MeteSertkan/ranger

Ranger helps you see the forest among the trees - Ranger is an effect-size meta analysis library creating beautiful forest plots!

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Experimental

When evaluating machine learning models for tasks in fields like Natural Language Processing (NLP) or Information Retrieval (IR), it can be hard to compare results across different metrics or scenarios. This tool helps you combine these varied evaluation outcomes into a single, comprehensive statistical view. You input the performance metrics from your different model evaluations, and it generates a "forest plot" — a visual summary that shows the overall effectiveness and reliability of your model across all tasks. Data scientists, researchers, and machine learning engineers who need to robustly assess and present their model's performance on multiple tasks would use this.

No commits in the last 6 months.

Use this if you need to aggregate and visually represent the impact of a model or 'treatment' across multiple, potentially incomparable, evaluation tasks in NLP, IR, or similar fields.

Not ideal if you are looking for a tool to train models, perform single-task evaluations, or visualize basic statistical distributions outside of meta-analysis.

NLP-evaluation IR-metrics model-comparison research-evaluation statistical-analysis
Stale 6m No Package No Dependents
Maintenance 0 / 25
Adoption 5 / 25
Maturity 16 / 25
Community 7 / 25

How are scores calculated?

Stars

11

Forks

1

Language

Python

License

Apache-2.0

Last pushed

Jun 12, 2023

Commits (30d)

0

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