NLP Server


This is a ready to use NLP Server for analyzing text documents using NLU library. Over 4000+ industry grade NLP Models in 300+ Languages are available to use via a simple and intuitive UI, without writing a line of code. For more expert users and more complex tasks, NLP Server also provides a REST API that can be used to process high amounts of data.

The models, refered to as spells, are provided by the NLU library and powered by the most widely used NLP library in the industry, Spark NLP.

NLP Server is free for everyone to download and use. There is no limitation in the amount of text to analyze.

You can setup NLP-Server as a Docker Machine in any enviroment or get it via the AWS Marketplace in just 1 click.

Web UI

The Web UI is accessible at the following URL: http://localhost:5000/ It allows a very simple and intuitive interaction with the NLP Server. As a first step the user chooses the spell from the first dropdown. All NLU spells are available. Then the user has to provide a text document for analysis. This can be done by either copy/pasting text on the text box, or by uploading a csv/json file. After selecting the grouping option, the user clicks on the Preview button to get the results for the first 10 rows of text.


NLP Server includes a REST API which can be used to process any amount of data using NLU. Once you deploy the NLP Server, you can access the API documentation at the following URL http://localhost:5000/docs.

How to use in Python

import requests
# Invoke Processing with tokenization spell
r ='http://localhost:5000/api/results',json={"spell": "tokenize","data": "I love NLU! <3"})
# Use the uuid to get your processed data
uuid = r.json()['uuid']

# Get status of processing
r = requests.get(f'http://localhost:5000/api/results/{uuid}/status').json
>>> {'status': {'code': 'success', 'message': None}}

# Get results
r = requests.get(f'http://localhost:5000/api/results/{uuid}').json()
>>> {'sentence': {'0': ['I love NLU! <3']}, 'document': {'0': 'I love NLU! <3'}, 'token': {'0': ['I', 'love', 'NLU', '!', '<3']}}

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