Named Entity Recognition with FastAPI

This project implements a Named Entity Recognition (NER) over an example text using FastAPI.
In the named entity recognition pipeline, following components were used:

• Document assembler
• Tokenizer
• Pretrained spell checker spellcheck_dl
• Pretrained word embeddings glove_100d
• Pretrained NER model onto_100
• NER converter to create NER chunks.

The pipeline created in this project is able to detect following entity types from given text:

CARDINAL, EVENT, WORK_OF_ART, ORG, DATE, GPE, PERSON, PRODUCT, NORP, ORDINAL, MONEY, LOC, FAC, LAW, TIME, PERCENT, QUANTITY, LANGUAGE

Files

• extraction_model/:
  • config/
    • config.py: Configuration settings for the NER model.
  • pretrained_models/: A folder containing pre-trained sparknlp models.
    • glove_100d_en_2.4.0_2.4_1579690104032: Pretrained word embeddings model
    • onto_100_en_2.4.0_2.4_1579729071672: Pretrained NER model
    • spellcheck_dl_en_3.4.1_3.0_1648457196011: Pretrained spell checker model
  • saved_ner_pipeline/: A folder containing saved NER pipeline.
    • ner_pipeline: Named Entity Recognition pipeline that is created with this project.
  • saprknlp_jar/
    • spark-nlp-assembly-5.3.2.jar: Jar file for the sparknlp library.
  • extraction.py: FastAPI application code containing the endpoints for NER.
  • log_manager.py: Module for initializing the logger.
  • pipeline_manager.py: Module for managing the Spark NLP pipeline.
• requirements.txt: List of Python dependencies required to run the project.
• README.md: This README file providing information about the project.