BERT-NER and ner-bert

These are competitors offering similar PyTorch implementations of Named Entity Recognition using BERT, with the first being a more popular standalone project while the second is a wrapper around Google's official BERT model.

BERT-NER
51
Established
ner-bert
50
Established
Maintenance 0/25
Adoption 10/25
Maturity 16/25
Community 25/25
Maintenance 0/25
Adoption 10/25
Maturity 16/25
Community 24/25
Stars: 1,249
Forks: 272
Downloads:
Commits (30d): 0
Language: Python
License: AGPL-3.0
Stars: 408
Forks: 100
Downloads:
Commits (30d): 0
Language: Jupyter Notebook
License: MIT
Stale 6m No Package No Dependents
Stale 6m No Package No Dependents

About BERT-NER

kamalkraj/BERT-NER

Pytorch-Named-Entity-Recognition-with-BERT

This tool helps extract key entities like people, organizations, and locations from text. You provide raw text documents, and it identifies and labels these specific entities within the content. This is useful for anyone who needs to quickly find and categorize important information from large volumes of unstructured text, such as researchers, analysts, or content managers.

information-extraction text-analysis data-tagging content-categorization document-processing

About ner-bert

ai-forever/ner-bert

BERT-NER (nert-bert) with google bert https://github.com/google-research.

This project helps you automatically identify and extract key entities like names, locations, or organizations from text documents. You provide raw text data, and it outputs the same text with specific words or phrases tagged with their respective categories. This is useful for data scientists, natural language processing engineers, or anyone working with large volumes of unstructured text who needs to quickly find specific information.

natural-language-processing information-extraction text-analytics data-labeling semantic-search

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