https://github.com/pluslabnlp/zero_shot_cqa
공식 코드Zero-shot Commonsense Question Answering with Cloze Translation and Consistency Optimization
Papers with Code 아카이브
Text Classification
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Text Classification is the task of assigning a sentence or document an appropriate category. The categories depend on the chosen dataset and can range from topics.
Text Classification problems include emotion classification, news classification, citation intent classification, among others. Benchmark datasets for evaluating text classification capabilities include GLUE, AGNews, among others.
In recent years, deep learning techniques like XLNet and RoBERTa have attained some of the biggest performance jumps for text classification problems.
<span style="color:grey; opacity: 0.6">( Image credit: Text Classification Algorithms: A Survey )</span>
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/pmichel31415/P-DRO
공식 코드Distributionally Robust Models with Parametric Likelihood Ratios
https://github.com/poseidon-workflows/llm_ad
공식 코드https://github.com/prakash-aryan/qwen-arabic-project
공식 코드Resource-Aware Arabic LLM Creation: Model Adaptation, Integration, and Multi-Domain Testing
https://github.com/prateeky2806/exessnet
공식 코드Exclusive Supermask Subnetwork Training for Continual Learning
https://github.com/primerai/primer-research
공식 코드BabyBear: Cheap inference triage for expensive language models
https://github.com/princeton-nlp/datamux
공식 코드https://github.com/princeton-nlp/shortcutgrammar
공식 코드https://github.com/princeton-nlp/transformerprograms
공식 코드https://github.com/projectdossier/citationscreeningreplicability
공식 코드https://github.com/projectdossier/cruise-screening
공식 코드https://github.com/punyajoy/HateXplain
공식 코드HateXplain: A Benchmark Dataset for Explainable Hate Speech Detection
https://github.com/puredefence/advbench4text
공식 코드Tougher Text, Smarter Models: Raising the Bar for Adversarial Defence Benchmarks
https://github.com/pyg-team/pytorch-frame
공식 코드PyTorch Frame: A Modular Framework for Multi-Modal Tabular Learning
https://github.com/pytorch/fairseq
공식 코드https://github.com/qas-lab/piersonreu
공식 코드Automated Bug Report Prioritization in Large Open-Source Projects
https://github.com/qianbozang/kg-htc
공식 코드https://github.com/qianc62/MGTC
공식 코드An Approach for Process Model Extraction By Multi-Grained Text Classification
https://github.com/qianjunlang/placeholdingparallelprediction
공식 코드https://github.com/qitianwu/difformer
공식 코드DIFFormer: Scalable (Graph) Transformers Induced by Energy Constrained Diffusion
https://github.com/qkrdmsghk/textssl
공식 코드Sparse Structure Learning via Graph Neural Networks for Inductive Document Classification
https://github.com/qq31415926/cd
공식 코드Concept Based Continuous Prompts for Interpretable Text Classification
https://github.com/queez0405/A-Confidence-Calibrated-MOBA-Game-Winner-Predictor
공식 코드https://github.com/qwenlm/qwen
공식 코드https://github.com/r-three/t-few
공식 코드Few-Shot Parameter-Efficient Fine-Tuning is Better and Cheaper than In-Context Learning
https://github.com/racai-ai/EuroVoc-BERT
공식 코드PyEuroVoc: A Tool for Multilingual Legal Document Classification with EuroVoc Descriptors
https://github.com/racai-ai/pyeurovoc
공식 코드PyEuroVoc: A Tool for Multilingual Legal Document Classification with EuroVoc Descriptors
https://github.com/raduionescu/vlawe-boswe
공식 코드Vector of Locally-Aggregated Word Embeddings (VLAWE): A Novel Document-level Representation
https://github.com/rafa-zy/sent
공식 코드https://github.com/raffaelemarino/learning_in_wilsoncowan
공식 코드https://github.com/raipranav/fcl-fedseit
공식 코드Federated Continual Learning for Text Classification via Selective Inter-client Transfer
https://github.com/rajamohanharesh/Emotion-Recognition
공식 코드https://github.com/rasmuskaer/mdapt_supplements
공식 코드MDAPT: Multilingual Domain Adaptive Pretraining in a Single Model
https://github.com/rayaneghilene/ARENAS_Automatic_Extremist_Analysis/tree/main/Entailment_framework
공식 코드Analysis of Socially Unacceptable Discourse with Zero-shot Learning
https://github.com/raymondzmc/topic-model-diversity-aware-coherence-loss
공식 코드Diversity-Aware Coherence Loss for Improving Neural Topic Models
https://github.com/raytsang123/clibe
공식 코드CLIBE: Detecting Dynamic Backdoors in Transformer-based NLP Models
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.