This repository contains the implementation for FilBench, an Open LLM Leaderboard and Evaluation Suite for Filipino. It is a fork of HuggingFace's lighteval library, with new Filipino-focused benchmarks implemented under the hood.
First, clone the repository and install all dependencies:
git clone [email protected]:filbench/lighteval.git
# Create a virtualenv
python3 -m venv venv
pip install -e .[dev]If you're developing FilBench, we encourage installing a pre-commit hook:
pre-commit install
pre-commit run --all-filesFilBench contains a suite of evaluation benchmarks from a variety of Filipino datasets.
They are in the following format filbench|{task_name}|{few_shot}|{truncate_few_shots}
You can find all tasks in the examples/tasks/all_filbench_tasks.txt file.
For example, let's inspect the anatomy subset of Global-MMLU:
python -m lighteval tasks inspect "filbench|global_mmlu_all_tgl_mcf:anatomy|0|0" \
--num-samples 1 \
--custom-tasks community_tasks/filbench_evals.pyOutput:
{ 'choices': [' A', ' B', ' C', ' D'],
'ctx': '',
'fewshot_sorting_class': None,
'gold_index': [0],
'instruction': '',
'num_asked_few_shots': -1,
'num_effective_few_shots': -1,
'original_query': '',
'query': 'Tanong: Ang isang sugat na nagdudulot ng compression ng facial '
'nerve sa stylomastoid foramen ay magdudulot ng ipsilateral\n'
' A. Paralysis ng facial muscles.\n'
' B. Paralysis ng facial muscles at pagkawala ng panlasa.\n'
' C. Paralysis ng facial muscles, pagkawala ng lasa at '
'lacrimation.\n'
' D. Paralysis ng facial muscles, pagkawala ng lasa, lacrimation at '
'pagbaba ng salivation.\n'
'Sagot:',
'specific': None,
'task_name': 'filbench|global_mmlu_all_tgl_mcf:anatomy',
'unconditioned_query': 'Sagot:'}Tip
Always remember to pass community_tasks/filbench_evals.py in the --custom-tasks parameter. In addition, running all commands as a module (i.e., using python -m lighteval instead of lighteval) solves some pathing or weird errors.
You can also check all tasks available in filbench (and all of lighteval) via this command:
# Saves all tasks in a file called `all_tasks.txt`
python -m lighteval tasks list --custom-tasks community_tasks/filbench_evals.py > all_tasks.txtPlease check lighteval's official documentation on running tasks.
Nothing much differs except that all of FilBench's tasks are registered in the filbench suite.
Our structure differs quite a bit from the community tasks in lighteval.
Specifically, we implement one task per file in the filbench/ directory.
This helps a lot in organization and for multiple people working on different benchmarks at the same time.
- Implement the task as a new file in the
filbench/directory. Check if there are similar implementations in the existing tasks inlighteval. By default, we follow their implementations to ensure that we're consistent with existing benchmarks. You can check all existing implementations in thefilbench/directory as reference. - Add the task in the
TASK_TABLEconstant in thecommunity_tasks/filbench_evals.pyfile. This file is our main entrypoint for running evaluations. - Ensure that nothing is amiss— inspect the task using
python -m lighteval tasks inspectto examine a single sample. - If everything looks good, add the task string, i.e.,
filbench|{task_name}|{few_shot}|{truncate_few_shots}in theexamples/tasks/all_filbench_tasks.txtfile.
Your go-to toolkit for lightning-fast, flexible LLM evaluation, from Hugging Face's Leaderboard and Evals Team.
Documentation: Lighteval's Wiki
Lighteval is your all-in-one toolkit for evaluating LLMs across multiple backends—whether it's transformers, tgi, vllm, or nanotron—with ease. Dive deep into your model’s performance by saving and exploring detailed, sample-by-sample results to debug and see how your models stack-up.
Customization at your fingertips: letting you either browse all our existing tasks and metrics or effortlessly create your own, tailored to your needs.
Seamlessly experiment, benchmark, and store your results on the Hugging Face Hub, S3, or locally.
- Speed: Use vllm as backend for fast evals.
- Completeness: Use the accelerate backend to launch any models hosted on Hugging Face.
- Seamless Storage: Save results in S3 or Hugging Face Datasets.
- Python API: Simple integration with the Python API.
- Custom Tasks: Easily add custom tasks.
- Versatility: Tons of metrics and tasks ready to go.
pip install lightevalLighteval allows for many extras when installing, see here for a complete list.
If you want to push results to the Hugging Face Hub, add your access token as an environment variable:
huggingface-cli loginLighteval offers two main entry points for model evaluation:
lighteval accelerate: evaluate models on CPU or one or more GPUs using 🤗 Acceleratelighteval nanotron: evaluate models in distributed settings using ⚡️ Nanotronlighteval vllm: evaluate models on one or more GPUs using 🚀 VLLMlighteval endpointinference-endpoint: evaluate models on one or more GPUs using 🔗 Inference Endpointtgi: evaluate models on one or more GPUs using 🔗 Text Generation Inferenceopenai: evaluate models on one or more GPUs using 🔗 OpenAI API
Here’s a quick command to evaluate using the Accelerate backend:
lighteval accelerate \
"pretrained=gpt2" \
"leaderboard|truthfulqa:mc|0|0"Lighteval started as an extension of the fantastic Eleuther AI Harness (which powers the Open LLM Leaderboard) and draws inspiration from the amazing HELM framework.
While evolving Lighteval into its own standalone tool, we are grateful to the Harness and HELM teams for their pioneering work on LLM evaluations.
Got ideas? Found a bug? Want to add a task or metric? Contributions are warmly welcomed!
If you're adding a new feature, please open an issue first.
If you open a PR, don't forget to run the styling!
pip install -e .[dev]
pre-commit install
pre-commit run --all-files@misc{lighteval,
author = {Fourrier, Clémentine and Habib, Nathan and Wolf, Thomas and Tunstall, Lewis},
title = {LightEval: A lightweight framework for LLM evaluation},
year = {2023},
version = {0.5.0},
url = {https://github.com/huggingface/lighteval}
}