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| """Generate json file for webpage.""" | |
| import json | |
| import os | |
| import re | |
| # models = ['llama', 'alpaca', 'gpt35', 'bard'] | |
| models = ["vicuna"] | |
| def read_jsonl(path: str, key: str = None): | |
| data = [] | |
| with open(os.path.expanduser(path)) as f: | |
| for line in f: | |
| if not line: | |
| continue | |
| data.append(json.loads(line)) | |
| if key is not None: | |
| data.sort(key=lambda x: x[key]) | |
| data = {item[key]: item for item in data} | |
| return data | |
| def trim_hanging_lines(s: str, n: int) -> str: | |
| s = s.strip() | |
| for _ in range(n): | |
| s = s.split("\n", 1)[1].strip() | |
| return s | |
| if __name__ == "__main__": | |
| questions = read_jsonl("table/question.jsonl", key="question_id") | |
| # alpaca_answers = read_jsonl('table/answer/answer_alpaca-13b.jsonl', key='question_id') | |
| # bard_answers = read_jsonl('table/answer/answer_bard.jsonl', key='question_id') | |
| # gpt35_answers = read_jsonl('table/answer/answer_gpt35.jsonl', key='question_id') | |
| # llama_answers = read_jsonl('table/answer/answer_llama-13b.jsonl', key='question_id') | |
| vicuna_answers = read_jsonl( | |
| "table/answer/answer_vicuna-13b.jsonl", key="question_id" | |
| ) | |
| ours_answers = read_jsonl( | |
| "table/results/llama-13b-hf-alpaca.jsonl", key="question_id" | |
| ) | |
| review_vicuna = read_jsonl( | |
| "table/review/review_vicuna-13b_llama-13b-hf-alpaca.jsonl", key="question_id" | |
| ) | |
| # review_alpaca = read_jsonl('table/review/review_alpaca-13b_vicuna-13b.jsonl', key='question_id') | |
| # review_bard = read_jsonl('table/review/review_bard_vicuna-13b.jsonl', key='question_id') | |
| # review_gpt35 = read_jsonl('table/review/review_gpt35_vicuna-13b.jsonl', key='question_id') | |
| # review_llama = read_jsonl('table/review/review_llama-13b_vicuna-13b.jsonl', key='question_id') | |
| records = [] | |
| for qid in questions.keys(): | |
| r = { | |
| "id": qid, | |
| "category": questions[qid]["category"], | |
| "question": questions[qid]["text"], | |
| "answers": { | |
| # 'alpaca': alpaca_answers[qid]['text'], | |
| # 'llama': llama_answers[qid]['text'], | |
| # 'bard': bard_answers[qid]['text'], | |
| # 'gpt35': gpt35_answers[qid]['text'], | |
| "vicuna": vicuna_answers[qid]["text"], | |
| "ours": ours_answers[qid]["text"], | |
| }, | |
| "evaluations": { | |
| # 'alpaca': review_alpaca[qid]['text'], | |
| # 'llama': review_llama[qid]['text'], | |
| # 'bard': review_bard[qid]['text'], | |
| "vicuna": review_vicuna[qid]["content"], | |
| # 'gpt35': review_gpt35[qid]['text'], | |
| }, | |
| "scores": { | |
| "vicuna": review_vicuna[qid]["tuple"], | |
| # 'alpaca': review_alpaca[qid]['score'], | |
| # 'llama': review_llama[qid]['score'], | |
| # 'bard': review_bard[qid]['score'], | |
| # 'gpt35': review_gpt35[qid]['score'], | |
| }, | |
| } | |
| # cleanup data | |
| cleaned_evals = {} | |
| for k, v in r["evaluations"].items(): | |
| v = v.strip() | |
| lines = v.split("\n") | |
| # trim the first line if it's a pair of numbers | |
| if re.match(r"\d+[, ]+\d+", lines[0]): | |
| lines = lines[1:] | |
| v = "\n".join(lines) | |
| cleaned_evals[k] = v.replace("Assistant 1", "**Assistant 1**").replace( | |
| "Assistant 2", "**Assistant 2**" | |
| ) | |
| r["evaluations"] = cleaned_evals | |
| records.append(r) | |
| # Reorder the records, this is optional | |
| for r in records: | |
| if r["id"] <= 20: | |
| r["id"] += 60 | |
| else: | |
| r["id"] -= 20 | |
| for r in records: | |
| if r["id"] <= 50: | |
| r["id"] += 10 | |
| elif 50 < r["id"] <= 60: | |
| r["id"] -= 50 | |
| for r in records: | |
| if r["id"] == 7: | |
| r["id"] = 1 | |
| elif r["id"] < 7: | |
| r["id"] += 1 | |
| records.sort(key=lambda x: x["id"]) | |
| # Write to file | |
| with open("webpage/data.json", "w") as f: | |
| json.dump({"questions": records, "models": models}, f, indent=2) | |