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from model_finetuning import formatting_func_without_space, formatting_func_space, trajectory_region_formatting, sft
from model_inference import gemma2b
from config import ScriptArguments, sft_files, dataset_files, max_tokens, result_parsers
from tqdm import tqdm
import json
import os
models_path = '~/.cache/modelscope/hub/AI-ModelScope/gemma-2b'
tasks2formatting = {"administrative_region_determination": formatting_func_without_space, "direction_determination": formatting_func_without_space, "trajectory_anomaly_detection": formatting_func_space, "trajectory_prediction": formatting_func_space, "trajectory_region": trajectory_region_formatting, "trajectory_trajectory": formatting_func_without_space}
if not os.path.exists("./save"):
os.mkdir("./save")
if not os.path.exists("./logs"):
os.mkdir("./logs")
for task, formatting_func in tasks2formatting.items():
save_path = "/save/{}/".format(task)
if not os.path.exists(save_path):
os.mkdir(save_path)
sft(ScriptArguments, models_path, formatting_func, sft_files[task], save_path)
model = gemma2b(save_path)
error_writer = open("./logs/{}.log".format(task), 'a')
error_writer.write(save_path+'\n')
result_parser = result_parsers[task]
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)
response = model.generate(item["Question"], max_tokens[task])
score = result_parser(response, item["Answer"], error_writer)
if task!='trajectory_prediction' or score is not None:
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()
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