Datasets:
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from importlib import import_module
from src.datasets import *
import os
from os import path
import numpy as np
from train_eval import train, eval
import argparse
from src.Models import *
from ray import tune
from ray.tune.schedulers import ASHAScheduler
parser = argparse.ArgumentParser(description='Chinese Toxic Classification')
parser.add_argument('--mode', default="train", type=str, help='train/test')
# 是否微调超参数
parser.add_argument('--tune_param', default=False, type=bool, help='True for param tuning')
parser.add_argument('--tune_samples', default=1, type=int, help='Number of tuning experiments to run')
parser.add_argument('--tune_asha', default=False, type=bool, help='If use ASHA scheduler for early stopping')
parser.add_argument('--tune_file', default='RoBERTa', type=str, help='Suffix of filename for parameter tuning results')
parser.add_argument('--tune_gpu', default=True, type=bool, help='Use GPU to tune parameters')
args = parser.parse_args()
# TOC
# search_space = {
# 'learning_rate': tune.choice([1e-5, 5e-5]),
# 'num_epochs': tune.choice([5]),
# 'batch_size': tune.choice([32, 64]),
# 'dropout': tune.choice([0.2, 0.5]),
# 'seed': tune.choice([1]),
# "pad_size" : tune.choice([50, 100])
# }
# THUCnews
search_space = {
'learning_rate': tune.choice([1e-5]),
'num_epochs': tune.choice([5]),
'batch_size': tune.choice([32]),
# 'dropout': tune.choice([0.2, 0.3, 0.5]),
'seed': tune.choice([1]),
"pad_size" : tune.choice([80]),
"alpha1" : tune.choice([0.5])
}
def convert_label(preds):
final_pred = []
for pred in preds:
if pred == [1, 0]:
final_pred.append("offensive")
else:
final_pred.append("non-offensive")
return final_pred
if __name__ == '__main__':
# dataset = 'TOC' # 数据集
dataset = "ToxiCN"
# model_name = "bert-base-chinese"
model_name = "hfl/chinese-roberta-wwm-ext"
# model_name = "junnyu/ChineseBERT-base"
np.random.seed(1)
torch.manual_seed(1)
torch.cuda.manual_seed_all(1)
torch.backends.cudnn.deterministic = True # 保证每次结果一样
start_time = time.time()
print("Loading data...")
x = import_module('model.' + "Config_base")
config = x.Config_base(model_name, dataset) # 引入Config参数，包括Config_base和各私有Config
if not os.path.exists(config.data_path):
trn_data = Datasets(config, config.train_path)
dev_data = Datasets(config, config.dev_path)
test_data = Datasets(config, config.test_path)
torch.save({
'trn_data' : trn_data,
'dev_data' : dev_data,
'test_data' : test_data,
}, config.data_path)
else:
checkpoint = torch.load(config.data_path)
trn_data = checkpoint['trn_data']
dev_data = checkpoint['dev_data']
test_data = checkpoint['test_data']
print('The size of the Training dataset: {}'.format(len(trn_data)))
print('The size of the Validation dataset: {}'.format(len(dev_data)))
print('The size of the Test dataset: {}'.format(len(test_data)))
train_iter = Dataloader(trn_data, batch_size=int(config.batch_size), SEED=config.seed)
dev_iter = Dataloader(dev_data, batch_size=int(config.batch_size), shuffle=False)
test_iter = Dataloader(test_data, batch_size=int(config.batch_size), shuffle=False)
time_dif = get_time_dif(start_time)
print("Time usage:", time_dif)
def experiment(tune_config):
if tune_config:
for param in tune_config:
setattr(config, param, tune_config[param])
train(config, train_iter, dev_iter, test_iter)
if args.mode == "train":
if args.tune_param:
scheduler = ASHAScheduler(metric='metric', mode="max") if args.tune_asha else None
analysis = tune.run(experiment, num_samples=args.tune_samples, config=search_space, resources_per_trial={'gpu':int(args.tune_gpu)},
scheduler=scheduler,
verbose=3)
analysis.results_df.to_csv('tune_results_'+args.tune_file+'.csv')
# if not tune parameters
else:
experiment(tune_config=None)
else:
embed_model = Bert_Layer(config).to(config.device)
model = TwoLayerFFNNLayer(config).to(config.device)
# model_name = "ckp-bert-base-chinese-NN_ML-100_D-0.2_B-64_E-5_Lr-1e-05-BEST.tar"
model_name = "ckp-bert-base-chinese-NN_ML-150_D-0.5_B-32_E-2_Lr-1e-05-BEST.tar"
path = '{}/{}'.format(config.checkpoint_path, model_name)
checkpoint = torch.load(path)
embed_model.load_state_dict(checkpoint['embed_model_state_dict'])
model.load_state_dict(checkpoint['model_state_dict'])
loss_fn = nn.BCEWithLogitsLoss()
dev_scores, preds = eval(config, embed_model, model, loss_fn, dev_iter, data_name='TEST')
print(convert_label(preds)) |