Natural Language Processing (NLP) NLP has become faster and more efficient thanks to compact models like DistilGPT and DistilRoBERTa. These lightweight versions of larger AI models powerchatbots, virtual assistants, and search engines to deliver quick and accurate responses while using fewer resources...
xP379MMulti-lingual78,883,588 instructions collected by prompts & datasets across 46 languages & 16 NLP tasks- CodeParrot-pythonThe database was queried for all Python files with less than 1MB in size resulting in a 180GB dataset with over 20M files.- ...
We as humans rely on language to talk to people, but it cannot be used when interacting with a computer system.This is where natural language processing (NLP) comes in, playing a central role in the world of modern AI. It transforms how machines understand and interact with human language....
Syntax parsing contains most of the common NLP activities found in better libraries, likelemmatization,part-of-speech tagging, anddependency-tree parsing. NLP mainly deals with helping machines understand text and the relationship between words. Syntax parsing is a foundational part of most language-pr...
Data Visualization: Presenting data in a graphical format (like charts, graphs, and maps) to make the analysis understandable and accessible to a wider audience. Tools like Tableau, PowerBI, or libraries in Python (like Matplotlib, Seaborn) are commonly used. Data Gove...
Answer– This section is reserved for the model to output the SQL query response to the natural language input. An example of the database schema and prompt used in this section is available in theGitHub Repo. ### TaskGenerate aSQLquerytoanswer[Q...
Natural Language Processing (NLP) is the subfield in computational linguistics that enables computers to understand, process, and analyze text. This book caters to the unmet demand for hands-on training of NLP concepts and provides exposure to real-world applications along with a solid theoretical ...