Papers with Code 아카이브
Document Classification
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 1
- 평가표
- 22
- 보존된 평가 기록
- 51
Document Classification is a procedure of assigning one or more labels to a document from a predetermined set of labels.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/coastalcph/trldc
공식 코드Revisiting Transformer-based Models for Long Document Classification
https://github.com/constantinouchristos/ood-ahm
공식 코드Out-of-Distribution Detection with Attention Head Masking for Multimodal Document Classification
https://github.com/csebuetnlp/banglabert
공식 코드https://github.com/dallal9/bibrank
공식 코드BibRank: Automatic Keyphrase Extraction Platform Using~Metadata
https://github.com/damithdr/legal-classification
공식 코드Can Model Fusing Help Transformers in Long Document Classification? An Empirical Study
https://github.com/dao-ailab/flash-attention
공식 코드FlashAttention: Fast and Memory-Efficient Exact Attention with IO-Awareness
https://github.com/dbmdz/berts
공식 코드https://github.com/ddhruvkr/contradoc
공식 코드ContraDoc: Understanding Self-Contradictions in Documents with Large Language Models
https://github.com/deepopinion/anls_star_metric
공식 코드ANLS* -- A Universal Document Processing Metric for Generative Large Language Models
https://github.com/dinghanshen/SWEM
공식 코드Baseline Needs More Love: On Simple Word-Embedding-Based Models and Associated Pooling Mechanisms
https://github.com/divelab/lgcn
공식 코드https://github.com/dki-lab/covid19-classification
공식 코드https://github.com/epfml/Bi-Sent2Vec
공식 코드https://github.com/eternityyw/llm_healthcare
공식 코드https://github.com/etsurin/summaug
공식 코드Summarization-based Data Augmentation for Document Classification
https://github.com/facebookresearch/LASER
공식 코드Massively Multilingual Sentence Embeddings for Zero-Shot Cross-Lingual Transfer and Beyond
https://github.com/facebookresearch/MLDoc
공식 코드A Corpus for Multilingual Document Classification in Eight Languages
https://github.com/fitz-like-coding/cwtm
공식 코드CWTM: Leveraging Contextualized Word Embeddings from BERT for Neural Topic Modeling
https://github.com/geobrain-ai/geogalactica
공식 코드GeoGalactica: A Scientific Large Language Model in Geoscience
https://github.com/giannisnik/mpad
공식 코드Message Passing Attention Networks for Document Understanding
https://github.com/giannisnik/repset
공식 코드Rep the Set: Neural Networks for Learning Set Representations
https://github.com/gordon-lim/tobacco3482-mistakes
공식 코드https://github.com/gouwsmeister/bilbowa
공식 코드BilBOWA: Fast Bilingual Distributed Representations without Word Alignments
https://github.com/goyalanil/PB-MVBoost
공식 코드Multiview Boosting by Controlling the Diversity and the Accuracy of View-specific Voters
https://github.com/gpleiss/temperature_scaling
공식 코드https://github.com/grig-guz/coherence-rst
공식 코드https://github.com/gxlarson/rvl-cdip-ood
공식 코드Evaluating Out-of-Distribution Performance on Document Image Classifiers
https://github.com/hazdzz/Converter
공식 코드https://github.com/henrymoss/COLING2018
공식 코드Using J-K fold Cross Validation to Reduce Variance When Tuning NLP Models
https://github.com/hugochan/KATE
공식 코드https://github.com/idiap/mhan
공식 코드Multilingual Hierarchical Attention Networks for Document Classification
https://github.com/irenezihuili/hipool
공식 코드https://github.com/jaaack-wang/detect-influence-campaigns
공식 코드Clustering Document Parts: Detecting and Characterizing Influence Campaigns from Documents
https://github.com/jerbarnes/hier_vs_transfer
공식 코드A Systematic Comparison of Architectures for Document-Level Sentiment Classification
https://github.com/jjacampos/FeedbackWeightedLearning
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.