https://github.com/maknotavailable/pytorch-pretrained-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.
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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/mandarjoshi90/coref
SpanBERT: Improving Pre-training by Representing and Predicting Spans
https://github.com/mandubian/codenets
ALBERT: A Lite BERT for Self-supervised Learning of Language Representations
https://github.com/manideep2510/siamese-BERT-fake-news-detection-LIAR
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/manshri/tesum
mT5: A massively multilingual pre-trained text-to-text transformer
https://github.com/mario-holubar/ATCS-InferSent
Supervised Learning of Universal Sentence Representations from Natural Language Inference Data
https://github.com/martinsbruveris/tensorflow-image-models
https://github.com/maxudong1993/mybert
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/mayurnewase/Quora-Bert
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/mbforbes/physical-commonsense
Do Neural Language Representations Learn Physical Commonsense?
https://github.com/mblondel/fenchel-young-losses
https://github.com/mblondel/projection-losses
https://github.com/mbzuai-paris/lm-evaluation-harness-atlas-chat
https://github.com/mcao610/Factual-Error-Correction
Evaluating the Factual Consistency of Abstractive Text Summarization
https://github.com/mcgill-nlp/llm2vec
https://github.com/meelfy/pytorch_pretrained_bert
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/meghu2791/DeepLearningModels
Bilateral Multi-Perspective Matching for Natural Language Sentences
https://github.com/meizi1114/bert
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/menajosep/AleatoricSent
Supervised Learning of Universal Sentence Representations from Natural Language Inference Data
https://github.com/mhaseeb123/DeepMSim
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/microsoft/LoNLI
Trusting RoBERTa over BERT: Insights from CheckListing the Natural Language Inference Task
https://github.com/microsoft/M3P
M3P: Learning Universal Representations via Multitask Multilingual Multimodal Pre-training
https://github.com/microsoft/MT-DNN
https://github.com/microsoft/Table-Pretraining
https://github.com/microsoft/fastseq
https://github.com/milmor/GPT
https://github.com/mim-solutions/bert_for_longer_texts
https://github.com/mim-solutions/roberta_for_longer_texts
https://github.com/mindspore-ai/models/tree/master/official/nlp/bert
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/mindspore-ai/models/tree/master/official/nlp/bert_thor
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/mindspore-ai/models/tree/master/official/nlp/gpt
https://github.com/mindspore-ai/models/tree/master/official/nlp/pangu_alpha
https://github.com/mindspore-ai/models/tree/master/official/nlp/q8bert
https://github.com/mindspore-ai/models/tree/master/official/nlp/tinybert
TinyBERT: Distilling BERT for Natural Language Understanding
https://github.com/mindspore-courses/ABSA-MindSpore
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/mindspore-courses/d2l-mindspore/tree/master/chapter_14_natural_language_processing_pretraining
SqueezeBERT: What can computer vision teach NLP about efficient neural networks?
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.