---
library_name: transformers
license: mit
base_model: adity12345/RoBerta_covi19_rumor
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: Roberta_feverous
  results: []
---

<!-- 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_feverous

This model is a fine-tuned version of [adity12345/RoBerta_covi19_rumor](https://huggingface.co/adity12345/RoBerta_covi19_rumor) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6168
- Accuracy: 0.674
- Auc: 0.67
- Precision: 0.677
- Recall: 0.897
- F1: 0.771
- F1-macro: 0.6
- F1-micro: 0.674
- F1-weighted: 0.639

## 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 |
|:-------------:|:------:|:----:|:---------------:|:--------:|:-----:|:---------:|:------:|:-----:|:--------:|:--------:|:-----------:|
| 0.6385        | 0.2896 | 500  | 0.6240          | 0.666    | 0.646 | 0.656     | 0.96   | 0.779 | 0.546    | 0.666    | 0.599       |
| 0.6294        | 0.5793 | 1000 | 0.6270          | 0.665    | 0.652 | 0.673     | 0.885  | 0.764 | 0.593    | 0.665    | 0.632       |
| 0.627         | 0.8689 | 1500 | 0.6192          | 0.669    | 0.658 | 0.674     | 0.891  | 0.768 | 0.595    | 0.669    | 0.634       |
| 0.6126        | 1.1581 | 2000 | 0.6185          | 0.674    | 0.662 | 0.665     | 0.945  | 0.781 | 0.573    | 0.674    | 0.621       |
| 0.6044        | 1.4478 | 2500 | 0.6155          | 0.673    | 0.665 | 0.669     | 0.927  | 0.777 | 0.582    | 0.673    | 0.627       |
| 0.5942        | 1.7374 | 3000 | 0.6168          | 0.674    | 0.67  | 0.677     | 0.897  | 0.771 | 0.6      | 0.674    | 0.639       |


### Framework versions

- Transformers 4.55.2
- Pytorch 2.8.0+cu126
- Datasets 4.0.0
- Tokenizers 0.21.4