Explore Arabic NLP tools
Classify text into categories
This is for learning purpose, don't take it seriously :)
Ask questions about air quality data with pre-built prompts or your own queries
Analyze sentiment of text input as positive or negative
Explore BERT model interactions
Demo emotion detection
Playground for NuExtract-v1.5
Detect emotions in text sentences
Analyze content to detect triggers
Generate vector representations from text
Track, rank and evaluate open LLMs and chatbots
Calculate patentability score from application
The Arabic NLP Demo is a web-based platform designed to demonstrate cutting-edge Natural Language Processing (NLP) capabilities for the Arabic language. It provides an intuitive interface to explore various NLP tools and features, making it easy for researchers, developers, and students to experiment with Arabic text analysis. The demo leverages state-of-the-art models to address complex linguistic challenges unique to Arabic, such as its rich morphology, dialect variations, and script-specific characteristics.
• Text Tokenization: Split Arabic text into words and subwords for further analysis.
• Named Entity Recognition (NER): Identify and classify named entities such as names, locations, and organizations.
• Sentiment Analysis: Determine the emotional tone of text (e.g., positive, negative, neutral).
• Machine Translation: Translate Arabic text to other languages and vice versa.
• Text Summarization: Generate concise summaries of long Arabic documents.
• Dialect Detection: Identify the dialect of Arabic being used in the text (e.g., Egyptian, Gulf, Levantine).
What languages are supported for machine translation?
The Arabic NLP Demo supports translation between Arabic and multiple languages, including English, French, and Spanish.
Can I customize the models used in the demo?
Currently, the demo uses pre-trained models, but you can provide feedback to suggest additional customization options for future updates.
Is the demo suitable for analyzing Arabic dialects?
Yes, the demo includes dialect detection and can handle various Arabic dialects to some extent, though accuracy may vary depending on the dialect and input quality.