Lighteval provides comprehensive logging and result management through the EvaluationTracker class. This system allows you to save results locally and optionally push them to various platforms for collaboration and analysis.
Lighteval automatically saves results and evaluation details in the
directory specified with the --output-dir option. The results are saved in
{output_dir}/results/{model_name}/results_{timestamp}.json. Here is an
example of a result file. The output path can be
any fsspec
compliant path (local, S3, Hugging Face Hub, Google Drive, FTP, etc.).
To save detailed evaluation information, you can use the --save-details
option. The details are saved in Parquet files at
{output_dir}/details/{model_name}/{timestamp}/details_{task}_{timestamp}.parquet.
If you want results to be saved in a custom path structure, you can set the results-path-template option.
This allows you to specify a string template for the path. The template must contain the following
variables: output_dir, model_name, org. For example:
{output_dir}/{org}_{model}. The template will be used to create the path for the results file.
You can push results and evaluation details to the Hugging Face Hub. To do
so, you need to set the --push-to-hub option as well as the --results-org
option. The results are saved in a dataset with the name
{results_org}/{model_org}/{model_name}. To push the details, you need to set
the --save-details option.
The dataset created will be private by default. You can make it public by
setting the --public-run option.
You can push results to TensorBoard by setting --push-to-tensorboard.
This creates a TensorBoard dashboard in a Hugging Face organization specified with the --results-org
option.
You can push results to Weights & Biases by setting --wandb. This initializes a W&B
run and logs the results.
W&B arguments need to be set in your environment variables:
export WANDB_PROJECT="lighteval"You can find a complete list of variables in the W&B documentation.
If Trackio is available in your environment (pip install lighteval[trackio]), it will be used to log and push results to a
Hugging Face dataset. Choose the dataset name and organization with:
export WANDB_SPACE_ID="org/name"from datasets import load_dataset
import os
import glob
output_dir = "evals_doc"
model_name = "HuggingFaceH4/zephyr-7b-beta"
timestamp = "latest"
task = "lighteval|gsm8k|0"
if timestamp == "latest":
path = f"{output_dir}/details/{model_name}/*/"
timestamps = glob.glob(path)
timestamp = sorted(timestamps)[-1].split("/")[-2]
print(f"Latest timestamp: {timestamp}")
details_path = f"{output_dir}/details/{model_name}/{timestamp}/details_{task}_{timestamp}.parquet"
# Load the details
details = load_dataset("parquet", data_files=details_path, split="train")
for detail in details:
print(detail)from datasets import load_dataset
results_org = "SaylorTwift"
model_name = "HuggingFaceH4/zephyr-7b-beta"
sanitized_model_name = model_name.replace("/", "__")
task = "lighteval|gsm8k|0"
public_run = False
dataset_path = f"{results_org}/details_{sanitized_model_name}{'_private' if not public_run else ''}"
details = load_dataset(dataset_path, task.replace("|", "_"), split="latest")
for detail in details:
print(detail)The detail file contains the following columns:
__doc__: The document used for evaluation, containing the gold reference, few-shot examples, and other hyperparameters used for the task.__model_response__: Contains model generations, log probabilities, and the input that was sent to the model.__metric__: The value of the metrics for this sample.The EvaluationTracker class provides several configuration options for customizing how results are saved and pushed:
from lighteval.logging.evaluation_tracker import EvaluationTracker
tracker = EvaluationTracker(
output_dir="./results",
save_details=True,
push_to_hub=True,
hub_results_org="your_username",
public=False
)tracker = EvaluationTracker(
output_dir="./results",
results_path_template="{output_dir}/custom/{org}_{model}",
save_details=True,
push_to_hub=True,
push_to_tensorboard=True,
hub_results_org="my-org",
tensorboard_metric_prefix="eval",
public=True,
use_wandb=True
)output_dir: Local directory to save evaluation results and logsresults_path_template: Template for results directory structuresave_details: Whether to save detailed evaluation records (default: True)push_to_hub: Whether to push results to Hugging Face Hub (default: False)push_to_tensorboard: Whether to push metrics to TensorBoard (default: False)hub_results_org: Hugging Face Hub organization to push results totensorboard_metric_prefix: Prefix for TensorBoard metrics (default: “eval”)public: Whether to make Hub datasets public (default: False)use_wandb: Whether to log to Weights & Biases or Trackio (default: False)The main results file contains several sections:
config_general: Overall evaluation configuration including model information, timing, and system detailssummary_general: General statistics about the evaluation runconfig_tasks: Configuration details for each evaluated tasksummary_tasks: Task-specific statistics and metadataversions: Version information for tasks and datasetsresults: Actual evaluation metrics and scores for each task{
"config_general": {
"lighteval_sha": "203045a8431bc9b77245c9998e05fc54509ea07f",
"num_fewshot_seeds": 1,
"max_samples": 1,
"job_id": "",
"start_time": 620979.879320166,
"end_time": 621004.632108041,
"total_evaluation_time_secondes": "24.752787875011563",
"model_name": "gpt2",
"model_sha": "607a30d783dfa663caf39e06633721c8d4cfcd7e",
"model_dtype": null,
"model_size": "476.2 MB"
},
"results": {
"lighteval|gsm8k|0": {
"em": 0.0,
"em_stderr": 0.0,
"maj@8": 0.0,
"maj@8_stderr": 0.0
},
"all": {
"em": 0.0,
"em_stderr": 0.0,
"maj@8": 0.0,
"maj@8_stderr": 0.0
}
},
"versions": {
"lighteval|gsm8k|0": 0
},
"config_tasks": {
"lighteval|gsm8k": {
"name": "gsm8k",
"prompt_function": "gsm8k",
"hf_repo": "gsm8k",
"hf_subset": "main",
"metric": [
{
"metric_name": "em",
"higher_is_better": true,
"category": "3",
"use_case": "5",
"sample_level_fn": "compute",
"corpus_level_fn": "mean"
},
{
"metric_name": "maj@8",
"higher_is_better": true,
"category": "5",
"use_case": "5",
"sample_level_fn": "compute",
"corpus_level_fn": "mean"
}
],
"hf_avail_splits": [
"train",
"test"
],
"evaluation_splits": [
"test"
],
"few_shots_split": null,
"few_shots_select": "random_sampling_from_train",
"generation_size": 256,
"generation_grammar": null,
"stop_sequence": [
"Question="
],
"num_samples": null,
"suite": [
"lighteval"
],
"original_num_docs": 1319,
"effective_num_docs": 1,
"must_remove_duplicate_docs": null,
"version": 0
}
},
"summary_tasks": {
"lighteval|gsm8k|0": {
"hashes": {
"hash_examples": "8517d5bf7e880086",
"hash_full_prompts": "8517d5bf7e880086",
"hash_input_tokens": "29916e7afe5cb51d",
"hash_cont_tokens": "37f91ce23ef6d435"
},
"padded": 0,
"non_padded": 2,
"effective_few_shots": 0.0,
}
},
"summary_general": {
"hashes": {
"hash_examples": "5f383c395f01096e",
"hash_full_prompts": "5f383c395f01096e",
"hash_input_tokens": "ac933feb14f96d7b",
"hash_cont_tokens": "9d03fb26f8da7277"
},
"padded": 0,
"non_padded": 2,
}
}