---
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: roberta-mqa
  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

This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.4631
- Accuracy: 0.3793
- F1: 0.3774
- Precision: 0.3819
- Recall: 0.3760

## 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: 28
- eval_batch_size: 28
- seed: 42
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 1.5076        | 1.0   | 1061 | 1.4901          | 0.3372   | 0.3328 | 0.3366    | 0.3321 |
| 1.4244        | 2.0   | 2122 | 1.4584          | 0.3594   | 0.3560 | 0.3615    | 0.3545 |
| 1.3553        | 3.0   | 3183 | 1.4631          | 0.3793   | 0.3774 | 0.3819    | 0.3760 |


### Framework versions

- Transformers 4.40.2
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1