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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/boschresearch/mulms-az-codi2023
공식 코드MuLMS-AZ: An Argumentative Zoning Dataset for the Materials Science Domain
https://github.com/botondbarta/hunsum
공식 코드From News to Summaries: Building a Hungarian Corpus for Extractive and Abstractive Summarization
https://github.com/boyuanfeng/faith
공식 코드Faith: An Efficient Framework for Transformer Verification on GPUs
https://github.com/brainsharks-fyp17/sinhala-text-simplification-dataset-and-evaluation
공식 코드https://github.com/brcsomnath/semae
공식 코드Unsupervised Extractive Opinion Summarization Using Sparse Coding
https://github.com/brendanartley/CipherSniffer
공식 코드https://github.com/brettfyt/ss_via_llms
공식 코드https://github.com/brmson/dataset-sts
공식 코드Sentence Pair Scoring: Towards Unified Framework for Text Comprehension
https://github.com/brxx122/HeterSUMGraph
공식 코드Heterogeneous Graph Neural Networks for Extractive Document Summarization
https://github.com/brychcin/LTLM
공식 코드https://github.com/bujol12/bert-seq-interpretability
공식 코드Zero-shot Sequence Labeling for Transformer-based Sentence Classifiers
https://github.com/bunsenfeng/kalm
공식 코드https://github.com/burgerburgerburger/aa
공식 코드https://github.com/bwallace/rationale-CNN
공식 코드Rationale-Augmented Convolutional Neural Networks for Text Classification
https://github.com/byinhao/bvsp
공식 코드BvSP: Broad-view Soft Prompting for Few-Shot Aspect Sentiment Quad Prediction
https://github.com/byinhao/uaul
공식 코드Uncertainty-Aware Unlikelihood Learning Improves Generative Aspect Sentiment Quad Prediction
https://github.com/bytedance/neurst
공식 코드Towards Making the Most of BERT in Neural Machine Translation
https://github.com/bytedance/neurst/tree/master/examples/ctnmt
공식 코드Towards Making the Most of BERT in Neural Machine Translation
https://github.com/c-juhwan/adverb-deletion-aug
공식 코드Adverb Is the Key: Simple Text Data Augmentation with Adverb Deletion
https://github.com/callsys/textvr
공식 코드A Large Cross-Modal Video Retrieval Dataset with Reading Comprehension
https://github.com/cambridgeltl/mirror-bert
공식 코드https://github.com/cambridgeltl/mirrorwic
공식 코드MirrorWiC: On Eliciting Word-in-Context Representations from Pretrained Language Models
https://github.com/caoyichao/unihoi
공식 코드https://github.com/caoyu-noob/tasa
공식 코드TASA: Deceiving Question Answering Models by Twin Answer Sentences Attack
https://github.com/carina-kauf/better-mlm-scoring
공식 코드https://github.com/carina-kauf/llm-plaus-prob
공식 코드https://github.com/carina-kauf/lm-event-knowledge
공식 코드Event knowledge in large language models: the gap between the impossible and the unlikely
https://github.com/caskcsg/CoT-MAE
공식 코드https://github.com/caskcsg/TextSmoothing
공식 코드Text Smoothing: Enhance Various Data Augmentation Methods on Text Classification Tasks
https://github.com/caskcsg/ir
공식 코드https://github.com/caskcsg/sentemb
공식 코드InfoCSE: Information-aggregated Contrastive Learning of Sentence Embeddings
https://github.com/caskcsg/sentemb/tree/main/ESimCSE
공식 코드ESimCSE: Enhanced Sample Building Method for Contrastive Learning of Unsupervised Sentence Embedding
https://github.com/caskcsg/sentemb/tree/main/InfoCSE
공식 코드InfoCSE: Information-aggregated Contrastive Learning of Sentence Embeddings
https://github.com/caskcsg/sentemb/tree/main/gsInfoNCE
공식 코드Smoothed Contrastive Learning for Unsupervised Sentence Embedding
https://github.com/catrin-baze/ino-of-factify
공식 코드INO at Factify 2: Structure Coherence based Multi-Modal Fact Verification
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.