Does this skill support GPU acceleration?

The skill utilizes PyTorch and Transformers patterns which can be configured to run on GPU (CUDA) for faster inference and training if the hardware is available.

Which transformer models are supported by the NLP Basics skill?

By default, the skill uses 'bert-base-uncased', but it is designed to be compatible with most models available via the Hugging Face Transformers library.

Can I use this skill to build a search engine?

Yes, it includes specific components for semantic search, allowing you to index documents and perform similarity-based queries using sentence embeddings.

What is the default token limit for text processing?

The skill defaults to a max_length of 512 tokens, which aligns with the architectural limits of standard BERT and RoBERTa models.

What preprocessing steps are included in the cleaning pipeline?

The provided preprocessor handles lowercasing, URL removal, special character stripping, tokenization, and lemmatization using NLTK.

NLP Basics

Name: NLP Basics
Author: pluginagentmarketplace

bypluginagentmarketplace

•

Data Science & ML

Simplifies text analysis and processing using modern NLP techniques including embeddings, tokenization, and transformer models.

About

The NLP Basics skill provides a comprehensive toolkit for implementing natural language processing workflows within Claude Code. It streamlines common tasks such as text preprocessing (cleaning and lemmatization), generating high-quality vector embeddings, performing sentiment analysis, and extracting named entities. Whether you are building a semantic search engine or fine-tuning transformer models like BERT, this skill provides standardized patterns and best practices to transform unstructured text into actionable, structured insights using industry-standard libraries like Hugging Face and NLTK.

Key Features

Pre-configured pipelines for Text Classification and Sentiment Analysis
Semantic search implementation using cosine similarity and vector indexing
Named Entity Recognition (NER) for identifying organizations, people, and locations
Automated text preprocessing including cleaning, tokenization, and lemmatization
1 GitHub stars
Support for static and contextual word and sentence embeddings

Use Cases

Developing automated sentiment analysis systems for processing customer feedback
Extracting key entities from unstructured reports and legal documents
Building a vector-based semantic search engine for large document collections

About

Key Features

Pre-configured pipelines for Text Classification and Sentiment Analysis
Semantic search implementation using cosine similarity and vector indexing
Named Entity Recognition (NER) for identifying organizations, people, and locations
Automated text preprocessing including cleaning, tokenization, and lemmatization
1 GitHub stars
Support for static and contextual word and sentence embeddings

Use Cases

Developing automated sentiment analysis systems for processing customer feedback
Extracting key entities from unstructured reports and legal documents
Building a vector-based semantic search engine for large document collections