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- import torch
- from transformers import BertTokenizer, BertModel, BertConfig
-
- from data.config import Config
- from data.weibo.data_loader import WeiboDatasetLoader
-
-
- class WeiboConfig(Config):
- name = 'weibo'
- DatasetLoader = WeiboDatasetLoader
-
- data_path = '../../../../../media/external_3TB/3TB/ghorbanpoor/weibo/'
- # data_path = '/home/faeze/PycharmProjects/fake_news_detection/data/weibo/'
- output_path = '../../../../../media/external_10TB/10TB/ghorbanpoor/'
- # output_path = ''
-
- rumor_image_path = data_path + 'rumor_images/'
- nonrumor_image_path = data_path + 'nonrumor_images/'
-
- train_text_path = data_path + 'weibo_train.csv'
- validation_text_path = data_path + 'weibo_train.csv'
- test_text_path = data_path + 'weibo_test.csv'
-
- batch_size = 64
- epochs = 100
- num_workers = 4
- head_lr = 1e-03
- image_encoder_lr = 1e-02
- text_encoder_lr = 1e-05
- weight_decay = 0.001
- classification_lr = 1e-02
-
- device = torch.device("cuda:1" if torch.cuda.is_available() else "cpu")
-
- image_model_name = '../../../../../media/external_10TB/10TB/ghorbanpoor/vit-base-patch16-224'
- image_embedding = 768
- text_encoder_model = "../../../../../media/external_10TB/10TB/ghorbanpoor/chinese-xlnet-base"
- # text_encoder_model = "/home/faeze/PycharmProjects/new_fake_news_detectioin/bert/bert-base-uncased"
- text_tokenizer = "../../../../../media/external_10TB/10TB/ghorbanpoor/chinese-xlnet-base"
- # text_tokenizer = "/home/faeze/PycharmProjects/new_fake_news_detectioin/bert/bert-base-uncased"
- text_embedding = 768
- max_length = 200
-
- pretrained = True
- trainable = False
- temperature = 1.0
-
- labels = ['real', 'fake']
-
- wanted_accuracy = 0.80
-
- def optuna(self, trial):
- self.head_lr = trial.suggest_loguniform('head_lr', 1e-5, 1e-1)
- self.image_encoder_lr = trial.suggest_loguniform('image_encoder_lr', 1e-6, 1e-3)
- self.text_encoder_lr = trial.suggest_loguniform('text_encoder_lr', 1e-6, 1e-3)
- self.classification_lr = trial.suggest_loguniform('classification_lr', 1e-5, 1e-1)
-
- self.head_weight_decay = trial.suggest_loguniform('head_weight_decay', 1e-5, 1e-1)
- self.classification_weight_decay = trial.suggest_loguniform('classification_weight_decay', 1e-5, 1e-1)
-
- self.projection_size = trial.suggest_categorical('projection_size', [256, 128, 64])
- self.hidden_size = trial.suggest_categorical('hidden_size', [256, 128, 64, ])
- self.dropout = trial.suggest_categorical('drop_out', [0.1, 0.3, 0.5, ])
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