Agent skill · research science · aj-geddes
Natural Language Processing
Build NLP applications using transformers library, BERT, GPT, text classification, named entity recognition, and sentiment analysis
Why this skill is useful
Includes executable scripts for text preprocessing and classification that enhance NLP capabilities beyond standard knowledge.
What it needs
Requires nltk, sklearn, torch, transformers installed locally. About 5k tokens when loaded. Last updated 2026-03-04. 307 stars on the source repository.
What this skill does
Natural Language Processing Overview This skill provides comprehensive tools for building NLP applications using modern transformers, BERT, GPT, and classical NLP techniques for text classification, named entity recognition, sentiment analysis, and more. When to Use Building text classification systems for sentiment analysis, topic categorization, or intent detection Extracting named entities (people, places, organizations) from unstructured text Implementing machine translation, text summarization, or question answering systems Processing and analyzing large volumes of textual data for insights Creating chatbots, virtual assistants, or conversational AI applications Fine-tuning pre-trained transformer models for domain-specific NLP tasks NLP Core Tasks Text Classification: Sentiment, topic, intent classification Named Entity Recognition: Identifying people, places, organizations Machine Translation: Text translation between languages Text Summarization: Extracting key information Question Answering: Finding answers in documents Text Generation: Generating coherent text Popular Models and Libraries Transformers: BERT, GPT, RoBERTa, T5 spaCy: Industrial NLP pipeline NLTK: Classic NLP toolkit Hugging Face: Pre-trained models hub PyTorch/TensorFlow: Deep learning frameworks Python Implementation Common NLP Tasks and Models Classification: DistilBERT, RoBERTa, ELECTRA NER: BioBERT, SciBERT, spaCy models Translation: MarianMT, M2M-100 Summarization: BART, Pegasus, T5 QA: BERT, RoBERTa, DeBERTa Text Preprocessing Pipeline 1. Lowercasing and cleaning 2. Tokenization 3. Stopword removal 4. Lemmatization/Stemming 5. Vectorization Best Practices Use pre-trained models when available Fine-tune on task-specific data Handle out-of-vocabulary words Batch process for efficiency Monitor for bias in models Deliverables Trained NLP model Text classification results Named entities extracted Performance metrics Visualization dashboard Inference API
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills aj-geddes/natural-language-processing