nlp-basics

Preprocess unstructured text for transformer models by cleaning, normalizing, and tokenizing input to ensure structured representations. This skill handles text normalization and tokenization to prepare raw text for downstream NLP tasks.

Generate embeddings for semantic search using transformer models to create semantic vector representations. This skill supports configurable model selection to produce embeddings directly applicable to search and classification tasks.

Configure model selection and logging for NLP pipelines using assets and defined parameters. This skill ensures reproducible pipelines through configurable settings, retry logic, and observability features validated by scripts.

This text processing tool supports named-entity recognition alongside semantic search and basic classification. It provides ready-to-use patterns for transformer-based analysis to extract structured insights from unstructured text.

You need pyyaml to run these text processing pipelines because the skill uses it to manage configurable parameters. Configuration files rely on this dependency to ensure reliable and reproducible NLP workflows.

NLP preprocessing pipelines lack reproducibility without defined parameters and validation. This skill solves that by using configurable assets and validation scripts to ensure consistent, observable text transformations across runs.