Papers with Code 아카이브
Named Entity Recognition (NER)
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- 평가표
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Named Entity Recognition (NER) is a task of Natural Language Processing (NLP) that involves identifying and classifying named entities in a text into predefined categories such as person names, organizations, locations, and others. The goal of NER is to extract structured information from unstructured text data and represent it in a machine-readable format. Approaches typically use BIO notation, which differentiates the beginning (B) and the inside (I) of entities. O is used for non-entity tokens.
Example:
| Mark | Watney | visited | Mars |
|---|---|---|---|
| B-PER | I-PER | O | B-LOC |
<span style="color:grey; opacity: 0.6">( Image credit: Zalando )</span>
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://dagshub.com/Dean/BioBERT-DAGsHub
BioBERT: a pre-trained biomedical language representation model for biomedical text mining
https://github.com/1wy/bert
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/2023-MindSpore-1/ms-code-11
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/2023-MindSpore-1/ms-code-12
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/2023-MindSpore-1/ms-code-150
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/2023-MindSpore-1/ms-code-156
LUKE: Deep Contextualized Entity Representations with Entity-aware Self-attention
https://github.com/2023-MindSpore-1/ms-code-160
NEZHA: Neural Contextualized Representation for Chinese Language Understanding
https://github.com/2023-MindSpore-1/ms-code-190
https://github.com/2023-MindSpore-1/ms-code-5/tree/main/emotect
ERNIE: Enhanced Representation through Knowledge Integration
https://github.com/2023-MindSpore-1/ms-code-5/tree/main/ernie
ERNIE: Enhanced Representation through Knowledge Integration
https://github.com/2023-MindSpore-1/ms-code-5/tree/main/lstm_crf
https://github.com/2023-MindSpore-1/ms-code-56
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/2023-MindSpore-4/Code12/tree/main/MindFormers/bert
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/2023-MindSpore-4/Code16/tree/main/guitao/MINER-main
https://github.com/2024-MindSpore-1/Code2/tree/main/model-1/bertweet
https://github.com/2024-MindSpore-1/Code2/tree/main/model-1/camembert
https://github.com/2024-MindSpore-1/Code2/tree/main/model-1/deberta
https://github.com/2024-MindSpore-1/Code2/tree/main/model-1/deberta_v2
https://github.com/2024-MindSpore-1/Code2/tree/main/model-1/ernie
ERNIE: Enhanced Representation through Knowledge Integration
https://github.com/2024-MindSpore-1/Code2/tree/main/model-1/ernie_m
ERNIE: Enhanced Representation through Knowledge Integration
https://github.com/2024-MindSpore-1/Code3/tree/main/scBERT
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/27182812/ChineseBERT_paddle
ChineseBERT: Chinese Pretraining Enhanced by Glyph and Pinyin Information
https://github.com/2miatran/Natural-Language-Processing
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/4ai/bellm
https://github.com/8400thehealthnetwork/hebsafeharbor
AlephBERT:A Hebrew Large Pre-Trained Language Model to Start-off your Hebrew NLP Application With
https://github.com/ABaldrati/MT-BERT
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/AGiannoutsos/COVID19-document-retrieval-with-BERT
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/AbhishekD15/Sentiment-Analysis_BERT
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/AdamStein97/Semi-Supervised-BERT-NER
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/Adapter-Hub/adapter-transformers
MAD-X: An Adapter-Based Framework for Multi-Task Cross-Lingual Transfer
https://github.com/AhmedYounes94/Semi-supervised-BERT-NER
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/AindriyaBarua/Contextual-vs-Non-Contextual-Word-Embeddings-For-Hindi-NER-With-WebApp
https://github.com/Aitslab/BioNLP
Interpretable Multi-dataset Evaluation for Named Entity Recognition
https://github.com/Akshayanti/supersense-sequence-labelling
https://github.com/Alibaba-NLP/CLNER
Improving Named Entity Recognition by External Context Retrieving and Cooperative Learning
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.