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-0.7.8.tar.gz (192.4 kB view details)

Uploaded Source

Built Distribution

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

flexeval-0.7.8-py3-none-any.whl (267.3 kB view details)

Uploaded Python 3

File details

Details for the file flexeval-0.7.8.tar.gz.

File metadata

  • Download URL: flexeval-0.7.8.tar.gz
  • Upload date:
  • Size: 192.4 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-0.7.8.tar.gz
Algorithm Hash digest
SHA256 57d44ef38b59cc8a6a861191ff936747fb0162d590e0148356f7c7f3b4c609db
MD5 4857d31e5821f0483b788f61ff94db33
BLAKE2b-256 5bd79e564253625a631b7655b144dd526467caf055f895fa8376ad96cd65e1cd

See more details on using hashes here.

File details

Details for the file flexeval-0.7.8-py3-none-any.whl.

File metadata

  • Download URL: flexeval-0.7.8-py3-none-any.whl
  • Upload date:
  • Size: 267.3 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-0.7.8-py3-none-any.whl
Algorithm Hash digest
SHA256 a8963285e5d03bcb33c42d3f3d6fa53e23edc87f22799cb7d67487e909d95d0e
MD5 c915eb2fbc90709f3622c49a8cb32c42
BLAKE2b-256 a1dc0889c45d09654a7a1a3a9471321cf490f8868727aabeefef36f1c9fea806

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