| | from model_inference import * |
| | from config import dataset_files, cot_files |
| | from result_parser import find_option_number_for_cot |
| | from tqdm import tqdm |
| | import json |
| | import os |
| |
|
| | models = [gemma2b] |
| | tasks = ["urban_region_function_recognition", "trajectory_region", "trajectory_trajectory", "trajectory_classification"] |
| |
|
| | if not os.path.exists("./logs"): |
| | os.mkdir("./logs") |
| |
|
| | for fun in models: |
| | model = fun() |
| | for task in tasks: |
| | error_writer = open("./logs/cot_{}.log".format(task), 'a') |
| | error_writer.write(model.model_path+'\n') |
| |
|
| | context_samples = open(cot_files[task]) |
| | prompt = "" |
| | for _i, sample in enumerate(context_samples.readlines()): |
| | sample = json.loads(sample) |
| | prompt += "{}{}\n".format(sample['Question'], sample['Answer']) |
| |
|
| | for dataset_path in dataset_files[task]: |
| | dataset = open(dataset_path, 'r') |
| | dataset = dataset.readlines() |
| |
|
| | correct = 0 |
| | total = 0 |
| | exception = 0 |
| |
|
| | for i, item in tqdm(enumerate(dataset), total=len(dataset)): |
| | item = json.loads(item) |
| |
|
| | |
| | if task=="urban_region_function_recognition": |
| | question = item['Question'].replace("Please just answer the number of your option with no other texts. Answer: Option (", "") |
| | elif task=="trajectory_trajectory": |
| | question = item['Question'].replace(" with no other texts. Answer: Option (", ".") |
| | elif task=="trajectory_region": |
| | question = item['Question'].replace(" with no other texts. Answer: Option ", ".") |
| | elif task=="trajectory_classification": |
| | question = item['Question'].replace("Answer: The trajectory is most likely to be generated by", "") |
| |
|
| | response = model.generate(prompt+question, 100) |
| | score = find_option_number_for_cot(response, item["Answer"], error_writer) |
| | |
| | total +=1 |
| | if score is None: |
| | exception += 1 |
| | else: |
| | correct += score |
| |
|
| | if i%100==0: |
| | print("Dataset: {}\nTotal: {}, correct:{}, exception:{}, accuracy:{}\n\n".format(dataset_path, total, correct, exception, correct/total)) |
| | |
| | error_writer.write("Dataset: {}\nTotal: {}, correct:{}, exception:{}, accuracy:{}\n\n".format(dataset_path, total, correct, exception, correct/total)) |
| | error_writer.flush() |
| | error_writer.write("\n") |
| | error_writer.close() |
| |
|
