| --- |
| license: mit |
| tags: |
| - generated_from_trainer |
| metrics: |
| - accuracy |
| - f1 |
| - precision |
| - recall |
| model-index: |
| - name: clickbait_binary_detection_DeBERTa |
| results: [] |
| datasets: |
| - christinacdl/clickbait_notclickbait_dataset |
| language: |
| - en |
| pipeline_tag: text-classification |
| --- |
| |
| <!-- This model card has been generated automatically according to the information the Trainer had access to. You |
| should probably proofread and complete it, then remove this comment. --> |
|
|
| # clickbait_binary_detection_DeBERTa |
| |
| This model is a fine-tuned version of [microsoft/deberta-v3-large](https://huggingface.co/microsoft/deberta-v3-large) on an unknown dataset. |
| It achieves the following results on the evaluation set: |
| - Loss: 0.7269 |
| - Macro F1: 0.9010 |
| - Micro F1: 0.9069 |
| - Accuracy: 0.9069 |
| |
| Performance on test set: |
| |
| - Accuracy: 0.911986301369863 |
| - F1 score: 0.9053903329555788 |
| - Precision: 0.9069346899004087 |
| - Recall : 0.9039394560612273 |
| - Matthews Correlation Coefficient: 0.8108686139956713 |
| |
| - Precision of each class: [0.92560647 0.88826291] |
| - Recall of each class: [0.93518519 0.87269373] |
| - F1 score of each class: [0.93037117 0.88040949] |
| |
| ## 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 |
| - train_batch_size: 4 |
| - eval_batch_size: 8 |
| - seed: 42 |
| - gradient_accumulation_steps: 2 |
| - total_train_batch_size: 8 |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
| - lr_scheduler_type: linear |
| - num_epochs: 10 |
| - mixed_precision_training: Native AMP |
|
|
| ### Training results |
|
|
| | Training Loss | Epoch | Step | Validation Loss | Macro F1 | Micro F1 | Accuracy | |
| |:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------:|:--------:| |
| | 0.2692 | 1.0 | 5475 | 0.2676 | 0.9051 | 0.9142 | 0.9142 | |
| | 0.2492 | 2.0 | 10951 | 0.3331 | 0.9078 | 0.9156 | 0.9156 | |
| | 0.2189 | 3.0 | 16426 | 0.3909 | 0.9107 | 0.9169 | 0.9169 | |
| | 0.1769 | 4.0 | 21902 | 0.3799 | 0.9114 | 0.9178 | 0.9178 | |
| | 0.1479 | 5.0 | 27377 | 0.5103 | 0.8980 | 0.9032 | 0.9032 | |
| | 0.108 | 6.0 | 32853 | 0.5215 | 0.9123 | 0.9183 | 0.9183 | |
| | 0.0957 | 7.0 | 38328 | 0.6549 | 0.8974 | 0.9028 | 0.9028 | |
| | 0.0773 | 8.0 | 43804 | 0.6768 | 0.9044 | 0.9101 | 0.9101 | |
| | 0.0586 | 9.0 | 49279 | 0.6837 | 0.9023 | 0.9083 | 0.9083 | |
| | 0.0439 | 10.0 | 54750 | 0.7269 | 0.9010 | 0.9069 | 0.9069 | |
|
|
|
|
| ### Framework versions |
|
|
| - Transformers 4.27.1 |
| - Pytorch 2.0.1+cu118 |
| - Datasets 2.9.0 |
| - Tokenizers 0.13.3 |
