Image processing

UX Design Handbook for Documentation Engineers

broken-links-checker.py: A Python script to scan broken links from a given web domain.

translate.py: Translate documentations (.md files) to destined language.

• This pipeline uses a high-performance Neural Machine Translation (NMT) system. The current code is running on Helsinki-NLP/opus-mt-en-zh model, which is trained on a diverse range of parallel texts from the internet. Switch to your favourite pre-trainded moddel to translate between any pair of languages from the OPUS corpus.
• (Optional) Fully-automated Documentation publication workflow, by push & commit to GitHub, and subsequently Docs auto deployment.

✨✨ml-docs-scanner.py✨✨: The udpated version of NLP Docs Scanner.

• Using machine learning techniques to train models on the cleaned data, make predictions on the data, score the documentation based on the criteria you specified.
• Use supervised learning techniques to train a model to predict the quality of a document based on a set of labeled examples. For example, you can use grammatical error correction models, spell checker models, readability metrics such as the Flesch-Kincaid readability test, sentiment analysis models to measure the objectivity and tone of a document.
• (Not Open Sourced) Utilize AI models to check for consistency and coherence in style, tone, and terminology throughout the text, and give improved readability scores.
• Screenshot:
• Dependenceis:

## prereq: python3, jre
## Install dependenceis:
pip3 install nltk textstat markdown textblob language-tool-python pyfiglet textblob

nlp-docs-scanner.py: Automated Documentation Scanner. Features:

• Scan all .md files in a given directory and all the sub-directories and use natural language processing(NLP) techniques to determine complicated words by breaking down the text into individual sentences.
• Grammar and Spelling checker.
• Evaluate readability: the Flesch-Kincaid Reading Ease score.
• Evalute the objectivity: by computing the Automated Readability Index (ARI) and Flesch-Kincaid Grade Level.
• Evalute clearity: Apply named entity recognition (NER) to identify specific words within the text and make suggestions for improvements.
• Evalue the tone: Apply Sentiment analysis using Machine learning (ML) techniques.
• Evalute the consistency: Analyze the text based on NLP and ML, which, detects terms and check consistency.

Note 1: You are obligated to create a terminology_dict.json file in the following format:

{
    "word1": count1,
    "word2": count2,
    ...
}

Note 2: Grammar check, spelling check & clearity check on a word-based level proven to be unreliable for generating too many false positives. Best pracitce: use grammarly instead.