Export data from model run v2 (beta)
Export v2 gives you more flexibility and control to retrieve the most valuable information from your Model Runs. You can now select and export a subselection of the data rows of most interest based on your predefined or new parameters in the Model Run filters You can also export more detailed information about the Model run's predictions, and include or exclude relevant data row attributes in your export. We also simplified and standardized the annotation formats.
We encourage you to use exports v2 over exports v1. We are collecting feedback during this beta period. The final format of the export json is subject to minor changes until end of June, 2023. We will continue to support export v1 and its existing features.
Export fields
When you export annotations from a Model Run, the export will contain an NDJSON file where all labels on a data row will be grouped together. This structure makes it easy to include/exclude certain fields within the export. To see a sample NDJSON export, see the section on Export v2 examples.
When you request an export (via the UI or SDK), you must select to include the optional fields in order for the optional fields to appear in the export file.
Predictions are now included in Model Run Export
If you have uploaded predictions to model run, they will also show up in the export file along with the data rows and model run's versioned label information.
Nested classifications and frame-based classifications
The nested classifications will maintain their nested structure in export v2; video and DICOM exports will include all frame-based annotations directly in the export file.
Each data row in the NDJSON file can have the following information.
| Field | Description | Included |
|---|---|---|
data_row | Contains the basic information of the data row: - id- row_data- global_key- data_row_details (optional, see below) | Always |
details(data row) | Contains additional details of the data row: - dataset_id- created_at- updated_at- created_by | Optional |
media_attributes | See Media attributes | Optional |
attachments | See Attachments | Optional |
metadata_fields | See Metadata | Optional |
models | Contains the ID of the model in which the data row was stored. | Always |
<model_id> | Contains the following sections, which are expanded on below: - model_name- model_runs | Always |
model_name | Name of the model | Always |
model_runs | Contains the ID of the model run in which the data row was stored. | Always |
<model_run_id> | Contains the following sections, which are expanded on below: - model_run_name- annotation_group_id- labels- predictions | Always |
model_run_name | Name of the model run | Always |
annotation_group_id | Model Run Data Row id, similar to data_row_id but in a Model Run's context | Always |
labels | Contains a list of labels attached to this data row versioned in this model run - label_kind- version- id- annotations | Always |
predictions | Contains a list of predictions attached to this data row: - label_kind- version- id- annotations | Optional |
Export v2 examples
{
"data_row": {
"id": "<id>",
"external_id": "<id>",
"global_key": "<id>",
"row_data": "<url>",
"details": {
"dataset_id": "<id>",
"created_at": "<time>",
"updated_at": "<time>",
"created_by": "<email>"
}
},
"media_attributes": {},
"attachments": [],
"metadata_fields": [],
"models": {
"<model_id>": {
"model_name": "",
"model_runs": {
"<model_run_id>": {
"model_run_name": "",
"annotation_group_id": "<id>",
"labels": [
{
"label_kind": "",
"version": "1.0.0",
"id": "<id>",
"annotations": {
"objects": [],
"classifications": [],
"relationships": []
}
}
],
"predictions": [
{
"label_kind": "",
"version": "1.0.0",
"id": "<id>",
"annotations": {
"objects": [],
"classifications": [],
"relationships": []
}
}
]
}
}
}
}
}
{
"data_row": {
"id": "cldw8hs6l1ii9070lcysma1zk",
"global_key": "5e3648e7-a888-4f03-b3e6-b277d42a795a",
