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---
library_name: transformers
license: mit
base_model: xlm-roberta-base
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: output_fp16
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. -->
# output_fp16
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4230
- Accuracy: 0.8382
## 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: 0.0003
- train_batch_size: 32
- eval_batch_size: 8
- seed: 42
- 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: 15
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:------:|:---------------:|:--------:|
| 0.5485 | 1.0 | 12272 | 0.4865 | 0.8100 |
| 0.4989 | 2.0 | 24544 | 0.4720 | 0.8193 |
| 0.4743 | 3.0 | 36816 | 0.5417 | 0.7859 |
| 0.4762 | 4.0 | 49088 | 0.4359 | 0.8313 |
| 0.4525 | 5.0 | 61360 | 0.4297 | 0.8365 |
| 0.4457 | 6.0 | 73632 | 0.4273 | 0.8398 |
| 0.4205 | 7.0 | 85904 | 0.4343 | 0.8321 |
| 0.4315 | 8.0 | 98176 | 0.4287 | 0.8357 |
| 0.4271 | 9.0 | 110448 | 0.4299 | 0.8394 |
| 0.4031 | 10.0 | 122720 | 0.4250 | 0.8353 |
| 0.4 | 11.0 | 134992 | 0.4401 | 0.8345 |
| 0.3899 | 12.0 | 147264 | 0.4178 | 0.8418 |
| 0.3921 | 13.0 | 159536 | 0.4313 | 0.8386 |
| 0.3849 | 14.0 | 171808 | 0.4212 | 0.8378 |
| 0.3777 | 15.0 | 184080 | 0.4230 | 0.8382 |
### Framework versions
- Transformers 4.53.2
- Pytorch 2.6.0+cu124
- Datasets 2.18.0
- Tokenizers 0.21.2
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