Lighteval will automatically save results and evaluation details in the
directory set with the --output-dir option. The results will be 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, hf hub, gdrive, ftp, etc).
To save the details of the evaluation, you can use the --save-details
option. The details will be saved in a parquet file
{output_dir}/details/{model_name}/{timestamp}/details_{task}_{timestamp}.parquet.
You can push the results and evaluation details to the HuggingFace hub. To do
so, you need to set the --push-to-hub as well as the --results-org
option. The results will be saved in a dataset with the name at
{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 the results to Tensorboard by setting --push-to-tensorboard.
This will create a Tensorboard dashboard in a HF org set with the --results-org
option.
from datasets import load_dataset
import os
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_org}/{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:
choices: The choices presented to the model in the case of mutlichoice tasks.gold: The gold answer.gold_index: The index of the gold answer in the choices list.cont_tokens: The continuation tokens.example: The input in text form.full_prompt: The full prompt, that will be inputed to the model.input_tokens: The tokens of the full prompt.instruction: The instruction given to the model.metrics: The metrics computed for the example.num_asked_few_shots: The number of few shots asked to the model.num_effective_few_shots: The number of effective few shots.padded: Whether the input was padded.pred_logits: The logits of the model.predictions: The predictions of the model.specifics: The specifics of the task.truncated: Whether the input was truncated.{
"config_general": {
"lighteval_sha": "203045a8431bc9b77245c9998e05fc54509ea07f",
"num_fewshot_seeds": 1,
"override_batch_size": 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": {
"qem": 0.0,
"qem_stderr": 0.0,
"maj@8": 0.0,
"maj@8_stderr": 0.0
},
"all": {
"qem": 0.0,
"qem_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": "qem",
"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,
"trust_dataset": true,
"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"
},
"truncated": 2,
"non_truncated": 0,
"padded": 0,
"non_padded": 2,
"effective_few_shots": 0.0,
"num_truncated_few_shots": 0
}
},
"summary_general": {
"hashes": {
"hash_examples": "5f383c395f01096e",
"hash_full_prompts": "5f383c395f01096e",
"hash_input_tokens": "ac933feb14f96d7b",
"hash_cont_tokens": "9d03fb26f8da7277"
},
"truncated": 2,
"non_truncated": 0,
"padded": 0,
"non_padded": 2,
"num_truncated_few_shots": 0
}
}