xiapk7/ANT-A-Benchmark

📊 ANT-A Benchmark

This directory contains the ANT-A Benchmark in HuggingFace-compatible Parquet format, ready for upload to the HuggingFace Hub.

ANT-A is a benchmark of 7 real-world annotation tasks (10,500 samples, 5 annotators each), covering text and multimodal modalities. All 5 annotations are preserved per sample — no forced majority vote — supporting research on soft labels and annotator disagreement.

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🗂️ Dataset Structure

Each task is provided as two Parquet files (_en and _zh) containing 1,500 samples each.

benchmark-huggingface/
├── billing_scenario_classification/
│   ├── billing_scenario_classification_en.parquet
│   └── billing_scenario_classification_zh.parquet
├── disease_privacy_assessment/
│   ├── disease_privacy_assessment_en.parquet
│   └── disease_privacy_assessment_zh.parquet
├── entity_extraction/
│   ├── entity_extraction_en.parquet
│   └── entity_extraction_zh.parquet
├── social_text_classification/
│   ├── social_text_classification_en.parquet
│   └── social_text_classification_zh.parquet
├── image_classification/
│   ├── image_classification_en.parquet
│   ├── image_classification_zh.parquet
│   └── images/                    # Image files referenced by image_url
│       ├── d04483052ca827b9a8fb7f6d038284dc.jpg
│       └── ...
├── image_text_relevance/
│   ├── image_text_relevance_en.parquet
│   ├── image_text_relevance_zh.parquet
│   └── images/                    # Image files referenced by image_url
│       ├── f8bbec0e98904e16a740a519cf307ce1.jpg
│       └── ...
└── image_attribution_recognition/
    ├── image_attribution_recognition_en.parquet
    ├── image_attribution_recognition_zh.parquet
    └── images/                    # Image files referenced by image_url
        ├── 03bf28fe75314bbe94e00e1dbc535446.jpg
        └── ...

Column Schema

Column	Type	Description
query	string	The input query (text prompt)
image_url	string (vision tasks only)	Relative path to the image file (e.g., images/abc123.jpg)
annotation_1 ~ annotation_5	string	5 independent annotator labels, JSON-encoded for structured tasks

Task Overview

Task	Modality	Description	Output Type	Agreement (5/5)
billing_scenario_classification	text	Billing scenario classification	single label	90.1%
disease_privacy_assessment	text	Disease privacy compliance assessment	binary (Yes/No)	52.4%
entity_extraction	text	Medical entity extraction	list of typed spans	45.2%
social_text_classification	text	Social media text classification	single label	21.7%
image_classification	image	Category / subcategory / guidance analysis	hierarchical multi-label	35.5%
image_text_relevance	image + text	Image–text relevance assessment	ordinal score	75.9%
image_attribution_recognition	image	Image attribute recognition	binary	56.4%

Annotator Agreement Distribution

Task	Full agreement (5/5)	Majority (3–4/5)	No majority
billing_scenario_classification	90.1%	9.7%	0.2%
image_text_relevance	75.9%	23.9%	0.3%
image_attribution_recognition	56.4%	42.5%	1.1%
disease_privacy_assessment	52.4%	47.5%	0.1%
entity_extraction	45.2%	37.4%	17.4%
image_classification	35.5%	50.3%	14.2%
social_text_classification	21.7%	51.2%	27.1%

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🚀 Quick Start

from datasets import load_dataset

# Load from local parquet files
ds = load_dataset("parquet", data_files="billing_scenario_classification_en.parquet")["train"]

# Or load from HuggingFace Hub (replace with actual repo_id)
ds = load_dataset("xiapk7/ANT-A-Benchmark", "billing_scenario_classification")

For vision tasks, access images via image_url:

from PIL import Image
from pathlib import Path

ds = load_dataset("parquet", data_files="image_classification_en.parquet")["train"]
img_url = ds[0]["image_url"]  # "images/abc123.jpg"
img = Image.open(Path("image_classification") / img_url)

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📝 Data Format Details

Text Tasks

billing_scenario_classification

• query: Dict with keys Merchant Name, Invoice Title, Counterparty Name (JSON-encoded string)
• annotation_1~`annotation_5`: Single label string

disease_privacy_assessment

• query: Medical query text
• annotation_1~`annotation_5: "Yes"or"No"`

entity_extraction

• query: Medical text to extract entities from
• annotation_1~`annotation_5: JSON list of {"text": "...", "category": "..."}` objects

social_text_classification

• query: Social media post content
• annotation_1~`annotation_5`: Single label string

Vision / Multimodal Tasks

image_classification

• image_url: Relative path to the image
• annotation_1~`annotation_5: JSON dict with Primary Category, Secondary Category, Discourse Analysis`

image_text_relevance

• query: Text description
• image_url: Relative path to the image
• annotation_1~`annotation_5: Ordinal score (e.g., "1 point", "2 points"`)

image_attribution_recognition

• image_url: Relative path to the image
• annotation_1~`annotation_5: Binary string "0"or"1"`

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📋 Annotation Rules

Each task includes its original annotation guideline (*_rules_en.md / *_rules_zh.md) shipped alongside the data. These are real production-grade rule documents (34–467 lines) that define the annotation schema and serve as prompts for rule-grounding research.

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ℹ️ Notes

• Language: Original annotations are in Chinese; English translations are provided for the *_en files.
• Privacy: All personal-privacy data has been anonymized.
• Soft labels: No majority-vote aggregation is applied. All 5 annotator labels are preserved verbatim for research on label distribution and disagreement.
• Images: For the 3 vision tasks, images are stored in the images/ subdirectory and referenced by image_url in the parquet files.

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