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pythondatasetsactive-learningtext-annotationdatasetnatural-language-processingdata-labelingmachine-learningannotation-tool
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39 lines
1.3 KiB
39 lines
1.3 KiB
from typing import Type
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from auto_labeling_pipeline.labels import SequenceLabels, Seq2seqLabels, ClassificationLabels, Labels
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from auto_labeling_pipeline.mappings import MappingTemplate
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from auto_labeling_pipeline.models import RequestModelFactory
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from auto_labeling_pipeline.pipeline import pipeline
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from auto_labeling_pipeline.postprocessing import PostProcessor
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from .labels import create_labels
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from auto_labeling.models import AutoLabelingConfig
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def get_label_collection(task_type: str) -> Type[Labels]:
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return {
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'Category': ClassificationLabels,
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'Span': SequenceLabels,
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'Text': Seq2seqLabels
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}[task_type]
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def execute_pipeline(data: str, config: AutoLabelingConfig):
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label_collection = get_label_collection(config.task_type)
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model = RequestModelFactory.create(
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model_name=config.model_name,
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attributes=config.model_attrs
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)
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template = MappingTemplate(
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label_collection=label_collection,
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template=config.template
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)
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post_processor = PostProcessor(config.label_mapping)
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labels = pipeline(
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text=data,
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request_model=model,
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mapping_template=template,
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post_processing=post_processor
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)
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labels = create_labels(config.task_type, labels)
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return labels
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