"row_data": "https://storage.googleapis.com/labelbox-datasets/image_sample_data/2560px-Kitano_Street_Kobe01s5s4110.jpeg",
"details": {
"dataset_id": "cldw8hrhr1r6107xz65bxd2ah",
"created_at": "2023-02-08",
"updated_at": "2023-02-08",
"created_by": "[email protected]"
}
},
"media_attributes": {
"height": 1707,
"width": 2560,
"mime_type": "image/jpeg"
},
"attachments": [],
"metadata_fields": [],
"models": {
"a08c8320-f6da-01c7-ddfc-440ea660acc2": {
"model_name": "image_model_run_748bf36b-5bac-4d5a-8511-a8d8a22f1398",
"model_runs": {
"a08c8321-3e07-06d8-43d2-a26ce0d9862c": {
"model_run_name": "iteration 1",
"annotation_group_id": "78e2c7ca-eaba-48da-b582-a1fe5565dea1",
"labels": [
{
"label_kind": "Default",
"version": "1.0.0",
"id": "cldw8i75c000m13vuk7xzvtho",
"annotations": {
"objects": [
{
"feature_id": "4a4ae9b1-df44-4551-8303-67b1fee61c14",
"name": "bbox_with_radio_subclass",
"annotation_kind": "ImageBoundingBox",
"classifications": [
{
"feature_id": "a98ec212-d2d2-4ca4-8648-595e2470c7a8",
"name": "sub_radio_question",
"radio_answer": {
"feature_id": "43868193-a513-4287-a2fb-ac7f62f09148",
"name": "first_sub_radio_answer",
"classifications": []
}
}
],
"bounding_box": {
"top": 933,
"left": 541,
"height": 191,
"width": 330
}
},
{
"feature_id": "6d059ab4-a0e1-4b6a-ba69-9d28bd111274",
"name": "polygon",
"annotation_kind": "ImagePolygon",
"classifications": [],
"polygon": [
{
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},
{
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},
{
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},
{
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},
{
"x": 2557.386,
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},
{
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},
{
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},
{
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},
{
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{
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}
]
},
{
"feature_id": "844936f6-6dc8-44fe-8305-376a9e663946",
"name": "bounding_box",
"annotation_kind": "ImageBoundingBox",
"classifications": [],
"bounding_box": {
"top": 977,
"left": 1690,
"height": 330,
"width": 225
}
},
{
"feature_id": "9af7a10a-d84d-4f63-a2cb-87ca55f407d7",
"name": "point",
"annotation_kind": "ImagePoint",
"classifications": [],
"point": {
"x": 1166.606,
"y": 1441.768
}
},
{
"feature_id": "a51c0659-5a29-4c09-a1ec-1bcb259b0439",
"name": "mask",
"annotation_kind": "ImageSegmentationMask",
"classifications": [],
"mask": {
"url": "https://api.labelbox.com/api/v1/models/a08c8320-f6da-01c7-ddfc-440ea660acc2/annotations/a51c0659-5a29-4c09-a1ec-1bcb259b0439/mask"
}
},
{
"feature_id": "ae445b95-d532-4bf8-926d-a213c50899c6",
"name": "polyline",
"annotation_kind": "ImageLine",
"classifications": [],
"line": [
{
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},
{
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},
{
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},
{
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},
{
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},
{
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},
{
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{
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{
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{
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},
{
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},
{
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{
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},
{
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{
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{
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{
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{
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{
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{
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{
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}
