---
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