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---
library_name: transformers
license: mit
base_model: FacebookAI/roberta-base
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: roberta-base-cpp
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. -->
# roberta-base-cpp
This model is a fine-tuned version of [FacebookAI/roberta-base](https://huggingface.co/FacebookAI/roberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0844
- Accuracy: 0.9550
## 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: 2
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 4
- optimizer: Use OptimizerNames.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: 1
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.1085 | 0.05 | 1000 | 0.1080 | 0.9183 |
| 0.0716 | 0.1 | 2000 | 0.2524 | 0.8473 |
| 0.0615 | 0.15 | 3000 | 0.1182 | 0.9299 |
| 0.0648 | 0.2 | 4000 | 0.0757 | 0.9498 |
| 0.0522 | 0.25 | 5000 | 0.1201 | 0.9273 |
| 0.0377 | 0.3 | 6000 | 0.0846 | 0.9555 |
| 0.0447 | 0.35 | 7000 | 0.1036 | 0.9323 |
| 0.0421 | 0.4 | 8000 | 0.1804 | 0.8914 |
| 0.0364 | 0.45 | 9000 | 0.0494 | 0.9628 |
| 0.0301 | 0.5 | 10000 | 0.0583 | 0.9689 |
| 0.0281 | 0.55 | 11000 | 0.0554 | 0.9689 |
| 0.0362 | 0.6 | 12000 | 0.0898 | 0.9428 |
| 0.022 | 0.65 | 13000 | 0.0772 | 0.9687 |
| 0.0221 | 0.7 | 14000 | 0.0706 | 0.9613 |
| 0.0256 | 0.75 | 15000 | 0.0487 | 0.9719 |
| 0.0215 | 0.8 | 16000 | 0.0427 | 0.9765 |
| 0.0162 | 0.85 | 17000 | 0.0437 | 0.9742 |
| 0.0186 | 0.9 | 18000 | 0.0613 | 0.9680 |
| 0.0211 | 0.95 | 19000 | 0.0950 | 0.9514 |
| 0.0136 | 1.0 | 20000 | 0.0844 | 0.9550 |
### Framework versions
- Transformers 4.57.3
- Pytorch 2.8.0+cu126
- Datasets 4.4.2
- Tokenizers 0.22.1
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