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Few-Shot Text Classification

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평가표: 8
보존된 평가 기록: 19

Few-shot Text Classification predicts the semantic label of a given text with a handful of supporting instances 1

코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/BDBC-KG-NLP/AAAI2022_ContrastNet

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ContrastNet: A Contrastive Learning Framework for Few-Shot Text Classification

https://github.com/Gorov/DiverseFewShot_Amazon

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Diverse Few-Shot Text Classification with Multiple Metrics

https://github.com/Tianyi-Lei/Adaptive-Meta-learner-via-Gradient-Similarity-for-Few-shot-Text-Classification

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Adaptive Meta-learner via Gradient Similarity for Few-shot Text Classification

https://github.com/YujiaBao/Distributional-Signatures

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Few-shot Text Classification with Distributional Signatures

https://github.com/alibaba/EasyNLP

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Towards Unified Prompt Tuning for Few-shot Text Classification

https://github.com/amazon-research/label-aware-pretrain

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Label Semantic Aware Pre-training for Few-shot Text Classification

https://github.com/canyuchen/promptda

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PromptDA: Label-guided Data Augmentation for Prompt-based Few-shot Learners

https://github.com/dousia/metricprompt

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MetricPrompt: Prompting Model as a Relevance Metric for Few-shot Text Classification

https://github.com/hannight/amulap

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Automatic Multi-Label Prompting: Simple and Interpretable Few-Shot Classification

https://github.com/hccngu/MLADA

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Meta-Learning Adversarial Domain Adaptation Network for Few-Shot Text Classification

https://github.com/hccngu/meta-sn

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Meta-Learning Siamese Network for Few-Shot Text Classification

https://github.com/hongyuanluke/pcc

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PCC: Paraphrasing with Bottom-k Sampling and Cyclic Learning for Curriculum Data Augmentation

https://github.com/huggingface/setfit

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Efficient Few-Shot Learning Without Prompts

https://github.com/inimah/protoinfomax

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ProtoInfoMax: Prototypical Networks with Mutual Information Maximization for Out-of-Domain Detection

https://github.com/ink-usc/fewner

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Good Examples Make A Faster Learner: Simple Demonstration-based Learning for Low-resource NER

https://github.com/jasonwei20/triplet-loss

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Few-Shot Text Classification with Triplet Networks, Data Augmentation, and Curriculum Learning

https://github.com/jixuan-wang/grad2task

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Grad2Task: Improved Few-shot Text Classification Using Gradients for Task Representation

https://github.com/katbailey/few-shot-text-classification

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Few-Shot Text Classification with Pre-Trained Word Embeddings and a Human in the Loop

https://github.com/kddubey/pretrain-on-test

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Evaluating the fairness of task-adaptive pretraining on unlabeled test data before few-shot text classification

https://github.com/ltian678/metatroll-code

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MetaTroll: Few-shot Detection of State-Sponsored Trolls with Transformer Adapters

https://github.com/oughtinc/raft-baselines

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RAFT: A Real-World Few-Shot Text Classification Benchmark

https://github.com/r-three/t-few

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Few-Shot Parameter-Efficient Fine-Tuning is Better and Cheaper than In-Context Learning

https://github.com/rickltt/evs

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Evolutionary Verbalizer Search for Prompt-based Few Shot Text Classification

https://github.com/saeednajafi/riff

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RIFF: Learning to Rephrase Inputs for Few-shot Fine-tuning of Language Models

https://github.com/shaharlutatipersonal/ocd

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OCD: Learning to Overfit with Conditional Diffusion Models

https://github.com/shmsw25/Channel-LM-Prompting

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Noisy Channel Language Model Prompting for Few-Shot Text Classification

https://github.com/slei109/tart

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TART: Improved Few-shot Text Classification Using Task-Adaptive Reference Transformation

https://github.com/symanto-research/few-shot-learning-label-tuning

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Few-Shot Learning with Siamese Networks and Label Tuning

https://github.com/tdopierre/FewShotText

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A Neural Few-Shot Text Classification Reality Check

https://github.com/thunlp/knowledgeableprompttuning

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Knowledgeable Prompt-tuning: Incorporating Knowledge into Prompt Verbalizer for Text Classification

https://github.com/timoschick/pet

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Exploiting Cloze Questions for Few Shot Text Classification and Natural Language Inference

https://github.com/ukplab/lagonn

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Like a Good Nearest Neighbor: Practical Content Moderation and Text Classification

https://github.com/wzy232303/fpt

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FPT: Feature Prompt Tuning for Few-shot Readability Assessment

https://github.com/yookyungkho/mav

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Boosting Prompt-Based Self-Training With Mapping-Free Automatic Verbalizer for Multi-Class Classification

https://github.com/youseob/pin

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Hard Prompts Made Interpretable: Sparse Entropy Regularization for Prompt Tuning with RL

https://github.com/yvogao/laqda

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Improve Meta-learning for Few-Shot Text Classification with All You Can Acquire from the Tasks

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