archai/research/lm_eval_harness
Gustavo Rosa c77676af4d fix(research): Adds evaluate requirement. 2023-03-28 12:05:32 -03:00
..
lm_eval_harness chore(research): Adds hard-coded values as arguments. 2023-03-27 16:40:54 -03:00
tutorials chore(research): Renames harness to lm_eval_harness research project. 2023-01-24 16:02:58 -03:00
.gitignore chore(research): Renames harness to lm_eval_harness research project. 2023-01-24 16:02:58 -03:00
README.md chore(research): Renames harness to lm_eval_harness research project. 2023-01-24 16:02:58 -03:00
evaluate_with_lm_eval_harness.py chore(research): Adds package for performing harness evaluation with evaluate. 2023-01-30 17:16:52 -03:00
requirements.txt fix(research): Adds evaluate requirement. 2023-03-28 12:05:32 -03:00
setup.py chore(research): Renames harness to lm_eval_harness research project. 2023-01-24 16:02:58 -03:00

README.md

LM-Eval-Harness

Installation

To install lm_eval_harness, run the following commands in your command line:

conda create -n lm_eval_harness python=3.8
conda activate lm_eval_harness

pip install -e .

Evaluating with lm_eval_harness

To evaluate your model with lm_eval_harness, run the following command:

python evaluate_with_lm_eval.py --help

This will give you a list of options and arguments that can be passed to the script to evaluate your model. For example:

python evaluate_with_lm_eval.py gpt2 gpt2 --tasks cb,copa

This will evaluate a pre-trained GPT-2 from Hugging Face's Hub, using the gpt2 pre-trained tokenizer on two SuperGLUE tasks: CommitmentBank and Choice of Plausible Alternatives.