Oracle Cloud Infrastructure-Dokumentation

Label Studio-Integration

Oracle Data Labeling Service ist veraltet. Wir empfehlen Ihnen, Ihre beschrifteten Datensätze zu Label Studio zu migrieren, einem Open-Source- und Marketplace-unterstützten Etikettierungstool.

Führen Sie die folgenden Schritte aus, um Data Labeling-Snapshot-Exporte in Label Studio-Import- und Label Studio-Raw-JSON-Exportformate zu konvertieren. Sie können diese Formate für weitere Anmerkungen in Label Studio oder direktes Modelltraining verwenden.

Workflows für benutzerdefiniertes NER oder benutzerdefiniertes TXTC beschriften

Kommentieren Sie ein neues Dataset in Label Studio.
  1. Laden Sie ein Dataset hoch, entweder eine Textdatei oder vorab gekennzeichnete Datasets, die aus Data Labeling konvertiert wurden.
  2. (Optional) Falls erforderlich, kommentieren Sie die Entity-Spans.
  3. Exportieren Sie die annotierten Daten.
    • Das Dataset wird im Raw-JSON-Format aus Label Studio exportiert.
    • Diese exportierten Daten können zur weiteren Verarbeitung in Object Storage hochgeladen werden.

Verwenden Sie die Daten im Modelltrainingsworkflow als Teil der Language-Pipeline.

Vorhandene Data Labeling-annotierte Daten zu Label Studio migrieren

Wenn Sie bereits Daten in Data Labeling mit Anmerkungen versehen haben, wird ein Migrationsskript bereitgestellt, um das vorhandene Dataset in ein Label Studio-kompatibles Format zu konvertieren.
  1. Exportieren Sie das Dataset aus Data Labeling im JSONL-Format in einen Objektspeicher-Bucket.
  2. Führen Sie das Konvertierungsskript aus.
    Ein Python-Skript verarbeitet das Dataset und konvertiert es in:
    • Label Studio-Importformat zur direkten Verwendung in Label Studio für weitere Anmerkungen.
    • Label Studio-Exportformat, strukturiert für die direkte Verwendung in Schulungsworkflows ohne weitere Änderungen.
  3. Laden Sie die konvertierte Importformatdatei für Annotationsaktualisierungen in Label Studio hoch.
  4. Exportieren Sie das aktualisierte Dataset im JSONL-Rawformat in den Objektspeicher.
Verwenden Sie die Daten für das Sprachtraining.

Datenformate

CNER

CNER-Daten können in folgenden Formaten gespeichert werden:
  • Datenlabelformat
  • Portable JSNOL-Format (selbstständiger Text)
  • Label Studio Raw-JSON-Exportformat

Datenlabeling-Format (Portable JSONL)

Der Hauptunterschied zwischen Data Labeling und Portable JSONL-Format besteht darin, wie der Text gespeichert wird:

• Portable JSONL: Der eigentliche Text ist in der gleichen Datei unter sourceDetails['text'] vorhanden.

• Datenlabeling-Format: Anstelle von Text wird in sourceDetails['path'] ein sourcePath angegeben, der auf einen externen Speicherort verweist, an dem der Text gespeichert ist.

