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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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/facebookresearch/GPU-DPF
공식 코드GPU-based Private Information Retrieval for On-Device Machine Learning Inference
https://github.com/facebookresearch/LAMA
공식 코드https://github.com/facebookresearch/Mask-Predict
공식 코드Mask-Predict: Parallel Decoding of Conditional Masked Language Models
https://github.com/facebookresearch/MemoryMosaics
공식 코드https://github.com/facebookresearch/XLM
공식 코드https://github.com/facebookresearch/adaptive-span
공식 코드https://github.com/facebookresearch/alma
공식 코드https://github.com/facebookresearch/atlas
공식 코드Atlas: Few-shot Learning with Retrieval Augmented Language Models
https://github.com/facebookresearch/bart_ls
공식 코드Adapting Pretrained Text-to-Text Models for Long Text Sequences
https://github.com/facebookresearch/bitsandbytes
공식 코드https://github.com/facebookresearch/cc_net
공식 코드https://github.com/facebookresearch/coder_reviewer_reranking
공식 코드https://github.com/facebookresearch/colorlessgreenRNNs
공식 코드https://github.com/facebookresearch/crag
공식 코드https://github.com/facebookresearch/diffq
공식 코드Differentiable Model Compression via Pseudo Quantization Noise
https://github.com/facebookresearch/dual-system-for-visual-language-reasoning
공식 코드DOMINO: A Dual-System for Multi-step Visual Language Reasoning
https://github.com/facebookresearch/evaluation-of-nmt-bt
공식 코드On The Evaluation of Machine Translation Systems Trained With Back-Translation
https://github.com/facebookresearch/fairseq/tree/main/examples/linformer
공식 코드https://github.com/facebookresearch/large_concept_model
공식 코드Large Concept Models: Language Modeling in a Sentence Representation Space
https://github.com/facebookresearch/lingua
공식 코드From Bytes to Ideas: Language Modeling with Autoregressive U-Nets
https://github.com/facebookresearch/llama-hd-dataset
공식 코드https://github.com/facebookresearch/maze_navigation_MLMU
공식 코드https://github.com/facebookresearch/mega
공식 코드https://github.com/facebookresearch/metaicl
공식 코드https://github.com/facebookresearch/metaseq
공식 코드https://github.com/facebookresearch/minif2f
공식 코드Draft, Sketch, and Prove: Guiding Formal Theorem Provers with Informal Proofs
https://github.com/facebookresearch/motif
공식 코드Motif: Intrinsic Motivation from Artificial Intelligence Feedback
https://github.com/facebookresearch/multisense_consistency
공식 코드From Form(s) to Meaning: Probing the Semantic Depths of Language Models Using Multisense Consistency
https://github.com/facebookresearch/npm
공식 코드https://github.com/facebookresearch/oni
공식 코드Online Intrinsic Rewards for Decision Making Agents from Large Language Model Feedback
https://github.com/facebookresearch/online-dt
공식 코드https://github.com/facebookresearch/polymath
공식 코드Logic.py: Bridging the Gap between LLMs and Constraint Solvers
https://github.com/facebookresearch/radioactive-watermark
공식 코드https://github.com/facebookresearch/rlcd
공식 코드RLCD: Reinforcement Learning from Contrastive Distillation for Language Model Alignment
https://github.com/facebookresearch/shepherd
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.