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pythondatasetsactive-learningtext-annotationdatasetnatural-language-processingdata-labelingmachine-learningannotation-tool
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61 lines
1.9 KiB
61 lines
1.9 KiB
import abc
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from typing import List, Type
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from auto_labeling_pipeline.labels import Labels
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from django.contrib.auth.models import User
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from examples.models import Example
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from label_types.models import CategoryType, LabelType, SpanType
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from labels.models import Category, Label, Span, TextLabel
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from projects.models import Project
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class LabelCollection(abc.ABC):
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label_type: Type[LabelType]
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model: Type[Label]
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def __init__(self, labels):
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self.labels = labels
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def transform(self, project: Project, example: Example, user: User) -> List[Label]:
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mapping = {c.text: c for c in self.label_type.objects.filter(project=project)}
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annotations = []
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for label in self.labels:
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if label["label"] not in mapping:
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continue
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label["example"] = example
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label["label"] = mapping[label["label"]]
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label["user"] = user
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annotations.append(self.model(**label))
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return annotations
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def save(self, project: Project, example: Example, user: User):
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labels = self.transform(project, example, user)
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labels = self.model.objects.filter_annotatable_labels(labels, project)
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self.model.objects.bulk_create(labels)
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class Categories(LabelCollection):
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label_type = CategoryType
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model = Category
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class Spans(LabelCollection):
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label_type = SpanType
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model = Span
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class Texts(LabelCollection):
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model = TextLabel
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def transform(self, project: Project, example: Example, user: User) -> List[Label]:
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annotations = []
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for label in self.labels:
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label["example"] = example
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label["user"] = user
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annotations.append(self.model(**label))
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return annotations
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def create_labels(task_type: str, labels: Labels) -> LabelCollection:
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return {"Category": Categories, "Span": Spans, "Text": Texts}[task_type](labels.dict())
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