survey of the Effectiveness of Debiasing Techniques for Pre-trained Language Models
https://github.com/mcgill-nlp/debias-eval
공식 코드An Empirical Survey of the Effectiveness of Debiasing Techniques for Pre-trained Language Models
https://github.com/mcgill-nlp/logicalknowedit
공식 코드Evaluating Dependencies in Fact Editing for Language Models: Specificity and Implication Awareness
https://github.com/mcgill-nlp/retriever-lm-reasoning
공식 코드https://github.com/mcgill-nlp/syntactic-substitutability
공식 코드Syntactic Substitutability as Unsupervised Dependency Syntax
https://github.com/mchen24/iclr2017
공식 코드Efficient Vector Representation for Documents through Corruption
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.