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sqc_score.py
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sqc_score.py
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import json
import os
import re
import copy
import torch
import argparse
import numpy as np
from tqdm import tqdm
from train import train
from menli.MENLI import MENLI
from collections import OrderedDict
from transformers import AutoModelForCausalLM, AutoTokenizer
def post_process_grading_process(text, gold_answer, test_answer):
if "That's the end of my answer" in text:
text = text[text.find('<|assistant|>'):]
text = text[:text.find('That\'s the end of my answer.')]
else:
return None
grading_list = list(set(text.split('\n')))
good_match = 0
good_match_list = []
for grading in grading_list:
if re.findall(r'\d+', grading) == []:
continue
valid = False
for gold_point in gold_answer:
gold_point = gold_point.replace('.', '').strip()
gold_point = gold_point.replace('(2 points)', '')
if gold_point in grading:
valid = True
break
if not valid:
continue
for test_point in test_answer:
test_point = test_point.replace('.', '').strip()
if test_point in grading:
if gold_point == 'none':
if test_point == 'none':
good_match += 1
good_match_list.append(grading)
break
else:
if all(int(x) <= 0 for x in re.findall(r'\d+', grading)):
continue
else:
good_match += 1
good_match_list.append(grading)
break
if good_match == 0:
P = 0
F = 0
R = 0
else:
P = min(1, good_match / len(test_answer))
R = min(1, good_match / len(gold_answer))
F = min(1, 2 * P * R / (P + R))
return ((P, R, F), good_match_list)
def paraphrase(triples, device='cuda'):
with open('./prompt_paraphrase.txt', 'r') as f:
prompt = f.read()
paraphrases = []
model = AutoModelForCausalLM.from_pretrained("mistralai/Mistral-7B-Instruct-v0.2", torch_dtype=torch.bfloat16).to(device)
tokenizer = AutoTokenizer.from_pretrained("mistralai/Mistral-7B-Instruct-v0.2")
for triple_list in triples:
if len(triple_list) != 3:
continue
triple = {'h': triple_list[0], 'r': triple_list[1], 't': triple_list[2]}
new_prompt = prompt + '\n' + str(triple)
messages = [
{'role': 'user', 'content': new_prompt}
]
encodeds = tokenizer.apply_chat_template(messages, return_tensors="pt")
model_inputs = encodeds.to(device)
generated_ids = model.generate(model_inputs, max_new_tokens=128, do_sample=True)
decoded = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
decoded = decoded[decoded.find('[/INST]') + 7:]
decoded = decoded[:decoded.find('(')]
paraphrases.append(decoded.strip())
return paraphrases
def get_threshold(dataset, ratio):
assert ratio > 0 and ratio < 1, "ratio should be in (0, 1)"
# refs -> list of strings
refs = [data['text'] for data in dataset]
# hyps -> list of list of strings
hyps = [[s.replace("(2 points)", '')] for data in dataset for s in data["standard_answer"]]
scores = []
nli_scorer = MENLI(direction="rh", formula="e", nli_weight=1.0, combine_with="None", model="D")
for ref, hyp in zip(refs, hyps):
score = []
ref = [copy.deepcopy(ref) for i in range(len(hyp))]
nli_score = nli_scorer.score_all([], ref, hyp)
score = [float(s) for s in nli_score]
scores.append(score)
scores = [s for score in scores for s in score]
scores = np.array(sorted(scores))
threshold = np.percentile(scores, 100 * (1 - ratio))
print(f"Now NLI threshold is {threshold}")
return threshold
def get_nli_valid(dataset, threshold):
refs = [data['text'] for data in dataset]
hyps = [data['student_answer'] for data in dataset]
scores = []
nli_scorer = MENLI(direction="rh", formula="e", nli_weight=1.0, combine_with="None", model="D")
for ref, hyp in zip(refs, hyps):
score = []
ref = [copy.deepcopy(ref) for i in range(len(hyp))]
nli_score = nli_scorer.score_all([], ref, hyp)
score = [float(s) for s in nli_score]
scores.append(score)
valid = []
for score in scores:
cur_valid = []
for s in score:
cur_valid.append(s > threshold)
