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pythonannotation-tooldatasetsactive-learningtext-annotationdatasetnatural-language-processingdata-labelingmachine-learning
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40 lines
1.4 KiB
40 lines
1.4 KiB
from ...models import (DOCUMENT_CLASSIFICATION, IMAGE_CLASSIFICATION, SEQ2SEQ,
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SEQUENCE_LABELING)
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from . import catalog, data, dataset, label
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def get_data_class(project_type: str):
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text_projects = [DOCUMENT_CLASSIFICATION, SEQUENCE_LABELING, SEQ2SEQ]
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if project_type in text_projects:
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return data.TextData
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else:
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return data.FileData
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def get_dataset_class(format: str):
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mapping = {
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catalog.TextFile.name: dataset.TextFileDataset,
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catalog.TextLine.name: dataset.TextLineDataset,
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catalog.CSV.name: dataset.CsvDataset,
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catalog.JSONL.name: dataset.JSONLDataset,
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catalog.JSON.name: dataset.JSONDataset,
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catalog.FastText.name: dataset.FastTextDataset,
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catalog.Excel.name: dataset.ExcelDataset,
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catalog.CoNLL.name: dataset.CoNLLDataset,
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catalog.ImageFile.name: dataset.FileBaseDataset,
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}
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if format not in mapping:
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ValueError(f'Invalid format: {format}')
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return mapping[format]
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def get_label_class(project_type: str):
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mapping = {
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DOCUMENT_CLASSIFICATION: label.CategoryLabel,
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SEQUENCE_LABELING: label.OffsetLabel,
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SEQ2SEQ: label.TextLabel,
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IMAGE_CLASSIFICATION: label.CategoryLabel,
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}
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if project_type not in mapping:
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ValueError(f'Invalid project type: {project_type}')
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return mapping[project_type]
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