Lm-Evaluation-Harness

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Lm-evaluation-harness is a framework for few-shot evaluation of language models. It helps researchers and engineers assess model performance across various tasks.

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At a glance

Pricing

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Free tier

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API

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Skill level

Technical

About

What is lm-evaluation-harness?

Lm-evaluation-harness is a framework specifically designed for the few-shot evaluation of language models. It provides a robust environment for researchers and engineers to assess the performance of different models across a variety of tasks. The tool is built with a focus on usability, offering CLI refactoring with subcommands and support for YAML configuration files. Additionally, it provides lighter installation options through separate model backends, making it more flexible for different setups.

Best used for

Evaluating and benchmarking language models using few-shot learning across various tasks.

Common actions

Evaluate language models

Benchmark AI models

Assess model performance

Configure evaluations

Compare language models

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Capabilities

Key features

Few-shot evaluation
CLI subcommands
YAML config files
Separate model backends

Target Audience

ResearchersEngineersData ScientistsML Developers

Integrations

Not yet documented

Pricing & Plans

unknown

Free

FAQs

Does lm-evaluation-harness support evaluating custom language models or only pre-trained ones?

Yes, lm-evaluation-harness is designed to be flexible. While it supports many popular pre-trained models, its architecture allows researchers to integrate and evaluate their own custom language models by implementing the necessary backend interfaces.

What kind of tasks can I evaluate my language models on using this framework?

The framework supports a wide variety of tasks, particularly those suitable for few-shot evaluation. This includes common NLP benchmarks like question answering, summarization, translation, and text classification, with extensibility to add new tasks.

Are there lighter installation options available for users who don't need all model backends?

Yes, lm-evaluation-harness offers separate model backends. This means you can install only the dependencies required for the specific models you intend to evaluate, leading to a lighter and more focused installation.

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Research & Education › Academic Research

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