https://github.com/douglasraevan/covid19-indonesian-tweets
공식 코드Two-Stage Classifier for COVID-19 Misinformation Detection Using BERT: a Study on Indonesian Tweets
Papers with Code 아카이브
Text Classification
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 16
- 평가표
- 101
- 보존된 평가 기록
- 341
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/dousia/metricprompt
공식 코드MetricPrompt: Prompting Model as a Relevance Metric for Few-shot Text Classification
https://github.com/drimpossible/Sampling-Bias-Active-Learning
공식 코드Sampling Bias in Deep Active Classification: An Empirical Study
https://github.com/drndr/bert_ent_attack
공식 코드https://github.com/drndr/multilabel-text-clf
공식 코드Are We Really Making Much Progress in Text Classification? A Comparative Review
https://github.com/dsfsi/textaugment
공식 코드Improving short text classification through global augmentation methods
https://github.com/dslaborg/germeval2021
공식 코드https://github.com/dswang2011/guided-attention-transformer
공식 코드https://github.com/dteodore/emotionarcs
공식 코드Evaluating Emotion Arcs Across Languages: Bridging the Global Divide in Sentiment Analysis
https://github.com/dutir-bionlp/taiyi-llm
공식 코드Taiyi: A Bilingual Fine-Tuned Large Language Model for Diverse Biomedical Tasks
https://github.com/dutir-ysq/emometa
공식 코드EmoMeta: A Multimodal Dataset for Fine-grained Emotion Classification in Chinese Metaphors
https://github.com/dwzhu-pku/couda
공식 코드https://github.com/dykuang/bci-attention
공식 코드A Monotonicity Constrained Attention Module for Emotion Classification with Limited EEG Data
https://github.com/e-pet/adni-bias
공식 코드https://github.com/ecielyang/smallest_set
공식 코드How Many and Which Training Points Would Need to be Removed to Flip this Prediction?
https://github.com/eduard6421/ronli
공식 코드https://github.com/eleutherai/gpt-neox
공식 코드Synthetic Data Generation in Low-Resource Settings via Fine-Tuning of Large Language Models
https://github.com/eligugliotta/tarc
공식 코드Multi-Task Sequence Prediction For Tunisian Arabizi Multi-Level Annotation
https://github.com/elisaF/structured
공식 코드https://github.com/elmurod1202/textclassification
공식 코드https://github.com/embeddings-benchmark/mteb
공식 코드https://github.com/emerging-welfare/glocongold
공식 코드Cross-context News Corpus for Protest Events related Knowledge Base Construction
https://github.com/emory-irlab/PHM2017
공식 코드https://github.com/emorynlp/CMCL-2021
공식 코드Enhancing Cognitive Models of Emotions with Representation Learning
https://github.com/emorynlp/personality-detection
공식 코드https://github.com/emu1729/gist
공식 코드GIST: Generating Image-Specific Text for Fine-grained Object Classification
https://github.com/epfl-dlab/gpturk
공식 코드https://github.com/epfml/Bi-Sent2Vec
공식 코드https://github.com/eqtpartners/ptec
공식 코드Prompt Tuned Embedding Classification for Multi-Label Industry Sector Allocation
https://github.com/eric11eca/disco
공식 코드DISCO: Distilling Counterfactuals with Large Language Models
https://github.com/eshaant/x-insta
공식 코드Multilingual LLMs are Better Cross-lingual In-context Learners with Alignment
https://github.com/estherrolf/implicit-posterior
공식 코드https://github.com/eternityyw/llm_healthcare
공식 코드https://github.com/eth-lre/llm_icl
공식 코드What Do Language Models Learn in Context? The Structured Task Hypothesis
https://github.com/ethanhezhao/DirBN
공식 코드https://github.com/ethanhezhao/MetaLDA
공식 코드MetaLDA: a Topic Model that Efficiently Incorporates Meta information
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.