PybiberPipeline

pipeline.PybiberPipeline(
    nlp=None,
    model='en_core_web_sm',
    disable_ner=True,
    n_process=CONFIG.DEFAULT_N_PROCESS,
    batch_size=CONFIG.DEFAULT_BATCH_SIZE,
    show_progress=None,
)

End-to-end convenience wrapper for common pybiber workflows.

Parameters

Name	Type	Description	Default
nlp	Optional[Language]	Pre-loaded spaCy model. If None, the model named by model will be loaded lazily on first use.	None
model	str	Name of the spaCy model to load when nlp is None. Defaults to “en_core_web_sm”.	'en_core_web_sm'
disable_ner	bool	When True, disable spaCy’s NER component for speed and stability. Parser is still enabled and required. Defaults to True.	True
n_process	int	Number of processes to use for spaCy’s pipe.	CONFIG.DEFAULT_N_PROCESS
batch_size	int	Batch size for spaCy’s pipe.	CONFIG.DEFAULT_BATCH_SIZE
show_progress	Optional[bool]	Whether to show internal progress indicators when processing. If None, it is determined based on corpus size.	None

Methods

Name	Description
features	Compute Biber features from token-level parses.
from_folder	Read .txt files from a folder into a corpus DataFrame.
parse	Parse a corpus with spaCy using the configured settings.
run	Parse and compute features from an in-memory corpus DataFrame.
run_from_folder	Read, parse, and compute features from a folder of .txt files.
to_analyzer	Create a BiberAnalyzer from a Biber feature matrix.

features

pipeline.PybiberPipeline.features(tokens, normalize=True, force_ttr=False)

Compute Biber features from token-level parses.

from_folder

pipeline.PybiberPipeline.from_folder(directory, recursive=False)

Read .txt files from a folder into a corpus DataFrame.

parse

pipeline.PybiberPipeline.parse(corpus)

Parse a corpus with spaCy using the configured settings.

run

pipeline.PybiberPipeline.run(
    corpus,
    return_tokens=False,
    normalize=True,
    force_ttr=False,
)

Parse and compute features from an in-memory corpus DataFrame.

run_from_folder

pipeline.PybiberPipeline.run_from_folder(
    directory,
    recursive=False,
    return_tokens=False,
    normalize=True,
    force_ttr=False,
)

Read, parse, and compute features from a folder of .txt files.

to_analyzer

pipeline.PybiberPipeline.to_analyzer(biber_df)

Create a BiberAnalyzer from a Biber feature matrix.