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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/gblackout/logicllama
공식 코드Harnessing the Power of Large Language Models for Natural Language to First-Order Logic Translation
https://github.com/gchronis/features_in_context
공식 코드https://github.com/gchrupala/imaginet
공식 코드https://github.com/gcyzsl/cvlmn
공식 코드Learning to Collocate Visual-Linguistic Neural Modules for Image Captioning
https://github.com/geekjuruo/gapa
공식 코드Automatic Context Pattern Generation for Entity Set Expansion
https://github.com/gengchenmai/space2vec
공식 코드Multi-Scale Representation Learning for Spatial Feature Distributions using Grid Cells
https://github.com/george0828zhang/sinkhorn-simultrans
공식 코드Anticipation-Free Training for Simultaneous Machine Translation
https://github.com/getalp/Flaubert
공식 코드FlauBERT: Unsupervised Language Model Pre-training for French
https://github.com/getalp/mass-dataset
공식 코드https://github.com/getao/human-performance-gec
공식 코드Reaching Human-level Performance in Automatic Grammatical Error Correction: An Empirical Study
https://github.com/ghazalkhalighinejad/approximating-cky
공식 코드https://github.com/ghazi-f/Disentanglement_Transformer
공식 코드Disentangling semantics in language through VAEs and a certain architectural choice
https://github.com/ghazi-f/advae
공식 코드Towards Unsupervised Content Disentanglement in Sentence Representations via Syntactic Roles
https://github.com/giannispana/ails-ntua-at-semeval-2024-task-9-brainteaser
공식 코드https://github.com/gimpong/WWW22-HCQ
공식 코드Hybrid Contrastive Quantization for Efficient Cross-View Video Retrieval
https://github.com/gingasan/lemon
공식 코드https://github.com/gingsi/coot-videotext
공식 코드COOT: Cooperative Hierarchical Transformer for Video-Text Representation Learning
https://github.com/githubarry/docie-probing
공식 코드https://github.com/giuliarambelli/transformers_thematic_fit
공식 코드Did the Cat Drink the Coffee? Challenging Transformers with Generalized Event Knowledge
https://github.com/gjiaying/zs-ska
공식 코드Prompt-based Zero-shot Relation Extraction with Semantic Knowledge Augmentation
https://github.com/gkuling/birads_bert
공식 코드BI-RADS BERT & Using Section Segmentation to Understand Radiology Reports
https://github.com/glouppe/recnn
공식 코드https://github.com/gmichalo/lexsubcon
공식 코드https://github.com/gmichalo/question_identification_on_medical_logs
공식 코드https://github.com/google-research-datasets/C4_200M-synthetic-dataset-for-grammatical-error-correction
공식 코드Synthetic Data Generation for Grammatical Error Correction with Tagged Corruption Models
https://github.com/google-research-datasets/MultiReQA
공식 코드MultiReQA: A Cross-Domain Evaluation for Retrieval Question Answering Models
https://github.com/google-research-datasets/ToTTo
공식 코드https://github.com/google-research-datasets/cvss
공식 코드CVSS Corpus and Massively Multilingual Speech-to-Speech Translation
https://github.com/google-research-datasets/dakshina
공식 코드Processing South Asian Languages Written in the Latin Script: the Dakshina Dataset
https://github.com/google-research-datasets/paws
공식 코드PAWS-X: A Cross-lingual Adversarial Dataset for Paraphrase Identification
https://github.com/google-research-datasets/wiki-translated-clusters-nli
공식 코드Stretching Sentence-pair NLI Models to Reason over Long Documents and Clusters
https://github.com/google-research/ALBERT
공식 코드ALBERT: A Lite BERT for Self-supervised Learning of Language Representations
https://github.com/google-research/big_vision
공식 코드https://github.com/google-research/deduplicate-text-datasets
공식 코드https://github.com/google-research/google-research
공식 코드SummAE: Zero-Shot Abstractive Text Summarization using Length-Agnostic Auto-Encoders
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.