lemonhu/NER-BERT-pytorch
PyTorch solution of named entity recognition task Using Google AI's pre-trained BERT model.
This project helps you automatically identify and categorize key entities like people, organizations, and locations within Chinese text. You input raw Chinese text data, and it outputs the same text with specific words or phrases tagged as a 'person,' 'organization,' or 'location.' This is ideal for natural language processing engineers or data scientists who need to extract structured information from unstructured Chinese documents.
449 stars. No commits in the last 6 months.
Use this if you need a reliable way to automatically find and classify named entities in large volumes of Chinese text.
Not ideal if you require named entity recognition for languages other than Chinese or English, or if you need to identify a very broad range of custom entity types beyond people, organizations, and locations.
Stars
449
Forks
107
Language
Python
License
MIT
Category
Last pushed
Mar 30, 2023
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/nlp/lemonhu/NER-BERT-pytorch"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Related tools
hellohaptik/chatbot_ner
chatbot_ner: Named Entity Recognition for chatbots.
openeventdata/mordecai
Full text geoparsing as a Python library
Rostlab/nalaf
NLP framework in python for entity recognition and relationship extraction
mpuig/spacy-lookup
Named Entity Recognition based on dictionaries
NorskRegnesentral/skweak
skweak: A software toolkit for weak supervision applied to NLP tasks