https://github.com/karta282950/bert-multiclass
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.
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- 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/kaushalshetty/Structured-Self-Attention
https://github.com/kaushaltrivedi/fast-bert
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/kawine/dataset_difficulty
A large annotated corpus for learning natural language inference
https://github.com/keiyamashita111/aaproject
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/kelly2016/multi-label-bert
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/kelvinguu/qanli
Transforming Question Answering Datasets Into Natural Language Inference Datasets
https://github.com/kenkenling/NLP
https://github.com/kevin-rn/Efficient-Fact-checking
https://github.com/kevinduh/san_mrc
https://github.com/khalidsaifullaah/bertify
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/kheeong/XLM_OWN
https://github.com/khhaliil/avatario
https://github.com/khushsi/Finetuning_BART_for_FACET_Summarization
https://github.com/kiko441500/google_bert
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/kingcheng2000/bert
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/kingcong/gpu_albert
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/kingcong/models/tree/main/bert
NEZHA: Neural Contextualized Representation for Chinese Language Understanding
https://github.com/kinimod23/ATS_Project
ABCNN: Attention-Based Convolutional Neural Network for Modeling Sentence Pairs
https://github.com/kinimod23/NMT_Project
https://github.com/kmkwon94/ainize-laser
Massively Multilingual Sentence Embeddings for Zero-Shot Cross-Lingual Transfer and Beyond
https://github.com/knuddj1/op_text
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/knuddy/op_text
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/ko-nlp/Korpora
KorNLI and KorSTS: New Benchmark Datasets for Korean Natural Language Understanding
https://github.com/kobowon/cs470_project_version2
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/kohjingyu/fromage
LLM.int8(): 8-bit Matrix Multiplication for Transformers at Scale
https://github.com/kpe/bert-for-tf2
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/kpot/keras-transformer
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/krantirk/BERT-Pretrained-model
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/kriesbeck/masked-language-prediction
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/kriskorrel/sparsemax-pytorch
From Softmax to Sparsemax: A Sparse Model of Attention and Multi-Label Classification
https://github.com/krlgroup/clustered-compositional-explanations
https://github.com/kuleshov-group/llmtune
ModuLoRA: Finetuning 2-Bit LLMs on Consumer GPUs by Integrating with Modular Quantizers
https://github.com/kunde122/bilm-tf
https://github.com/kunj17/keras-quora-question-pair
Bilateral Multi-Perspective Matching for Natural Language Sentences
https://github.com/kushalchauhan98/dynamic-meta-embeddings
Dynamic Meta-Embeddings for Improved Sentence Representations
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.