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---
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
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