]
}
],
"classifications": [
{
"feature_id": "5236b2ae-a2bc-4db3-80db-cd6bcef3cc7b",
"name": "radio_question",
"radio_answer": {
"feature_id": "c8f365a4-6cf8-4210-a27d-3fca48e7e23d",
"name": "second_radio_answer",
"classifications": []
}
},
{
"feature_id": "66595ecd-72ff-4ff1-b906-00c1a11e4614",
"name": "free_text",
"text_answer": {
"content": "sample text"
}
},
{
"feature_id": "824350d5-b17f-40f0-adff-87a149d2b1e4",
"name": "nested_radio_question",
"radio_answer": {
"feature_id": "f8933b4c-5b79-4099-b3d5-cf5296819c1f",
"name": "first_radio_answer",
"classifications": [
{
"feature_id": "a82fa120-a07b-400b-ad7d-a750f2f26632",
"name": "sub_radio_question",
"radio_answer": {
"feature_id": "cec45106-3efb-48b9-9b02-7d0a0acfce02",
"name": "first_sub_radio_answer",
"classifications": []
}
}
]
}
},
{
"feature_id": "b4b2ea8f-7d2b-421e-a9ee-5c237e871a4e",
"name": "nested_checklist_question",
"checklist_answers": [
{
"feature_id": "4d686853-92a9-455a-bb24-a3f98b713423",
"name": "first_checklist_answer",
"classifications": [
{
"feature_id": "9d14b980-56da-4f30-938b-6a1ccc31fcb4",
"name": "sub_checklist_question",
"checklist_answers": [
{
"feature_id": "7d3ec4d8-7baf-4e80-a3d7-cc07ae67f114",
"name": "first_sub_checklist_answer",
"classifications": []
}
]
}
]
}
]
},
{
"feature_id": "c070476e-e57a-4bec-91f3-58adab50c86b",
"name": "checklist_question",
"checklist_answers": [
{
"feature_id": "45714163-42b7-41d6-a8a7-324ff45367b4",
"name": "first_checklist_answer",
"classifications": []
},
{
"feature_id": "71f30b0a-89e0-484e-b5ab-c835fe425c32",
"name": "second_checklist_answer",
"classifications": []
}
]
}
],
"relationships": []
}
}
],
"predictions": [
{
"label_kind": "Default",
"version": "1.0.0",
"id": "cldw8i75c000m13vuk7xzvtho",
"annotations": {
"objects": [
{
"feature_id": "05969c6f-5a75-4859-a17b-bdb2fec41489",
"name": "bounding_box",
"annotation_kind": "ImageBoundingBox",
"classifications": [],
"bounding_box": {
"top": 977,
"left": 1690,
"height": 330,
"width": 225
}
},
{
"feature_id": "49b6d892-6058-45ea-a393-39c54235d97b",
"name": "mask",
"annotation_kind": "ImageSegmentationMask",
"classifications": [],
"mask": {
"url": "https://api.labelbox.com/api/v1/models/a08c8320-f6da-01c7-ddfc-440ea660acc2/annotations/49b6d892-6058-45ea-a393-39c54235d97b/mask"
}
},
{
"feature_id": "dcf5eac7-a2eb-4665-8ea9-5be43690da0e",
"name": "polygon",
"annotation_kind": "ImagePolygon",
"classifications": [],
"polygon": [
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{
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},
{
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{
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{
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},
{
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{
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{
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{
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{
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{
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{
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]
},
{
"feature_id": "f3cdd6b1-0119-4d02-8774-173ae88324be",
"name": "bbox_with_radio_subclass",
"annotation_kind": "ImageBoundingBox",
"classifications": [
{
"feature_id": "84b35f92-a15e-41d1-8d2b-8f2ffc96f425",
"name": "sub_radio_question",
"radio_answer": {
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"name": "first_sub_radio_answer",
"classifications": []
}
}
],
"bounding_box": {
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"left": 541,
"height": 191,
"width": 330
}
},
{
"feature_id": "fc343a15-e1fb-4780-93c3-14b50f8647ab",
"name": "point",
"annotation_kind": "ImagePoint",
"classifications": [],
"point": {
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}
},
{
"feature_id": "fcbb7514-6f61-4965-961b-5db4b9ebdeef",
"name": "polyline",
"annotation_kind": "ImageLine",
"classifications": [],
"line": [
