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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/2023-MindSpore-1/ms-code-156
LUKE: Deep Contextualized Entity Representations with Entity-aware Self-attention
https://github.com/2023-MindSpore-1/ms-code-161
https://github.com/2023-MindSpore-1/ms-code-163
https://github.com/2023-MindSpore-1/ms-code-169
https://github.com/2023-MindSpore-1/ms-code-190
https://github.com/2023-MindSpore-1/ms-code-219/tree/main/cpm
CPM: A Large-scale Generative Chinese Pre-trained Language Model
https://github.com/2023-MindSpore-1/ms-code-220/tree/main/transformer_xl
Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context
https://github.com/2023-MindSpore-1/ms-code-5/tree/main/luke
mLUKE: The Power of Entity Representations in Multilingual Pretrained Language Models
https://github.com/2023-MindSpore-1/ms-code-97
https://github.com/2023-MindSpore-4/Code-5/tree/main/IPT
Is Supervised Syntactic Parsing Beneficial for Language Understanding? An Empirical Investigation
https://github.com/2023-MindSpore-4/Code10/tree/main/DeepText
https://github.com/2023-MindSpore-4/Code12/tree/main/MindFormers/bloom
BLOOM: A 176B-Parameter Open-Access Multilingual Language Model
https://github.com/2023-MindSpore-4/Code12/tree/main/MindFormers/glm
https://github.com/2023-MindSpore-4/Code12/tree/main/MindFormers/glm2
WebGLM: Towards An Efficient Web-Enhanced Question Answering System with Human Preferences
https://github.com/2023-MindSpore-4/Code12/tree/main/MindFormers/glm3
https://github.com/2023-MindSpore-4/Code12/tree/main/MindFormers/pangualpha
PanGu-Bot: Efficient Generative Dialogue Pre-training from Pre-trained Language Model
https://github.com/2023-MindSpore-4/Code12/tree/main/ganleilei/SemGloVeMindspore-master
https://github.com/2024-MindSpore-1/Code2/tree/main/model-1/bert_generation
https://github.com/2024-MindSpore-1/Code2/tree/main/model-1/bertweet
https://github.com/2024-MindSpore-1/Code2/tree/main/model-1/blip_2
https://github.com/2024-MindSpore-1/Code2/tree/main/model-1/bloom
BLOOM: A 176B-Parameter Open-Access Multilingual Language Model
https://github.com/2024-MindSpore-1/Code2/tree/main/model-1/bros
https://github.com/2024-MindSpore-1/Code2/tree/main/model-1/camembert
https://github.com/2024-MindSpore-1/Code2/tree/main/model-1/codegen
CodeGen: An Open Large Language Model for Code with Multi-Turn Program Synthesis
https://github.com/2024-MindSpore-1/Code2/tree/main/model-1/cogvlm
https://github.com/2024-MindSpore-1/Code2/tree/main/model-1/cpm
CPM: A Large-scale Generative Chinese Pre-trained Language Model
https://github.com/2024-MindSpore-1/Code2/tree/main/model-1/cpmbee
CPM: A Large-scale Generative Chinese Pre-trained Language Model
https://github.com/2024-MindSpore-1/Code2/tree/main/model-1/ctrl
CTRL: A Conditional Transformer Language Model for Controllable Generation
https://github.com/2024-MindSpore-1/Code2/tree/main/model-1/decision_transformer
Decision Transformer: Reinforcement Learning via Sequence Modeling
https://github.com/2024-MindSpore-1/Code2/tree/main/model-1/distilbert
DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter
https://github.com/2024-MindSpore-1/Code2/tree/main/model-1/electra
ELECTRA: Pre-training Text Encoders as Discriminators Rather Than Generators
https://github.com/2024-MindSpore-1/Code2/tree/main/model-1/gemma
LLaVA-Gemma: Accelerating Multimodal Foundation Models with a Compact Language Model
https://github.com/2024-MindSpore-1/Code2/tree/main/model-1/gpt_neox_japanese
https://github.com/2024-MindSpore-1/Code2/tree/main/model-1/gptj
https://github.com/2024-MindSpore-1/Code2/tree/main/tengzhiyang/LogiQA2.0-main
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.