---
license: mit
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: clickbait_binary_detection
  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

This model is a fine-tuned version of [roberta-large](https://huggingface.co/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
- train_batch_size: 6
- eval_batch_size: 10
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 12
- 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------:|:--------:|
| 0.2296        | 1.0   | 3650  | 0.2236          | 0.9105   | 0.9183   | 0.9183   |
| 0.228         | 2.0   | 7301  | 0.2708          | 0.9115   | 0.9192   | 0.9192   |
| 0.2075        | 3.0   | 10951 | 0.3141          | 0.9164   | 0.9224   | 0.9224   |
| 0.1881        | 4.0   | 14602 | 0.3211          | 0.9143   | 0.9201   | 0.9201   |
| 0.18          | 5.0   | 18252 | 0.3852          | 0.9130   | 0.9188   | 0.9188   |
| 0.1818        | 6.0   | 21903 | 0.3784          | 0.9110   | 0.9174   | 0.9174   |
| 0.1495        | 7.0   | 25553 | 0.4606          | 0.9106   | 0.9156   | 0.9156   |
| 0.1453        | 8.0   | 29204 | 0.4630          | 0.9155   | 0.9215   | 0.9215   |


### Framework versions

- Transformers 4.27.1
- Pytorch 2.0.1+cu118
- Datasets 2.9.0
- Tokenizers 0.13.3