Beispiele für verschiedene Formate:
CNER portables JSONL-Format
{"labelsSet": [{"name": "PER"}, {"name": "MISC"}, {"name": "LOC"}, {"name": "ORG"}], "annotationFormat": "ENTITY_EXTRACTION", "datasetFormatDetails": {"formatType": "TEXT"}}
{"sourceDetails": {"text": "His current band , Gigi , was formed in 1994 with Baron ( guitar ) , Thomas ( bass ) , Armand ( vocal ) and Ronald ( drum ) ."}, "annotations": [{"entities": [{"entityType": "TEXTSELECTION", "labels": [{"label_name": "ORG"}], "textSpan": {"offset": 19, "length": 4}}, {"entityType": "TEXTSELECTION", "labels": [{"label_name": "PER"}], "textSpan": {"offset": 50, "length": 5}}, {"entityType": "TEXTSELECTION", "labels": [{"label_name": "PER"}], "textSpan": {"offset": 69, "length": 6}}, {"entityType": "TEXTSELECTION", "labels": [{"label_name": "PER"}], "textSpan": {"offset": 87, "length": 6}}, {"entityType": "TEXTSELECTION", "labels": [{"label_name": "PER"}], "textSpan": {"offset": 108, "length": 6}}]}]}
{"sourceDetails": {"text": "Since then , Gigi has released six albums ."}, "annotations": [{"entities": [{"entityType": "TEXTSELECTION", "labels": [{"label_name": "ORG"}], "textSpan": {"offset": 13, "length": 4}}]}]}
{"sourceDetails": {"text": "He has also released his own solo albums : Nusa Damai ; Gitarku ; Samsara ; and Home , a tribute album to the December 2004 tsunami victims ."}, "annotations": [{"entities": [{"entityType": "TEXTSELECTION", "labels": [{"label_name": "MISC"}], "textSpan": {"offset": 43, "length": 10}}, {"entityType": "TEXTSELECTION", "labels": [{"label_name": "MISC"}], "textSpan": {"offset": 56, "length": 7}}, {"entityType": "TEXTSELECTION", "labels": [{"label_name": "MISC"}], "textSpan": {"offset": 66, "length": 7}}, {"entityType": "TEXTSELECTION", "labels": [{"label_name": "MISC"}], "textSpan": {"offset": 80, "length": 4}}]}]}
{"sourceDetails": {"text": "Since Dewa Budjana started his professional career as a musician , he has mainly used a Parker Fly Delux as his main guitar , occasionally using Klein and Gibson guitars SG series instead ."}, "annotations": [{"entities": [{"entityType": "TEXTSELECTION", "labels": [{"label_name": "PER"}], "textSpan": {"offset": 6, "length": 12}}, {"entityType": "TEXTSELECTION", "labels": [{"label_name": "MISC"}], "textSpan": {"offset": 88, "length": 16}}, {"entityType": "TEXTSELECTION", "labels": [{"label_name": "ORG"}], "textSpan": {"offset": 145, "length": 16}}]}]}
{"sourceDetails": {"text": "Budjana also owns a double neck Klein guitar which was used on his latest album , Home , and pictured on the CD cover ."}, "annotations": [{"entities": [{"entityType": "TEXTSELECTION", "labels": [{"label_name": "PER"}], "textSpan": {"offset": 0, "length": 7}}, {"entityType": "TEXTSELECTION", "labels": [{"label_name": "ORG"}], "textSpan": {"offset": 32, "length": 5}}, {"entityType": "TEXTSELECTION", "labels": [{"label_name": "MISC"}], "textSpan": {"offset": 82, "length": 4}}]}]}
Label Studio-Importformat
[{'data': {'text': 'His current band , Gigi , was formed in 1994 with Baron ( guitar ) , Thomas ( bass ) , Armand ( vocal ) and Ronald ( drum ) .'},
  'predictions': [{'result': [{'value': {'start': 19,
       'end': 23,
       'text': 'Gigi',
       'labels': ['ORG']},
      'from_name': 'label',
      'to_name': 'text',
      'type': 'labels'},
     {'value': {'start': 50, 'end': 55, 'text': 'Baron', 'labels': ['PER']},
      'from_name': 'label',
      'to_name': 'text',
      'type': 'labels'},
     {'value': {'start': 69, 'end': 75, 'text': 'Thomas', 'labels': ['PER']},
      'from_name': 'label',
      'to_name': 'text',
      'type': 'labels'},
     {'value': {'start': 87, 'end': 93, 'text': 'Armand', 'labels': ['PER']},
      'from_name': 'label',
      'to_name': 'text',
      'type': 'labels'},
     {'value': {'start': 108, 'end': 114, 'text': 'Ronald', 'labels': ['PER']},
      'from_name': 'label',
      'to_name': 'text',
      'type': 'labels'}]}]},
 {'data': {'text': 'Since then , Gigi has released six albums .'},
  'predictions': [{'result': [{'value': {'start': 13,
       'end': 17,
       'text': 'Gigi',
       'labels': ['ORG']},
      'from_name': 'label',
      'to_name': 'text',
      'type': 'labels'}]}]},
 {'data': {'text': 'He has also released his own solo albums : Nusa Damai ; Gitarku ; Samsara ; and Home , a tribute album to the December 2004 tsunami victims .'},
  'predictions': [{'result': [{'value': {'start': 43,
       'end': 53,
       'text': 'Nusa Damai',
       'labels': ['MISC']},
      'from_name': 'label',
      'to_name': 'text',
      'type': 'labels'},
     {'value': {'start': 56, 'end': 63, 'text': 'Gitarku', 'labels': ['MISC']},
      'from_name': 'label',
      'to_name': 'text',
      'type': 'labels'},
     {'value': {'start': 66, 'end': 73, 'text': 'Samsara', 'labels': ['MISC']},
      'from_name': 'label',
      'to_name': 'text',
      'type': 'labels'},
     {'value': {'start': 80, 'end': 84, 'text': 'Home', 'labels': ['MISC']},
      'from_name': 'label',
      'to_name': 'text',
      'type': 'labels'}]}]},
 {'data': {'text': 'Since Dewa Budjana started his professional career as a musician , he has mainly used a Parker Fly Delux as his main guitar , occasionally using Klein and Gibson guitars SG series instead .'},
  'predictions': [{'result': [{'value': {'start': 6,
       'end': 18,
       'text': 'Dewa Budjana',
       'labels': ['PER']},
      'from_name': 'label',
      'to_name': 'text',
      'type': 'labels'},
     {'value': {'start': 88,
       'end': 104,
       'text': 'Parker Fly Delux',
       'labels': ['MISC']},
      'from_name': 'label',
      'to_name': 'text',
      'type': 'labels'},
     {'value': {'start': 145,
       'end': 161,
       'text': 'Klein and Gibson',
       'labels': ['ORG']},
      'from_name': 'label',
      'to_name': 'text',
      'type': 'labels'}]}]}]
Label Studio-Exportformat (Raw-JSON)
[{'id': 141,
  'annotations': [{'id': 42,
    'completed_by': 4,
    'result': [{'value': {'start': 19,
