https://github.com/MindCode-4/code-3/tree/main/ernie_m
ERNIE: Enhanced Representation through Knowledge Integration
Papers with Code 아카이브
Named Entity Recognition (NER)
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- 평가표
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- 보존된 평가 기록
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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://github.com/MindCode-4/code-5/tree/main/whisper
WhisperNER: Unified Open Named Entity and Speech Recognition
https://github.com/MindCode-4/code-5/tree/main/xlm
XLM-K: Improving Cross-Lingual Language Model Pre-training with Multilingual Knowledge
https://github.com/MindCode-4/code-6/tree/main/drop-an-octave-reducing-spatial
https://github.com/MindCode-4/code-9/tree/main/RITA
RITA: Automatic Framework for Designing of Resilient IoT Applications
https://github.com/MindSpore-paper-code-3/code9/tree/main/emotect
ERNIE: Enhanced Representation through Knowledge Integration
https://github.com/MindSpore-paper-code-3/code9/tree/main/lstm_crf
https://github.com/MindSpore-paper-code-3/code9/tree/main/soft_masked_bert
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/MindSpore-scientific-2/code-14/tree/main/layoutlmv3
LayoutLMv3: Pre-training for Document AI with Unified Text and Image Masking
https://github.com/MindSpore-scientific/code-12/tree/main/Cybertron
DiffusionNER: Boundary Diffusion for Named Entity Recognition
https://github.com/MindSpore-scientific/code-5/tree/main/RITA
RITA: Automatic Framework for Designing of Resilient IoT Applications
https://github.com/MindSpore-scientific/code-7/tree/main/bert
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/MirunaPislar/multi-head-attention-labeller
https://github.com/Misoknisky/Bert-MultiGpu
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/NEUNLP-RE/Entity-aware-RC
https://github.com/Nealcly/BiLSTM-LAN
Hierarchically-Refined Label Attention Network for Sequence Labeling
https://github.com/NehaTamore/TextSimilarity
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/Nilanshrajput/Intent_classification
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/Nimesh-Patel/BERT
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/Nstats/bert_MRC
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/Nstats/bert_senti_analysis_ch
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/Nstats/my_bert
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/OnlpLab/AlephBERT
https://github.com/Osobarako/alduswarrensewell
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/OweysMomenzada/Evergreen-Content-Classifier-for-german-Text
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/PKU-TANGENT/nlp-tutorial
https://github.com/PaddlePaddle/PaddleNLP
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/PaddlePaddle/PaddleNLP/blob/develop/paddlenlp/transformers/nezha/modeling.py
NEZHA: Neural Contextualized Representation for Chinese Language Understanding
https://github.com/PaddlePaddle/PaddleNLP/tree/develop/examples/language_model/chinesebert
ChineseBERT: Chinese Pretraining Enhanced by Glyph and Pinyin Information
https://github.com/PaddlePaddle/PaddleNLP/tree/develop/examples/language_model/rembert
Rethinking embedding coupling in pre-trained language models
https://github.com/PaddlePaddle/PaddleNLP/tree/develop/examples/language_model/roformer
LUKE: Deep Contextualized Entity Representations with Entity-aware Self-attention
https://github.com/PaddlePaddle/PaddleNLP/tree/develop/examples/lexical_analysis
https://github.com/PaddlePaddle/models
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/Pirata-Codex/Tag-Persian-Entities-Using-Bert
https://github.com/PrashantRanjan09/Elmo-Tutorial
https://github.com/PrashantRanjan09/WordEmbeddings-Elmo-Fasttext-Word2Vec
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.