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-0.7.1.tar.gz (186.4 kB view details)

Uploaded Source

Built Distribution

flexeval-0.7.1-py3-none-any.whl (258.1 kB view details)

Uploaded Python 3

File details

Details for the file flexeval-0.7.1.tar.gz.

File metadata

  • Download URL: flexeval-0.7.1.tar.gz
  • Upload date:
  • Size: 186.4 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: poetry/1.8.3 CPython/3.9.20 Linux/6.8.0-1014-azure

File hashes

Hashes for flexeval-0.7.1.tar.gz
Algorithm Hash digest
SHA256 13046ad0c293b9b7ec7d9943929709126148f194872c75a97d686c6e121caf4d
MD5 cc5b233618f5de7227de4aac222535a9
BLAKE2b-256 b6cd6d13ee4b91a879ad1f8f62eaa1c4a79e2ea08941ca1d91cfe39714905273

See more details on using hashes here.

File details

Details for the file flexeval-0.7.1-py3-none-any.whl.

File metadata

  • Download URL: flexeval-0.7.1-py3-none-any.whl
  • Upload date:
  • Size: 258.1 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: poetry/1.8.3 CPython/3.9.20 Linux/6.8.0-1014-azure

File hashes

Hashes for flexeval-0.7.1-py3-none-any.whl
Algorithm Hash digest
SHA256 e32ef5a7f365ec611c423b30a22b915493bf374650b9e7b4190fbbf93096f30d
MD5 34066ffa1dccb30cfe8b754766f86bc7
BLAKE2b-256 547a6a085bfdac69be9e4658c5163a3f23ccae4dfec64687dd12f30a7f7a4f72

See more details on using hashes here.

Supported by

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