valid.append(cur_valid)
return valid
def report(dataset):
precisions = [data['Precision'] for data in dataset]
recalls = [data['Recall'] for data in dataset]
f1s = [data['F1'] for data in dataset]
precision = sum(precisions) / len(precisions)
recall = sum(recalls) / len(recalls)
f1 = sum(f1s) / len(f1s)
print(f'Precision: {precision}')
print(f'Recall: {recall}')
print(f'F1: {f1}')
def sqc_score(in_file, score_model, out_dir=None, do_nli=True, gold_ratio=0.4):
os.makedirs(out_dir, exist_ok=True)
dataset = []
with open(in_file, 'r') as f:
dataset = json.load(f)
device = "cuda" if torch.cuda.is_available() else "cpu"
os.makedirs(out_dir, exist_ok=True)
out_path = os.path.join(out_dir, 'scored_' + os.path.basename(in_file))
model = AutoModelForCausalLM.from_pretrained(score_model).to(device)
tokenizer = AutoTokenizer.from_pretrained(score_model)
for data in tqdm(dataset):
gold = data['standard_answer']
gold_list = paraphrase(gold)
part_score = 2
total_score = part_score * len(gold_list)
gold_list = [f'{s}(2 points)' for s in gold_list]
gold_str = '\n'.join(gold_list)
unique_tuples = list(OrderedDict.fromkeys(tuple(item) for item in data['student_answer']))
test = [list(item) for item in unique_tuples]
test_list = paraphrase(test)
test_str = '\n'.join(test_list)
with open("./prompt.txt", "r") as f:
prompt = f.read()
prompt += "\n\nStandard Answer:\n" + gold_str + "\n<end-of-standard-answer>" + "\n\nStudent Answer:\n" + test_str + "\n<end-of-student-answer>" + "\n\nTotal Score:\n" + str(total_score) + " points"
if 'tulu' in score_model.lower():
prompt = '<|user|>\n' + prompt + '\n<|assistant|>'
input_ids = tokenizer(prompt, return_tensors='pt')[
'input_ids'].to(device)
pred = model.generate(input_ids, max_new_tokens=512, do_sample=True)
pred = tokenizer.decode(pred[0], skip_special_tokens=True)
assert pred != [], "No grading process generated"
results = post_process_grading_process(
pred, gold_list, test_list)
while results is None:
pred = model.generate(input_ids, max_new_tokens=256, do_sample=True)
pred = tokenizer.decode(pred[0], skip_special_tokens=True)
results = post_process_grading_process(
pred, gold_list, test_list)
P, R, F = results[0]
good_match_list = results[1]
data['standard_answer'] = gold_list
data['student_answer'] = test_list
data['Precision'] = P
data['Recall'] = R
data['F1'] = F
data['good_match'] = good_match_list
if not do_nli:
report(dataset)
print(f"Writing to {out_path}")
with open(out_path, 'w') as f:
for data in dataset:
f.write(json.dumps(data) + '\n')
return
nli_threshold = get_threshold(dataset, gold_ratio)
for data in dataset:
if data['F1'] == 0:
continue
for match in data['good_match']:
for gold_point in data['standard_answer']:
if gold_point in match:
data['standard_answer'].remove(gold_point)
for test_point in data['student_answer']:
if test_point in match:
data['student_answer'].remove(test_point)
nlid_valid = get_nli_valid(dataset, nli_threshold)
for data, valid in zip(dataset, nlid_valid):
cur_data_nli_valid = len([v for v in valid if v])
gold_num = len(data['standard_answer']) + cur_data_nli_valid
test_num = len(data['student_answer'])
correct_num = len(data['good_match']) + cur_data_nli_valid
P = correct_num / test_num
R = correct_num / gold_num
F = 2 * P * R / (P + R)
print(f"Precision: {P}, Recall: {R}, F1: {F}")
with open(out_path, 'w') as f:
for data in dataset:
f.write(json.dumps(data) + '\n')
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--in_file', type=str, required=True, help='input file')
parser.add_argument('--score_model', type=str, required=True, help='score model')
parser.add_argument('--out_dir', type=str, required=True, help='output directory')
parser.add_argument('--do-nli', type=str, required=True, help='whether to use nli')
args = parser.parse_args()
sqc_score(args.in_file, args.score_model, args.out_dir, args.do_nli)
if __name__ == '__main__':
main()