       'end': 23,
       'text': 'Gigi',
       'labels': ['ORG']},
      'id': '467b95b0',
      'from_name': 'label',
      'to_name': 'text',
      'type': 'labels',
      'origin': 'prediction'},
     {'value': {'start': 50, 'end': 55, 'text': 'Baron', 'labels': ['PER']},
      'id': '8f12fa43',
      'from_name': 'label',
      'to_name': 'text',
      'type': 'labels',
      'origin': 'prediction'},
     {'value': {'start': 69, 'end': 75, 'text': 'Thomas', 'labels': ['PER']},
      'id': '936f63f0',
      'from_name': 'label',
      'to_name': 'text',
      'type': 'labels',
      'origin': 'prediction'},
     {'value': {'start': 87, 'end': 93, 'text': 'Armand', 'labels': ['PER']},
      'id': 'f02d2509',
      'from_name': 'label',
      'to_name': 'text',
      'type': 'labels',
      'origin': 'prediction'},
     {'value': {'start': 108, 'end': 114, 'text': 'Ronald', 'labels': ['PER']},
      'id': 'cb479df1',
      'from_name': 'label',
      'to_name': 'text',
      'type': 'labels',
      'origin': 'prediction'}],
    'was_cancelled': False,
    'ground_truth': False,
    'created_at': '2025-01-19T16:47:10.630009Z',
    'updated_at': '2025-01-19T16:47:10.630009Z',
    'draft_created_at': None,
    'lead_time': 0,
    'prediction': {'id': 95,
     'result': [{'value': {'start': 19,
        'end': 23,
        'text': 'Gigi',
        'labels': ['ORG']},
       'id': '467b95b0',
       'from_name': 'label',
       'to_name': 'text',
       'type': 'labels',
       'origin': 'prediction'},
      {'value': {'start': 50, 'end': 55, 'text': 'Baron', 'labels': ['PER']},
       'id': '8f12fa43',
       'from_name': 'label',
       'to_name': 'text',
       'type': 'labels',
       'origin': 'prediction'},
      {'value': {'start': 69, 'end': 75, 'text': 'Thomas', 'labels': ['PER']},
       'id': '936f63f0',
       'from_name': 'label',
       'to_name': 'text',
       'type': 'labels',
       'origin': 'prediction'},
      {'value': {'start': 87, 'end': 93, 'text': 'Armand', 'labels': ['PER']},
       'id': 'f02d2509',
       'from_name': 'label',
       'to_name': 'text',
       'type': 'labels',
       'origin': 'prediction'},
      {'value': {'start': 108,
        'end': 114,
        'text': 'Ronald',
        'labels': ['PER']},
       'id': 'cb479df1',
       'from_name': 'label',
       'to_name': 'text',
       'type': 'labels',
       'origin': 'prediction'}],
     'model_version': 'undefined',
     'created_ago': '0\xa0minutes',
     'score': None,
     'cluster': None,
     'neighbors': None,
     'mislabeling': 0.0,
     'created_at': '2025-01-19T16:47:10.630009Z',
     'updated_at': '2025-01-19T16:47:10.630009Z',
     'model': None,
     'model_run': None,
     'task': 141,
     'project': 14},
    'result_count': 5,
    'unique_id': '54a60d2b-6960-48c9-baae-bf24b15ce273',
    'import_id': None,
    'last_action': None,
    'task': 141,
    'project': 14,
    'updated_by': 4,
    'parent_prediction': 95,
    'parent_annotation': None,
    'last_created_by': None}],
  'file_upload': 'example_file.json',
  'drafts': [],
  'predictions': [95],
  'data': {'text': 'His current band , Gigi , was formed in 1994 with Baron ( guitar ) , Thomas ( bass ) , Armand ( vocal ) and Ronald ( drum ) .'},
  'meta': {},
  'created_at': '2025-01-19T16:47:10.630009Z',
  'updated_at': '2025-01-19T16:47:10.630009Z',
  'inner_id': 1,
  'total_annotations': 1,
  'cancelled_annotations': 0,
  'total_predictions': 1,
  'comment_count': 0,
  'unresolved_comment_count': 0,
  'last_comment_updated_at': None,
  'project': 14,
  'updated_by': 4,
  'comment_authors': []},
 {'id': 141,
  'annotations': [{'id': 60,
    'completed_by': 4,
    'result': [{'value': {'start': 13,
       'end': 17,
       'text': 'Gigi',
       'labels': ['ORG']},
      'id': 'ce521973',
      'from_name': 'label',
      'to_name': 'text',
      'type': 'labels',
      'origin': 'prediction'}],
    'was_cancelled': False,
    'ground_truth': False,
    'created_at': '2025-01-19T16:47:10.630104Z',
    'updated_at': '2025-01-19T16:47:10.630104Z',
    'draft_created_at': None,
    'lead_time': 0,
    'prediction': {'id': 95,
     'result': [{'value': {'start': 13,
        'end': 17,
        'text': 'Gigi',
        'labels': ['ORG']},
       'id': 'ce521973',
       'from_name': 'label',
       'to_name': 'text',
       'type': 'labels',
       'origin': 'prediction'}],
     'model_version': 'undefined',
     'created_ago': '0\xa0minutes',
     'score': None,
     'cluster': None,
     'neighbors': None,
     'mislabeling': 0.0,
     'created_at': '2025-01-19T16:47:10.630104Z',
     'updated_at': '2025-01-19T16:47:10.630104Z',
     'model': None,
     'model_run': None,
     'task': 141,
     'project': 14},
    'result_count': 1,
    'unique_id': '4a4b5631-4b51-4a09-99c4-ad8927074503',
    'import_id': None,
    'last_action': None,
    'task': 141,
    'project': 14,
    'updated_by': 4,
    'parent_prediction': 95,
    'parent_annotation': None,
    'last_created_by': None}],
  'file_upload': 'example_file.json',
  'drafts': [],
  'predictions': [95],
  'data': {'text': 'Since then , Gigi has released six albums .'},
  'meta': {},
  'created_at': '2025-01-19T16:47:10.630104Z',
  'updated_at': '2025-01-19T16:47:10.630104Z',
  'inner_id': 1,
  'total_annotations': 1,
  'cancelled_annotations': 0,
  'total_predictions': 1,
  'comment_count': 0,
  'unresolved_comment_count': 0,
  'last_comment_updated_at': None,
  'project': 14,
  'updated_by': 4,
  'comment_authors': []},
 {'id': 141,
  'annotations': [{'id': 96,
    'completed_by': 4,
    'result': [{'value': {'start': 43,
       'end': 53,
       'text': 'Nusa Damai',
       'labels': ['MISC']},
      'id': 'a013849e',
      'from_name': 'label',
      'to_name': 'text',
      'type': 'labels',
      'origin': 'prediction'},
     {'value': {'start': 56, 'end': 63, 'text': 'Gitarku', 'labels': ['MISC']},
      'id': '57423aa5',
      'from_name': 'label',
