Introduction to Spacy-Legacy

The spacy-legacy library provides a collection of legacy features and APIs that have been retired in newer versions of Spacy but are still essential for certain applications. These legacy API functions help in maintaining older Spacy models and workflows without breaking compatibility. In this blog post, we will delve into understanding and using various APIs offered by spacy-legacy with code snippets and a complete app example.

Example APIs and Their Usage

1. Legacy Pipeline Components

spacy-legacy provides pipeline components that were deprecated in newer versions but can still be used for backward compatibility.

  import spacy
  from spacy_legacy import ArcEagerParser

  nlp = spacy.blank("en")
  arc_eager_parser = ArcEagerParser(nlp.vocab)
  nlp.add_pipe(arc_eager_parser, last=True)

2. Legacy Serialization Methods

Serialization methods for old models help in saving and loading models that are no longer supported by the current Spacy version.

  from spacy_legacy import serialize, deserialize

  # Example model
  model = {"config": {...}}

  # Serialize the model
  bytes_data = serialize(model)

  # Deserialize the model
  loaded_model = deserialize(bytes_data)

3. Legacy Tokenizer

Support for older tokenizer configurations.

  from spacy_legacy import V1Tokenizer

  vocab = spacy.blank("en").vocab
  tokenizer = V1Tokenizer(vocab)
  
  doc = tokenizer("This is a sample text.")
  for token in doc:
      print(token.text)

Application Example Using Spacy-Legacy

Below is a complete app example that demonstrates the use of multiple legacy components in a single workflow.

  import spacy
  from spacy_legacy import ArcEagerParser, BILOU, serialize, deserialize

  # Example application using spacy-legacy
  nlp = spacy.blank("en")

  # Add legacy arc eager parser
  arc_eager_parser = ArcEagerParser(nlp.vocab)
  nlp.add_pipe(arc_eager_parser, last=True)

  # Use legacy BILOU tagging
  tokens = ["This", "is", "a", "sample", "text", "."]
  bilou_tags = ["B", "I", "L", "U", "O", "O"]
  bilou = BILOU(tokens, bilou_tags)

  # Serialize the model pipeline
  model = {"config": {...}, "pipeline": nlp.pipe_names}
  serialized_model = serialize(model)

  # Deserialize the model
  loaded_model = deserialize(serialized_model)

  # Example doc
  doc = nlp("This is a new example.")
  for token in doc:
      print(token.text, token.dep_)

As demonstrated, the spacy-legacy API allows for the seamless integration of older pipelines and models into current workflows, ensuring that your NLP projects remain functional and effective.

Hash: b1fc719f385ed79511cc24799a196cac8c421697efea5806230000bfa336fec9