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pythondatasetsactive-learningtext-annotationdatasetnatural-language-processingdata-labelingmachine-learningannotation-tool
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75 lines
2.7 KiB
75 lines
2.7 KiB
from typing import Dict, List, Type
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from django.db.models import QuerySet
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from . import catalog, formatters, labels, writers
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from .labels import Labels
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from data_export.models import ExportedExample
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from projects.models import (
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DOCUMENT_CLASSIFICATION,
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IMAGE_CLASSIFICATION,
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INTENT_DETECTION_AND_SLOT_FILLING,
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SEQ2SEQ,
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SEQUENCE_LABELING,
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SPEECH2TEXT,
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)
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def select_writer(file_format: str) -> Type[writers.Writer]:
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mapping = {
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catalog.CSV.name: writers.CsvWriter,
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catalog.JSON.name: writers.JsonWriter,
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catalog.JSONL.name: writers.JsonlWriter,
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# catalog.FastText.name: writers.FastTextWriter,
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}
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if file_format not in mapping:
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ValueError(f"Invalid format: {file_format}")
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return mapping[file_format]
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def select_formatter(project, file_format: str) -> List[Type[formatters.Formatter]]:
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use_relation = getattr(project, "use_relation", False)
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mapping: Dict[str, Dict[str, List[Type[formatters.Formatter]]]] = {
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DOCUMENT_CLASSIFICATION: {
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catalog.CSV.name: [formatters.JoinedCategoryFormatter],
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catalog.JSON.name: [formatters.ListedCategoryFormatter],
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catalog.JSONL.name: [formatters.ListedCategoryFormatter],
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},
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SEQUENCE_LABELING: {
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catalog.JSONL.name: [formatters.DictFormatter, formatters.DictFormatter]
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if use_relation
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else [formatters.TupledSpanFormatter]
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},
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SEQ2SEQ: {
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catalog.CSV.name: [formatters.JoinedCategoryFormatter],
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catalog.JSON.name: [formatters.ListedCategoryFormatter],
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catalog.JSONL.name: [formatters.ListedCategoryFormatter],
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},
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IMAGE_CLASSIFICATION: {
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catalog.JSONL.name: [formatters.ListedCategoryFormatter],
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},
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SPEECH2TEXT: {
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catalog.JSONL.name: [formatters.ListedCategoryFormatter],
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},
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INTENT_DETECTION_AND_SLOT_FILLING: {
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catalog.JSONL.name: [formatters.ListedCategoryFormatter, formatters.TupledSpanFormatter]
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},
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}
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return mapping[project.project_type][file_format]
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def select_label_collection(project):
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use_relation = getattr(project, "use_relation", False)
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mapping = {
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DOCUMENT_CLASSIFICATION: [labels.Categories],
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SEQUENCE_LABELING: [labels.Spans, labels.Relations] if use_relation else [labels.Spans],
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SEQ2SEQ: [labels.Texts],
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IMAGE_CLASSIFICATION: [labels.Categories],
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SPEECH2TEXT: [labels.Texts],
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INTENT_DETECTION_AND_SLOT_FILLING: [labels.Categories, labels.Spans],
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}
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return mapping[project.project_type]
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def create_labels(label_collection_class: Type[Labels], examples: QuerySet[ExportedExample], user=None):
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return label_collection_class(examples, user)
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