      'to_name': 'text',
      'type': 'labels',
      'origin': 'prediction'},
     {'value': {'start': 66, 'end': 73, 'text': 'Samsara', 'labels': ['MISC']},
      'id': 'eeee84bb',
      'from_name': 'label',
      'to_name': 'text',
      'type': 'labels',
      'origin': 'prediction'},
     {'value': {'start': 80, 'end': 84, 'text': 'Home', 'labels': ['MISC']},
      'id': '3df96d57',
      'from_name': 'label',
      'to_name': 'text',
      'type': 'labels',
      'origin': 'prediction'}],
    'was_cancelled': False,
    'ground_truth': False,
    'created_at': '2025-01-19T16:47:10.630126Z',
    'updated_at': '2025-01-19T16:47:10.630126Z',
    'draft_created_at': None,
    'lead_time': 0,
    'prediction': {'id': 95,
     'result': [{'value': {'start': 43,
        'end': 53,
        'text': 'Nusa Damai',
        'labels': ['MISC']},
       'id': 'a013849e',
       'from_name': 'label',
       'to_name': 'text',
       'type': 'labels',
       'origin': 'prediction'},
      {'value': {'start': 56,
        'end': 63,
        'text': 'Gitarku',
        'labels': ['MISC']},
       'id': '57423aa5',
       'from_name': 'label',
       'to_name': 'text',
       'type': 'labels',
       'origin': 'prediction'},
      {'value': {'start': 66,
        'end': 73,
        'text': 'Samsara',
        'labels': ['MISC']},
       'id': 'eeee84bb',
       'from_name': 'label',
       'to_name': 'text',
       'type': 'labels',
       'origin': 'prediction'},
      {'value': {'start': 80, 'end': 84, 'text': 'Home', 'labels': ['MISC']},
       'id': '3df96d57',
       'from_name': 'label',
       'to_name': 'text',
       'type': 'labels',
       'origin': 'prediction'}],
     'model_version': 'undefined',
     'created_ago': '0\xa0minutes',
     'score': None,
     'cluster': None,
     'neighbors': None,
     'mislabeling': 0.0,
     'created_at': '2025-01-19T16:47:10.630126Z',
     'updated_at': '2025-01-19T16:47:10.630126Z',
     'model': None,
     'model_run': None,
     'task': 141,
     'project': 14},
    'result_count': 4,
    'unique_id': 'd2b8ec9a-cc62-4881-a42b-833546df7953',
    'import_id': None,
    'last_action': None,
    'task': 141,
    'project': 14,
    'updated_by': 4,
    'parent_prediction': 95,
    'parent_annotation': None,
    'last_created_by': None}],
  'file_upload': 'example_file.json',
  'drafts': [],
  'predictions': [95],
  'data': {'text': 'He has also released his own solo albums : Nusa Damai ; Gitarku ; Samsara ; and Home , a tribute album to the December 2004 tsunami victims .'},
  'meta': {},
  'created_at': '2025-01-19T16:47:10.630126Z',
  'updated_at': '2025-01-19T16:47:10.630126Z',
  'inner_id': 1,
  'total_annotations': 1,
  'cancelled_annotations': 0,
  'total_predictions': 1,
  'comment_count': 0,
  'unresolved_comment_count': 0,
  'last_comment_updated_at': None,
  'project': 14,
  'updated_by': 4,
  'comment_authors': []},
 {'id': 141,
  'annotations': [{'id': 7,
    'completed_by': 4,
    'result': [{'value': {'start': 6,
       'end': 18,
       'text': 'Dewa Budjana',
       'labels': ['PER']},
      'id': '2d675f56',
      'from_name': 'label',
      'to_name': 'text',
      'type': 'labels',
      'origin': 'prediction'},
     {'value': {'start': 88,
       'end': 104,
       'text': 'Parker Fly Delux',
       'labels': ['MISC']},
      'id': '13f6536b',
      'from_name': 'label',
      'to_name': 'text',
      'type': 'labels',
      'origin': 'prediction'},
     {'value': {'start': 145,
       'end': 161,
       'text': 'Klein and Gibson',
       'labels': ['ORG']},
      'id': '283b9e25',
      'from_name': 'label',
      'to_name': 'text',
      'type': 'labels',
      'origin': 'prediction'}],
    'was_cancelled': False,
    'ground_truth': False,
    'created_at': '2025-01-19T16:47:10.630165Z',
    'updated_at': '2025-01-19T16:47:10.630165Z',
    'draft_created_at': None,
    'lead_time': 0,
    'prediction': {'id': 95,
     'result': [{'value': {'start': 6,
        'end': 18,
        'text': 'Dewa Budjana',
        'labels': ['PER']},
       'id': '2d675f56',
       'from_name': 'label',
       'to_name': 'text',
       'type': 'labels',
       'origin': 'prediction'},
      {'value': {'start': 88,
        'end': 104,
        'text': 'Parker Fly Delux',
        'labels': ['MISC']},
       'id': '13f6536b',
       'from_name': 'label',
       'to_name': 'text',
       'type': 'labels',
       'origin': 'prediction'},
      {'value': {'start': 145,
        'end': 161,
        'text': 'Klein and Gibson',
        'labels': ['ORG']},
       'id': '283b9e25',
       'from_name': 'label',
       'to_name': 'text',
       'type': 'labels',
       'origin': 'prediction'}],
     'model_version': 'undefined',
     'created_ago': '0\xa0minutes',
     'score': None,
     'cluster': None,
     'neighbors': None,
     'mislabeling': 0.0,
     'created_at': '2025-01-19T16:47:10.630165Z',
     'updated_at': '2025-01-19T16:47:10.630165Z',
     'model': None,
     'model_run': None,
     'task': 141,
     'project': 14},
    'result_count': 3,
    'unique_id': '21115ec1-4a96-43c4-b1ec-1734cd004160',
    'import_id': None,
    'last_action': None,
    'task': 141,
    'project': 14,
    'updated_by': 4,
    'parent_prediction': 95,
    'parent_annotation': None,
    'last_created_by': None}],
  'file_upload': 'example_file.json',
  'drafts': [],
  'predictions': [95],
  'data': {'text': 'Since Dewa Budjana started his professional career as a musician , he has mainly used a Parker Fly Delux as his main guitar , occasionally using Klein and Gibson guitars SG series instead .'},
  'meta': {},
  'created_at': '2025-01-19T16:47:10.630165Z',
  'updated_at': '2025-01-19T16:47:10.630165Z',
  'inner_id': 1,
  'total_annotations': 1,
  'cancelled_annotations': 0,
  'total_predictions': 1,
  'comment_count': 0,
  'unresolved_comment_count': 0,
  'last_comment_updated_at': None,
  'project': 14,
  'updated_by': 4,
  'comment_authors': []}]

