How to import annotations on conversational text data and sample import formats.
Open this Colab for an interactive tutorial on importing annotations on conversational data.
Supported annotations
To import annotations in Labelbox, you need to create an annotations payload. In this section, we provide this payload for every supported annotation type.
Labelbox supports two formats for the annotations payload:
- Python annotation types (recommended)
- NDJSON
Both are described below.
Entity (Message-based)
ner_annotation = lb_types.ObjectAnnotation(
name="ner",
value=lb_types.ConversationEntity(
start=0,
end=8,
message_id="4"
)
)
ner_annotation = {
"name": "ner",
"location": {
"start": 0,
"end": 8
},
"messageId": "4" # this should match the message
}
Classification: Free-form text (Message-based)
text_annotation = lb_types.ClassificationAnnotation(
name="text_convo",
value=lb_types.Text(answer="the answer to the text questions right here"),
message_id="0" # Remove argument if importing annotation as a global classification (not message-based)
)
text_annotation_ndjson = {
"name": "text_convo",
"answer": "the answer to the text questions right here",
"messageId": "0" # Remove argument if importing annotation as a global classification (not message-based)
}
Classification: Checklist (Multi-choice, Message-based)
checklist_annotation= lb_types.ClassificationAnnotation(
name="checklist_convo", # must match your ontology feature"s name
value=lb_types.Checklist(
answer = [
lb_types.ClassificationAnswer(
name = "first_checklist_answer"
),
lb_types.ClassificationAnswer(
name = "second_checklist_answer"
)
]
),
message_id="2" # Remove argument if importing annotation as a global classification (not message-based)
)
checklist_annotation_ndjson = {
"name": "checklist_convo",
"answers": [
{"name": "first_checklist_answer"},
{"name": "second_checklist_answer"}
],
"messageId": "2" # Remove argument if importing annotation as a global classification (not message-based)
}
Classification: Radio (Single-choice, Message-based)
radio_annotation = lb_types.ClassificationAnnotation(
name="radio_convo",
value=lb_types.Radio(answer = lb_types.ClassificationAnswer(name = "first_radio_answer")),
message_id="0" # Remove argument if importing annotation as a global classification (not message-based)
)
radio_annotation_ndjson = {
"name": "radio_convo",
"answer": {
"name": "first_radio_answer"
},
"messageId": "0", # Remove argument if importing global classifications
}
Relationship with Entity (Message-based)
Relationship annotations are only supported for MAL import jobs.
ner_source = lb_types.ObjectAnnotation(
name="ner",
value=lb_types.ConversationEntity(
start=16,
end=26,
message_id="4"
)
)
ner_target = lb_types.ObjectAnnotation(
name="ner",
value=lb_types.ConversationEntity(
start=29,
end=34,
message_id="4"
)
)
ner_relationship = lb_types.RelationshipAnnotation(
name="relationship",
value=lb_types.Relationship(
source=ner_source,
target=ner_target,
type=lb_types.Relationship.Type.UNIDIRECTIONAL,
))
uuid_source = str(uuid.uuid4())
uuid_target = str(uuid.uuid4())
ner_source_ndjson = {
"uuid": uuid_source,
"name": "ner",
"location": {
"start": 16,
"end": 26
},
"messageId": "4"
}
ner_target_ndjson = {
"uuid": uuid_target,
"name": "ner",
"location": {
"start": 29,
"end": 34
},
"messageId": "4"
}
ner_relationship_annotation_ndjson = {
"name": "relationship",
"relationship": {
"source": uuid_source, #UUID reference to the source annotation
"target": uuid_target, # UUID reference to the target annotation
"type": "bidirectional"
}
}
End-to-end example: Import pre-labels or ground truth
Whether you are importing annotations as pre-labels or as ground truth, the steps are very similar. Steps 5 and 6 (creating and importing the annotation payload) are where the process becomes slightly different and is explained below in detail.
Before you start
You must import these libraries to use the code examples in this section.
import labelbox as lb
import labelbox.types as lb_types
from labelbox.schema.queue_mode import QueueMode
import uuid
import json
import numpy as np
Replace with your API key
API_KEY = ""
client = lb.Client(api_key=API_KEY)
Step 1: Import data rows
To attach annotations to a data row, it must first be uploaded to Catalog. Here we create an example data row in Catalog.
