---
library_name: transformers
license: mit
base_model: FacebookAI/roberta-large
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: fine_tuned_main_raid
  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. -->

# fine_tuned_main_raid

This model is a fine-tuned version of [FacebookAI/roberta-large](https://huggingface.co/FacebookAI/roberta-large) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0711
- Accuracy: 0.9843

## 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
- 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
- num_epochs: 3

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:--------:|
| 0.2533        | 0.0139 | 100  | 0.1743          | 0.9663   |
| 0.1848        | 0.0277 | 200  | 0.1058          | 0.9768   |
| 0.1832        | 0.0416 | 300  | 0.0924          | 0.9796   |
| 0.1199        | 0.0554 | 400  | 0.0854          | 0.9813   |
| 0.1294        | 0.0693 | 500  | 0.2504          | 0.9471   |
| 0.1755        | 0.0832 | 600  | 0.1885          | 0.9646   |
| 0.0831        | 0.0970 | 700  | 0.0831          | 0.9855   |
| 0.1051        | 0.1109 | 800  | 0.0711          | 0.9843   |
| 0.1411        | 0.1248 | 900  | 0.2770          | 0.9637   |
| 0.0761        | 0.1386 | 1000 | 0.0922          | 0.9835   |
| 0.1178        | 0.1525 | 1100 | 0.2174          | 0.9649   |


### Framework versions

- Transformers 4.46.2
- Pytorch 2.5.0+cu121
- Datasets 3.1.0
- Tokenizers 0.20.3