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---
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 |