---
library_name: transformers
license: mit
base_model: FacebookAI/roberta-large
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: fine_tuned_raid_cleaned
  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_raid_cleaned

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: 0.1136
- Accuracy: 0.9800

## 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 |
|:-------------:|:------:|:----:|:---------------:|:--------:|
| 0.3826        | 0.0139 | 100  | 0.1739          | 0.9443   |
| 0.2556        | 0.0277 | 200  | 0.2700          | 0.9408   |
| 0.3383        | 0.0416 | 300  | 0.1667          | 0.9529   |
| 0.3672        | 0.0554 | 400  | 0.9354          | 0.7975   |
| 0.2223        | 0.0693 | 500  | 0.1584          | 0.9673   |
| 0.2197        | 0.0832 | 600  | 0.6363          | 0.8793   |
| 0.2873        | 0.0970 | 700  | 0.2169          | 0.9462   |
| 0.2201        | 0.1109 | 800  | 0.1366          | 0.9621   |
| 0.1695        | 0.1248 | 900  | 0.2912          | 0.9554   |
| 0.1912        | 0.1386 | 1000 | 0.2287          | 0.9542   |
| 0.131         | 0.1525 | 1100 | 0.1136          | 0.9800   |
| 0.1764        | 0.1663 | 1200 | 0.1770          | 0.9645   |
| 0.1195        | 0.1802 | 1300 | 0.1255          | 0.9755   |
| 0.09          | 0.1941 | 1400 | 0.1285          | 0.9758   |


### Framework versions

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