Extracting named entities (NER) from contract texts using spaCy. 📝

Manually parsing legal documents to find organizations, dates, and amounts is too time-consuming. The spaCy library allows you to automatically find and classify named entities in unstructured text. We will write a Python script that loads a language model and recognizes key entities in a contract excerpt. 🚀

We will install the spaCy library and download a pre-trained language model for working with the Russian language. 🇷🇺

pip install spacy && python3 -m spacy download ru_core_news_sm

The spaCy library and the Russian language model have been successfully loaded into the environment. ✅

We will create a Python script called extract_ner.py that processes the contract text through a language pipeline and prints the identified entities. 🐍

import spacy

nlp = spacy.load("ru_core_news_sm")
text = "OOO 'Vector' undertakes to pay 500,000 rubles by October 15, 2026, in Moscow."
doc = nlp(text)

for ent in doc.ents:
    print(f"{ent.text}

#spacy #python #nlp #dataScience #programming #automation

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