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import os
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import argparse
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import re
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import torch
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import pandas as pd
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from thefuzz import process
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from tqdm import tqdm
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from transformers.trainer_utils import set_seed
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from transformers import AutoModelForCausalLM, AutoTokenizer
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from transformers.generation import GenerationConfig
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'''
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wget https://huggingface.co/datasets/ceval/ceval-exam/resolve/main/ceval-exam.zip
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mkdir data/ceval
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mv ceval-exam.zip data/ceval
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cd data/ceval; unzip ceval-exam.zip
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cd ../../
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pip install thefuzz
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python eval/evaluate_chat_ceval.py -d data/ceval
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'''
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def load_models_tokenizer(args):
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tokenizer = AutoTokenizer.from_pretrained(
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args.checkpoint_path, trust_remote_code=True
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)
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model = AutoModelForCausalLM.from_pretrained(
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args.checkpoint_path, device_map="auto", trust_remote_code=True
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).eval()
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model.generation_config = GenerationConfig.from_pretrained(
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args.checkpoint_path, trust_remote_code=True
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)
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model.generation_config.do_sample = False # use greedy decoding
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model.generation_config.repetition_penalty = 1.0 # disable repetition penalty
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return model, tokenizer
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def process_before_extraction(gen, question, choice_dict):
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# Example Prompt:
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# 关于传输层的面向连接服务的特性是____。
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# A. 既不保证可靠，也不保证按序交付
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# B. 不保证可靠，但保证按序交付
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# C. 保证可靠，但不保证按序交付
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# D. 既保证可靠，也保证按序交付
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# Example Model Output：
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# 关于传输层的面向连接服务的特性是既保证可靠，也保证按序交付
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# Processed Output:
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# 答案是D
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question_split = question.rstrip("。").split("。")[-1].split("_")
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# replacing the question
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if len(question_split[0].strip()) > 4:
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gen = gen.replace(question_split[0], "答案是")
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if len(question_split[-1].strip()) > 4:
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gen = gen.replace(question_split[-1], "")
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# replace the choice by letter in the generated sentence
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# from longest one to shortest one
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for key, val in sorted(choice_dict.items(), key=lambda x: len(x[1]), reverse=True):
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gen = gen.replace(val.rstrip("。"), key)
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return gen
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def count_substr(gen, pattern):
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return len(re.findall(pattern, gen))
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def extract_choice(gen, prompt, choice_list):
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# 答案是A | 选项是A | 应该选A选项
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res = re.search(
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r"(?:(?:选|选择|选定)[：:]?\s*|(?:(?:答案|选项)(?![^ABCD]{0,10}?(?:不|非)[^ABCD]{0,10}?(?:是|选|为|：|:|】))[^ABCD]{0,10}?(?:是|选|为|：|:|】))[^ABCD]{0,10}?)(A|B|C|D)(?:选项)?(?:\)|。|\.|，|,|．|、|A|B|C|D|$|：|:|\)|）)",
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gen,
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)
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# A选项正确 | A选项符合题意
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if res is None:
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res = re.search(
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r"(A|B|C|D)(?:选?项)?(?![^ABCD]{0,4}?(?:不|非)[^ABCD]{0,4}?(?:正确|对[的，。：]|符合))[^ABCD]{0,4}?(?:正确|对[的，。：]|符合)",
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gen,
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)
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# 直接输出 A
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if res is None:
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res = re.search(r"^[\(（]?(A|B|C|D)(?:。|\)|）|\.|，|,|．|：|:|$)", gen)
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# 获取第一个出现的字母
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if res is None:
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res = re.search(r"(?<![a-zA-Z])(A|B|C|D)(?![a-zA-Z=])", gen)
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if res is None:
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return choices[choice_list.index(process.extractOne(gen, choice_list)[0])]
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return res.group(1)
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def format_example(line):
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example = line["question"] + "\n\n"
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for choice in choices:
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example += f'{choice}. {line[f"{choice}"]}\n'
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return example
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def extract_answer(response, row):
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prompt = row["question"]
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gen = process_before_extraction(
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response, prompt, {choice: row[choice] for choice in choices}
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)
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if not isinstance(prompt, str):
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prompt = prompt[0]
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pred = extract_choice(gen, prompt, [row[choice] for choice in choices])
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return pred
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@torch.no_grad()
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def eval_subject(
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model,
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tokenizer,
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subject_name,
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test_df,
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save_result_dir=None,
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overwrite=False,
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**kwargs
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):
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result_path = os.path.join(save_result_dir, f"{subject_name}_result.csv")
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if not overwrite and os.path.exists(result_path):
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print(f"{result_path} existed, skip!")
