Papers with Code 아카이브
Coreference Resolution
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 2
- 평가표
- 16
- 보존된 평가 기록
- 154
Coreference resolution is the task of clustering mentions in text that refer to the same underlying real world entities.
Example:
+-----------+
| |
I voted for Obama because he was most aligned with my values", she said.
| | |
+-------------------------------------------------+------------+
"I", "my", and "she" belong to the same cluster and "Obama" and "he" belong to the same cluster.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/LamLauChiu/Tensorflow_Learning
https://github.com/Lemoners/Attention-Is-All-You-Need
https://github.com/LeoLaugier/conditional-auto-encoder-text-to-text-transfer-transformer
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/LeoWood/bert-horovod
https://github.com/LiamMaclean216/Pytorch-Chatbot
https://github.com/LiamMaclean216/Pytorch-Transfomer
https://github.com/Lightning-AI/lit-gpt
Pythia: A Suite for Analyzing Large Language Models Across Training and Scaling
https://github.com/Linar23/Research_work
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/LorenzoAgnolucci/BERT_for_ABSA
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/LoveYang/bert_test
https://github.com/Lsdefine/attention-is-all-you-need-keras
https://github.com/MCLYang/RhythmGAN_pytorch
https://github.com/MINGG2333/bert_ms
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/MLH-Fellowship/Social-BERTerfly
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/MOHAMEDELDAKDOUKY/bert_adjusted
https://github.com/MS-Mind/MS-Code-01/tree/main/soft_masked_bert
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/MS-P3/code5/tree/main/bert
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/MS-P3/code5/tree/main/gpt2
https://github.com/MS-P3/code5/tree/main/t5
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/MS-P3/code6/tree/main/finetune
https://github.com/MaZhiyuanBUAA/bert-tf1.4.0
https://github.com/MalteHB/-l-ctra
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/ManasRMohanty/DS5500-capstone
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/MansMeg/IntroML
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/Maple728/transformer
https://github.com/MarcBS/keras
https://github.com/MarcosGrzeca/drunktweets
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/MashaKubyshina/learning_ML
https://github.com/MatthewSwahn/news-headlines-sarcasm-detector
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/Matthewdowney18/Transformer_Dialogue
https://github.com/Maz101/Bert
https://github.com/Maz101/pretrainingdataset
https://github.com/MichaelZhouwang/LMlexsub
https://github.com/Microsoft/AzureML-BERT
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/Mind23-2/MindCode-133
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.