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Language Modeling
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코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/MindCode-4/code-14/tree/main/CAJ
Learn from Downstream and Be Yourself in Multimodal Large Language Model Fine-Tuning
https://github.com/MindCode-4/code-2/tree/main/openelm
OpenELM: An Efficient Language Model Family with Open Training and Inference Framework
https://github.com/MindCode-4/code-2/tree/main/pix2struct
Pix2Struct: Screenshot Parsing as Pretraining for Visual Language Understanding
https://github.com/MindCode-4/code-3/tree/main/cpm
CPM: A Large-scale Generative Chinese Pre-trained Language Model
https://github.com/MindCode-4/code-3/tree/main/cpmbee
CPM: A Large-scale Generative Chinese Pre-trained Language Model
https://github.com/MindCode-4/code-3/tree/main/ctrl
CTRL: A Conditional Transformer Language Model for Controllable Generation
https://github.com/MindCode-4/code-3/tree/main/decision_transformer
Decision Transformer: Reinforcement Learning via Sequence Modeling
https://github.com/MindCode-4/code-3/tree/main/deepseek_v2
DeepSeek-V2: A Strong, Economical, and Efficient Mixture-of-Experts Language Model
https://github.com/MindCode-4/code-3/tree/main/distilbert
DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter
https://github.com/MindCode-4/code-3/tree/main/esm
ESM All-Atom: Multi-scale Protein Language Model for Unified Molecular Modeling
https://github.com/MindCode-4/code-5/tree/main/tapex
TAPEX: Table Pre-training via Learning a Neural SQL Executor
https://github.com/MindCode-4/code-5/tree/main/trocr
TrOCR: Transformer-based Optical Character Recognition with Pre-trained Models
https://github.com/MindCode-4/code-5/tree/main/visual_bert
VisualBERT: A Simple and Performant Baseline for Vision and Language
https://github.com/MindCode-4/code-5/tree/main/xlm
XLM-K: Improving Cross-Lingual Language Model Pre-training with Multilingual Knowledge
https://github.com/MindCode-4/code-5/tree/main/xlm_roberta_xl
https://github.com/MindCode-4/code-5/tree/main/xlnet
XLNet: Generalized Autoregressive Pretraining for Language Understanding
https://github.com/MindCode-4/code-5/tree/main/xmod
Xmodel-VLM: A Simple Baseline for Multimodal Vision Language Model
https://github.com/MindCode-4/code-7/tree/main/gated-word-character-recurrent-language-model
https://github.com/MindSpore-paper-code-2/code3/tree/main/t2t-vit
Tokens-to-Token ViT: Training Vision Transformers from Scratch on ImageNet
https://github.com/MindSpore-paper-code-2/code399/tree/main/Bert
DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter
https://github.com/MindSpore-paper-code-2/code399/tree/main/GPT
https://github.com/MindSpore-paper-code-2/code399/tree/main/Pangu_alpha
PanGu-Coder: Program Synthesis with Function-Level Language Modeling
https://github.com/MindSpore-paper-code-2/code399/tree/main/jasper
https://github.com/MindSpore-paper-code-2/code400/tree/main/DeepText
https://github.com/MindSpore-paper-code-2/code400/tree/main/Roberta_Seq2Seq
https://github.com/MindSpore-paper-code-3/code10/tree/main/guided_anchoring
https://github.com/MindSpore-paper-code-3/code9/tree/main/DeepText
https://github.com/MindSpore-paper-code-3/code9/tree/main/cpm
CPM: A Large-scale Generative Chinese Pre-trained Language Model
https://github.com/MindSpore-paper-code-3/code9/tree/main/mass
MASS: Masked Sequence to Sequence Pre-training for Language Generation
https://github.com/MindSpore-scientific-2/code-10/tree/main/QuantExplainNLP_AlgoAnalysis
NLP Evaluation in trouble: On the Need to Measure LLM Data Contamination for each Benchmark
https://github.com/MindSpore-scientific-2/code-12/tree/main/gated-word-character-recurrent-language-model
https://github.com/MindSpore-scientific-2/code-14/tree/main/gpt_neox_japanese
https://github.com/MindSpore-scientific-2/code-14/tree/main/gptj
https://github.com/MindSpore-scientific-2/code-14/tree/main/jamba
https://github.com/MindSpore-scientific-2/code-14/tree/main/layoutlmv2
LayoutLMv2: Multi-modal Pre-training for Visually-Rich Document Understanding
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.