Load a model: nlp = spacy.load('en_core_web_sm')

Tokenize text: doc = nlp('Hello world!')

Extract named entities: [(ent.text, ent.label_) for ent in doc.ents]

Part-of-speech tagging: [(token.text, token.pos_) for token in doc]

Custom rule matching using Matcher or PhraseMatcher

Ensure correct model is downloaded and loaded

Check language model compatibility with spaCy version

Handle Unicode and encoding issues in text

Ensure custom components are added correctly to pipeline

Optimize memory usage for large text corpora

Verify tokenization matches expectations

Check named entity recognition accuracy

Validate syntactic dependencies

Benchmark processing speed for large corpora

Ensure pipeline reproducibility with unit tests

Local scripts or notebooks

ETL pipelines for text preprocessing

Integration with web services or chatbots

Cloud NLP pipelines using Docker or Kubernetes

Embedding in ML inference pipelines

scikit-learn for ML pipelines

TensorFlow/PyTorch for custom NLP models

Textacy for advanced NLP tasks

Prodigy for data annotation

Thinc for neural network components

Integrate with ML pipelines via scikit-learn or PyTorch

Use custom token vectors for similarity tasks

Rule-based matching for extraction

NER training with custom datasets

Export processed data for visualization or analytics

Use pre-trained models for common tasks

Batch process large text corpora with nlp.pipe

Disable unused pipeline components for speed

Document pipelines and preprocessing steps

Leverage custom components for reusable workflows

Process multilingual text efficiently

Handle ambiguous or noisy text data

Build accurate custom NER models

Integrate spaCy pipelines with ML/DL workflows

Deploy NLP pipelines at scale