Papers with Code 아카이브

Coreference Resolution

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

전체 기법 전체 데이터셋

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/nvshrao/Bidirectional-Transformer

Attention Is All You Need

https://github.com/octanove/grammartagger

BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

https://github.com/okanvk/ENELPI---Soru-Cevap-Sistemi

BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

https://github.com/okanvk/Turkish-Reading-Comprehension-Question-Answering-Dataset

BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

https://github.com/okkteam/Transformer-Transducer

Attention Is All You Need

https://github.com/ololo123321/maestro

Attention Is All You Need

https://github.com/om00839/machine-suneung

BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

https://github.com/open-mmlab/mmclassification

Attention Is All You Need

https://github.com/openai/gpt-2

https://github.com/openbiolink/promptsource

Language Models are Few-Shot Learners

https://github.com/opengptx/lm-evaluation-harness

Language Models are Few-Shot Learners

https://github.com/opennlg/openba-v2

UL2: Unifying Language Learning Paradigms

https://github.com/opennmt/ctranslate2

Attention Is All You Need

https://github.com/osu-nlp-group/mind2web

DeBERTa: Decoding-enhanced BERT with Disentangled Attention

https://github.com/overfitter/biobert_embedding

BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

https://github.com/owainwest/uniprot_bert

Attention Is All You Need

https://github.com/paolanu/BERT_epitope

Attention Is All You Need

https://github.com/parcorelab/comscribe

Attention Is All You Need

https://github.com/patrickhart/jaxdl

Attention Is All You Need

https://github.com/paulskeie/stadnamn

Attention Is All You Need

https://github.com/pengming617/bert_classification

Attention Is All You Need

https://github.com/pengshuyuan/Bert

Attention Is All You Need

https://github.com/penny9287/sockeye

Attention Is All You Need

https://github.com/pennylaneai/generative-quantum-states

Attention Is All You Need

https://github.com/pfecht/bert-exploration

BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

https://github.com/phohenecker/pytorch-transformer

Attention Is All You Need

https://github.com/piegu/language-models

BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

https://github.com/pingheng001/Cnn-Bert

Attention Is All You Need

https://github.com/piquil/rydberggpt

Attention Is All You Need

https://github.com/pisalore/roberta_results

BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

https://github.com/pitrack/incremental-coref

Moving on from OntoNotes: Coreference Resolution Model Transfer

https://github.com/plkmo/Transformer-Eng2French

Attention Is All You Need

https://github.com/polzinben/TensorFlow_NLP

BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

https://github.com/pomonam/AttentionCluster

Attention Is All You Need

https://github.com/postech-ami/smile-dataset

Language Models are Few-Shot Learners

https://github.com/ppriyank/Bert-Coref-Resolution-Lee-

End-to-end Neural Coreference Resolution

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.