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score = []
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for (_, datarow), (_, resultrow) in zip(
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test_df.iterrows(), pd.read_csv(result_path).iterrows()
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):
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pred = extract_answer(resultrow["model_response"], datarow)
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correct = 1 if pred == datarow["answer"] else 0
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score.append(correct)
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correct_ratio = 100 * sum(score) / len(score)
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return correct_ratio
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responses = []
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result = []
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score = []
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for _, row in tqdm(test_df.iterrows(), total=len(test_df)):
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question = format_example(row)
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response, _ = model.chat(
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tokenizer,
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question,
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history=None,
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)
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print(question)
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print(response)
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pred = extract_answer(response, row)
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print(pred)
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print("======================")
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if "answer" in row:
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correct = 1 if pred == row["answer"] else 0
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score.append(correct)
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if args.debug:
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print(f'{question} pred: {pred} ref: {row["answer"]}')
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responses.append(response)
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result.append(pred)
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if score:
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correct_ratio = 100 * sum(score) / len(score)
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if args.debug:
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print(subject_name, correct_ratio)
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else:
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correct_ratio = 0
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if save_result_dir:
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test_df["model_response"] = responses
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test_df["model_output"] = result
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if score:
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test_df["correctness"] = score
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os.makedirs(save_result_dir, exist_ok=True)
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test_df.to_csv(result_path, encoding="utf-8", index=False)
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return correct_ratio
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def cal_ceval(res):
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acc_sum_dict = dict()
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acc_norm_sum_dict = dict()
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cnt_dict = dict()
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acc_sum = 0.0
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cnt = 0
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hard_cnt = 0
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hard_acc_sum = 0.0
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for tt in res.keys():
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name = tt.split("-")[-1]
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acc_sum += float(res[tt])
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cnt += 1
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class_ = TASK_NAME_MAPPING[name][2]
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if class_ not in acc_sum_dict:
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acc_sum_dict[class_] = 0.0
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acc_norm_sum_dict[class_] = 0.0
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cnt_dict[class_] = 0.0
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if name in hard_list:
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hard_cnt += 1
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hard_acc_sum += float(res[tt])
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acc_sum_dict[class_] += float(res[tt])
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cnt_dict[class_] += 1
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print("\n\n\n")
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for k in ["STEM", "Social Science", "Humanities", "Other"]:
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if k in cnt_dict:
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print("%s acc: %.2f " % (k, acc_sum_dict[k] / cnt_dict[k]))
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if hard_cnt > 0:
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print("Hard acc:%.2f " % (hard_acc_sum / hard_cnt))
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print("AVERAGE acc:%.2f " % (acc_sum / cnt))
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TASK_NAME_MAPPING = {
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"computer_network": ["Computer Network", "\u8ba1\u7b97\u673a\u7f51\u7edc", "STEM"],
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"operating_system": ["Operating System", "\u64cd\u4f5c\u7cfb\u7edf", "STEM"],
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"computer_architecture": [
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"Computer Architecture",
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"\u8ba1\u7b97\u673a\u7ec4\u6210",
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"STEM",
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],
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"college_programming": ["College Programming", "\u5927\u5b66\u7f16\u7a0b", "STEM"],
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"college_physics": ["College Physics", "\u5927\u5b66\u7269\u7406", "STEM"],
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"college_chemistry": ["College Chemistry", "\u5927\u5b66\u5316\u5b66", "STEM"],
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"advanced_mathematics": [
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"Advanced Mathematics",
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"\u9ad8\u7b49\u6570\u5b66",
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"STEM",
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],
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"probability_and_statistics": [
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"Probability and Statistics",
