https://github.com/jieyuz2/taskmeanything
DataComp-LM: In search of the next generation of training sets for language models
Papers with Code 아카이브
Natural Language Understanding
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 2
- 평가표
- 6
- 보존된 평가 기록
- 42
Natural Language Understanding is an important field of Natural Language Processing which contains various tasks such as text classification, natural language inference and story comprehension. Applications enabled by natural language understanding range from question answering to automated reasoning.
Source: Find a Reasonable Ending for Stories: Does Logic Relation Help the Story Cloze Test?
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/jim-schwoebel/allie
https://github.com/jinzhenfan/BERT
https://github.com/jiyuan/ainote
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/jl-er/rwkv-peft
Balancing LoRA Performance and Efficiency with Simple Shard Sharing
https://github.com/jmnolte/hccnet
https://github.com/joebluems/Soul_Of_AI
https://github.com/johnr0/TaleBrush-backend
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/joongbo/tta
https://github.com/jopokemine/Dissertation
https://github.com/josselinsomervilleroberts/ptsl
BERT and PALs: Projected Attention Layers for Efficient Adaptation in Multi-Task Learning
https://github.com/joytianya/google_bert
https://github.com/jsalt18-sentence-repl/jiant
GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding
https://github.com/jsantoso2/yelp-clone-ml-project
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/jungomi/swiss-language-model
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/junhahyung/bert_finetune
https://github.com/junhahyung/bert_transfer
https://github.com/jxmorris12/unsupervised-text-deidentification
Probabilistically Masked Language Model Capable of Autoregressive Generation in Arbitrary Word Order
https://github.com/jyhengcoder/lans_optimizer
Accelerated Large Batch Optimization of BERT Pretraining in 54 minutes
https://github.com/kainoj/run_glue
GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding
https://github.com/kaituoxu/Speech-Transformer
https://github.com/kaituoxu/SpeechTransformer
https://github.com/kamalkraj/BERT-SQuAD
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/kamalkraj/BERT_NER
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/karenacorn99/explore-bert
https://github.com/karpathy/makemore
https://github.com/karpathy/minGPT
https://github.com/karta282950/bert-multiclass
https://github.com/kaushaltrivedi/fast-bert
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/keiyamashita111/aaproject
https://github.com/kelly2016/multi-label-bert
https://github.com/keonlee9420/Parallel-Tacotron2
https://github.com/kh-kim/simple-nmt
https://github.com/khalidsaifullaah/bertify
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/kheeong/XLM_OWN
https://github.com/khwajawisal/Attention-augmented-Convolutional-Neural-Networks
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.