https://github.com/qbxlvnf11/contextualized-embedding-BERT
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
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/qbxlvnf11/pretrained-language-model-BERT
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/qema/orca-py
https://github.com/qianliu0708/ToWE
https://github.com/qijindou/CRAB
LLM Reading Tea Leaves: Automatically Evaluating Topic Models with Large Language Models
https://github.com/qinhanmin2014/fine-tune-bert-for-text-classification
https://github.com/qxtaiba/Emotional-Dystopia
https://github.com/qywu/Chinese-GPT
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/ra1nty/sru-naive
https://github.com/rachit-shah/News-Classfication-using-DNN-models
https://github.com/radoslawkrolikowski/sentiment-analysis-pytorch
https://github.com/rafaelcl292/projeto1-cdados
Naive Bayes and Text Classification I - Introduction and Theory
https://github.com/rahul-1996/KGraphs-QA
BioBERT: a pre-trained biomedical language representation model for biomedical text mining
https://github.com/rainorangelemon/TextCNN-with-Attention
https://github.com/rainyBJ/Fastformer-Paddle
https://github.com/rajlm10/Chandler
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/rajs96/ULMFiT-Twitter-US-Airline-Sentiment
Universal Language Model Fine-tuning for Text Classification
https://github.com/rakeshmp18/Text_Analytics
https://github.com/ramasesh/rnn_dynamics
https://github.com/randomrandom/deep-atrous-cnn-sentiment
https://github.com/rania000/SentAnalyser
Universal Language Model Fine-tuning for Text Classification
https://github.com/ranjanisubramanyan/Patient-data-representation
https://github.com/rasta-nitzsche/Bert-For-Algerian-Dialect-DariBERTo
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/raulsteleac/Speech_Emotion_Recognition
Transfer Learning for Improving Speech Emotion Classification Accuracy
https://github.com/raymondhs/fairseq-laser
Massively Multilingual Sentence Embeddings for Zero-Shot Cross-Lingual Transfer and Beyond
https://github.com/re-search/DocProduct
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/refuel-ai/autolabel
https://github.com/reneang17/authorencoder
https://github.com/renmada/squeezebert-paddle
SqueezeBERT: What can computer vision teach NLP about efficient neural networks?
https://github.com/reppy4620/Dialog
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/rforgione/deepwalk
https://github.com/rgsachin/DMTN
Ask Me Anything: Dynamic Memory Networks for Natural Language Processing
https://github.com/ricaelum42/Contextual-Twitter-Sarcasm-Detection
https://github.com/ricardordb/bert
https://github.com/richinkabra/CoVe-BCN
https://github.com/rickyHong/Google-BERT-repl
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.