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"\u6982\u7387\u7edf\u8ba1",
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"STEM",
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],
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"discrete_mathematics": [
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"Discrete Mathematics",
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"\u79bb\u6563\u6570\u5b66",
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"STEM",
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],
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"electrical_engineer": [
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"Electrical Engineer",
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"\u6ce8\u518c\u7535\u6c14\u5de5\u7a0b\u5e08",
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"STEM",
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],
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"metrology_engineer": [
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"Metrology Engineer",
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"\u6ce8\u518c\u8ba1\u91cf\u5e08",
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"STEM",
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],
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"high_school_mathematics": [
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"High School Mathematics",
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"\u9ad8\u4e2d\u6570\u5b66",
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"STEM",
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],
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"high_school_physics": ["High School Physics", "\u9ad8\u4e2d\u7269\u7406", "STEM"],
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"high_school_chemistry": [
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"High School Chemistry",
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"\u9ad8\u4e2d\u5316\u5b66",
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"STEM",
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],
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"high_school_biology": ["High School Biology", "\u9ad8\u4e2d\u751f\u7269", "STEM"],
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"middle_school_mathematics": [
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"Middle School Mathematics",
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"\u521d\u4e2d\u6570\u5b66",
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"STEM",
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],
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"middle_school_biology": [
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"Middle School Biology",
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"\u521d\u4e2d\u751f\u7269",
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"STEM",
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],
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"middle_school_physics": [
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"Middle School Physics",
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"\u521d\u4e2d\u7269\u7406",
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"STEM",
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],
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"middle_school_chemistry": [
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"Middle School Chemistry",
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"\u521d\u4e2d\u5316\u5b66",
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"STEM",
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],
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"veterinary_medicine": ["Veterinary Medicine", "\u517d\u533b\u5b66", "STEM"],
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"college_economics": [
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"College Economics",
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"\u5927\u5b66\u7ecf\u6d4e\u5b66",
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"Social Science",
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],
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"business_administration": [
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"Business Administration",
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"\u5de5\u5546\u7ba1\u7406",
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"Social Science",
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],
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"marxism": [
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"Marxism",
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"\u9a6c\u514b\u601d\u4e3b\u4e49\u57fa\u672c\u539f\u7406",
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"Social Science",
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],
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"mao_zedong_thought": [
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"Mao Zedong Thought",
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"\u6bdb\u6cfd\u4e1c\u601d\u60f3\u548c\u4e2d\u56fd\u7279\u8272\u793e\u4f1a\u4e3b\u4e49\u7406\u8bba\u4f53\u7cfb\u6982\u8bba",
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"Social Science",
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],
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"education_science": ["Education Science", "\u6559\u80b2\u5b66", "Social Science"],
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"teacher_qualification": [
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"Teacher Qualification",
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"\u6559\u5e08\u8d44\u683c",
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"Social Science",
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],
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"high_school_politics": [
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"High School Politics",
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"\u9ad8\u4e2d\u653f\u6cbb",
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"Social Science",
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],
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"high_school_geography": [
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"High School Geography",
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"\u9ad8\u4e2d\u5730\u7406",
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"Social Science",
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],
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"middle_school_politics": [
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"Middle School Politics",
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"\u521d\u4e2d\u653f\u6cbb",
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"Social Science",
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],
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"middle_school_geography": [
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"Middle School Geography",
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"\u521d\u4e2d\u5730\u7406",
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"Social Science",
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],
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"modern_chinese_history": [
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"Modern Chinese History",
