https://github.com/eunseo1092/Graduation_project
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Papers with Code 아카이브
Text Classification
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- 보존된 평가 기록
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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/evgeniael/probar
Predict the Next Word: Humans exhibit uncertainty in this task and language models _____
https://github.com/evtaktasheva/dependency_extraction
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/expertailab/spaceqa
https://github.com/explosion/floret
https://github.com/ezelikman/STaR
RoFormer: Enhanced Transformer with Rotary Position Embedding
https://github.com/f-data/ADD
https://github.com/fabiocorreacordeiro/Elsevier_abstracts-Classification
https://github.com/fabyangliu/AGCNN-Sentence-Classification
An Attention-Gated Convolutional Neural Network for Sentence Classification
https://github.com/fabyangliu/AGCNN_sentence_classification
An Attention-Gated Convolutional Neural Network for Sentence Classification
https://github.com/facebookresearch/CrypTen
CrypTen: Secure Multi-Party Computation Meets Machine Learning
https://github.com/facebookresearch/InferSent
https://github.com/facebookresearch/ParlAI
https://github.com/facebookresearch/anli
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/facebookresearch/pytext
https://github.com/facebookresearch/vizseq
Massively Multilingual Sentence Embeddings for Zero-Shot Cross-Lingual Transfer and Beyond
https://github.com/facebookresearch/xformers
https://github.com/facebookresearch/xformers/blob/main/xformers/ops/fmha/flash.py
FlashAttention: Fast and Memory-Efficient Exact Attention with IO-Awareness
https://github.com/faizansuhail89/bert
https://github.com/fanchenyou/transformer-study
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/fancyerii/bert
https://github.com/fangrouli/Document-embedding-generation-models
https://github.com/fanzhenliu/dagad
Semi-Supervised Classification with Graph Convolutional Networks
https://github.com/farbodtaymouri/BERT-GAN
An Evaluation Dataset for Intent Classification and Out-of-Scope Prediction
https://github.com/fastnlp/fastNLP
https://github.com/fbenites/sklearn-hierarchical-classification
Evaluation Measures for Hierarchical Classification: a unified view and novel approaches
https://github.com/fciannel/bert_fciannel
https://github.com/few-shot-NER-benchmark/BaselineCode
https://github.com/firojalam/domain-adaptation
https://github.com/flairNLP/flair
https://github.com/flexible-fl/flex-nlp
https://github.com/floleuerer/fastai_ulmfit
Universal Language Model Fine-tuning for Text Classification
https://github.com/floraxinru/HotelReviews_SentimentAnalysis
Universal Language Model Fine-tuning for Text Classification
https://github.com/floraxinru/NLP_HotelReviews
Universal Language Model Fine-tuning for Text Classification
https://github.com/flxst/nerblackbox
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/flyliu2017/bert
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.