# Create one Labelbox dataset
global_key = "conversation-1.json"
asset = {
"row_data": "https://storage.googleapis.com/labelbox-developer-testing-assets/conversational_text/1000-conversations/conversation-1.json",
"global_key": global_key
}
dataset = client.create_dataset(name="conversational_annotation_import_demo_dataset")
task = dataset.create_data_rows([asset])
task.wait_till_done()
print("Errors:", task.errors)
print("Failed data rows: ", task.failed_data_rows)
Step 2: Create an ontology
Your project should have the correct ontology set up with all the tools and classifications supported for your annotations. The value for the name parameter should match the name field in your annotations to ensure the correct feature schemas are matched.
Here is an example of creating an ontology programmatically for all the sample annotations above.
ontology_builder = lb.OntologyBuilder(
tools=[
lb.Tool(tool=lb.Tool.Type.NER,name="ner"),
lb.Tool(tool=lb.Tool.Type.RELATIONSHIP,name="relationship")
],
classifications=[
lb.Classification(
class_type=lb.Classification.Type.TEXT,
scope=lb.Classification.Scope.INDEX, # Remove this line or set scope to "GLOBAL" if importing global text annotations
instructions="text_convo"),
lb.Classification(
class_type=lb.Classification.Type.CHECKLIST,
scope=lb.Classification.Scope.INDEX, # Remove this line or set scope to "GLOBAL" if importing global checklist annotations
instructions="checklist_convo",
options=[
lb.Option(value="first_checklist_answer"),
lb.Option(value="second_checklist_answer")
]
),
lb.Classification(
class_type=lb.Classification.Type.RADIO,
instructions="radio_convo",
scope=lb.Classification.Scope.INDEX, # Remove this line or set scope to "GLOBAL" if importing global radio annotations
options=[
lb.Option(value="first_radio_answer"),
lb.Option(value="second_radio_answer")
]
)
]
)
ontology = client.create_ontology("Ontology Conversation Annotations", ontology_builder.asdict())
Step 3: Create a labeling project
Create a project and connect the ontology created above
# Create Labelbox project
project = client.create_project(name="conversational_project",
media_type=lb.MediaType.Conversational)
# Setup your ontology
project.setup_editor(ontology) # Connect your ontology and editor to your project
Step 4: Send a batch of data rows to the project
# Setup Batches and Ontology
# Create a batch to send to your MAL project
batch = project.create_batch(
"first-batch-convo-demo", # Each batch in a project must have a unique name
global_keys=[global_key], # a list of global keys, data row ids or global keys
priority=5 # priority between 1(highest) - 5(lowest)
)
print("Batch: ", batch)
Step 5: Create the annotation payload
Create the annotations payload using the snippets of code shown above.
Labelbox supports two formats for the annotations payload: NDJSON and Python annotation types. Both approaches are described below with instructions to compose annotations into Labels attached to the data rows.
The resulting label_ndjson
and label
from each approach will include every annotation (created above) supported by the respective method.
label = []
label.append(
lb_types.Label(
data=lb_types.ConversationData(
global_key=global_key
),
annotations=[
ner_annotation,
text_annotation,
checklist_annotation,
radio_annotation,
ner_source,
ner_target,
ner_relationship
]
)
)
label_ndjson = []
for annotations in [
ner_annotation_ndjson,
text_annotation_ndjson,
checklist_annotation_ndjson,
radio_annotation_ndjson,
ner_source_ndjson,
ner_target_ndjson,
ner_relationship_annotation_ndjson,
]:
annotations.update({
"dataRow": {
"globalKey": global_key
}
})
label_ndjson.append(annotations)
Step 6: Import the annotation payload
For both options, you can pass either the label_ndjson
and label
payload as the value for the predictions or labels parameter.
Here, we opt to use the payload from the NDJSON approach since more example annotations are supported.
Option A: Upload to a labeling project as pre-labels (Model-assisted labeling)
# Upload our label using Model-Assisted Labeling
upload_job = lb.MALPredictionImport.create_from_objects(
client = client,
project_id = project.uid,
name=f"mal_job-{str(uuid.uuid4())}",
predictions=label)
upload_job.wait_until_done()
print("Errors:", upload_job.errors)
print("Status of uploads: ", upload_job.statuses)
Option B: Upload to a labeling project as ground truth
Relationship annotations are not supported in label import jobs
# Upload label for this data row in project
upload_job = lb.LabelImport.create_from_objects(
client = client,
project_id = project.uid,
name="label_import_job"+str(uuid.uuid4()),
labels=label)
upload_job.wait_until_done();
print("Errors:", upload_job.errors)
print("Status of uploads: ", upload_job.statuses)