# Model Documentation Evaluation Template

# Metadata
model_name: "Apertus"
model_link: "https://huggingface.co/swiss-ai/Apertus-70B-2509"
organization: "Swiss AI Initiative"
org_link: "https://www.swiss-ai.org/"
evaluation_date: "2026-01-12"
public_summary_link: "https://huggingface.co/swiss-ai/Apertus-70B-2509/blob/main/Apertus_EU_Public_Summary.pdf"
public_summary_date: "2025-09-01"
public_summary_location: "https://huggingface.co/swiss-ai/Apertus-70B-2509"
model_publication_date: "2025-09-02"
category: "New model" # New model, fine-tuned model
archive_file_name: "Apertus_EU_Public_Summary -- 2025_11_12.pdf"

# Requirement assessments
# Each requirement should be assessed once with a score from 0-10
# Leave blank for N/A, and assign a max score of 0
S1: # Document-level requirements
  D1: # Clarity
    score: 9
    max_score: 11
    notes: ""

  D2: # Completeness
    score: 4
    max_score: 4
    notes: ""

  D3: # Consistency
    score: 7
    max_score: 7
    notes: ""

  D4: # Correctness
    score: 3.5
    max_score: 5
    notes: ""

  D5: # Accessibility
    score: 10
    max_score: 10
    notes: ""

  D6: # Comprehension
    score: 6
    max_score: 9
    notes: ""

S2: # General information
  D1: # Clarity
    score: 20
    max_score: 42
    notes: ""

  D2: # Completeness
    score: 51
    max_score: 62
    notes: ""

  D3: # Consistency
    score: 8
    max_score: 8
    notes: ""

  D4: # Correctness
    score: 18
    max_score: 18
    notes: ""

  D5: # Accessibility
    score: 5
    max_score: 5
    notes: ""

  D6: # Comprehension
    score: 10
    max_score: 11
    notes: ""

S3: # Public datasets
  D1: # Clarity
    score: 92
    max_score: 92
    notes: ""

  D2: # Completeness
    score: 95
    max_score: 95
    notes: ""

  D3: # Consistency
    score: 23
    max_score: 23
    notes: ""

  D4: # Correctness
    score: 23
    max_score: 23
    notes: ""

  D5: # Accessibility
    score: 20
    max_score: 20
    notes: ""

  D6: # Comprehension
    score: 19
    max_score: 19
    notes: ""

S4: # Private datasets
  D1: # Clarity
    score: 0
    max_score: 0
    notes: ""

  D2: # Completeness
    score: 2
    max_score: 2
    notes: ""

  D3: # Consistency
    score: 3
    max_score: 3
    notes: ""

  D4: # Correctness
    score: 2
    max_score: 2
    notes: ""

  D5: # Accessibility
    score: 0
    max_score: 0
    notes: ""

  D6: # Comprehension
    score: 0
    max_score: 0
    notes: ""

S5: # Scraped/crawled data
  D1: # Clarity
    score: 0
    max_score: 0
    notes: ""

  D2: # Completeness
    score: 3
    max_score: 3
    notes: ""

  D3: # Consistency
    score: 0
    max_score: 0
    notes: ""

  D4: # Correctness
    score: 3
    max_score: 3
    notes: ""

  D5: # Accessibility
    score: 0
    max_score: 0
    notes: ""

  D6: # Comprehension
    score: 0
    max_score: 0
    notes: ""

S6: # User data
  D1: # Clarity
    score: 0
    max_score: 0
    notes: ""

  D2: # Completeness
    score: 8
    max_score: 8
    notes: ""

  D3: # Consistency
    score: 0
    max_score: 0
    notes: ""

  D4: # Correctness
    score: 8
    max_score: 8
    notes: ""

  D5: # Accessibility
    score: 0
    max_score: 0
    notes: ""

  D6: # Comprehension
    score: 0
    max_score: 0
    notes: ""

S7: # Synthetic & other
  D1: # Clarity
    score: 0
    max_score: 0
    notes: ""

  D2: # Completeness
    score: 3
    max_score: 3
    notes: ""

  D3: # Consistency
    score: 6
    max_score: 6
    notes: ""

  D4: # Correctness
    score: 3
    max_score: 3
    notes: ""

  D5: # Accessibility
    score: 0
    max_score: 0
    notes: ""

  D6: # Comprehension
    score: 0
    max_score: 0
    notes: ""

S8: # Data processing
  D1: # Clarity
    score: 24
    max_score: 24
    notes: ""

  D2: # Completeness
    score: 19
    max_score: 19
    notes: ""

  D3: # Consistency
    score: 3
    max_score: 3
    notes: ""

  D4: # Correctness
    score: 37
    max_score: 37
    notes: ""

  D5: # Accessibility
    score: 15
    max_score: 15
    notes: ""

  D6: # Comprehension
    score: 51
    max_score: 51
    notes: ""


general_notes: "The public summary for the Apertus model family can be found as a PDF on HuggingFace in the same context as its models, for instance at <a href=\"https://huggingface.co/swiss-ai/Apertus-70B-2509/blob/main/Apertus_EU_Public_Summary.pdf\" target=\"_blank\">the repository of Apertus-70B-2509</a>. Each field of the template was filled in, including explicitly marking sections as not applicable, though some fields included superfluous information not relevant to the topic or question which caused a few points deduction. We assessed its score to be <span class=\"openness open\">92.90%</span> with Grade <strong>A</strong> for <strong><i>transparency</i></strong>, and <span class=\"openness open\">97.14%</span> with Grade <strong>A+</strong> for <strong><i>usefulness</i></strong>, which were the highest of all assessed summaries published before and during our initial research."