{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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}
]
}
],
"classifications": [
{
"feature_id": "0ba27448-8bc5-4745-94bd-99c778f3168c",
"name": "free_text",
"text_answer": {
"content": "sample text"
}
},
{
"feature_id": "11e26ef0-fde5-40bc-99d7-78e2874ff1ce",
"name": "radio_question",
"radio_answer": {
"feature_id": "5cb8e2e4-5057-4c31-b60f-d1857c39adbc",
"name": "second_radio_answer",
"classifications": []
}
},
{
"feature_id": "120d6ef7-7d78-420c-986e-8f80c9973efb",
"name": "checklist_question",
"checklist_answers": [
{
"feature_id": "16427678-a4c7-408b-bab1-86fc5f9955c4",
"name": "second_checklist_answer",
"classifications": []
},
{
"feature_id": "e753a2b9-a565-4785-8c66-cefd80a6999c",
"name": "first_checklist_answer",
"classifications": []
}
]
},
{
"feature_id": "a584a4ef-8876-4d96-9386-c5ca9064ec72",
"name": "nested_radio_question",
"radio_answer": {
"feature_id": "0d69b9f8-9bc6-4bc6-97ec-8e7afac44ef9",
"name": "first_radio_answer",
"classifications": [
{
"feature_id": "8c1701b2-5807-4e7e-96a2-3e7c78711602",
"name": "sub_radio_question",
"radio_answer": {
"feature_id": "0ba750f5-7ceb-4ee8-a3a4-2a1a3ffdfd0a",
"name": "first_sub_radio_answer",
"classifications": []
}
}
]
}
},
{
"feature_id": "ede5db24-e7ed-4250-a1e4-0ec227c58fd0",
"name": "nested_checklist_question",
"checklist_answers": [
{
"feature_id": "340672db-03e7-4646-a5ef-21c1027c5697",
"name": "first_checklist_answer",
"classifications": [
{
"feature_id": "dcff1e19-209b-4101-a27d-436dd8c84814",
"name": "sub_checklist_question",
"checklist_answers": [
{
"feature_id": "d61c28f1-872e-4ee7-9705-8f985d890aed",
"name": "first_sub_checklist_answer",
"classifications": []
}
]
}
]
}
]
}
],
"relationships": []
}
}
]
}
}
}
}
}
{
"data_row": {
"id": "clduj5b8103z407uqhlalhu25",
"global_key": "84a24fab-bf79-449d-831d-b3b3002affe9",
"row_data": "https://storage.googleapis.com/labelbox-sample-datasets/nlp/lorem-ipsum.txt",
"details": {
"dataset_id": "clduj5ary09eh07zsf3btbgk9",
"created_at": "2023-02-07",
"updated_at": "2023-02-07",
"created_by": "[email protected]"
}
},
"media_attributes": {
"mime_type": "text/plain"
},
"attachments": [],
"metadata_fields": [],
"models": {
"a089fc39-5fb0-0107-5948-d9963f668d76": {
"model_name": "text_model_run_4dfb57ac-54a4-40da-89ff-b4930f388953",
"model_runs": {
"a089fc39-d06d-00c1-ee95-e49521276427": {
"model_run_name": "iteration 1",
"annotation_group_id": "f86738d9-3438-4b71-9625-232ff2b0009c",
"labels": [
{
"label_kind": "Default",
"version": "1.0.0",
"id": "cldujpk1702p113ydw8vi6qra",
"annotations": {
"objects": [
{
"feature_id": "79cf45ed-d0ad-4a41-a107-686de471dc6b",
"name": "named_entity",
"annotation_kind": "TextEntity",
"classifications": [],
"location": {
"start": 67,
"end": 128
}
}
],
"classifications": [
{
"feature_id": "5c3d1d41-d69c-49ff-b6a7-5b8a50c45569",
"name": "radio_question",
"radio_answer": {
"feature_id": "92ffa583-8ab8-408e-8e2d-2e600a522525",
"name": "first_radio_answer",
"classifications": []
}
},
{
"feature_id": "80ed4712-7ef7-454e-972c-b4db5dd62a0b",
"name": "nested_checklist_question",
"checklist_answers": [
{
"feature_id": "41a8857a-597f-490c-977e-9e2e84c184df",
"name": "first_checklist_answer",
"classifications": [
{
"feature_id": "2d91f11c-fdaf-40c6-893a-f1ae73d1154d",
"name": "sub_checklist_question",
"checklist_answers": [
{
"feature_id": "a0bfd338-6957-41bc-a00d-4464673aa732",
"name": "first_sub_checklist_answer",
"classifications": []
}
]
}
]
}
]
},
{
"feature_id": "8d604648-cfe5-4ac9-bebc-01bf439661f4",
"name": "free_text",
"text_answer": {
"content": "sample text"
}
},
{
"feature_id": "b2ae102b-783f-4511-a5f5-61aae62ad312",
"name": "radio_question_sub",
"radio_answer": {
"feature_id": "18a702ed-6b88-42b8-9753-50caf990dc04",
"name": "first_radio_answer",
"classifications": [
{
"feature_id": "4d3757b0-55b4-41ee-87c9-c7fc9b4c5342",
"name": "sub_radio_question",
"radio_answer": {
