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import os
import numpy as np
import wandb
import json
import argparse
RANDOM_BASELINE={
"arc_challenge": 0.2502, # Source: https://arxiv.org/pdf/1803.05457.pdf table 6
"arc_easy": 0.2502, # Source: https://arxiv.org/pdf/1803.05457.pdf table 6
"boolq": 0.5,
"copa": 0.5,
"headqa_en": 0.25,
"hellaswag": 0.25,
"lambada": 0., # Safe to say that random models won't perform well at all.
"logiqa": 0.25,
"mathqa": (4360 * 1/ 5 - (4475 - 4360) * 1/ 4) / 4475,
"mrpc": 0.5,
"multirc": 0., # TODO: I couldn't figure it out
"openbookqa": 0.25,
"piqa": 0.5,
"prost": 0.25,
"pubmedqa": 1/3,
"qnli": 0.5,
"qqp": 0.5,
"race": 0.25, # Source: https://arxiv.org/pdf/1704.04683.pdf table 5
"rte": 0.5,
"sciq": 0.25,
"sst": 0.5,
"triviaqa": 0.,
"webqs": 0.,
"wic": 0.5,
"winogrande": 0.5,
"wnli": 0.5,
"wsc": 0.5
}
def normalise(score, task):
return (score - RANDOM_BASELINE[task]) / (1. - RANDOM_BASELINE[task])
def parse_args():
parser = argparse.ArgumentParser()
parser.add_argument("--input_files", type=lambda s: s.split(','), required=True)
parser.add_argument("--all_tasks", action="store_true")
parser.add_argument("--naive_average", action="store_true")
parser.add_argument("--acc_average", action="store_true")
parser.add_argument("--normalised_acc_average", action="store_true")
return parser.parse_args()
def main():
args = parse_args()
for input_file in args.input_files:
assert os.path.basename(input_file).endswith("_agg.json")
experiment_name = os.path.basename(input_file).split("_agg.json")[0]
with open(input_file, "r") as fi:
experiment = json.load(fi)
results = experiment["results"]
tokens = experiment["tokens"]
run = wandb.init(project="bigscience-tr3-evaluation-through-training", entity="timerobber", name=experiment_name,
reinit=True)
for i, n_tokens in enumerate(tokens):
all_values = []
acc_average = []
normalised_acc_average = []
for task, task_results in results.items():
values = None
for metric, values in task_results.items():
if args.all_tasks:
wandb.log({f"{task}_{metric}": values[i], "tokens": tokens[i]})
if "stderr" not in metric and "ppl" not in metric:
all_values.append(values[i])
if metric == "acc":
acc_average.append(values[i])
normalised_acc_average.append(normalise(values[i], task))
if args.naive_average:
wandb.log({f"naive_average": np.mean(all_values), "tokens": tokens[i]})
if args.acc_average:
wandb.log({f"acc_average": np.mean(acc_average), "tokens": tokens[i]})
if args.normalised_acc_average:
wandb.log({f"normalised_acc_average": np.mean(normalised_acc_average), "tokens": tokens[i]})
run.finish()
if __name__ == "__main__":
main()
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