Add results parser

parse_results.py

@@ -0,0 +1,76 @@
+"""Parse all results and upload it into a single leaderboard"""
+
+import argparse
+import os
+import sys
+import logging
+import json
+from typing import Any
+
+import pandas as pd
+from datasets import load_dataset, Dataset, DownloadMode
+from huggingface_hub import list_datasets
+
+logging.basicConfig(
+    format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
+    datefmt="%Y-%m-%d %H:%M:%S",
+    handlers=[logging.StreamHandler(sys.stdout)],
+    level=logging.INFO,
+)
+
+
+def get_args():
+    # fmt: off
+    parser = argparse.ArgumentParser(description="Parse all results from datasets of a given HF org, and upload it into a new dataset.")
+    parser.add_argument("--hf_org", type=str, default="UD-Filipino", help="HuggingFace org to parse results from.")
+    parser.add_argument("--hf_repo_output", type=str, default="UD-Filipino/filbench-results", help="HuggingFace dataset to upload all parsed results.")
+    # fmt: on
+    return parser.parse_args()
+
+
+def main():
+    args = get_args()
+    if not os.getenv("HF_TOKEN"):
+        raise ValueError("HF_TOKEN environment variable not set!")
+
+    # List datasets with 'details' in their name within a given org
+    datasets = [ds.id for ds in list_datasets(search="details", author=args.hf_org)]
+    logging.info(f"Found {len(datasets)} datasets")
+    parsed_results = pd.DataFrame([parse_outputs(dataset) for dataset in datasets])
+    logging.info(f"Uploading to {args.hf_repo_output}")
+    Dataset.from_pandas(parsed_results).push_to_hub(repo_id=args.hf_repo_output, private=True, split="train")
+
+
+def parse_outputs(dataset_id: str) -> dict[str, Any]:
+    """Parse a dataset ID and output a dataframe containing the relevant fields
+
+    Based from: https://huggingface.co/docs/lighteval/en/saving-and-reading-results
+    """
+    logging.info(f"Parsing results from dataset {dataset_id}")
+    ds = load_dataset(dataset_id, "results", trust_remote_code=True, download_mode=DownloadMode.FORCE_REDOWNLOAD)
+
+    # Save all metrics and versions for each task
+    metrics = {}
+    versions = {}
+    for run in ds.keys():
+        df = ds[run].to_pandas()
+        for task, result in json.loads(df.results.iloc[0]).items():
+            if task != "all":
+                metrics[task] = result
+
+        versions.update(json.loads(df.versions.iloc[0]))
+
+    logging.info(f"Found {len(metrics)} tasks!")
+
+    latest_config = json.loads(ds["latest"].to_pandas().config_general.iloc[0])
+    model_config = {
+        "model_name": latest_config.get("model_name"),
+        "model_dtype": latest_config.get("model_dtype"),
+        "model_size": latest_config.get("model_size"),
+    }
+
+    return {"config": model_config, "results": metrics, "versions": versions}
+
+
+if __name__ == "__main__":
+    main()