https://github.com/bigscience-workshop/architecture-objective
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
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/bigscience-workshop/biomedical
https://github.com/bigscience-workshop/promptsource
https://github.com/binhetech/bert-application
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/bino282/bert4news
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/bit-ml/anoshift
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/bitextor/bicleaner-ai
A Decomposable Attention Model for Natural Language Inference
https://github.com/blawok/named-entity-recognition
https://github.com/blazejdolicki/multilingual-analysis
https://github.com/blcunlp/CNLI
A Decomposable Attention Model for Natural Language Inference
https://github.com/bluejurand/Kaggle_QA_Google_Labeling
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/bm-k/kosimcse-skt
https://github.com/bourrel/French-News-Clustering
https://github.com/bplank/teaching-dl4nlp
https://github.com/brightmart/albert_zh
ALBERT: A Lite BERT for Self-supervised Learning of Language Representations
https://github.com/brightmart/bert_customized
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/brightmart/bert_original
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/brightmart/roberta_zh
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/brightmart/text_classification
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/broccolik/BERT-NER
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/bzhangGo/lrn
Simplifying Neural Machine Translation with Addition-Subtraction Twin-Gated Recurrent Networks
https://github.com/cactilab/hateguard
https://github.com/castorini/d-bert
Distilling Task-Specific Knowledge from BERT into Simple Neural Networks
https://github.com/cccntu/ft5-demo
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/cccntu/ft5-demo-space
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/cdpierse/transformers-interpret
Supervised Learning of Universal Sentence Representations from Natural Language Inference Data
https://github.com/cedrickchee/pytorch-pretrained-BERT
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/celsolbm/Attention_Text
https://github.com/celsolbm/imdb_sentiment_experiments
https://github.com/chakravarthi-v/Polaroid-1
https://github.com/chalothon/BERT_Practice
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/chambliss/Multilingual_NER
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/chandu7077/mybert
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/charles9n/bert-sklearn
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/chen-xiong-yi/OwnBERT
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/cheng-Ye/daguan-competition-master
The Natural Language Decathlon: Multitask Learning as Question Answering
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.