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library_name: transformers
license: mit
base_model: FacebookAI/roberta-base
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: my-roberta-RQ3
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. -->
# my-roberta-RQ3
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: 0.4223
- Accuracy: 0.9462
- F1 Macro: 0.5894
- F1 Weighted: 0.9458
## 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: 1e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 0.1
- num_epochs: 10
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 Macro | F1 Weighted |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:-----------:|
| 0.3736 | 1.0 | 539 | 0.3766 | 0.9376 | 0.3041 | 0.9287 |
| 0.3358 | 2.0 | 1078 | 0.3330 | 0.9445 | 0.4096 | 0.9412 |
| 0.2991 | 3.0 | 1617 | 0.3182 | 0.9503 | 0.4425 | 0.9472 |
| 0.2410 | 4.0 | 2156 | 0.3319 | 0.9480 | 0.4913 | 0.9460 |
| 0.1962 | 5.0 | 2695 | 0.3398 | 0.9487 | 0.6223 | 0.9484 |
| 0.1727 | 6.0 | 3234 | 0.3461 | 0.9517 | 0.6410 | 0.9510 |
| 0.1506 | 7.0 | 3773 | 0.3586 | 0.9515 | 0.6507 | 0.9514 |
| 0.1136 | 8.0 | 4312 | 0.3881 | 0.9510 | 0.6519 | 0.9506 |
| 0.1293 | 9.0 | 4851 | 0.4080 | 0.9515 | 0.6367 | 0.9513 |
| 0.1075 | 10.0 | 5390 | 0.4134 | 0.9501 | 0.6309 | 0.9499 |
### Framework versions
- Transformers 5.0.0
- Pytorch 2.10.0+cu128
- Datasets 4.8.3
- Tokenizers 0.22.2
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