README.md

metadata

language:
  - en
license: mit
tags:
  - generated_from_trainer
datasets:
  - christinacdl/clickbait_notclickbait_dataset
metrics:
  - accuracy
  - f1
  - precision
  - recall
pipeline_tag: text-classification
base_model: microsoft/deberta-v3-large
model-index:
  - name: clickbait_binary_detection_DeBERTa
    results: []

clickbait_binary_detection_DeBERTa

This model is a fine-tuned version of 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