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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)
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