# Model Documentation Evaluation Template

# Metadata
model_name: "Apertus v1.5"
model_link: "https://huggingface.co/swiss-ai/Apertus-v1.5-70B/tree/main"
organization: "Swiss AI Initiative"
org_link: "https://www.swiss-ai.org/"
evaluation_date: "2026-08-05"
public_summary_link: "https://huggingface.co/swiss-ai/Apertus-v1.5-70B/blob/main/Apertus_1_5_EU_Public_Summary.pdf"
public_summary_date: "2026-07-17"
public_summary_location: "https://huggingface.co/swiss-ai/Apertus-v1.5-70B"
model_publication_date: "2026-07-14"
category: "New model" # New model, fine-tuned model
archive_file_name: "Apertus_1_5_2026_08_05.pdf"
previous_versions:

# 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: 21
    max_score: 21
    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: 129
    notes: ""

  D2: # Completeness
    score: 3
    max_score: 158
    notes: ""

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

  D4: # Correctness
    score: 3
    max_score: 93
    notes: ""

  D5: # Accessibility
    score: 0
    max_score: 25
    notes: ""

  D6: # Comprehension
    score: 0
    max_score: 58
    notes: ""

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

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

  D3: # Consistency
    score: 3
    max_score: 3
    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 1.5 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-v1.5-70B/blob/main/Apertus_1_5_EU_Public_Summary.pdf\" target=\"_blank\">the repository of Apertus-v1.5-70B</a>. This repository is gated and requires providing contact information, however <a href=\"https://github.com/swiss-ai/apertus-legal/blob/main/apertus_1.5/Apertus_1_5_EU_Public_Summary.pdf\" target=\"_blank\">the summary can also be found in a publicly accessible manner on an Apertus's GitHub repository</a>. Despite declaring use of crawlers, the summary does not mention any further details regarding how the crawlers were used. Aside from this, the training data summary for Apertus 1.5 largely meets the same standards as the summary for Apertus v1.0. We assessed its score to be <span class=\"openness partial\">54.24%</span> with Grade <strong>C</strong> for <strong><i>transparency</i></strong>, and <span class=\"openness partial\">60.99%</span> with Grade <strong>C+</strong> for <strong><i>usefulness</i></strong>."