Welcome to the Comprehensive Guide to Spacy-Loggers

Spacy-loggers provide a powerful and extensible way to monitor and log information about your spaCy models and pipelines. This guide will walk you through various APIs offered by `spacy-loggers` and give code examples to illustrate their use.

Getting Started with Spacy-Loggers

To get started, first, make sure you have installed the `spacy-loggers` package:

  
    pip install spacy-loggers
  

Basic Logging Functions

Spacy-loggers offer the following basic logging functions:

  
    import spacy
    from spacy_loggers import logger

    nlp = spacy.load("en_core_web_sm")
    logger.set_logger_level("INFO")

    logger.info("This is an info log.")
    logger.warn("This is a warning log.")
    logger.error("This is an error log.")
    logger.debug("This is a debug log.")
  

Using Loggers with Spacy Pipelines

Incorporate logging within your spaCy pipelines to monitor specific stages of your NLP processing. Here’s an example:

  
    from spacy.language import Language
    from spacy_loggers.pipeline_logger import PipelineLogger

    nlp = spacy.load("en_core_web_sm")
    pipeline_logger = PipelineLogger(nlp)

    @Language.component("custom_component")
    def custom_component(doc):
        logger.info(f"Processing document: {doc.text}")
        return doc

    nlp.add_pipe("custom_component", last=True)
    text = "SpaCy is an amazing NLP library!"
    doc = nlp(text)
  

Advanced Usage: Metrics Logging

Track custom metrics during your NLP processes using spacy-loggers. Here’s how you can set this up:

  
    from spacy_loggers.metrics_logger import MetricsLogger

    nlp = spacy.load("en_core_web_sm")
    metrics_logger = MetricsLogger(nlp)

    @Language.component("metrics_component")
    def metrics_component(doc):
        metrics_logger.log_metric("doc_length", len(doc))
        return doc

    nlp.add_pipe("metrics_component", last=True)
    text = "Tracking metrics is essential in NLP processing."
    doc = nlp(text)
    metrics_logger.export_metrics("metrics.json")
  

Example Application with Spacy-Loggers

Here’s a complete example of an application that uses spacy-loggers for logging and monitoring:

  
    import spacy 
    from spacy.language import Language
    from spacy_loggers import logger
    from spacy_loggers.pipeline_logger import PipelineLogger
    from spacy_loggers.metrics_logger import MetricsLogger

    # Load Spacy model and initialize loggers
    nlp = spacy.load("en_core_web_sm")
    pipeline_logger = PipelineLogger(nlp)
    metrics_logger = MetricsLogger(nlp)

    # Log basic messages
    logger.info("Starting the NLP application.")

    # Custom spaCy component for logging and metrics
    @Language.component("log_metrics_component")
    def log_metrics_component(doc):
        logger.info(f"Processing document: {doc.text}")
        metrics_logger.log_metric("token_count", len(doc))
        return doc

    # Adding custom component to pipeline
    nlp.add_pipe("log_metrics_component", last=True)

    # Document to process
    text = "Spacy-loggers provide efficient logging and monitoring for NLP models."
    doc = nlp(text)

    # Export metrics to JSON
    metrics_logger.export_metrics("app_metrics.json")
    logger.info("NLP application processing completed.")
  

With these examples, you can now effectively utilize spacy-loggers to monitor and log various aspects of your NLP pipelines, ensuring smoother debugging and better performance tracking.

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