https://github.com/2023-MindSpore-4/Code8/tree/main/cnn_direction_model
DiSAN: Directional Self-Attention Network for RNN/CNN-Free 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
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- 보존된 평가 기록
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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/2024-MindSpore-1/Code2/tree/main/model-1/bart
https://github.com/2024-MindSpore-1/Code2/tree/main/model-1/bert
https://github.com/2024-MindSpore-1/Code2/tree/main/model-1/big_bird
https://github.com/2024-MindSpore-1/Code2/tree/main/model-1/byt5
ByT5: Towards a token-free future with pre-trained byte-to-byte models
https://github.com/2024-MindSpore-1/Code2/tree/main/model-1/camembert
https://github.com/2024-MindSpore-1/Code2/tree/main/model-1/data2vec
data2vec: A General Framework for Self-supervised Learning in Speech, Vision and Language
https://github.com/2024-MindSpore-1/Code2/tree/main/model-1/deberta
https://github.com/2024-MindSpore-1/Code2/tree/main/model-1/deberta_v2
https://github.com/2024-MindSpore-1/Code2/tree/main/model-1/distilbert
DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter
https://github.com/2024-MindSpore-1/Code2/tree/main/model-1/ernie
ERNIE: Enhanced Representation through Knowledge Integration
https://github.com/2024-MindSpore-1/Code2/tree/main/model-1/ernie_m
ERNIE: Enhanced Representation through Knowledge Integration
https://github.com/2024-MindSpore-1/Code2/tree/main/model-1/fnet
https://github.com/2024-MindSpore-1/Code3/tree/main/gMLP
https://github.com/2024-MindSpore-1/Code3/tree/main/scBERT
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/27182812/ChineseBERT_paddle
ChineseBERT: Chinese Pretraining Enhanced by Glyph and Pinyin Information
https://github.com/2miatran/Natural-Language-Processing
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/ABaldrati/MT-BERT
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/AGiannoutsos/COVID19-document-retrieval-with-BERT
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/AI-Mart/distill_lstm
Distilling Task-Specific Knowledge from BERT into Simple Neural Networks
https://github.com/AbhilashaRavichander/NLI_StressTest
https://github.com/AbhishekD15/Sentiment-Analysis_BERT
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/AdamStein97/Semi-Supervised-BERT-NER
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/AdityaGovardhan/cosmosQA-research
Recent Advances in Natural Language Inference: A Survey of Benchmarks, Resources, and Approaches
https://github.com/AhmedYounes94/Semi-supervised-BERT-NER
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/Alihassan7726/Fine-tuning-BERT-for-semantic-sentence-pairs-classification
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/Alihassan7726/Multi-Class-classification-using-BERT-
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/AmanDaVinci/Universal-Sentence-Representations
Supervised Learning of Universal Sentence Representations from Natural Language Inference Data
https://github.com/AndreasMadsen/course-02456-sparsemax
From Softmax to Sparsemax: A Sparse Model of Attention and Multi-Label Classification
https://github.com/Arthurizijar/Bert_Airport
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/ArvinZhuang/BiTAG
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/AryanShekarlaban/data2vec-pytorch
data2vec: A General Framework for Self-supervised Learning in Speech, Vision and Language
https://github.com/AshwinDeshpande96/Hierarchical-Softmax
https://github.com/AtmaHou/MetaDialog
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/AutomatedProgramRepair-2021/dear-auto-fix
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/BM-K/KoSimCSE
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.