covid_fakenews_longformer_model
This model is a fine-tuned version of allenai/longformer-base-4096 on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.0571
- Accuracy: 0.9787
- F1: 0.9816
- Precision: 0.9796
- Recall: 0.9836
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: 5e-05
- train_batch_size: 8
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 64
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 200
- num_epochs: 5
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
|---|---|---|---|---|---|---|---|
| No log | 1.0 | 27 | 0.6234 | 0.7938 | 0.8324 | 0.7855 | 0.8852 |
| No log | 2.0 | 54 | 0.3374 | 0.8744 | 0.8959 | 0.8604 | 0.9344 |
| No log | 3.0 | 81 | 0.1397 | 0.9645 | 0.9702 | 0.9421 | 1.0 |
| No log | 4.0 | 108 | 0.1219 | 0.9621 | 0.9672 | 0.9672 | 0.9672 |
| No log | 4.8341 | 130 | 0.0571 | 0.9787 | 0.9816 | 0.9796 | 0.9836 |
Framework versions
- Transformers 4.49.0
- Pytorch 2.2.1
- Datasets 3.3.2
- Tokenizers 0.21.0