Papers with Code 아카이브
Natural Language Inference
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 3
- 평가표
- 43
- 보존된 평가 기록
- 505
Natural language inference (NLI) is the task of determining whether a "hypothesis" is true (entailment), false (contradiction), or undetermined (neutral) given a "premise".
Example:
| Premise | Label | Hypothesis |
|---|---|---|
| A man inspects the uniform of a figure in some East Asian country. | contradiction | The man is sleeping. |
| An older and younger man smiling. | neutral | Two men are smiling and laughing at the cats playing on the floor. |
| A soccer game with multiple males playing. | entailment | Some men are playing a sport. |
Approaches used for NLI include earlier symbolic and statistical approaches to more recent deep learning approaches. Benchmark datasets used for NLI include SNLI, MultiNLI, SciTail, among others. You can get hands-on practice on the SNLI task by following this d2l.ai chapter.
Further readings:
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/xiaoqian19940510/text-classification-surveys
https://github.com/xieyxclack/factual_coco
https://github.com/xinyooo/ALBERT4Rec
ALBERT: A Lite BERT for Self-supervised Learning of Language Representations
https://github.com/xitianxiaofeixue/BERT
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/xiuyu0000/new_papers_codes/tree/main/bert
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/xiuyu0000/new_papers_codes/tree/main/gpt
https://github.com/xiuyu0000/new_papers_codes/tree/main/t5
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/xiuyu0000/papers_with_examples/tree/main/vogue
https://github.com/xiuyu0000/tutorials/tree/main/bert
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/xmu-xiaoma666/External-Attention-pytorch
https://github.com/xuefuzhao/openmoe
ST-MoE: Designing Stable and Transferable Sparse Expert Models
https://github.com/xuetianci/pacit
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/xuzhezhaozhao/bert_reading
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/xycforgithub/MultiTask-MRC
Multi-Task Deep Neural Networks for Natural Language Understanding
https://github.com/yahah100/text_summarization
ALBERT: A Lite BERT for Self-supervised Learning of Language Representations
https://github.com/yakazimir/semantic_fragments
Probing Natural Language Inference Models through Semantic Fragments
https://github.com/yangalan123/amortized-interpretability
Can Explanations Be Useful for Calibrating Black Box Models?
https://github.com/yangarbiter/dp-dg
https://github.com/yangletliu/finlora
https://github.com/yangrui123/Hidden
https://github.com/yangyucheng000/University/tree/main/model-1/nystromformer
Nyströmformer: A Nyström-Based Algorithm for Approximating Self-Attention
https://github.com/yangyucheng000/University/tree/main/model-1/rembert
Rethinking embedding coupling in pre-trained language models
https://github.com/yangyucheng000/University/tree/main/model-2/ernie
ERNIE: Enhanced Representation through Knowledge Integration
https://github.com/yangyucheng000/University/tree/main/model-2/fnet
https://github.com/yangyucheng000/University/tree/main/model-3/rembert
Rethinking embedding coupling in pre-trained language models
https://github.com/yangyucheng000/University/tree/main/model-3/roberta
https://github.com/yangyucheng000/University/tree/main/model-3/rwkv
https://github.com/yangyucheng000/University/tree/main/model-3/squeezebert
SqueezeBERT: What can computer vision teach NLP about efficient neural networks?
https://github.com/yangyucheng000/VOGUE
https://github.com/yangyucheng000/ascend_albert
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/yangyucheng000/bert_Ascend
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/yangyucheng000/bert_GPU
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/yangyucheng000/papercode-2/tree/main/LRSL-main
https://github.com/yangzonglin1994/bilm-tf-extended
https://github.com/yannvgn/laserembeddings
Massively Multilingual Sentence Embeddings for Zero-Shot Cross-Lingual Transfer and Beyond
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.