Benutzerdefiniertes TXTC (CTXTC) - Textklassifizierung

CTXTC-Datasets unterstützen verschiedene Formate für Flexibilität bei der Annotation und Verarbeitung:
  • Datenlabelformat
  • CSV-Format
  • Label Studio-Format
Datenlabelformat
CTXTC portablejsonl-Format
CSV-Format
CTXTC CSV-Format - EINZELTABEL
CTXTC CSV-Format - MULTI LABEL
Label Studio-Importformat
CTXTC CSV-Format - EINZELTABEL
CTXTC CSV-Format - MULTI LABEL
[{'data': {'text': "I need to book a hotel in the east that has 4 stars. I can help you with that. What is your price range? That doesn't matter as long as it has free wifi and parking. If you'd like something cheap, I recommend the Allenbell. For something moderately priced, I would recommend the Warkworth House. Could you book the Wartworth for one night, 1 person? What day will you be staying? Friday and Can you book it for me and get a reference number ? Booking was successful. Reference number is : BMUKPTG6.  Can I help you with anything else today? I am looking to book a train that is leaving from Cambridge to Bishops Stortford on Friday. There are a number of trains leaving throughout the day.  What time would you like to travel? I want to get there by 19:45 at the latest. Okay! The latest train you can take leaves at 17:29, and arrives by 18:07. Would you like for me to book that for you? Yes please. I also need the travel time, departure time, and price. Reference number is : UIFV8FAS. The price is 10.1 GBP and the trip will take about 38 minutes. May I be of any other assistance? Yes. Sorry, but suddenly my plans changed. Can you change the Wartworth booking to Monday for 3 people and 4 nights? I have made that change and your reference number is YF86GE4J. Thank you very much, goodbye. You're welcome. Have a nice day!"},
  'predictions': [{'model_version': '1.3',
    'result': [{'id': '0',
      'from_name': 'textClassification',
      'to_name': 'text',
      'type': 'choices',
      'value': {'choices': ['hotel', 'train']}}]}]},
 {'data': {'text': 'Howdy, I need a train heading into cambridge. I would be happy to help you find a train.  Where are you departing from? I am departing from norwich.  I need to leave after 18:45 on Wednesday. I have several options for you. Where is your destination? I will be heading to cabridge. The earliest after 18:45 is the TR8658, leaving Norwich at 19:16. Can I reserve you one or more seats on this train? yeah, i need one ticket. Booking was successful, the total fee is 17.6 GBP payable at the station. Your reference number is AXH1NM1I. Do you need assistance with anything else? I am also looking for a multi sport in the East. It looks like there is The Cherry Hinton Village Centre.  Can I get you more information about it? I would like to get the phone number, please. their phone number is 01223576412. anything else? Oh, and what is their postcode, please? Sure, the postcode is cb19ej. Can I help you find any other information about Cambridge? That is all for now thank you. enjoy your time in Cambridge!'},
  'predictions': [{'model_version': '1.3',
    'result': [{'id': '1',
      'from_name': 'textClassification',
      'to_name': 'text',
      'type': 'choices',
      'value': {'choices': ['attraction', 'train']}}]}]},
 {'data': {'text': "What can you tell me about the Riverside Brasserie? It is a restaurant that serves modern european food near the centre of town. It is moderately priced. The phone number is 01223259988. Perfect. Can you help me with a reservation for 6 people at 14:30 this coming sunday? And please make sure I have a confirmation number to use. Your reservation is set! The table will be reserved for 15 minutes. Your reference number is LZLUDTVI. Is there anything else you need? I'm also looking for a place to stay.  In the south preferably. What price range were you thinking? No particular price range, but I would like it to be a 4 star hotel. There are no hotels that fit your criteria in the South, but there are two Guesthouses. Would you like to book one of those? Sure, that will work. Can you tell me more about them? Aylesbray Lodge Guesthouse and Rosa's Bed and Breakfast, both are rated at 4 stars and both include free parking and internet. Would you like a reservation for one of them? Can I get the postcode for both of them? Aylesbray postcode is cd17sr and Rosa's postcode is cb22ha. Is there anything else I can help you with today? No thanks. That's all the help I need. Take care. Bye. thank you! Enjoy your stay!"},
  'predictions': [{'model_version': '1.3',
    'result': [{'id': '2',
      'from_name': 'textClassification',
      'to_name': 'text',
      'type': 'choices',
      'value': {'choices': ['hotel', 'restaurant']}}]}]},
 {'data': {'text': "I am looking for a specific hotel, its name is express by holiday inn cambridge. I have the Express by Holiday Inn Cambridge located on 15-17 norman way, coldhams business park. Their phone number is 01223866800. Would you like to know anything else? Yes, could you book the hotel room for me for 7 people? Yes, of course. What day would you like to stay? Monday, please. There will be 7 of us and we'd like to stay for 4 days. Here is the booking information:Booking was successful. Reference number is : 5F8G6J1G. Thank you. I would also like to book a train, please. Sure, which stations will you be traveling between? I will be going from cambridge to birmingham new street. What time would you like to leave?  The trains depart every hour. Whenever will get me there by 17:30. I do need to leave on Friday and I will need the travel time please. There are 11 results. Would you prefer the earliest departure time or latest?"},
  'predictions': [{'model_version': '1.3',
    'result': [{'id': '3',
      'from_name': 'textClassification',
      'to_name': 'text',
      'type': 'choices',
      'value': {'choices': ['hotel', 'train']}}]}]}]
Label Studio-Exportformat
CTXTC LABEL Studio Exportformat - EINZELTABEL
CTXTC LABEL Studio Exportformat - MULTI LABEL
[{'id': 237,
  'annotations': [{'id': 110,
    'completed_by': 4,
    'result': [{'id': '0',
      'from_name': 'textClassification',
      'to_name': 'text',
      'type': 'choices',
      'value': {'choices': ['hotel', 'train']},
      'origin': 'prediction'}],
    'was_cancelled': False,
    'ground_truth': False,
    'created_at': '2025-01-19T19:15:56.447771Z',
    'updated_at': '2025-01-19T19:15:57.447771Z',
    'draft_created_at': None,
    'lead_time': 1.101,
    'prediction': {'id': 253,
     'result': [{'id': '0',
       'from_name': 'textClassification',
       'to_name': 'text',
       'type': 'choices',
       'value': {'choices': ['hotel', 'train']}}],
     'model_version': '1.3',
     'created_ago': '1\xa0minute',
     'score': None,
     'cluster': None,
     'neighbors': None,
     'mislabeling': 0.0,
     'created_at': '2025-01-19T19:15:56.447771Z',
     'updated_at': '2025-01-19T19:15:57.447771Z',
     'model': None,
     'model_run': None,
     'task': 237,
     'project': 22},
    'result_count': 1,
    'unique_id': '6aa5eb24-4c5b-4e7a-9770-eca304ee1420',
    'import_id': None,
    'last_action': None,
    'task': 237,
    'project': 22,
    'updated_by': 4,
    'parent_prediction': 253,
    'parent_annotation': None,
    'last_created_by': None}],
  'file_upload': '0606dc5a-multiLabel_ethos_train_LabelStudio_Upload.json',
  'drafts': [],
  'predictions': [253],
  'data': {'text': "I need to book a hotel in the east that has 4 stars. I can help you with that. What is your price range? That doesn't matter as long as it has free wifi and parking. If you'd like something cheap, I recommend the Allenbell. For something moderately priced, I would recommend the Warkworth House. Could you book the Wartworth for one night, 1 person? What day will you be staying? Friday and Can you book it for me and get a reference number ? Booking was successful. Reference number is : BMUKPTG6.  Can I help you with anything else today? I am looking to book a train that is leaving from Cambridge to Bishops Stortford on Friday. There are a number of trains leaving throughout the day.  What time would you like to travel? I want to get there by 19:45 at the latest. Okay! The latest train you can take leaves at 17:29, and arrives by 18:07. Would you like for me to book that for you? Yes please. I also need the travel time, departure time, and price. Reference number is : UIFV8FAS. The price is 10.1 GBP and the trip will take about 38 minutes. May I be of any other assistance? Yes. Sorry, but suddenly my plans changed. Can you change the Wartworth booking to Monday for 3 people and 4 nights? I have made that change and your reference number is YF86GE4J. Thank you very much, goodbye. You're welcome. Have a nice day!"},
  'meta': {},
  'created_at': '2025-01-19T19:15:56.447771Z',
  'updated_at': '2025-01-19T19:15:57.447771Z',
  'inner_id': 2,
  'total_annotations': 1,
  'cancelled_annotations': 0,
  'total_predictions': 1,
  'comment_count': 0,
  'unresolved_comment_count': 0,
  'last_comment_updated_at': None,
  'project': 22,
  'updated_by': 4,
  'comment_authors': []},
 {'id': 237,
  'annotations': [{'id': 110,
    'completed_by': 4,
    'result': [{'id': '1',
      'from_name': 'textClassification',
      'to_name': 'text',
      'type': 'choices',
      'value': {'choices': ['attraction', 'train']},
      'origin': 'prediction'}],
    'was_cancelled': False,
    'ground_truth': False,
    'created_at': '2025-01-19T19:15:56.447847Z',
    'updated_at': '2025-01-19T19:15:57.447847Z',
    'draft_created_at': None,
    'lead_time': 1.101,
    'prediction': {'id': 253,
     'result': [{'id': '1',
       'from_name': 'textClassification',
       'to_name': 'text',
       'type': 'choices',
       'value': {'choices': ['attraction', 'train']}}],
     'model_version': '1.3',
     'created_ago': '1\xa0minute',
     'score': None,
     'cluster': None,
     'neighbors': None,
     'mislabeling': 0.0,
     'created_at': '2025-01-19T19:15:56.447847Z',
     'updated_at': '2025-01-19T19:15:57.447847Z',
     'model': None,
     'model_run': None,
     'task': 237,
     'project': 22},
    'result_count': 1,
    'unique_id': 'adfa5ebc-bd45-4c9a-9f7e-1e797329dd85',
    'import_id': None,
    'last_action': None,
    'task': 237,
    'project': 22,
    'updated_by': 4,
    'parent_prediction': 253,
    'parent_annotation': None,
    'last_created_by': None}],
  'file_upload': '0606dc5a-multiLabel_ethos_train_LabelStudio_Upload.json',
  'drafts': [],
  'predictions': [253],
  'data': {'text': 'Howdy, I need a train heading into cambridge. I would be happy to help you find a train.  Where are you departing from? I am departing from norwich.  I need to leave after 18:45 on Wednesday. I have several options for you. Where is your destination? I will be heading to cabridge. The earliest after 18:45 is the TR8658, leaving Norwich at 19:16. Can I reserve you one or more seats on this train? yeah, i need one ticket. Booking was successful, the total fee is 17.6 GBP payable at the station. Your reference number is AXH1NM1I. Do you need assistance with anything else? I am also looking for a multi sport in the East. It looks like there is The Cherry Hinton Village Centre.  Can I get you more information about it? I would like to get the phone number, please. their phone number is 01223576412. anything else? Oh, and what is their postcode, please? Sure, the postcode is cb19ej. Can I help you find any other information about Cambridge? That is all for now thank you. enjoy your time in Cambridge!'},
  'meta': {},
  'created_at': '2025-01-19T19:15:56.447847Z',
  'updated_at': '2025-01-19T19:15:57.447847Z',
  'inner_id': 2,
  'total_annotations': 1,
  'cancelled_annotations': 0,
  'total_predictions': 1,
  'comment_count': 0,
  'unresolved_comment_count': 0,
  'last_comment_updated_at': None,
  'project': 22,
  'updated_by': 4,
  'comment_authors': []},
 {'id': 237,
  'annotations': [{'id': 110,
    'completed_by': 4,
    'result': [{'id': '2',
      'from_name': 'textClassification',
      'to_name': 'text',
      'type': 'choices',
      'value': {'choices': ['hotel', 'restaurant']},
      'origin': 'prediction'}],
    'was_cancelled': False,
    'ground_truth': False,
    'created_at': '2025-01-19T19:15:56.447870Z',
    'updated_at': '2025-01-19T19:15:57.447870Z',
    'draft_created_at': None,
    'lead_time': 1.101,
    'prediction': {'id': 253,
     'result': [{'id': '2',
       'from_name': 'textClassification',
       'to_name': 'text',
       'type': 'choices',
       'value': {'choices': ['hotel', 'restaurant']}}],
     'model_version': '1.3',
     'created_ago': '1\xa0minute',
     'score': None,
     'cluster': None,
     'neighbors': None,
     'mislabeling': 0.0,
     'created_at': '2025-01-19T19:15:56.447870Z',
     'updated_at': '2025-01-19T19:15:57.447870Z',
     'model': None,
     'model_run': None,
     'task': 237,
     'project': 22},
    'result_count': 1,
    'unique_id': '2b0c6f75-82a7-4a0f-a5d5-a5a697ef6798',
    'import_id': None,
    'last_action': None,
    'task': 237,
    'project': 22,
    'updated_by': 4,
    'parent_prediction': 253,
    'parent_annotation': None,
    'last_created_by': None}],
  'file_upload': '0606dc5a-multiLabel_ethos_train_LabelStudio_Upload.json',
  'drafts': [],
  'predictions': [253]]

