christinacdl/clickbait_binary_detection

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clickbaitbinarydetection

This model is a fine-tuned version of roberta-large on an unknown dataset. It achieves the following results on the evaluation set:

• —Loss: 0.4630
• —Macro F1: 0.9155
• —Micro F1: 0.9215
• —Accuracy: 0.9215

Performance on test set:

• —Accuracy: 0.9257990867579908

• —F1 score: 0.9199282431058413

• —Precision: 0.9233793490724882

• —Recall : 0.9168756883647268

• —Matthews Correlation Coefficient: 0.8402298675576902

• —Precision of each class: [0.931899 0.91485969]

• —Recall of each class: [0.95152505 0.88222632]

• —F1 score of each class: [0.94160977 0.89824671]

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

• —learning_rate: 2e-06
• —trainbatchsize: 6
• —evalbatchsize: 10
• —seed: 42
• —gradientaccumulationsteps: 2
• —totaltrainbatch_size: 12
• —optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• —lrschedulertype: linear
• —num_epochs: 10
• —mixedprecisiontraining: Native AMP

Training results

Framework versions

• —Transformers 4.27.1
• —Pytorch 2.0.1+cu118
• —Datasets 2.9.0
• —Tokenizers 0.13.3