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"\u8fd1\u4ee3\u53f2\u7eb2\u8981",
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"Humanities",
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],
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"ideological_and_moral_cultivation": [
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"Ideological and Moral Cultivation",
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"\u601d\u60f3\u9053\u5fb7\u4fee\u517b\u4e0e\u6cd5\u5f8b\u57fa\u7840",
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"Humanities",
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],
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"logic": ["Logic", "\u903b\u8f91\u5b66", "Humanities"],
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"law": ["Law", "\u6cd5\u5b66", "Humanities"],
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"chinese_language_and_literature": [
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"Chinese Language and Literature",
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"\u4e2d\u56fd\u8bed\u8a00\u6587\u5b66",
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"Humanities",
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],
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"art_studies": ["Art Studies", "\u827a\u672f\u5b66", "Humanities"],
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|
|
"professional_tour_guide": [
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|
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"Professional Tour Guide",
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|
"\u5bfc\u6e38\u8d44\u683c",
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|
|
"Humanities",
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],
|
|
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"legal_professional": [
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|
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"Legal Professional",
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|
"\u6cd5\u5f8b\u804c\u4e1a\u8d44\u683c",
|
|
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"Humanities",
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],
|
|
|
"high_school_chinese": [
|
|
|
"High School Chinese",
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|
|
"\u9ad8\u4e2d\u8bed\u6587",
|
|
|
"Humanities",
|
|
|
],
|
|
|
"high_school_history": [
|
|
|
"High School History",
|
|
|
"\u9ad8\u4e2d\u5386\u53f2",
|
|
|
"Humanities",
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|
|
],
|
|
|
"middle_school_history": [
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|
|
"Middle School History",
|
|
|
"\u521d\u4e2d\u5386\u53f2",
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|
|
"Humanities",
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|
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],
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|
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"civil_servant": ["Civil Servant", "\u516c\u52a1\u5458", "Other"],
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|
|
"sports_science": ["Sports Science", "\u4f53\u80b2\u5b66", "Other"],
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|
|
"plant_protection": ["Plant Protection", "\u690d\u7269\u4fdd\u62a4", "Other"],
|
|
|
"basic_medicine": ["Basic Medicine", "\u57fa\u7840\u533b\u5b66", "Other"],
|
|
|
"clinical_medicine": ["Clinical Medicine", "\u4e34\u5e8a\u533b\u5b66", "Other"],
|
|
|
"urban_and_rural_planner": [
|
|
|
"Urban and Rural Planner",
|
|
|
"\u6ce8\u518c\u57ce\u4e61\u89c4\u5212\u5e08",
|
|
|
"Other",
|
|
|
],
|
|
|
"accountant": ["Accountant", "\u6ce8\u518c\u4f1a\u8ba1\u5e08", "Other"],
|
|
|
"fire_engineer": [
|
|
|
"Fire Engineer",
|
|
|
"\u6ce8\u518c\u6d88\u9632\u5de5\u7a0b\u5e08",
|
|
|
"Other",
|
|
|
],
|
|
|
"environmental_impact_assessment_engineer": [
|
|
|
"Environmental Impact Assessment Engineer",
|
|
|
"\u73af\u5883\u5f71\u54cd\u8bc4\u4ef7\u5de5\u7a0b\u5e08",
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|
"Other",
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|
],
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|
"tax_accountant": ["Tax Accountant", "\u7a0e\u52a1\u5e08", "Other"],
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"physician": ["Physician", "\u533b\u5e08\u8d44\u683c", "Other"],
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|
}
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|
|
hard_list = [
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|
"advanced_mathematics",
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|
"discrete_mathematics",
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|
|
"probability_and_statistics",
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|
|
"college_physics",
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|
|
"college_chemistry",
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|
|
"high_school_mathematics",
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|
|
"high_school_physics",
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|
|
"high_school_chemistry",
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|
|
]
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|
|
choices = ["A", "B", "C", "D"]
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|
|
|
|
|
|
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|
def main(args):
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|
print("loading model weights")
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|
|
if args.checkpoint_path:
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model, tokenizer = load_models_tokenizer(args)
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|
else:
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|
|
model, tokenizer = None, None
|
|
|
print("model loaded")
|
|
|
dev_result = {}
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|
|
for subject_name in tqdm(TASK_NAME_MAPPING.keys()):
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|
|
val_file_path = os.path.join(
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|
|
args.eval_data_path, "val", f"{subject_name}_val.csv"
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|
|
)
|
|
|
val_df = pd.read_csv(val_file_path)
|
|
|
|
|
|
score = eval_subject(
|
|
|
model,
|
|
|
tokenizer,
|
|
|
subject_name,
|
|
|
val_df,
|
|
|
save_result_dir="outs_chat/ceval_eval_result",
|
|
|
overwrite=args.overwrite,
|
|
|
)
|
|
|
dev_result[subject_name] = score
|
|
|
cal_ceval(dev_result)
|
|
|
|
|
|
|
|
|
if __name__ == "__main__":
|
|
|
parser = argparse.ArgumentParser(description="Test HF checkpoint.")
|
|
|
parser.add_argument(
|
|
|
"-c",
|
|
|
"--checkpoint-path",
|
|
|
type=str,
|
|
|
help="Checkpoint path",
|
|
|
default="Qwen/Qwen-7B-Chat",
|
|
|
)
|
|
|
parser.add_argument("-s", "--seed", type=int, default=1234, help="Random seed")
|
|
|
|
|
|
# Provide extra arguments required for tasks
|
|
|
group = parser.add_argument_group(title="Evaluation options")
|
|
|
group.add_argument(
|
|
|
"-d", "--eval_data_path", type=str, required=True, help="Path to eval data"
|
|
|
)
|
|
|
group.add_argument(
|
|
|
"--debug", action="store_true", default=False, help="Print infos."
|
|
|
)
|
|
|
group.add_argument(
|
|
|
"--overwrite",
|
|
|
action="store_true",
|
|
|
default=False,
|
|
|
help="Overwrite existed results",
|
|
|
)
|
|
|
|
|
|
args = parser.parse_args()
|
|
|
set_seed(args.seed)
|
|
|
|
|
|
main(args)
|