ner_analyzer_cli_v2

A simple Named Entity Recognition (NER) analyzer package with a command-line interface (CLI) and export options for JSON and CSV. Powered by spaCy and supporting multiple languages.

Features

• Analyze text for named entities using spaCy.
• Supports multiple languages (en, fr, de, es, pt).
• Export results to JSON or CSV files.
• Shows entity frequency statistics.
• Easy-to-use CLI interface.

Installation

First, install the package (after cloning or downloading):

pip install ner_analyzer_cli_v2

You must also download the relevant spaCy language model(s) as needed. For example:

python -m spacy download en_core_web_sm
python -m spacy download fr_core_news_sm
# For other languages, see: https://spacy.io/models

CLI Usage

ner-analyze "Your text to analyze here"

Options

• --output <file>: Specify output file (.json or .csv).
• --lang <code>: Specify language code (en, fr, de, es, pt). Default is en.

Example

Analyze some text and export results:

ner-analyze "Barack Obama was born in Hawaii." --output results.json
ner-analyze "Emmanuel Macron est le président français." --lang fr --output results.csv

Output Example

Console

Entities:
[
  {
    "text": "Barack Obama",
    "label": "PERSON"
  },
  {
    "text": "Hawaii",
    "label": "GPE"
  }
]

Frequency:
{'PERSON': 1, 'GPE': 1}

JSON

{
  "entities": [
    {"text": "Barack Obama", "label": "PERSON"},
    {"text": "Hawaii", "label": "GPE"}
  ],
  "frequency": {"PERSON": 1, "GPE": 1}
}

CSV

Entity,Label
Barack Obama,PERSON
Hawaii,GPE

Label,Count
PERSON,1
GPE,1

API Usage

You can also use the NERAnalyzer class directly in your Python scripts:

from ner_analyzer.cli import NERAnalyzer

analyzer = NERAnalyzer(lang_model="en_core_web_sm")
entities = analyzer.analyze_text("Angela Merkel was Chancellor of Germany.")
print(entities)

License

MIT License

Author

Amal Alexander