Papers with Code 아카이브
Natural Language Inference
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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/abhaskumarsinha/Keras-implementation-of-Transformer-Architecture
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/abhaskumarsinha/MinimalGPT
https://github.com/abhiagg92/bert_sentiment
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/abhilash1910/BERTSimilarity
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/abhilashreddys/Fake-News-Article
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/abhineet/sentence_classification_pubmed_scibert
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/abhishekanand1710/noiseandbias
https://github.com/abmitra84/Language_Modeling
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/aced125/sparsemax
From Softmax to Sparsemax: A Sparse Model of Attention and Multi-Label Classification
https://github.com/adi2103/AML-CoVe
https://github.com/adriangrepo/mmbt_lightning
Supervised Multimodal Bitransformers for Classifying Images and Text
https://github.com/af-ai-center/bert
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/af-ai-center/nerblackbox
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/agrzheng/GRADA
PoisonedRAG: Knowledge Corruption Attacks to Retrieval-Augmented Generation of Large Language Models
https://github.com/ahmedssabir/Textual-Visual-Semantic-Dataset-for-Text-Spotting
https://github.com/ahmedssabir/dataset
https://github.com/ai21labs/lm-evaluation
https://github.com/airc-keti/ke-t5
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/airsplay/vimpac
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/aistairc/kirt_bert_on_abci
https://github.com/aivolcano/BERT_MRC_CLS
https://github.com/alankarj/robust_nlp
https://github.com/aleksac99/SQuAD-SR
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/alexa/ramen
From English To Foreign Languages: Transferring Pre-trained Language Models
https://github.com/alextmallen/adaptive-retrieval
LLM.int8(): 8-bit Matrix Multiplication for Transformers at Scale
https://github.com/algharak/BERTenhance
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/alibaba-edu/simple-effective-text-matching
Simple and Effective Text Matching with Richer Alignment Features
https://github.com/alibaba-edu/simple-effective-text-matching-pytorch
Simple and Effective Text Matching with Richer Alignment Features
https://github.com/alibaba/EasyNLP
GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding
https://github.com/alibaba/EasyNLP/tree/master/examples/fewshot_learning
Exploiting Cloze Questions for Few Shot Text Classification and Natural Language Inference
https://github.com/alipay/StructuredLM_RTDT
Generative Pretrained Structured Transformers: Unsupervised Syntactic Language Models at Scale
https://github.com/allenai/abductive-commonsense-reasoning
https://github.com/allenai/bilm-tf
https://github.com/allenai/c4-documentation
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/allenai/dolma
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.