https://github.com/saravanakumarjsk/BERT-collection
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
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/sarveshsparab/DeepElmoEmbedNer
https://github.com/saurabhkulkarni77/BERT_multilabel
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/saurabhnlp/bert
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/sberbank-ai/ner-bert
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/scotthlee/injury_autocoding
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/scrayish/ML_NLP
https://github.com/scut-bds/example_repo_from_scutbds
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/sdadas/polish-roberta
https://github.com/semal/bert
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/semantic-web-company/ptlm_wsid
DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter
https://github.com/sergeykramp/mthesis-bigbird-embeddings
https://github.com/seung7361/RWKV-Pytorch
https://github.com/seunghwan1228/ELMO
https://github.com/seungonekim/abductive-commonsense-reasoning
https://github.com/shaanchandra/InferSent
Supervised Learning of Universal Sentence Representations from Natural Language Inference Data
https://github.com/shaikhzhas/bert
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/shamalwinchurkar/question-classification
ABCNN: Attention-Based Convolutional Neural Network for Modeling Sentence Pairs
https://github.com/shaneding/bilm-tf-experimentation
https://github.com/shelleyHLX/bilm_EMLo
https://github.com/sherinaseri/bert
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/shijx12/AR-Tree
https://github.com/shijx12/kqapro_baselines
https://github.com/shishir-roy/decaNLP-with-BERT
The Natural Language Decathlon: Multitask Learning as Question Answering
https://github.com/shivamraval98/multitask-t5_ae
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/shmsw25/bart-closed-book-qa
https://github.com/shreyashankar/gpt3-sandbox
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/shuhewang1998/sim-gpt
https://github.com/shuxinyin/SimCSE-Pytorch
https://github.com/shyamupa/snli-entailment
https://github.com/sidak/SentEval
Supervised Learning of Universal Sentence Representations from Natural Language Inference Data
https://github.com/sigtyp/ST2024
The Natural Language Decathlon: Multitask Learning as Question Answering
https://github.com/simon-benigeri/narrative-generation
https://github.com/simonjisu/nsmc_study
https://github.com/simran-arora/focus
Ask Me Anything: A simple strategy for prompting language models
https://github.com/simran-arora/privacy_fm
Ask Me Anything: A simple strategy for prompting language models
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.