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Flexible evaluation tool for language models. Easy to extend, highly customizable!
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With FlexEval, you can evaluate language models with:
For more use cases, see the documentation.
flexeval
is flexible in terms of the evaluation setup and the language model to be evaluated.flexeval
are easily extensible and replaceable.flexeval
should be reproducible, with the ability to save and load configurations and results.pip install flexeval
The following minimal example evaluates the hugging face model sbintuitions/tiny-lm
with the commonsense_qa
task.
flexeval_lm \
--language_model HuggingFaceLM \
--language_model.model "sbintuitions/tiny-lm" \
--eval_setup "commonsense_qa" \
--save_dir "results/commonsense_qa"
(The model used in the example is solely for debugging purposes and does not perform well. Try switching to your favorite model!)
The results saved in --saved_dir
contain:
config.json
: The configuration of the evaluation, which can be used to replicate the evaluation.metrics.json
: The evaluation metrics.outputs.jsonl
: The outputs of the language model that comes with instance-level metrics.You can flexibly customize the evaluation by specifying command-line arguments or configuration files. Besides the Transformers model, you can also evaluate models via OpenAI ChatGPT and vLLM, and other models can be readily added!
flexeval_presets
to check the list of off-the-shelf presets in addition to commonsense_qa
. You can find the details in the Preset Configs section.FAQs
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We found that flexeval demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 1 open source maintainer collaborating on the project.
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