Skip to main content

No project description provided

Project description

FlexEval

logo

Flexible evaluation tool for language models. Easy to extend, highly customizable!

English | 日本語 |

With FlexEval, you can evaluate language models with:

  • Zero/few-shot in-context learning tasks
  • Open-ended text-generation benchmarks such as MT-Bench with automatic evaluation using GPT-4
  • Log-probability-based multiple-choice tasks
  • Computing perplexity of text data

For more use cases, see the documentation.

Key Features

  • Flexibility: flexeval is flexible in terms of the evaluation setup and the language model to be evaluated.
  • Modularity: The core components of flexeval are easily extensible and replaceable.
  • Clarity: The results of evaluation are clear and all the details are saved.
  • Reproducibility: flexeval should be reproducible, with the ability to save and load configurations and results.

Installation

pip install flexeval

Quick Start

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!

Next Steps

  • Run 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.
  • See Getting Started to check the tutorial examples for other kinds of tasks.
  • See the Configuration Guide to set up your evaluation.

Project details


Download files

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

Source Distribution

flexeval-0.9.3.tar.gz (195.7 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

flexeval-0.9.3-py3-none-any.whl (280.2 kB view details)

Uploaded Python 3

File details

Details for the file flexeval-0.9.3.tar.gz.

File metadata

  • Download URL: flexeval-0.9.3.tar.gz
  • Upload date:
  • Size: 195.7 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: poetry/2.0.1 CPython/3.9.21 Linux/6.8.0-1020-azure

File hashes

Hashes for flexeval-0.9.3.tar.gz
Algorithm Hash digest
SHA256 c3199fe68b48b3c1ecdf92756ef6ebfc38f42ea67235778020f18c2bd93712c5
MD5 754765d640d96abd4d806104477da3d6
BLAKE2b-256 45da32a71871c29ed27a5758569fd44145c2236bdbe7ff5388bdeea383b7e96a

See more details on using hashes here.

File details

Details for the file flexeval-0.9.3-py3-none-any.whl.

File metadata

  • Download URL: flexeval-0.9.3-py3-none-any.whl
  • Upload date:
  • Size: 280.2 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: poetry/2.0.1 CPython/3.9.21 Linux/6.8.0-1020-azure

File hashes

Hashes for flexeval-0.9.3-py3-none-any.whl
Algorithm Hash digest
SHA256 3d23babc84cd149adb20290e161f352cecbb98d9cbee3347abcd2dc12d4ad808
MD5 450cd26caaaf9547eb707741d690711d
BLAKE2b-256 045fc16807cfacb40c7bad3b5a82f3f0c66da644f8a43bf6afdc99162e75634b

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