Data Archive

spaCy

What is spaCy and how is it best used within Python?

spaCy is a fast, production-ready NLP library in Python, commonly used for tasks such as:

It is designed to work efficiently on large volumes of text and offers:

  • Pretrained pipelines (e.g., en_core_web_sm, en_core_web_trf)
  • Seamless integration with deep learning frameworks (e.g., PyTorch, Transformers via spacy-transformers)

Best practices for using spaCy:

  • Process documents as streams (e.g., use nlp.pipe with generators)
  • Avoid processing documents one-by-one unless debugging
  • Use spaCy’s DocBin for serialized storage of processed data
  • Combine with custom pipelines (e.g., text cleaning → spaCy → downstream classification)

Example:

import spacy
 
nlp = spacy.load("en_core_web_sm")
texts = ["This is sentence one.", "Here is another sentence."]
 
# Efficient [[batch processing]] using nlp.pipe
for doc in nlp.pipe(texts):
    print([ent.text for ent in doc.ents])

Exploratory Questions:

  • How does spaCy performance scale with document size and number of texts?
  • How can we integrate spaCy with a data pipeline (e.g., stream from disk/database)?
  • What are use cases where rule-based patterns outperform pretrained models?

Using Generators with spaCy

spaCy is designed to work well with generators—especially in nlp.pipe, which supports any iterable:

def text_stream(file_path):
    with open(file_path, "r") as f:
        for line in f:
            yield line.strip()
 
texts = text_stream("documents.txt")
for doc in nlp.pipe(texts, batch_size=32):
    yield doc  # or process and write to file

This approach is significantly more efficient than looping over nlp(text) one-by-one.

Exploratory Questions:

  • What are good strategies to pair nlp.pipe() with result-saving (e.g., JSONL or database)?
  • How do you monitor progress over long-running generators?
  • How do batch_size and n_process affect spaCy throughput?

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