---
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: 0.0284
- Accuracy: 0.9931

## 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 |
|:-------------:|:------:|:----:|:---------------:|:--------:|
| 0.3359        | 0.1018 | 100  | 0.1977          | 0.9703   |
| 0.17          | 0.2037 | 200  | 0.3161          | 0.9542   |
| 0.1525        | 0.3055 | 300  | 0.0936          | 0.9828   |
| 0.0874        | 0.4073 | 400  | 0.0900          | 0.9863   |
| 0.097         | 0.5092 | 500  | 0.0992          | 0.9863   |
| 0.0874        | 0.6110 | 600  | 0.1275          | 0.9851   |
| 0.0763        | 0.7128 | 700  | 0.1173          | 0.9840   |
| 0.1067        | 0.8147 | 800  | 0.0585          | 0.9874   |
| 0.0646        | 0.9165 | 900  | 0.0358          | 0.9943   |
| 0.0338        | 1.0183 | 1000 | 0.0413          | 0.9943   |
| 0.0463        | 1.1202 | 1100 | 0.0311          | 0.9943   |
| 0.0683        | 1.2220 | 1200 | 0.0473          | 0.9920   |
| 0.0315        | 1.3238 | 1300 | 0.0374          | 0.9931   |
| 0.0251        | 1.4257 | 1400 | 0.0335          | 0.9954   |
| 0.0238        | 1.5275 | 1500 | 0.0481          | 0.9931   |
| 0.0105        | 1.6293 | 1600 | 0.0555          | 0.9931   |
| 0.063         | 1.7312 | 1700 | 0.0343          | 0.9931   |
| 0.0389        | 1.8330 | 1800 | 0.0355          | 0.9931   |
| 0.0463        | 1.9348 | 1900 | 0.0584          | 0.9897   |
| 0.0075        | 2.0367 | 2000 | 0.0284          | 0.9931   |
| 0.0036        | 2.1385 | 2100 | 0.1225          | 0.9760   |
| 0.0062        | 2.2403 | 2200 | 0.0333          | 0.9943   |
| 0.0136        | 2.3422 | 2300 | 0.0379          | 0.9920   |


### Framework versions

- Transformers 4.46.2
- Pytorch 2.5.1+cu121
- Datasets 3.1.0
- Tokenizers 0.20.3