Comments (5)
Datasets are of course an important factor.
The average result of K-fold cross-validation can be used to evaluate the generalization ability of the model, which is useful for model design and hyperparamter selection.
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Hi Youwei, now I gain the results as following, whether they are normal or still problematic?
Twitter
(seed=0) test_acc: 0.7211, test_f1: 0.7101 10 fold mean_test_acc: 0.7211, mean_test_f1: 0.7075
Restaurant
(seed=0) test_acc: 0.8187, test_f1: 0.7066 10 fold mean_test_acc: 0.8036, mean_test_f1: 0.6787
the parameter settings are:
parser.add_argument('--model_name', default='aen_bert', type=str)
parser.add_argument('--dataset', default='twitter', type=str, help='twitter, restaurant, laptop')
parser.add_argument('--optimizer', default='adam', type=str)
parser.add_argument('--initializer', default='xavier_uniform_', type=str)
parser.add_argument('--learning_rate', default=2e-5, type=float, help='try 5e-5, 2e-5 for BERT, 1e-3 for others')
parser.add_argument('--dropout', default=0.1, type=float)
parser.add_argument('--l2reg', default=0.01, type=float)
parser.add_argument('--num_epoch', default=10, type=int, help='try larger number for non-BERT models')
parser.add_argument('--batch_size', default=32, type=int, help='try 16, 32, 64 for BERT models')
parser.add_argument('--log_step', default=10, type=int)
parser.add_argument('--embed_dim', default=300, type=int)
parser.add_argument('--hidden_dim', default=300, type=int)
parser.add_argument('--bert_dim', default=768, type=int)
parser.add_argument('--pretrained_bert_name', default='bert-base-uncased', type=str)
parser.add_argument('--max_seq_len', default=80, type=int)
parser.add_argument('--polarities_dim', default=3, type=int)
parser.add_argument('--hops', default=3, type=int)
parser.add_argument('--device', default=None, type=str, help='e.g. cuda:0')
parser.add_argument('--seed', default=0, type=int, help='set seed for reproducibility')
parser.add_argument('--cross_val_fold', default=10, type=int, help='k-fold cross validation')
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This result is obviously problematic. I'm not sure what the problem is, you can set batch_size=32, seed=0, and try again.
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This result is normal.
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ok. Thanks! Btw, the laptop results (seed=0) are: test_acc: 0.7915, test_f1: 0.7454 mean_test_acc: 0.7652, mean_test_f1: 0.7138.
It seems that the result will be influenced a lot by dataset? If I want to use model on a typical domain dataset(like financial text), what is the best way to create an innovative model? Mayby just try the 10-fold experiment and use the best record one?
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Related Issues (20)
- About LCF-BERT HOT 1
- argument 'input' (position 1) must be Tensor, not str HOT 2
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- Bert预处理中文词嵌入
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- 关于ASGCN中的位置权重? HOT 4
- 论文
- 关于LCF_BERT HOT 1
- 关于train_k_fold_cross_val.py的问题 HOT 1
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- What does 'stability' mean for a model here?
- 关于.xml.seg.graph文件的生成 HOT 2
- 使用MGAN模型报错
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- Why is the restaurant dataset designed for evaluating only one aspect? There is no evaluation for multiple aspects.
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