pytorch-bert-crf-ner and Bert-BiLSTM-CRF-pytorch

pytorch-bert-crf-ner
50
Established
Maintenance 0/25
Adoption 10/25
Maturity 16/25
Community 24/25
Maintenance 0/25
Adoption 10/25
Maturity 16/25
Community 22/25
Stars: 504
Forks: 108
Downloads:
Commits (30d): 0
Language: Jupyter Notebook
License: Apache-2.0
Stars: 283
Forks: 57
Downloads:
Commits (30d): 0
Language: Jupyter Notebook
License: MIT
Stale 6m No Package No Dependents
Stale 6m No Package No Dependents

About pytorch-bert-crf-ner

eagle705/pytorch-bert-crf-ner

KoBERT와 CRF로 만든 한국어 개체명인식기 (BERT+CRF based Named Entity Recognition model for Korean)

This tool helps anyone working with Korean text automatically identify and extract key pieces of information like names of people, locations, organizations, dates, and products. You input raw Korean sentences, and it outputs the same sentences with these important entities clearly tagged. This is useful for data analysts, researchers, or anyone needing to quickly structure information from unstructured Korean text.

Korean text analysis information extraction data structuring natural language processing content categorization

About Bert-BiLSTM-CRF-pytorch

cooscao/Bert-BiLSTM-CRF-pytorch

bert-bilstm-crf implemented in pytorch for named entity recognition.

This tool helps you automatically identify and extract specific types of entities, like names of people, places, or medical terms, from Chinese text. You input raw Chinese text that has been prepared into a specific 'BIO' format, and the system outputs the same text with the identified entities tagged. This is useful for anyone working with large volumes of Chinese text data, such as researchers, linguists, or data analysts.

Chinese-text-analysis information-extraction medical-data-processing linguistics data-annotation

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