Konvertierungsskripte - CNER

Skript: Datenlabeling-Format in Label Studio-Format konvertieren
Ein Python-Skript namens cner_export_to_LS.py wird zur Konvertierung des Data Labeling JSONL-Formats in Label Studio Import- und Exportformate bereitgestellt: 
import os
import argparse
import json
import uuid
from datetime import datetime
import random
import glob
 
 
def convert_to_label_studio_import(data):
    """ Convert portable JSONL format to Label Studio import format. """
    dd = []
    for i in data[1:]:
        ents = i['annotations'][0]['entities']
        ee = []
        text = i['sourceDetails']['text']
        for e in ents:
            start = e['textSpan']['offset']
            end = e['textSpan']['offset'] + e['textSpan']['length']
            ee.append({
                'value': {
                    'start': start,
                    'end': end,
                    'text': text[start:end],
                    'labels': [e['labels'][0]['label_name']]
                },
                'from_name': 'label',
                'to_name': 'text',
                'type': 'labels'
            })
        dd.append({
            'data': {'text': i['sourceDetails']['text']},
            'predictions': [{'result': ee}]
        })
    return dd
 
 
def convert_to_label_studio_raw_export(input_data):
    """ Convert portable JSONL format to Label Studio raw JSON export format. """
    # Extract text and annotations
    text = input_data['sourceDetails']['text']
    entities = input_data['annotations'][0]['entities']
     
    # Initialize variables
    task_id = 141
    project_id = 14
    now = datetime.now().isoformat() + "Z"
    unique_id = str(uuid.uuid4())
     
    # Process entities
    results = []
    for entity in entities:
        offset = entity['textSpan']['offset']
        length = entity['textSpan']['length']
        label_name = entity['labels'][0]['label_name']
        entity_text = text[offset:offset + length]
         
        result = {
            'value': {
                'start': offset,
                'end': offset + length,
                'text': entity_text,
                'labels': [label_name]
            },
            'id': str(uuid.uuid4())[:8],
            'from_name': 'label',
            'to_name': 'text',
            'type': 'labels',
            'origin': 'prediction'
        }
        results.append(result)
     
