---
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