데이터 형식

[
  {
    "id": "doc_001",
    "text": "This is the first document to annotate.",
    "source": "twitter",
    "date": "2024-01-15"
  },
  {
    "id": "doc_002",
    "text": "This is the second document.",
    "source": "reddit",
    "date": "2024-01-16"
  }
]

{"id": "doc_001", "text": "First document"}
{"id": "doc_002", "text": "Second document"}
{"id": "doc_003", "text": "Third document"}

id,text,source
doc_001,"This is the first document",twitter
doc_002,"This is the second document",reddit

data_files:
  - "data/documents.json"
 
item_properties:
  id_key: id      # Field name for unique ID
  text_key: text  # Field name for content to annotate

data_files:
  - "data/batch_1.json"
  - "data/batch_2.json"
  - "data/batch_3.jsonl"

{
  "id": "1",
  "text": "The product arrived quickly and works great!"
}

{
  "id": "1",
  "image_path": "images/photo_001.jpg"
}

item_properties:
  id_key: id
  image_key: image_path

{
  "id": "1",
  "text": ["Option A", "Option B", "Option C"]
}

{
  "id": "pair_001",
  "text": {
    "A": "Response from Model A",
    "B": "Response from Model B"
  }
}

{
  "id": "1",
  "text": "<embed width=\"1020\" height=\"440\" src=\"data/html_data/1.html\" type=\"text/html\">"
}

{
  "id": "1",
  "text": "This is great!",
  "context": "Previous message: How do you like the new feature?"
}

item_properties:
  id_key: id
  text_key: text
  kwargs:
    - context

list_as_text:
  # Prefix each item so an annotator can refer to one by name.
  text_list_prefix_type: alphabet   # alphabet, number, bullet, or none
 
  # Side-by-side suits a pairwise comparison. The default is vertical.
  horizontal: true
 
  # Shade alternate turns, which keeps a long dialogue readable.
  alternating_shading: true

{
  "id": "1",
  "text": "<p>This is <strong>formatted</strong> text.</p>"
}

output_annotation_dir: "output/"

export_annotation_format: "jsonl"   # or csv, tsv, parquet, coco, conll_2003, ...

{
  "user_id": "annotator_1",
  "instance_id_to_label_to_value": {
    "doc_001": {
      "sentiment": {"labels": {"positive": true}}
    }
  },
  "instance_id_to_span_to_value": {
    "doc_001": {
      "ner": [
        {"start": 0, "end": 5, "label": "PERSON", "text": "Alice"}
      ]
    }
  }
}

{
  "id": "1",
  "text": "Item A,Item B,Item C,Item D"
}

{
  "id": "1",
  "text": "Document content",
  "category": "news",
  "priority": "high",
  "custom_field": "any value"
}

database:
  type: mysql
  host: localhost
  port: 3306
  database: potato_db
  username: ${DB_USER}
  password: ${DB_PASSWORD}

annotation_task_name: "Document Classification"
task_dir: "."
port: 8000
 
# Data configuration
data_files:
  - "data/documents.json"
 
item_properties:
  id_key: id
  text_key: text
 
# Display settings
list_as_text:
  text_list_prefix_type: number
  horizontal: false
 
# Output
output_annotation_dir: "output/"
export_annotation_format: "jsonl"
 
# Annotation scheme
annotation_schemes:
  - annotation_type: radio
    name: category
    description: "Select the document category"
    labels:
      - News
      - Opinion
      - Tutorial
      - Other
 
user_config:
  allow_all_users: true
  users: []

{"id": "twitter_2024_001", "text": "..."}

{
  "id": "1",
  "text": "Content",
  "source": "twitter",
  "date": "2024-01-15",
  "language": "en"
}

import json
 
with open('data.json') as f:
    data = json.load(f)
 
# Check for required fields
for item in data:
    assert 'id' in item, f"Missing id: {item}"
    assert 'text' in item, f"Missing text: {item}"
 
# Check for duplicates
ids = [item['id'] for item in data]
assert len(ids) == len(set(ids)), "Duplicate IDs found"
 
print(f"Validated {len(data)} items")

# Convert JSON array to JSON Lines
cat data.json | jq -c '.[]' > data.jsonl