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

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: 0.0628
- Accuracy: 0.9905

## 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 |
|:-------------:|:------:|:----:|:---------------:|:--------:|
| 0.4396        | 0.0767 | 100  | 0.4779          | 0.8612   |
| 0.2322        | 0.1534 | 200  | 0.2148          | 0.9414   |
| 0.2867        | 0.2301 | 300  | 0.2022          | 0.9603   |
| 0.2758        | 0.3067 | 400  | 0.1828          | 0.9552   |
| 0.1543        | 0.3834 | 500  | 0.5250          | 0.9155   |
| 0.2348        | 0.4601 | 600  | 0.1141          | 0.9733   |
| 0.163         | 0.5368 | 700  | 0.1417          | 0.9733   |
| 0.1622        | 0.6135 | 800  | 0.0898          | 0.9810   |
| 0.174         | 0.6902 | 900  | 0.1013          | 0.9810   |
| 0.1398        | 0.7669 | 1000 | 0.3111          | 0.9241   |
| 0.1247        | 0.8436 | 1100 | 0.1722          | 0.9655   |
| 0.1559        | 0.9202 | 1200 | 0.2461          | 0.9629   |
| 0.0987        | 0.9969 | 1300 | 0.1538          | 0.9741   |
| 0.0431        | 1.0736 | 1400 | 0.1137          | 0.9828   |
| 0.0572        | 1.1503 | 1500 | 0.1094          | 0.9845   |
| 0.0509        | 1.2270 | 1600 | 0.1153          | 0.9836   |
| 0.0579        | 1.3037 | 1700 | 0.0736          | 0.9879   |
| 0.0773        | 1.3804 | 1800 | 0.1087          | 0.9802   |
| 0.062         | 1.4571 | 1900 | 0.0890          | 0.9853   |
| 0.0621        | 1.5337 | 2000 | 0.1404          | 0.9793   |
| 0.0324        | 1.6104 | 2100 | 0.0669          | 0.9888   |
| 0.0548        | 1.6871 | 2200 | 0.1057          | 0.9836   |
| 0.0201        | 1.7638 | 2300 | 0.0920          | 0.9853   |
| 0.0614        | 1.8405 | 2400 | 0.0696          | 0.9897   |
| 0.0312        | 1.9172 | 2500 | 0.0628          | 0.9905   |
| 0.0132        | 1.9939 | 2600 | 0.0976          | 0.9853   |
| 0.0108        | 2.0706 | 2700 | 0.0670          | 0.9914   |
| 0.0           | 2.1472 | 2800 | 0.1647          | 0.9802   |


### Framework versions

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