https://github.com/athms/learning-from-brains
공식 코드Self-Supervised Learning of Brain Dynamics from Broad Neuroimaging Data
Papers with Code 아카이브
Language Modeling
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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/atla-ai/selene-mini
공식 코드https://github.com/atosystem/speechclip
공식 코드SpeechCLIP: Integrating Speech with Pre-Trained Vision and Language Model
https://github.com/atten4vis/lw-detr
공식 코드OVLW-DETR: Open-Vocabulary Light-Weighted Detection Transformer
https://github.com/atulkum/pointer_summarizer
공식 코드https://github.com/aub-mind/araBERT
공식 코드AraELECTRA: Pre-Training Text Discriminators for Arabic Language Understanding
https://github.com/audioku/meta-transfer-learning
공식 코드https://github.com/augustinLib/SPIKE
공식 코드Imagine All The Relevance: Scenario-Profiled Indexing with Knowledge Expansion for Dense Retrieval
https://github.com/autextification/AuTexTification-Overview
공식 코드https://github.com/autograms/autograms
공식 코드https://github.com/automl/GoLU
공식 코드Gompertz Linear Units: Leveraging Asymmetry for Enhanced Learning Dynamics
https://github.com/automl/cpr
공식 코드Improving Deep Learning Optimization through Constrained Parameter Regularization
https://github.com/automl/unlocking_state_tracking
공식 코드Unlocking State-Tracking in Linear RNNs Through Negative Eigenvalues
https://github.com/autoscrum/autoscrum
공식 코드AutoScrum: Automating Project Planning Using Large Language Models
https://github.com/avduarte333/de-cop_method
공식 코드DE-COP: Detecting Copyrighted Content in Language Models Training Data
https://github.com/avduarte333/dis-co
공식 코드DIS-CO: Discovering Copyrighted Content in VLMs Training Data
https://github.com/avi-jit/etok
공식 코드Learn Your Tokens: Word-Pooled Tokenization for Language Modeling
https://github.com/aviaefrat/lmentry
공식 코드LMentry: A Language Model Benchmark of Elementary Language Tasks
https://github.com/aviclu/CD-LM
공식 코드https://github.com/aviclu/cdlm
공식 코드https://github.com/avramandrei/DeftEval
공식 코드UPB at SemEval-2020 Task 6: Pretrained Language Models for Definition Extraction
https://github.com/awaisrauf/agrogpt
공식 코드AgroGPT: Efficient Agricultural Vision-Language Model with Expert Tuning
https://github.com/awni00/dual-attention
공식 코드Disentangling and Integrating Relational and Sensory Information in Transformer Architectures
https://github.com/aws-health-ai/multi_domain_lm
공식 코드An Empirical Investigation Towards Efficient Multi-Domain Language Model Pre-training
https://github.com/awslabs/hlat
공식 코드HLAT: High-quality Large Language Model Pre-trained on AWS Trainium
https://github.com/awslabs/hypergraph-tabular-lm
공식 코드HYTREL: Hypergraph-enhanced Tabular Data Representation Learning
https://github.com/awslabs/language-model-prior-4-item-cold-start
공식 코드https://github.com/awslabs/mlm-scoring
공식 코드https://github.com/awslabs/rag-qa-arena
공식 코드RAG-QA Arena: Evaluating Domain Robustness for Long-form Retrieval Augmented Question Answering
https://github.com/awslabs/unsupervised-qa
공식 코드https://github.com/awwang10/llmpromptboosting
공식 코드https://github.com/awwkl/brain_language_narratives
공식 코드Training language models to summarize narratives improves brain alignment
https://github.com/awwkl/brain_language_summarization
공식 코드Training language models to summarize narratives improves brain alignment
https://github.com/axenov/BERT-Summ-OpenNMT
공식 코드Abstractive Text Summarization based on Language Model Conditioning and Locality Modeling
https://github.com/ayushjain1144/NukeBERT
공식 코드NukeBERT: A Pre-trained language model for Low Resource Nuclear Domain
https://github.com/azreasoners/llm-asp
공식 코드Coupling Large Language Models with Logic Programming for Robust and General Reasoning from Text
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.