"feature_id": "380ded50-a14c-41f5-9c78-b2ed821c6163",
"name": "first_sub_radio_answer",
"classifications": []
}
}
]
}
},
{
"feature_id": "ef6137ab-2c78-482c-8f59-b068ab826328",
"name": "checklist_question",
"checklist_answers": [
{
"feature_id": "160978a2-0caf-4bb2-ae74-6596cc2bcd5e",
"name": "third_checklist_answer",
"classifications": []
},
{
"feature_id": "a58e3c54-26e9-4980-bd6b-8d65034bdcf7",
"name": "first_checklist_answer",
"classifications": []
},
{
"feature_id": "f9b4e4b8-b8e3-468c-a404-1443d74a8b69",
"name": "second_checklist_answer",
"classifications": []
}
]
}
],
"relationships": []
}
}
],
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}
}
{
"data_row": {
"id": "cldyr6rgw7plt07ywc9qn4rxf",
"global_key": "278dd556-1a6a-4d66-8dfd-43167fb3fc05",
"row_data": "https://storage.googleapis.com/labelbox-datasets/html_sample_data/sample_html_2.html",
"details": {
"dataset_id": "cldyr6qo61tic07z1eieh3enb",
"created_at": "2023-02-10",
"updated_at": "2023-02-10",
"created_by": "[email protected]"
}
},
"media_attributes": {
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},
"attachments": [],
"metadata_fields": [],
"models": {
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},
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},
{
"feature_id": "9f4daa3c-b598-4721-bac6-cc0b5b667000",
"name": "free_text",
"text_answer": {
"content": "sample text"
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},
{
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"name": "radio_question_sub",
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},
{
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"name": "radio_question",
"radio_answer": {
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"name": "first_radio_answer",
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},
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"name": "first_radio_answer",
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},
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"name": "radio_question",
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}
Export data rows from a Model Run (UI)
You can use the filters to query data row label status, metadata, batch, annotations, and workflow history. Note that excluding fields from your export will make the export process faster and the export file smaller.
To export your annotations via the Labelbox UI, follow these steps:
- Select a model run.
- Configure your export
Option 1: Export the entire Model Run
Click on All (X number of data rows), then click the Export data v2 (beta) in the drop-down menu.
Option 2: Export a Split or a Slice
On the left side of the Model Run page, click on one of the Split options (Train, Validation, or Test) or Slice options. Click on Slice/Split (X number of data rows), then click the Export data v2 (beta) in the drop-down menu.
Option 3: Filter data rows
- You can also build your filters in the Model filters to query for data rows of the desired context.
- Then click the Done (X number of data rows) dropdown, and click Export data v2 (beta) to export the data rows.
Option 4: Select specific data rows
- Hand-select data rows to export using the check boxes next to each data row.
- Then, click the drop-down on the right side X selected, click Export data v2 (beta) to export the data rows.
- After you click Export JSON, you will see a notification banner telling you that you can track the progress of the export job in the Notifications center.
- Once the job is complete, download the export file by clicking the Download link.
Export from a Model Run (Python SDK)
# Set the export params to include/exclude certain fields. Make sure each of these fields are correctly grabbed
export_params= {
"attachments": True,
"metadata_fields": True,
"data_row_details": True,
}
export_task = model_run.export_v2(params=export_params)
export_task.wait_till_done()
print(export_task.errors)
export_json = export_task.result
Updated 5 days ago