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import os
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import re
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from tqdm import tqdm
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import torch
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from transformers import AutoTokenizer, AutoModel, AutoConfig
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from transformers.generation.logits_process import LogitsProcessor
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from transformers.generation.utils import LogitsProcessorList
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from evaluators.evaluator import Evaluator
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class ChatGLMMixin:
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def __init__(self):
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self.tokenizer = None
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self.model = None
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self.model_name = None
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self.k = None
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self.choices = None
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self.finetune_name = None
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def eval_subject(self, subject_name, test_df, dev_df=None, few_shot=False, cot=False, save_result_dir=None):
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correct_num = 0
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result = []
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score = []
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answer_list = []
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if few_shot:
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history = self.generate_few_shot_prompt(subject_name, dev_df, cot=cot)
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else:
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history = self.generate_zero_shot_prompt(is_choice_question=True)
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answers = list(test_df['answer'])
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for row_index, row in tqdm(test_df.iterrows(), total=len(test_df)):
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question = self.format_example(row, include_answer=False, cot=cot)
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if few_shot:
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response, _ = self.model.chat(self.tokenizer, question, max_length=2000,
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do_sample=False, history=history)
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response = response.strip()
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ans, direct_extract = self.extract_cot_answer(row, response)
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else: # zero-shot by extracting answer from distribution
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response, _ = self.model.chat(self.tokenizer, question, max_length=2000,
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do_sample=False, history=history)
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response = response.strip()
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ans, direct_extract = self.extract_cot_answer(row, response)
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if ans == answers[row_index]:
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correct_num += 1
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correct = 1
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else:
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correct = 0
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if save_result_dir:
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result.append(response)
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score.append(correct)
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answer_list.append(ans)
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correct_ratio = 100 * correct_num / len(answers)
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if save_result_dir:
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test_df['model_output'] = result
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test_df['correctness'] = score
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test_df['model_answer'] = answer_list
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result_file_name = f'{subject_name}_{correct_ratio}_test.csv'
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if few_shot:
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result_file_name = f'{subject_name}_{correct_ratio}_few_shot_test.csv'
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test_df.to_csv(os.path.join(save_result_dir, result_file_name))
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return correct_ratio
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def eval_qa(self, subject_name, qa_df, save_result_dir=None):
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history = self.generate_zero_shot_prompt(is_choice_question=False)
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for row_index, row in tqdm(qa_df.iterrows(), total=len(qa_df)):
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question = row['question']
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response, _ = self.model.chat(self.tokenizer, question, max_length=2000,
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do_sample=False, history=history)
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response = response.strip()
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qa_df.loc[row_index, 'model_output'] = response
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# current_length = 0
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# response = ""
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# for resp, _ in self.model.stream_chat(self.tokenizer, question, max_length=300,
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# do_sample=False, history=history):
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# print(resp[current_length:], end="", flush=True)
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# current_length = len(resp)
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# response = resp
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# print('')
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if save_result_dir and self.finetune_name is not None:
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result_file_name = f'{subject_name}_qa_test_result.csv'
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qa_df.to_csv(os.path.join(save_result_dir, result_file_name))
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return qa_df
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def generate_few_shot_prompt(self, subject, dev_df, cot=False):
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message = []
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k = self.k
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if self.k == -1:
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k = dev_df.shape[0]
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init_example = self.format_example(dev_df.iloc[0, :], cot=cot,
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add_prompt=f"以下是中国关于{subject}考试的单项选择题，请选出其中的正确答案。\n\n")
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if isinstance(init_example, list):
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message.extend(init_example)
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else:
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message.append(init_example)
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for i in range(1, k):
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example = self.format_example(dev_df.iloc[i, :], cot=cot)
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if isinstance(example, list):
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message.extend(example)
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else:
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message.append(example)
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return message
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def generate_zero_shot_prompt(self, is_choice_question=True):
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if self.model_name == 'chatglm3' and is_choice_question:
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return [{'role': 'user',
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'content': '接下来会提供给你一些选择题，请选出正确的答案，给出正确的选项即可。'},
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{'role': 'assistant',
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'content': '好的，我会尽力解答。'}]
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elif self.model_name == 'chatglm3' and not is_choice_question:
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return [{'role': 'user',
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'content': '接下来会给你一些一些汽车领域相关问题，请回答。'},
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{'role': 'assistant',
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'content': '好的，我会尽力解答。'}]
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else:
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return []
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def format_example(self, line, include_answer=True, cot=False, add_prompt=''):
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example = add_prompt + line['question']
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# print(example)
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for choice in self.choices:
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example += f'\n{choice}. {line[f"{choice}"]}'
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example += '\n答案：'
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if include_answer:
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if cot:
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ans = "让我们一步一步思考，\n" + line["explanation"] + f"\n所以答案是{line['answer']}。"
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else:
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ans = line["answer"]
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if self.model_name == 'chatglm3':
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m = [{
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'role': 'user',
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'content': example
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}, {
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'role': 'assistant',
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'content': ans
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}]
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else:
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m = (example, ans)
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return m
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return example
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def extract_cot_answer(self, line, gen_ans):
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m = re.findall(r'所以答案是(.+?)。', gen_ans, re.M)
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if len(m) > 0 and m[-1] in self.choices:
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return m[-1], True
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answer_patterns = [
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r'([ABCD])是正确的',
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r'选项([ABCD])正确',
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r'答案为([ABCD])',
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r'答案是([ABCD])',
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r'答案([ABCD])',
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r'选择([ABCD])',
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r'答案：([ABCD])',
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r'选择答案([ABCD])',
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r'正确答案是([ABCD])'
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]
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# RE extraction
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for answer_pattern in answer_patterns:
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m = re.search(answer_pattern, gen_ans, re.M)
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if m:
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answer = m.group(1)
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return answer, False
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# only containing one choice-character
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m = re.findall(r'[ABCD]', gen_ans, re.M)
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if len(m) == 1:
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answer = m[0]
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return answer, False
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answer_word_counter = 0
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# only containing one choice-context
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for c in self.choices:
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if str(line[f'{c}']) in gen_ans:
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answer = c
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answer_word_counter += 1
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if answer_word_counter == 1:
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return answer, False
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return '-', False
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@ -0,0 +1,7 @@
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import google.generativeai as genai
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genai.configure(api_key='AIzaSyAW_h8itGLwNhYTfx1EDLthhcHHlcIfs7w')
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model = genai.GenerativeModel('gemini-pro')
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response = model.generate_content("Write a story about a magic backpack.")
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print(response.text)
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