https://github.com/code2k13/nlphose

Enables creation of complex NLP pipelines in seconds, for processing static files or streaming text, using a set of simple command line tools. Perform multiple operation on text like NER, Sentiment Analysis, Chunking, Language Identification, Q&A, 0-shot Classification and more by executing a single command in the terminal. Can be used as a low code or no code Natural Language Processing solution. Also works with Kubernetes and PySpark !
https://github.com/code2k13/nlphose

ai artifical-intelligense data-science language-detection low-code machine-learning named-entity-recognition natural-language-processing nlp no-code sentiment-analysis text-mining twitter-sentiment-analysis

Last synced: about 1 month ago
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Enables creation of complex NLP pipelines in seconds, for processing static files or streaming text, using a set of simple command line tools. Perform multiple operation on text like NER, Sentiment Analysis, Chunking, Language Identification, Q&A, 0-shot Classification and more by executing a single command in the terminal. Can be used as a low code or no code Natural Language Processing solution. Also works with Kubernetes and PySpark !

• Host: GitHub
• URL: https://github.com/code2k13/nlphose
• Owner: code2k13
• License: apache-2.0
• Created: 2021-01-03T13:32:48.000Z (almost 4 years ago)
• Default Branch: main
• Last Pushed: 2022-01-25T06:18:35.000Z (almost 3 years ago)
• Last Synced: 2023-07-09T04:20:09.803Z (over 1 year ago)
• Topics: ai, artifical-intelligense, data-science, language-detection, low-code, machine-learning, named-entity-recognition, natural-language-processing, nlp, no-code, sentiment-analysis, text-mining, twitter-sentiment-analysis
• Language: Jupyter Notebook
• Homepage: https://ashishware.com
• Size: 2.38 MB
• Stars: 10
• Watchers: 3
• Forks: 1
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE.md

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README

        
![nlphose](logo.png)

Nlphose is a set of command-line tools that enables creation of complex NLP pipelines within seconds. 

It currently supports following operation on static files and streaming data:

 

* Sentiment Analysis (AFINN)

* NER (Spacy)

* Language Identification (FastText)

* Chunking (NLTK)

* Sentiment Analysis (Transformers)

* Question Answering (Transformers)

* Zero shot Classification (Transformers)

👇🏻Below is a sample pipeline that streams in 🗨️ tweets containing the term ⛈️'rainfall' and tries to guess the 🏙️ place it rained using extractive question answering.

```shell

twint -s 'rainfall' |\

./twint2json.py |\

./xformer.py --pipeline question-answering --param 'where did it rain' |\

jq '{"text":.text,"answer":.xfrmr_question_answering.answer}'

```

Nlphose pipelines can run on your laptop or on a cluster of computers using [Kubernetes](https://ashishware.com/2021/12/12/scalablenlp/) or [Pyspark](https://ashishware.com/2022/01/23/PysparkNlphose/).

## 😲😲😲 Looks interesting ??

Checkout the 🔗[installation guide ](https://github.com/code2k13/nlphose/wiki/Installing) and 🔎[some usage examples](https://github.com/code2k13/nlphose/wiki/Quickstart). Please refer to the wiki for 📖[detailed documentation](https://github.com/code2k13/nlphose/wiki)

## GUI Pipeline Builder (WIP)

I am also working on a GUI Pipeline buider tool which allows users to create a pipeline by simply drag-and-drop

![](https://github.com/code2k13/nlphoseGUI/blob/main/images/drag_drop_nlphose.gif?raw=true)

For more details visit it's repository : [https://github.com/code2k13/nlphoseGUI](https://github.com/code2k13/nlphoseGUI)