spaCy

A spaCy pipeline starts with text converted into a Doc containing tokens and can add components for tagging, parsing, lemmatization, entity recognition or custom annotations. Components may depend on attributes produced earlier in the pipeline. Tokenization is a separate initial step with language-specific rules, while trained packages supply learned models and configuration. Rule-based matching can operate on token attributes and combine with statistical predictions. Spans and token offsets link results to source text. Competence includes understanding component order, language and model compatibility, and whether an output is a deterministic rule match or a learned prediction. The library's structured object model does not make all annotations equally reliable.

Choose a language package and enable only components needed for the task, while retaining their prerequisites. Inspect token boundaries, entity spans and other relevant annotations on representative documents. Write precise rules with reviewed positive and negative examples. Batch processing where appropriate and measure end-to-end throughput. When training or tuning, keep documents and related sources separate across splits and evaluate each output type with suitable metrics. Save configuration, rules and model versions together. The useful result is a reproducible pipeline whose structured outputs remain aligned with the source text and whose observed failures are documented for downstream users.

Language packages and pipeline components have specific coverage and dependencies. Tokenization changes can invalidate span annotations or rules. Statistical entities and parses can be wrong despite a well-formed Doc object, while broad rules can overmatch. Fast processing does not establish task accuracy. spaCy differs from a single language model and from a complete document-understanding application. Pin assets, inspect effective component order and evaluate the exact annotations and downstream decisions required by the application.