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/pwc-1/Paper-8/tree/main/camembert
https://github.com/pwc-1/Paper-8/tree/main/deberta
https://github.com/pwc-1/Paper-8/tree/main/deberta_v2
https://github.com/pwc-1/Paper-9/tree/main/1/tinybert
TinyBERT: Distilling BERT for Natural Language Understanding
https://github.com/pwc-1/Paper-9/tree/main/1/xlnet
XLNet: Generalized Autoregressive Pretraining for Language Understanding
https://github.com/pwc-1/Paper-9/tree/main/2/nystromformer
Nyströmformer: A Nyström-Based Algorithm for Approximating Self-Attention
https://github.com/pwc-1/Paper-9/tree/main/3/temporal-and-aspectual-entailment
https://github.com/pwc-1/Paper-9/tree/main/4/temporal-and-aspectual-entailment
https://github.com/pwc-1/Paper-9/tree/main/5/xlm
XLM-V: Overcoming the Vocabulary Bottleneck in Multilingual Masked Language Models
https://github.com/pwc-1/Paper-9/tree/main/5/xlm_roberta
https://github.com/pwc-1/Paper-9/tree/main/5/xlnet
XLNet: Generalized Autoregressive Pretraining for Language Understanding
https://github.com/pwc-1/Paper-9/tree/main/ibert
https://github.com/pytorch/text
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/qbxlvnf11/BERT
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/qbxlvnf11/contextualized-embedding-BERT
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/qbxlvnf11/pretrained-language-model-BERT
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/qipengguo/p2_webnlg2020
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/qrfaction/keras-sparsemax
From Softmax to Sparsemax: A Sparse Model of Attention and Multi-Label Classification
https://github.com/qtli/eib
e-SNLI: Natural Language Inference with Natural Language Explanations
https://github.com/qywu/Chinese-GPT
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/qywu/memformers
https://github.com/radi-cho/gatedtabtransformer
https://github.com/radoslawkrolikowski/sentiment-analysis-pytorch
Supervised Learning of Universal Sentence Representations from Natural Language Inference Data
https://github.com/rajatgermany/qa-nlp
Supervised Learning of Universal Sentence Representations from Natural Language Inference Data
https://github.com/rajlm10/Chandler
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/ramanakshay/nanogpt
https://github.com/rasta-nitzsche/Bert-For-Algerian-Dialect-DariBERTo
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
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/recommeddit/labs
Utilizing BERT for Aspect-Based Sentiment Analysis via Constructing Auxiliary Sentence
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/reycn/multi-modal-scale
DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter
https://github.com/ricaelum42/Contextual-Twitter-Sarcasm-Detection
https://github.com/ricardordb/bert
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.