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-0.10.1.tar.gz (199.5 kB view details)

Uploaded Source

Built Distribution

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

flexeval-0.10.1-py3-none-any.whl (285.8 kB view details)

Uploaded Python 3

File details

Details for the file flexeval-0.10.1.tar.gz.

File metadata

  • Download URL: flexeval-0.10.1.tar.gz
  • Upload date:
  • Size: 199.5 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: poetry/2.1.1 CPython/3.9.21 Linux/6.8.0-1021-azure

File hashes

Hashes for flexeval-0.10.1.tar.gz
Algorithm Hash digest
SHA256 d15a06a241f5d665ac6b007867bb60dc0104bccd621e8bf8e8768d69414cd2f0
MD5 adb2a6e42626019c0aebbc87e9824d10
BLAKE2b-256 8f20a6b1ddce35f45ff976a829f8dba4222b75132dcf38883d687a76cccad62e

See more details on using hashes here.

File details

Details for the file flexeval-0.10.1-py3-none-any.whl.

File metadata

  • Download URL: flexeval-0.10.1-py3-none-any.whl
  • Upload date:
  • Size: 285.8 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: poetry/2.1.1 CPython/3.9.21 Linux/6.8.0-1021-azure

File hashes

Hashes for flexeval-0.10.1-py3-none-any.whl
Algorithm Hash digest
SHA256 2faf478b50ad7589dc184de23eaf42094f6f45bd46ffcec3eb0cce072b76ca7b
MD5 e624a36cd2b8082d1b20f1d378fd3786
BLAKE2b-256 ec14204317747ada18ba678fac2df0f6730756d9c8e45a7484d6c5da7d73f49c

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