Learn SPACY with Real Code Examples

Use nlp.pipe for batch processing of texts

Disable unused pipeline components for speed

Leverage GPU acceleration for vector-heavy tasks

Minimize repeated model loading

Profile pipeline steps to identify bottlenecks

Sanitize input text to prevent code injection

Avoid processing untrusted large text files in memory

Use secure storage for custom trained models

Validate entity recognition outputs before use

Protect sensitive data in logs and analytics pipelines

Track processing speed and memory usage

Validate entity recognition and tagging accuracy

Log NLP pipeline outputs for QA

Compare performance of models and pipelines

Visualize errors and distribution of predictions

Write modular pipeline components

Document preprocessing and annotation steps

Use type hints for functions

Test pipelines with unit tests

Maintain reproducibility of NLP workflows