fast_ner

A fast Named-Entity Recognition module

Description

fast_ner is built from ground-up for quick response time when extracting entity data from input query.
Utilizes a dictionary based approach to match and extract entity data from any text.

Performance:

Entity Dataset Size: 10k entity value
Input Query Size: 100k words
Response Time: under 0.5s

Installation

git clone https://github.com/loanchip/fast_ner.git

Navigate to the directory

python setup.py install

OR To use it as a Docker Container

docker-compose up

Docker Hub Image: loanchip/fast_ner

Usage

from fast_ner import ner

# to create new entities, provide folder path containing entity datasets
ner.add_new_entity(path_to_data_folder='abs_path_to_folder')

# to load already created entities, provide folder path to created entities,
# or use already provided entities by leaving that as None
data = ner.load_dict_data(path_to_data_folder='abs_path_to_folder')

# to perform NER extraction
query = 'Back to the Future is a great movie series'
output = ner.perform_ner(query, data)
# {'title': [(['back', 'to', 'the', 'future'], 0, 4)], 'review': [(['great'], 6, 7)]}

Contributing

Pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.

Please make sure to update tests as appropriate.

License

MIT