https://github.com/tvinith/bert
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
- 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/twobooks/intro-aws-training
DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter
https://github.com/typoverflow/decision-rwkv
https://github.com/tyxr/bert
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/uabinf/nlp-fall-2019-project-shuhui_shuqu
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/ucb-bar/gemmini
https://github.com/uci-soe/fairytaleqadata
It is AI's Turn to Ask Humans a Question: Question-Answer Pair Generation for Children's Story Books
https://github.com/ucinlp/null-prompts
AutoPrompt: Eliciting Knowledge from Language Models with Automatically Generated Prompts
https://github.com/uclanlp/socialbias-dataset-construction-biases
On Measuring and Mitigating Biased Inferences of Word Embeddings
https://github.com/uclnlp/jack
https://github.com/udnet96/BART-various-finetune
https://github.com/ufal/multilexnorm2021
ByT5: Towards a token-free future with pre-trained byte-to-byte models
https://github.com/ufal/wembedding_service
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/ukplab/arxiv2025-inherent-limits-plms
https://github.com/um-arm-lab/efficient-eng-2-ltl
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/utsjiyaoli/qa-attack
https://github.com/utterworks/fast-bert
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/uva-dsa/ems-pipeline
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/uygarkurt/BERT-PyTorch
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/vaibhav4595/BiMPM_PyTorch
Bilateral Multi-Perspective Matching for Natural Language Sentences
https://github.com/vanpersie32/Multigpu-Bert
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/vanslerry/long_text
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/vchopin/ERNIE_Emotect
ERNIE: Enhanced Representation through Knowledge Integration
https://github.com/vdogmcgee/SimCSE-Chinese-Pytorch
https://github.com/vectara/hallucination-leaderboard
TrueTeacher: Learning Factual Consistency Evaluation with Large Language Models
https://github.com/vene/sparse-structured-attention
From Softmax to Sparsemax: A Sparse Model of Attention and Multi-Label Classification
https://github.com/venkyr91193/Sentiment-Analysis-with-BERT
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/venkyr91193/Sentiment_Analysis_with_BERT
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/vgaraujov/seq2seq-spanish-plms
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/viethoang1512/kpa
https://github.com/vijay120/bert
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/vijayakuruba/IFT6010_Applying_Answer-Clue-Style_Approach_to_VQG
Asking Questions the Human Way: Scalable Question-Answer Generation from Text Corpus
https://github.com/vilm-ai/viet-llm-eval
https://github.com/vinayak19th/Brevis-2.0
https://github.com/vincent861223/Train_BERT_With_TextFooler
https://github.com/vineet54/fnet-google-pytorch
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.