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---
library_name: transformers
license: mit
base_model: roberta-base
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: results
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. -->
# results
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0528
- Accuracy: 0.9885
- Precision: 0.9885
- Recall: 0.9885
- F1: 0.9885
- Roc Auc: 0.9992
## 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: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | Roc Auc |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|:-------:|
| 0.1227 | 0.2 | 50 | 0.2116 | 0.935 | 0.9392 | 0.935 | 0.9338 | 0.9937 |
| 0.0744 | 0.4 | 100 | 0.0989 | 0.97 | 0.9705 | 0.97 | 0.9698 | 0.9960 |
| 0.0715 | 0.6 | 150 | 0.0651 | 0.982 | 0.9820 | 0.982 | 0.9820 | 0.9977 |
| 0.1218 | 0.8 | 200 | 0.1539 | 0.9555 | 0.9590 | 0.9555 | 0.9559 | 0.9961 |
| 0.0709 | 1.0 | 250 | 0.0528 | 0.9855 | 0.9855 | 0.9855 | 0.9855 | 0.9989 |
| 0.0602 | 1.2 | 300 | 0.0986 | 0.978 | 0.9782 | 0.978 | 0.9779 | 0.9986 |
| 0.034 | 1.4 | 350 | 0.0687 | 0.9835 | 0.9835 | 0.9835 | 0.9835 | 0.9986 |
| 0.0137 | 1.6 | 400 | 0.0613 | 0.9845 | 0.9845 | 0.9845 | 0.9845 | 0.9989 |
| 0.047 | 1.8 | 450 | 0.0472 | 0.9895 | 0.9895 | 0.9895 | 0.9895 | 0.9991 |
| 0.0617 | 2.0 | 500 | 0.0497 | 0.9885 | 0.9885 | 0.9885 | 0.9885 | 0.9991 |
| 0.0513 | 2.2 | 550 | 0.0534 | 0.987 | 0.9870 | 0.987 | 0.9870 | 0.9992 |
| 0.0269 | 2.4 | 600 | 0.0467 | 0.9885 | 0.9885 | 0.9885 | 0.9885 | 0.9993 |
| 0.001 | 2.6 | 650 | 0.0509 | 0.987 | 0.9870 | 0.987 | 0.9870 | 0.9994 |
| 0.0195 | 2.8 | 700 | 0.0521 | 0.9895 | 0.9895 | 0.9895 | 0.9895 | 0.9992 |
| 0.0011 | 3.0 | 750 | 0.0528 | 0.9885 | 0.9885 | 0.9885 | 0.9885 | 0.9992 |
### Framework versions
- Transformers 4.53.3
- Pytorch 2.6.0+cu124
- Datasets 4.4.1
- Tokenizers 0.21.2
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