Dataset Evaluation

Evaluate a submission by scoring content consistency of texts and quality of structured data based on completeness, accuracy, type correctness, and informati...

SKILL.md --- dataset_evaluation

Skill Name

dataset_evaluation

Description

Evaluate a miner submission by performing two evaluation steps:

1. Content Consistency Evaluation
2. Structured Data Quality Evaluation

The evaluator receives 5 cleaned data samples, the structured JSON, and the dataset schema, then computes a final score for the miner.

---

Input

json

{
  "cleaned_data_list": [
    "cleaned_text_1",
    "cleaned_text_2",
    "cleaned_text_3",
    "cleaned_text_4",
    "cleaned_text_5"
  ],
  "structured_data": {
    "field1": "value",
    "field2": "value"
  },
  "dataset_schema": {
    "fields": [
      {"name": "title", "type": "string", "required": true},
      {"name": "author", "type": "string", "required": false},
      {"name": "date", "type": "string", "required": false},
      {"name": "url", "type": "string", "required": true}
    ]
  }
}

---

Evaluation Procedure

Step 1 --- Content Consistency Evaluation (Weight 40%)

Goal: determine whether the 5 cleaned texts represent the same underlying content.

Method

1. Normalize text

• remove HTML
• lowercase
• remove excessive whitespace

2. Compute pairwise similarity across the 5 texts

Recommended metrics:

• cosine similarity (embedding based)
• OR Jaccard similarity

3. Compute the average similarity score.

Output

text

content_consistency_score (0-100)

Suggested mapping:

text

avg_similarity >= 0.9 → 100
0.8 – 0.9 → 80 – 100
0.6 – 0.8 → 60 – 80
0.4 – 0.6 → 40 – 60
< 0.4 → < 40

---

Step 2 --- Structured Data Quality Evaluation (Weight 60%)

Using the verified cleaned content, evaluate the structured JSON.

Compute four sub-scores.

---

2.1 Field Completeness (30%)

Evaluate whether all required fields exist.

Formula:

text

completeness_score =
    (# required fields present / total required fields) * 100

---

2.2 Value Accuracy (40%)

Evaluate whether each field value is consistent with the cleaned data.

Examples:

• title appears in cleaned text
• author name appears in text
• url matches source

Scoring guideline:

text

exact match → 100
partially correct → 60-80
inconsistent → <50

---

2.3 Type Correctness (15%)

Evaluate whether values match schema types.

Examples:

text

string
number
boolean
array

Formula:

text

type_score =
    (# correct types / total fields) * 100

---

2.4 Information Sufficiency (15%)

Evaluate whether the structured data misses obvious information present in the cleaned text.

Example:

Cleaned text contains:

text

title
author
date

But structured JSON only includes:

text

title

Then deduct score.

Guideline:

text

complete extraction → 100
minor missing info → 70–90
major missing info → <60

---

Structuring Quality Score

text

structuring_quality_score =
    completeness_score * 0.30
  + value_accuracy_score * 0.40
  + type_score * 0.15
  + information_sufficiency_score * 0.15

Range:

text

0 – 100

---

Step 3 --- Final Miner Score

text

miner_score =
    content_consistency_score * 0.4
  + structuring_quality_score * 0.6

Range:

text

0 – 100

---

Output Format

The evaluator must return:

json

{
  "content_consistency_score": 92,
  "structuring_quality_score": 85,
  "miner_score": 88.2,
  "details": {
    "completeness_score": 90,
    "value_accuracy_score": 88,
    "type_score": 100,
    "information_sufficiency_score": 80
  }
}

---

Evaluator Rules

The evaluator must follow these principles:

1. Be deterministic and reproducible
2. Base judgments only on provided inputs
3. Avoid hallucination
4. Penalize missing or inconsistent data
5. Return scores strictly in the 0--100 range