    # Construct the final output
    output = {
        'id': task_id,
        'annotations': [{
            'id': random.randint(1, 100),
            'completed_by': 4,
            'result': results,
            'was_cancelled': False,
            'ground_truth': False,
            'created_at': now,
            'updated_at': now,
            'draft_created_at': None,
            'lead_time': 0,
            'prediction': {
                'id': 95,
                'result': results,
                'model_version': 'undefined',
                'created_ago': '0\xa0minutes',
                'score': None,
                'cluster': None,
                'neighbors': None,
                'mislabeling': 0.0,
                'created_at': now,
                'updated_at': now,
                'model': None,
                'model_run': None,
                'task': task_id,
                'project': project_id
            },
            'result_count': len(results),
            'unique_id': unique_id,
            'import_id': None,
            'last_action': None,
            'task': task_id,
            'project': project_id,
            'updated_by': 4,
            'parent_prediction': 95,
            'parent_annotation': None,
            'last_created_by': None
        }],
        'file_upload': 'example_file.json',
        'drafts': [],
        'predictions': [95],
        'data': {'text': text},
        'meta': {},
        'created_at': now,
        'updated_at': now,
        'inner_id': 1,
        'total_annotations': 1,
        'cancelled_annotations': 0,
        'total_predictions': 1,
        'comment_count': 0,
        'unresolved_comment_count': 0,
        'last_comment_updated_at': None,
        'project': project_id,
        'updated_by': 4,
        'comment_authors': []
    }
     
    return output
 
 
def load_jsonl(file_path):
    """ Load a JSONL file and return a list of parsed JSON objects. """
    with open(file_path, 'r', encoding='utf-8') as f:
        return [json.loads(line) for line in f]
 
 
def save_jsonl(data, file_path):
    """ Save a list of JSON objects to a JSONL file. """
    with open(file_path, 'w', encoding='utf-8') as f:
        for entry in data:
            f.write(json.dumps(entry) + '\n')
 
 
def save_json(data, file_path):
    """ Save a JSON object to a JSON file. """
    with open(file_path, 'w', encoding='utf-8') as f:
        json.dump(data, f, indent=4)
 
 
def process_folder(input_folder, output_folder):
    """ Process all test, train, and dev files in the folder. """
    for split in ["test", "train", "dev"]:
        input_file = os.path.join(input_folder, f"{split}.jsonl")
        if not os.path.exists(input_file):
            print(f"Skipping {input_file}: File not found.")
            continue
 
        # Load input data
        data = load_jsonl(input_file)
         
        # Convert and save import format
        import_output_file = os.path.join(output_folder, f"{split}_import.jsonl")
        import_data = convert_to_label_studio_import(data)
        save_jsonl(import_data, import_output_file)
        print(f"Saved Label Studio import data to {import_output_file}")
         
        # Convert and save export format
        export_output_file = os.path.join(output_folder, f"{split}_export.json")
        export_data = [convert_to_label_studio_raw_export(item) for item in data]
        save_json(export_data, export_output_file)
        print(f"Saved Label Studio export data to {export_output_file}")
 
 
if __name__ == "__main__":
    parser = argparse.ArgumentParser(description="Convert JSONL files to Label Studio formats.")
    parser.add_argument("input_folder", type=str, help="Folder containing input JSONL files (test, train, dev).")
    parser.add_argument("output_folder", type=str, help="Folder to save converted files.")
    args = parser.parse_args()
 
    # Ensure output folder exists
    os.makedirs(args.output_folder, exist_ok=True)
 
    # Process input folder
    process_folder(args.input_folder, args.output_folder)

Verwendungsanweisungen

  1. Platzieren Sie alle Data Labeling JSONL-Dateien (test, train, dev) in einem Eingabeverzeichnis.
  2. Führen Sie das Skript cner_conversion.ipynb aus: 
    import glob
 
 
def list_folders_in_directory(directory_path):
    """List all folders in a given directory using glob."""
    folder_paths = glob.glob(os.path.join(directory_path, "*/"))
    # folder_names = [os.path.basename(os.path.normpath(folder)) for folder in folder_paths]
    return folder_paths
 
 
def list_files_in_directory(directory_path, extension="*"):
    """List all files in a directory with an optional extension filter."""
    file_paths = glob.glob(os.path.join(directory_path, f"*.{extension}"))
    # file_names = [os.path.basename(file) for file in file_paths]
    return file_paths
 
directory_path = "/home/niksoni/cner_labelstudio_integeration/datasets/cner"
output_folder = "/home/niksoni/cner_labelstudio_integeration/datasets/cner/label_studio_formats"
folders = list_folders_in_directory(directory_path)
for folder in folders:
    folder_name = os.path.basename(os.path.normpath(folder))
    output_folder_path = f"{output_folder}/{folder_name}"
    os.makedirs(output_folder_path, exist_ok=True)
    files = list_files_in_directory(folder)
    for input_file in files:
        if not os.path.exists(input_file):
                    print(f"Skipping {input_file}: File not found.")
                    continue 
        data = load_jsonl(input_file)
        # Convert and save import format
        file_name = os.path.basename(input_file)
        import_output_file = os.path.join(output_folder_path, f"{file_name}_LS_import.json")
        import_data = convert_to_label_studio_import(data)
        save_json(import_data, import_output_file)
        print(f"Saved Label Studio import data to {import_output_file}")
         
        export_output_file = os.path.join(output_folder_path, f"{file_name}_LS_export.json")
        export_data = [convert_to_label_studio_raw_export(item) for item in data[1:]]
        save_json(export_data, export_output_file)
        print(f"Saved Label Studio export data to {export_output_file}")

    Das Skript generiert:

    • *_import.jsonl für direkten Import in Label Studio
    • *_export.json für die direkte Verwendung in Schulungsworkflows
    Nach der Konvertierung enthält der Ausgabeordner Inhalt mit den folgenden Strukturen:
    /input_folder/
│── test.jsonl
│── train.jsonl
│── dev.jsonl

/output_folder/
│── test_import.json
│── test_export.json
│── train_import.json
│── train_export.json
│── dev_import.json
│── dev_export.json

Konvertierungsskripte - CTXTC

Skript: CSV-Format in Label Studio-Format konvertieren
Ein Python-Skript namens ctxtc_export_to_LS.py wird bereitgestellt, um das CSV-Format in Label Studio Import- und Exportformate zu konvertieren:
import os
import argparse
import json
import uuid
# from datetime import datetime
import random
import glob
from datetime import datetime, timedelta
 
import pandas as pd
 
def load_jsonl(file_path):
    """ Load a JSONL file and return a list of parsed JSON objects. """
    with open(file_path, 'r', encoding='utf-8') as f:
        return [json.loads(line) for line in f]
 
 
def save_jsonl(data, file_path):
    """ Save a list of JSON objects to a JSONL file. """
    with open(file_path, 'w', encoding='utf-8') as f:
        for entry in data:
            f.write(json.dumps(entry) + '\n')
             
             
def save_to_json(data, file_path):
    with open(file_path, 'w') as f:
        json.dump(data, f, indent=4)
    print(f"Data successfully saved to {file_path}")
 
def load_from_json(file_path):
    with open(file_path, 'r') as f:
        data = json.load(f)
    print(f"Data successfully loaded from {file_path}")
    return data
 
