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Documentation Index

Fetch the complete documentation index at: https://mintlify.com/GingerlyData247/SOTeam4-P2/llms.txt

Use this file to discover all available pages before exploring further.

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Endpoint

GET /artifact/model/{id}/rate
Computes and returns a comprehensive rating for a model artifact, including all Phase 2 trustworthiness metrics, performance indicators, and latency measurements.

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Path Parameters

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id
string
required
The unique identifier of the model artifact to rate

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Response

Returns a ModelRating object containing all computed metrics.
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name
string
The name of the rated model
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category
string
The artifact category (always “model” for this endpoint)
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net_score
number
Aggregate trustworthiness score (0.0 to 1.0) computed as the average of all individual metrics
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net_score_latency
number
Total time in milliseconds to compute all metrics

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Individual Metrics

Each metric includes both a score (0.0 to 1.0) and a latency measurement (milliseconds):
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ramp_up_time
number
Measures documentation quality and ease of onboarding for new users
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ramp_up_time_latency
number
Time in milliseconds to compute ramp-up time score
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bus_factor
number
Evaluates project resilience based on contributor distribution
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bus_factor_latency
number
Time in milliseconds to compute bus factor score
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performance_claims
number
Validates performance benchmarks mentioned in model documentation
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performance_claims_latency
number
Time in milliseconds to compute performance claims score
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license
number
License compatibility and permissiveness score
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license_latency
number
Time in milliseconds to compute license score
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dataset_and_code_score
number
Combined quality assessment of training datasets and associated code
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dataset_and_code_score_latency
number
Time in milliseconds to compute dataset and code score
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dataset_quality
number
Evaluates the quality and documentation of training datasets
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dataset_quality_latency
number
Time in milliseconds to compute dataset quality score
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code_quality
number
Assesses code maintainability, testing, and documentation
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code_quality_latency
number
Time in milliseconds to compute code quality score
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reproducibility
number
Measures availability of training scripts, configs, and reproducibility artifacts
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reproducibility_latency
number
Time in milliseconds to compute reproducibility score
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reviewedness
number
Evaluates community engagement, model card completeness, and peer review activity
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reviewedness_latency
number
Time in milliseconds to compute reviewedness score
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tree_score
number
Lineage-based score combining the model’s own metrics with ancestor model quality
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tree_score_latency
number
Time in milliseconds to compute tree score
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size_score
object
Hardware compatibility scores for different deployment targetsProperties:
  • raspberry_pi (number): Compatibility score for Raspberry Pi (0.0 to 1.0)
  • jetson_nano (number): Compatibility score for Jetson Nano (0.0 to 1.0)
  • desktop_pc (number): Compatibility score for desktop PC (0.0 to 1.0)
  • aws_server (number): Compatibility score for AWS server (0.0 to 1.0)
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size_score_latency
number
Time in milliseconds to compute size compatibility scores

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Example Request

curl -X GET https://api.example.com/artifact/model/42/rate

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Example Response

{
  "name": "facebook/wav2vec2-base",
  "category": "model",
  "net_score": 0.7234,
  "net_score_latency": 8250,
  "ramp_up_time": 0.85,
  "ramp_up_time_latency": 320,
  "bus_factor": 0.92,
  "bus_factor_latency": 450,
  "performance_claims": 0.78,
  "performance_claims_latency": 280,
  "license": 1.0,
  "license_latency": 120,
  "dataset_and_code_score": 0.73,
  "dataset_and_code_score_latency": 890,
  "dataset_quality": 0.81,
  "dataset_quality_latency": 540,
  "code_quality": 0.65,
  "code_quality_latency": 720,
  "reproducibility": 0.58,
  "reproducibility_latency": 380,
  "reviewedness": 0.91,
  "reviewedness_latency": 670,
  "tree_score": 0.68,
  "tree_score_latency": 2100,
  "size_score": {
    "raspberry_pi": 0.32,
    "jetson_nano": 0.45,
    "desktop_pc": 0.88,
    "aws_server": 1.0
  },
  "size_score_latency": 1780
}

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Error Responses

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404
error
Artifact not found - the specified model ID does not exist in the registry
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400
error
Bad request - rating is only supported for model artifacts (not datasets or code)
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500
error
Internal error during metric computation

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Behavior Notes

  • Metrics are computed at ingestion time and cached in artifact metadata
  • If cached metrics are available and valid, they are returned immediately
  • If metrics are missing or invalid, they are recomputed on-demand
  • All metric scores are normalized to the range [0.0, 1.0]
  • Latencies are measured in milliseconds and represent actual computation time
  • The net_score is the arithmetic mean of all individual metric scores

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