Papers with Code 아카이브
Natural Language Inference
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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- 평가표
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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/zer0sh0t/artificial_intelligence/tree/master/language_models/bidirectional_encoder_representations_from_transformers
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/zer0sh0t/artificial_intelligence/tree/master/multimodal_models/gmlp
https://github.com/zfj1998/CodeBert-Code2Text
https://github.com/zhang-huihui/git-repository
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/zhangbo2008/best_pytorch_transformer
https://github.com/zhdbwe/Paper-DailyReading
https://github.com/zhengcj1/ChID-Dataset
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/zhh6425/MotionPointNet
https://github.com/zhiguowang/BiMPM
Bilateral Multi-Perspective Matching for Natural Language Sentences
https://github.com/zhiqic/chartreader
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/zixinzeng-jennifer/spanbert_trans
SpanBERT: Improving Pre-training by Representing and Predicting Spans
https://github.com/ziyan-xiaoyu/spatialmqa
https://github.com/znhy1024/protoco
https://github.com/zphang/bert_on_stilts
Sentence Encoders on STILTs: Supplementary Training on Intermediate Labeled-data Tasks
https://github.com/zphang/lm_evaluation_harness
https://github.com/zsweet/BERT_zsw
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/zsweet/zsw_AI_model
https://gitlab.com/birder/birder
data2vec: A General Framework for Self-supervised Learning in Speech, Vision and Language
https://huggingface.co/datasets/jjjjh/MM-Instruct
MM-Instruct: Generated Visual Instructions for Large Multimodal Model Alignment
https://huggingface.co/manueldeprada/FactCC
Evaluating the Factual Consistency of Abstractive Text Summarization
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.