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- import os
- import sys
- import json
- from model_url import get_model_resp, get_url_tokenizer
-
-
- def run_predict(url, log_path, few_shot = True):
- import numpy as np
- tokenizer = get_url_tokenizer()
-
- id_label = {0: "Negative", 1: "Positive"}
- id_example = {"Negative": "不满意", "Positive": "满意"}
- MAIN_DIR = os.path.dirname(os.path.abspath(__file__))
- file_dir = MAIN_DIR + "/task_dataset/eprstmt/test_public.json"
- count = 0
- correct_num = 0
- acc = 0
-
- pre = ""
- devfile = MAIN_DIR + "/task_dataset/eprstmt/dev_few_all.json"
- cnt = 0
- if few_shot:
- with open(devfile, "r", encoding="utf8") as f:
- for line in f.readlines():
- cnt += 1
- line = json.loads(line)
- sentence, label = line["sentence"], line["label"]
- # pre += f"内容:{sentence}\n情感分类:{id_example[label]}\n" #hanjr_predict_200B_700b-EPRSTMT-copy-c1d8
- pre += f"{sentence}\n这句话表达的是{id_example[label]}\n"
- if cnt == 5:
- break
-
- with open(file_dir, "r", encoding="utf8") as f:
- for line in f.readlines():
- count += 1
- line = json.loads(line)
- sentence, label = line["sentence"], line["label"]
- # example = f"{pre}内容:{sentence}\n情感分类:"
- # input_str_one = f"{example}消极" #hanjr_predict_200B_700b-EPRSTMT-copy-c1d8 0.5
- # input_str_two = f"{example}积极"
-
- example = f"{pre}{sentence}\n这句话表达的是"
- input_str_one = f"{example}不满意" #
- input_str_two = f"{example}满意"
-
- input_str = []
- input_str.append(input_str_one)
- input_str.append(input_str_two)
- mask_length_list = []
- input_length_list = []
- for pred in input_str:
- input_length_list.append(len(tokenizer.encode(pred)))
- mask_length_list.append(len(tokenizer.encode(example)))
- model_resp = get_model_resp(url=url, input_str=input_str, tokens_to_generate=0, top_k=1, logprobs=True)
- return_resp = []
- for resp_item, input_length, mask_length in zip(model_resp, input_length_list, mask_length_list):
- # assert len(resp_item) == input_length - 1
- item = resp_item[mask_length - 1:input_length - 1]
- return_resp.append(item)
-
- pred_list = [sum(logprobs) / len(logprobs) for logprobs in return_resp]
- answers_pred = int(np.argmax(pred_list))
- if id_label[answers_pred] == label:
- correct_num += 1
- acc = correct_num / count
-
- print(f"eprstmt, 准确率Acc:{acc}, number: {count}")
-
- if not few_shot:
- with open(log_path + '/eprstmt_zeroshot.txt', 'w') as file:
- file.write(f"eprstmt, zero shot , Acc: {acc}, number: {count}")
- else:
- with open(log_path + '/eprstmt_fewshot.txt', 'w') as file:
- file.write(f"eprstmt, few shot , Acc: {acc}, number: {count}")
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