| --- |
| tags: |
| - generated_from_trainer |
| model-index: |
| - name: Fake_News_Classifier |
| results: [] |
| metrics: |
| - f1 |
| - accuracy |
| - roc_auc |
| 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. --> |
|
|
| # NELA-GT_Classifier |
| |
| This model was Fine-Tuned on a Fake News dataset. |
| |
| ## Model description |
| |
| This is a pretrained distilbert-uncased model finetuned for Fake News classification. |
| |
| ## 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: 5e-05 |
| - train_batch_size: 16 |
| - eval_batch_size: 16 |
| - seed: 42 |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
| - lr_scheduler_type: linear |
| - lr_scheduler_warmup_steps: 1500 |
| - num_epochs: 5 |
|
|
| ### Framework versions |
|
|
| - Transformers 4.29.2 |
| - Pytorch 2.0.1+cu118 |
| - Datasets 2.12.0 |
| - Tokenizers 0.13.3 |
