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# coding: utf8
from __future__ import unicode_literals
import prodigy
from prodigy.components.loaders import JSONL
from prodigy.util import split_string
from collections import Counter
# 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.
@prodigy.recipe('mark',
dataset=("The dataset to use", "positional", None, str),
source=("The source data as a JSONL file", "positional", None, str),
view_id=("ID of annotation interface", "option", "o", str),
exclude=("Names of datasets to exclude", "option", "e", split_string)
)
def mark(dataset, source, view_id, exclude=None):
"""
Click through pre-prepared examples, with no model in the loop.
"""
counts = Counter()
# Load the stream from a JSONL file and return a generator that yields a
# dictionary for each example in the data.
stream = JSONL(source)
def on_load(controller):
# Check if current dataset is available in database. The on_load
# callback receives the controller as an argument, which exposes the
# database via controller.db
if dataset in controller.db:
examples = controller.db.get_dataset(dataset)
for eg in examples:
# Update counts with existing answers
counts[eg['answer']] += 1
def receive_answers(answers):
for eg in answers:
# Update counts with new answers
counts[eg['answer']] += 1
def on_exit(controller):
# Output the total annotation counts
print('Accept:', counts['accept'])
print('Reject:', counts['reject'])
print('Ignore:', counts['ignore'])
print('Total: ', sum(counts.values()))
return {
'view_id': view_id, # Annotation interface to use
'dataset': dataset, # Name of dataset to save annotations
'stream': stream, # Incoming stream of examples
'update': receive_answers, # Update callback, called with answers
'on_load': on_load, # Called on first load
'on_exit': on_exit # Called when Prodigy server is stopped
}
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