 
# Function to convert dataframe to Label Studio format
def convert_to_label_studio(df, label_name="textClassification"):
    data = []
     
    for idx, row in df.iterrows():
        text = row['text']
        label = str(row['labels']).split("|")
         
        data_entry = {
            "data": {
                "text": text
            },
            "predictions": [
                {
                    "model_version": "1.3",  # This can be adjusted if needed
                    "result": [
                        {
                            "id": str(idx),
                            "from_name": label_name,  # Label name changed here
                            "to_name": "text",
                            "type": "choices",
                            "value": {
#                                 "score": 1.0,  # You can adjust the score based on confidence if needed
                                "choices": label
                            }
                        }
                    ]
                }
            ]
        }
        data.append(data_entry)
     
    return json.dumps(data, indent=4)
 
 
 
 
def convert_prediction_to_label_studio_export_format(input_data):
    # Generate placeholders
    current_time = datetime.utcnow()
    created_at = current_time.isoformat() + "Z"
    updated_at = (current_time + timedelta(seconds=1)).isoformat() + "Z"
    prediction_id = 253
    task_id = 237
    project_id = 22
    annotation_id = 110
    completed_by = 4
 
    # Extract values from input
    text = input_data['data']['text']
    predictions = input_data['predictions'][0]
    model_version = predictions['model_version']
    result = predictions['result']
 
    # Build the output structure
    converted_data = {
        'id': task_id,
        'annotations': [{
            'id': annotation_id,
            'completed_by': completed_by,
            'result': [
                {
                    **item,
                    'origin': 'prediction'
                }
                for item in result
            ],
            'was_cancelled': False,
            'ground_truth': False,
            'created_at': created_at,
            'updated_at': updated_at,
            'draft_created_at': None,
            'lead_time': 1.101,
            'prediction': {
                'id': prediction_id,
                'result': result,
                'model_version': model_version,
                'created_ago': '1\xa0minute',
                'score': None,
                'cluster': None,
                'neighbors': None,
                'mislabeling': 0.0,
                'created_at': created_at,
                'updated_at': updated_at,
                'model': None,
                'model_run': None,
                'task': task_id,
                'project': project_id
            },
            'result_count': 1,
            'unique_id': str(uuid.uuid4()),
            'import_id': None,
            'last_action': None,
            'task': task_id,
            'project': project_id,
            'updated_by': completed_by,
            'parent_prediction': prediction_id,
            'parent_annotation': None,
            'last_created_by': None
        }],
        'file_upload': '0606dc5a-multiLabel_ethos_train_LabelStudio_Upload.json',
        'drafts': [],
        'predictions': [prediction_id],
        'data': {
            'text': text
        },
        'meta': {},
        'created_at': created_at,
        'updated_at': updated_at,
        'inner_id': 2,
        'total_annotations': 1,
        'cancelled_annotations': 0,
        'total_predictions': 1,
        'comment_count': 0,
        'unresolved_comment_count': 0,
        'last_comment_updated_at': None,
        'project': project_id,
        'updated_by': completed_by,
        'comment_authors': []
    }
    return converted_data

Verwendungsanweisungen

  1. Platzieren Sie alle Data Labeling JSONL-Dateien (test, train, dev) in einem Eingabeverzeichnis.
  2. Führen Sie das Skript ctxtc_conversion.ipynb aus: 
    import json
from ctxtc_conversion_to_LS import *
import glob
 
def list_folders_in_directory(directory_path):
    """List all folders in a given directory using glob."""
    folder_paths = glob.glob(os.path.join(directory_path, "*/"))
    # folder_names = [os.path.basename(os.path.normpath(folder)) for folder in folder_paths]
    return folder_paths
 
 
def list_files_in_directory(directory_path, extension="*"):
    """List all files in a directory with an optional extension filter."""
    file_paths = glob.glob(os.path.join(directory_path, f"*.{extension}"))
    # file_names = [os.path.basename(file) for file in file_paths]
    return file_paths
 
# cls_types = ["singleLabel", "multilabel"]
cls_types = ["multilabel"]
directory_path = "/home/niksoni/cner_labelstudio_integeration/datasets/ctxtc"
output_folder = "/home/niksoni/cner_labelstudio_integeration/datasets/ctxtc/label_studio_formats"
for cls_type in cls_types:
    cls_directory_path = f"{directory_path}/{cls_type}"
    folders = list_folders_in_directory(cls_directory_path)
    for folder in folders:
        folder_name = os.path.basename(os.path.normpath(folder))
        output_folder_path = f"{output_folder}/{cls_type}/{folder_name}"
        os.makedirs(output_folder_path, exist_ok=True)
        files = list_files_in_directory(folder)
        # print(files)
        for input_file in files:
            if not os.path.exists(input_file):
                        print(f"Skipping {input_file}: File not found.")
                        continue 
            df = pd.read_csv(input_file)
            json_data = convert_to_label_studio(df)
            json_data = json.loads(json_data)
            file_name = os.path.basename(input_file)
            # save_to_json(json_data,file_name)
            import_output_file = os.path.join(output_folder_path, f"{file_name}_LS_import.json")
            # import_data = convert_to_label_studio(data)
            save_to_json(json_data, import_output_file)
            print(f"Saved Label Studio import data to {import_output_file}")
 
            export_output_file = os.path.join(output_folder_path, f"{file_name}_LS_export.json")
            export_data = [convert_prediction_to_label_studio_export_format(item) for item in json_data]
            save_to_json(export_data, export_output_file)
            print(f"Saved Label Studio export data to {export_output_file}")

    Das Skript generiert:

    • *_import.jsonl für direkten Import in Label Studio
    • *_export.json für die direkte Verwendung in Schulungsworkflows
    Nach der Konvertierung enthält der Ausgabeordner Inhalt mit den folgenden Strukturen:
    /input_folder/
│── test.jsonl
│── train.jsonl
│── dev.jsonl

/output_folder/
│── test_import.json
│── test_export.json
│── train_import.json
│── train_export.json
│── dev_import.json
│── dev_export.json

Integration mit Sprache

Folgen Sie diesem Schulungsablauf mit konvertierten Daten:
  1. Laden Sie ein Dataset hoch, entweder manuell mit Label Studio kommentiert oder aus Data Labeling konvertiert.
  2. Wählen Sie das in Object Storage gespeicherte Dataset für das Modelltraining aus.
    Sprache verarbeitet das Dataset und setzt die Trainingspipeline fort.

    Das Modell wird auf die gleiche Weise validiert, trainiert und bereitgestellt wie zuvor.

Abschluss

  • Das Migrationsskript stellt einen nahtlosen Übergang von Data Labeling zu Label Studio sicher.
  • Sie können mit der Annotierung vorhandener Daten in Label Studio fortfahren.
  • Das exportierte Dataset ist vollständig mit Sprachtrainingspipelines kompatibel.
  • Das vorhandene JSONL-Format wird aus Gründen der Abwärtskompatibilität weiterhin unterstützt.
Weitere Informationen finden Sie in der Dokumentation zu Label Studio: Siehe auch: