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---
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: roberta-mqa-formrat
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-mqa-formrat
This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1135
- Accuracy: 0.5671
- F1: 0.5659
- Precision: 0.5683
- Recall: 0.5650
## 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: 4
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
|:-------------:|:------:|:-----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 1.451 | 0.3233 | 1200 | 1.4125 | 0.4105 | 0.4093 | 0.4151 | 0.4107 |
| 1.416 | 0.6466 | 2400 | 1.3482 | 0.4412 | 0.4394 | 0.4438 | 0.4385 |
| 1.3157 | 0.9698 | 3600 | 1.2933 | 0.4788 | 0.4772 | 0.4776 | 0.4773 |
| 1.2616 | 1.2931 | 4800 | 1.2389 | 0.5032 | 0.5022 | 0.5053 | 0.5011 |
| 1.221 | 1.6164 | 6000 | 1.2049 | 0.5053 | 0.5039 | 0.5060 | 0.5029 |
| 1.1556 | 1.9397 | 7200 | 1.1792 | 0.5288 | 0.5276 | 0.5295 | 0.5265 |
| 1.082 | 2.2629 | 8400 | 1.1593 | 0.5451 | 0.5434 | 0.5487 | 0.5415 |
| 1.0692 | 2.5862 | 9600 | 1.1153 | 0.5613 | 0.5606 | 0.5641 | 0.5594 |
| 1.0066 | 2.9095 | 10800 | 1.1135 | 0.5671 | 0.5659 | 0.5683 | 0.5650 |
### Framework versions
- Transformers 4.40.2
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1
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