Papers with Code 아카이브
Natural Language Understanding
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 2
- 평가표
- 6
- 보존된 평가 기록
- 42
Natural Language Understanding is an important field of Natural Language Processing which contains various tasks such as text classification, natural language inference and story comprehension. Applications enabled by natural language understanding range from question answering to automated reasoning.
Source: Find a Reasonable Ending for Stories: Does Logic Relation Help the Story Cloze Test?
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/mayoor/attention_network_experiments
https://github.com/mayurnewase/Quora-Bert
https://github.com/mayurnewase/Translation
https://github.com/mediabrain-sjtu/medicalgpt-zh
https://github.com/meelfy/pytorch_pretrained_bert
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/mega002/annotator_bias
https://github.com/mehdibenamorr/Token-Positional-Bias
https://github.com/mehdibenamorr/bert-positional-bias-ner
https://github.com/meizi1114/bert
https://github.com/mertyg/post-hoc-cbm
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/mhamedLmarbouh/Attention-is-all-you-need
https://github.com/mhaseeb123/DeepMSim
https://github.com/michi-3000/eyeclip
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/microcoder-py/attn-is-all-you-need
https://github.com/microsoft/AdaMix
AdaMix: Mixture-of-Adaptations for Parameter-efficient Model Tuning
https://github.com/microsoft/LoNLI
Trusting RoBERTa over BERT: Insights from CheckListing the Natural Language Inference Task
https://github.com/microsoft/MT-DNN
https://github.com/microsoft/Unicoder
XGLUE: A New Benchmark Dataset for Cross-lingual Pre-training, Understanding and Generation
https://github.com/microsoft/XGLUE
XGLUE: A New Benchmark Dataset for Cross-lingual Pre-training, Understanding and Generation
https://github.com/microsoft/dialoglm
UniLMv2: Pseudo-Masked Language Models for Unified Language Model Pre-Training
https://github.com/microsoft/onnxruntime-training-examples
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/pangu_alpha
https://github.com/mindspore-ai/models/tree/master/official/nlp/tinybert
TinyBERT: Distilling BERT for Natural Language Understanding
https://github.com/mindspore-ai/models/tree/master/official/nlp/transformer
https://github.com/mindspore-courses/ABSA-MindSpore
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/mindspore-courses/Deep-Tutorials-for-MindSpore
https://github.com/mindspore-courses/External-Attention-MindSpore/blob/main/model/attention/SelfAttention.py
https://github.com/mindspore-lab/mindnlp/blob/master/mindnlp/models/bert/bert.py
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/ming024/FastSpeech2
https://github.com/minhanh151/pre
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/minhanh151/respro
Learning Transferable Visual Models From Natural Language Supervision
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.