JonathanRaiman/wikipedia_ner
:book: Labeled examples from wiki dumps in Python
This tool helps data scientists and NLP researchers generate labeled examples for named entity recognition (NER) tasks. It takes Wikipedia dumps as input and extracts entities like people, organizations, and locations, providing a rich dataset for training machine learning models. The output is a collection of articles with identified and categorized named entities.
No commits in the last 6 months. Available on PyPI.
Use this if you need to create a large, diverse dataset of text with named entities labeled for training or evaluating your NER models.
Not ideal if you're looking for a pre-trained NER model or if your specific domain entities are not well-represented in Wikipedia.
Stars
67
Forks
7
Language
Jupyter Notebook
License
—
Category
Last pushed
Aug 08, 2016
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/nlp/JonathanRaiman/wikipedia_ner"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
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