Skip to main content
Pre-release

This release is a pre-release and may not be stable for production use.

ATTENTION: ALPHA VERSION

Benchmarking procedure to test approaches on the PET-dataset (hosted on huggingface).

This is an beta version.

Documentation will come soon.

Example of ‘’how to benchmark an approach’’

from petbenchmarks.benchmarks import BenchmarkApproach

BenchmarkApproach(tested_approach_name='Approach-name',
                  predictions_file_or_folder='path-to-prediction-file.json')

The BenchmarkApproach object does all the job. It reads the prediction file, computes score and generates a reports.

Created by Patrizio Bellan.

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

petbenchmarks-0.0.1a2.tar.gz (13.5 kB view details)

Uploaded Source

File details

Details for the file petbenchmarks-0.0.1a2.tar.gz.

File metadata

  • Download URL: petbenchmarks-0.0.1a2.tar.gz
  • Upload date:
  • Size: 13.5 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.1 CPython/3.10.5

File hashes

Hashes for petbenchmarks-0.0.1a2.tar.gz
Algorithm Hash digest
SHA256 ef0fff6c4e2806d6441108f998825d927baa14efd19feb4366d5f699a29006b7
MD5 7d6c3d11d39045c383a15029d28c7a14
BLAKE2b-256 559c2aeb45475f5f269e39189bb7fe0c0fb75458215085a27c5518ea248ad74f

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page