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-0.7.7.tar.gz (189.5 kB view details)

Uploaded Source

Built Distribution

flexeval-0.7.7-py3-none-any.whl (262.5 kB view details)

Uploaded Python 3

File details

Details for the file flexeval-0.7.7.tar.gz.

File metadata

  • Download URL: flexeval-0.7.7.tar.gz
  • Upload date:
  • Size: 189.5 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: poetry/1.8.4 CPython/3.9.20 Linux/6.5.0-1025-azure

File hashes

Hashes for flexeval-0.7.7.tar.gz
Algorithm Hash digest
SHA256 7b83ba4a0d4efdf62ce3ef5929654d09bc1bc7c1721e5aaaaecefb6db7b980ad
MD5 145fe247322f9f10cf4f5f72eea10be8
BLAKE2b-256 a53bb178398b8c3913b04d1b72f5b017ed45d1cb66078cbbbd23a8d471e7cb54

See more details on using hashes here.

File details

Details for the file flexeval-0.7.7-py3-none-any.whl.

File metadata

  • Download URL: flexeval-0.7.7-py3-none-any.whl
  • Upload date:
  • Size: 262.5 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: poetry/1.8.4 CPython/3.9.20 Linux/6.5.0-1025-azure

File hashes

Hashes for flexeval-0.7.7-py3-none-any.whl
Algorithm Hash digest
SHA256 984d063e969b9f69f6e46399369f8ac1dd11f75af7fe17e70c36e304dea29741
MD5 ca93e78866e24bece6596488e21bf26f
BLAKE2b-256 8db99eb1a07d31005df46f9e44116e63cd7f5941033095be34fcea52f864c51d

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