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# coding: utf8
from __future__ import unicode_literals
import prodigy
from prodigy.components.loaders import JSONL
from prodigy.models.textcat import TextClassifier
from prodigy.models.matcher import PatternMatcher
from prodigy.components.sorters import prefer_uncertain
from prodigy.util import combine_models, split_string
import spacy
# Recipe decorator with argument annotations: (description, argument type,
# shortcut, type / converter function called on value before it's passed to
# the function). Descriptions are also shown when typing --help.
dataset=("The dataset to use", "positional", None, str),
spacy_model=("The base model", "positional", None, str),
source=("The source data as a JSONL file", "positional", None, str),
label=("One or more comma-separated labels", "option", "l", split_string),
patterns=("Optional match patterns", "option", "p", str),
exclude=("Names of datasets to exclude", "option", "e", split_string),
long_text=("Enable long-text classification mode", "flag", "L", bool)
def textcat_teach(dataset, spacy_model, source, label=None, patterns=None,
exclude=None, long_text=False):
Collect the best possible training data for a text classification model
with the model in the loop. Based on your annotations, Prodigy will decide
which questions to ask next.
# Load the stream from a JSONL file and return a generator that yields a
# dictionary for each example in the data.
stream = JSONL(source)
# Load the spaCy model
nlp = spacy.load(spacy_model)
# Initialize Prodigy's text classifier model, which outputs
# (score, example) tuples
model = TextClassifier(nlp, label, long_text=long_text)
if patterns is None:
# No patterns are used, so just use the model to suggest examples
# and only use the model's update method as the update callback
predict = model
update = model.update
# Initialize the pattern matcher and load in the JSONL patterns.
# Set the matcher to not label the highlighted spans, only the text.
matcher = PatternMatcher(nlp, prior_correct=5., prior_incorrect=5.,
label_span=False, label_task=True)
matcher = matcher.from_disk(patterns)
# Combine the NER model and the matcher and interleave their
# suggestions and update both at the same time
predict, update = combine_models(model, matcher)
# Use the prefer_uncertain sorter to focus on suggestions that the model
# is most uncertain about (i.e. with a score closest to 0.5). The model
# yields (score, example) tuples and the sorter yields just the example
stream = prefer_uncertain(predict(stream))
return {
'view_id': 'classification', # Annotation interface to use
'dataset': dataset, # Name of dataset to save annotations
'stream': stream, # Incoming stream of examples
'update': update, # Update callback, called with batch of answers
'exclude': exclude, # List of dataset names to exclude
'config': { # Additional config settings, mostly for app UI
'lang': nlp.lang,
'label': ', '.join(label) if label is not None else 'n/a'
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