https://github.com/zhuchen03/FreeLB
공식 코드FreeLB: Enhanced Adversarial Training for Natural Language Understanding
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/zhuchen03/maxva
공식 코드MaxVA: Fast Adaptation of Step Sizes by Maximizing Observed Variance of Gradients
https://github.com/zhzhengit/LinkNER
공식 코드LinkNER: Linking Local Named Entity Recognition Models to Large Language Models using Uncertainty
https://github.com/zihao-wang/number-nli
공식 코드A Neural-Symbolic Approach to Natural Language Understanding
https://github.com/zliucr/mixed-language-training
공식 코드https://github.com/zurichnlp/swissbert
공식 코드https://github.com/zycdev/cmploss
공식 코드Cross-Model Comparative Loss for Enhancing Neuronal Utility in Language Understanding
https://github.com/zzeng13/iekg
공식 코드IEKG: A Commonsense Knowledge Graph for Idiomatic Expressions
https://huggingface.co/lamm-mit/cephalo
공식 코드Cephalo: Multi-Modal Vision-Language Models for Bio-Inspired Materials Analysis and Design
https://huggingface.co/uitnlp/visobert
공식 코드ViSoBERT: A Pre-Trained Language Model for Vietnamese Social Media Text Processing
https://worksheets.codalab.org/worksheets/0x9a15a170809f4e2cb7940e1f256dee55
공식 코드https://bitbucket.org/robvanderg/sid4lr
https://github.com/04RR/SOTA-Vision
https://github.com/1-punchMan/CLTS
https://github.com/1wy/bert
https://github.com/2023-MindSpore-1/ms-code-11
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/2023-MindSpore-1/ms-code-12
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/2023-MindSpore-1/ms-code-14/tree/main/Pangu_alpha
https://github.com/2023-MindSpore-1/ms-code-14/tree/main/Transformer
https://github.com/2023-MindSpore-1/ms-code-150
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/2023-MindSpore-1/ms-code-160
NEZHA: Neural Contextualized Representation for Chinese Language Understanding
https://github.com/2023-MindSpore-1/ms-code-162
https://github.com/2023-MindSpore-1/ms-code-166
TinyBERT: Distilling BERT for Natural Language Understanding
https://github.com/2023-MindSpore-1/ms-code-169
https://github.com/2023-MindSpore-1/ms-code-222/tree/main/bert
https://github.com/2023-MindSpore-1/ms-code-56
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/2023-MindSpore-4/Code-5/tree/main/IPT
Is Supervised Syntactic Parsing Beneficial for Language Understanding? An Empirical Investigation
https://github.com/2023-MindSpore-4/Code12/tree/main/MindFormers/bert
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/2023-MindSpore-4/Code12/tree/main/MindFormers/clip
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/2023-MindSpore-4/Code8/tree/main/AttentionCluster
https://github.com/2024-MindSpore-1/Code2/tree/main/model-1/audio_spectrogram_transformer
https://github.com/2024-MindSpore-1/Code2/tree/main/model-1/barthez
BARThez: a Skilled Pretrained French Sequence-to-Sequence Model
https://github.com/2024-MindSpore-1/Code2/tree/main/model-1/bert
https://github.com/2024-MindSpore-1/Code2/tree/main/model-1/clip
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/2024-MindSpore-1/Code2/tree/main/model-1/convbert
ConvBERT: Improving BERT with Span-based Dynamic Convolution
https://github.com/2024-MindSpore-1/Code2/tree/main/model-1/data2vec
data2vec: A General Framework for Self-supervised Learning in Speech, Vision and Language
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.