Downloads · 30 days
5
11% of all-time downloads
Roberta_fakecovid
adity12345/Roberta_fakecovid
Roberta_fakecovid is a text classification model from adity12345. Use it when you need a label for a piece of text. It is set up for transformers. The card lists the license as mit.
should probably proofread and complete it, then remove this comment. --
All-time downloads
46
Public
Parameters
278M
1.1 GB on disk
Likes
0
Public
Repo makeup
Click a slice to open those files.
.safetensors1.1 GB · 98%
At a glance
- Task
- Text Classification
- Library
- transformers
- License
- mit
- Model type
- xlm-roberta
- Access
- Public
- Created
- Aug 24, 2025
- Updated
- Aug 24, 2025
- SHA
- 9c92f376
Try a prompt
Base models
Model card
From the Hugging Face model README
<!-- 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. -->
Roberta_fakecovid
This model is a fine-tuned version of FacebookAI/xlm-roberta-base on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.0417
- Accuracy: 0.988
- Auc: 0.941
- Precision: 0.0
- Recall: 0.0
- F1: 0.0
- F1-macro: 0.497
- F1-micro: 0.988
- F1-weighted: 0.982
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-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 2
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Auc | Precision | Recall | F1 | F1-macro | F1-micro | F1-weighted |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 0.1257 | 0.3017 | 50 | 0.0617 | 0.988 | 0.913 | 0.0 | 0.0 | 0.0 | 0.497 | 0.988 | 0.982 |
| 0.0482 | 0.6033 | 100 | 0.0696 | 0.988 | 0.944 | 0.0 | 0.0 | 0.0 | 0.497 | 0.988 | 0.982 |
| 0.0557 | 0.9050 | 150 | 0.0717 | 0.988 | 0.892 | 0.0 | 0.0 | 0.0 | 0.497 | 0.988 | 0.982 |
| 0.0419 | 1.2051 | 200 | 0.0547 | 0.988 | 0.909 | 0.0 | 0.0 | 0.0 | 0.497 | 0.988 | 0.982 |
| 0.051 | 1.5068 | 250 | 0.0472 | 0.988 | 0.916 | 0.0 | 0.0 | 0.0 | 0.497 | 0.988 | 0.982 |
| 0.0571 | 1.8084 | 300 | 0.0417 | 0.988 | 0.941 | 0.0 | 0.0 | 0.0 | 0.497 | 0.988 | 0.982 |
Framework versions
- Transformers 4.55.2
- Pytorch 2.8.0+cu126
- Datasets 4.0.0
- Tokenizers 0.21.4
- Task
- Text Classification
- Library
- transformers
- Type
- xlm-roberta
- License
- mit
- Created
- Aug 24, 2025
- Updated
- Aug 24, 2025