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/richinkabra/CoVe-BCN
https://github.com/rickyHong/Google-BERT-repl
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/roberttwomey/machine-imagination-isea
https://github.com/roberttwomey/machine-imagination-workshop
https://github.com/robinjia/certified-word-sub
https://github.com/rockandroll123/natural-language-inference
https://github.com/rohan400/PepperQA
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/rohanshad/cmr_transformer
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/roomylee/self-attentive-emb-tf
https://github.com/roshaan7zafar/-MAX-Text-Sentiment-Classifier
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/rpuiggari/bert2
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/ruc-nlpir/flashrag
Making Retrieval-Augmented Language Models Robust to Irrelevant Context
https://github.com/rwightman/pytorch-image-models
https://github.com/rwkv/rwkv-lm
https://github.com/rwkv/rwkv.cpp
https://github.com/rybread1/stack-overflow-question-classification
DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter
https://github.com/rzhangpku/MFAE
https://github.com/s-nlp/russe_detox_2022
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/safakkbilici/Academic-Paper-Title-Recommendation
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/sagorbrur/bangla-bert
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/sagorbrur/codeswitch
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/sajjjadayobi/ParsBigBird
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/salanueva/UniVSE
Evaluating Multimodal Representations on Visual Semantic Textual Similarity
https://github.com/salesforce/GeDi
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/salesforce/auditnlg
SummaC: Re-Visiting NLI-based Models for Inconsistency Detection in Summarization
https://github.com/salesforce/codet5
https://github.com/salesforceairesearch/answer-engine-eval
Do RAG Systems Cover What Matters? Evaluating and Optimizing Responses with Sub-Question Coverage
https://github.com/salesforceairesearch/crmarena
Do LVLMs Understand Charts? Analyzing and Correcting Factual Errors in Chart Captioning
https://github.com/salrowili/biom-transformers
Transferability of Natural Language Inference to Biomedical Question Answering
https://github.com/sambanova/generative_data_prep
https://github.com/sambanova/lm-evaluation-harness
https://github.com/samirsahoo007/Audio-and-Speech-Processing
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/samsam3232/qampari
https://github.com/samwisegamjeee/pytorch-transformers
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/saraswat/munkhdalai-nse
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.