| import os | |
| import json | |
| from glob import glob | |
| import pandas as pd | |
| from datasets import load_dataset | |
| os.makedirs("results/flan_ul2_additional_analysis", exist_ok=True) | |
| data = load_dataset("cardiffnlp/relentless", split="test") | |
| data = {i['relation_type']: i for i in data} | |
| pred_zero = {} | |
| for i in glob("results/lm_qa_zeroshot/flan-ul2/*.jsonl"): | |
| r = os.path.basename(i).replace("__", "/").replace("_", " ").replace("ppl.", "").replace("is ", "").replace(".jsonl", "") | |
| with open(i) as f: | |
| pred_zero[r] = [json.loads(l)['perplexity'] for l in f.read().split("\n")] | |
| pred_few = {} | |
| for i in glob("results/lm_qa_1shots_0seed/flan-ul2/*.jsonl"): | |
| r = os.path.basename(i).replace("__", "/").replace("_", " ").replace("ppl.", "").replace("is ", "").replace(".jsonl", "") | |
| with open(i) as f: | |
| pred_few[r] = [json.loads(l)['perplexity'] for l in f.read().split("\n")] | |
| def get_rank(score): | |
| s2r = {s: n for n, s in enumerate(sorted(score))} | |
| return [s2r[s] for s in score] | |
| for k, v in data.items(): | |
| df = pd.DataFrame({ | |
| "pairs": v['pairs'], | |
| "score_fewshot": pred_few[k], | |
| "score_zeroshot": pred_zero[k], | |
| "score_true": v["scores_mean"], | |
| "rank_fewshot": get_rank(pred_few[k]), | |
| "rank_zeroshot": get_rank(pred_zero[k]), | |
| "rank_true": v["ranks"], | |
| }) | |
| df.to_csv(f"results/flan_ul2_additional_analysis/{k[:4]}.csv", index=False) | |