Papers with Code 아카이브
Natural Language Inference
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
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- 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/jielin-qiu/mm_robustness
Benchmarking Robustness of Multimodal Image-Text Models under Distribution Shift
https://github.com/jihunchoi/unsupervised-treelstm
https://github.com/jingyuanz/keras-self-attention-layer
https://github.com/jinzhenfan/BERT
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/jiyuan/ainote
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/joeljang/rlphf
Exploring the Benefits of Training Expert Language Models over Instruction Tuning
https://github.com/john-bradshaw/rxn-lm
https://github.com/johnr0/TaleBrush-backend
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/jonahwinninghoff/Text-Summarization
XLNet: Generalized Autoregressive Pretraining for Language Understanding
https://github.com/jongwooko/nash-pruning-official
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/joongbo/tta
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/joshuaWang-bit/Textclassification-pytorch
XLNet: Generalized Autoregressive Pretraining for Language Understanding
https://github.com/josselinsomervilleroberts/ptsl
BERT and PALs: Projected Attention Layers for Efficient Adaptation in Multi-Task Learning
https://github.com/joytianya/google_bert
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/jpablou/Matching-The-Blanks-Ths
ALBERT: A Lite BERT for Self-supervised Learning of Language Representations
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/juletx/lm-evaluation-harness
https://github.com/junfenglx/reasoning_attention
https://github.com/jungomi/swiss-language-model
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/junhahyung/bert_finetune
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/junhahyung/bert_transfer
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/junnyu/paddle_t5
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/jvdbogae/artverc
Supervised Learning of Universal Sentence Representations from Natural Language Inference Data
https://github.com/kafura-kafiri/tf2-elmo
https://github.com/kainoj/run_glue
GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding
https://github.com/kaist-dmlab/BioNER
https://github.com/kalaivani-s/self-attentive-sent-emb
https://github.com/kamalkraj/ALBERT-TF2.0
ALBERT: A Lite BERT for Self-supervised Learning of Language Representations
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/kamalkraj/BioELECTRA
https://github.com/karenacorn99/explore-bert
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/karpathy/build-nanogpt
https://github.com/karpathy/llm.c
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.