---
license: apache-2.0
base_model: projecte-aina/roberta-base-ca-v2-cased-te
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: SYN_300524_epoch_5
  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. -->

# SYN_300524_epoch_5

This model is a fine-tuned version of [projecte-aina/roberta-base-ca-v2-cased-te](https://huggingface.co/projecte-aina/roberta-base-ca-v2-cased-te) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3372
- Accuracy: 0.98
- Precision: 0.9803
- Recall: 0.98
- F1: 0.9800
- Ratio: 0.488

## 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: 16
- eval_batch_size: 16
- seed: 47
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.06
- lr_scheduler_warmup_steps: 4
- num_epochs: 1
- label_smoothing_factor: 0.1

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Accuracy | Precision | Recall | F1     | Ratio |
|:-------------:|:------:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|:-----:|
| 0.3174        | 0.0533 | 10   | 0.3307          | 0.984    | 0.9840    | 0.984  | 0.9840 | 0.496 |
| 0.3202        | 0.1067 | 20   | 0.3258          | 0.986    | 0.9861    | 0.986  | 0.9860 | 0.494 |
| 0.3016        | 0.16   | 30   | 0.3282          | 0.986    | 0.9860    | 0.986  | 0.9860 | 0.504 |
| 0.3291        | 0.2133 | 40   | 0.3495          | 0.977    | 0.9774    | 0.977  | 0.9770 | 0.485 |
| 0.2942        | 0.2667 | 50   | 0.3602          | 0.973    | 0.9738    | 0.973  | 0.9730 | 0.479 |
| 0.3121        | 0.32   | 60   | 0.3586          | 0.973    | 0.9731    | 0.973  | 0.9730 | 0.493 |
| 0.3226        | 0.3733 | 70   | 0.3736          | 0.968    | 0.9681    | 0.968  | 0.9680 | 0.508 |
| 0.3226        | 0.4267 | 80   | 0.3515          | 0.979    | 0.9791    | 0.979  | 0.9790 | 0.493 |
| 0.3265        | 0.48   | 90   | 0.3697          | 0.97     | 0.9706    | 0.97   | 0.9700 | 0.482 |
| 0.3424        | 0.5333 | 100  | 0.3650          | 0.971    | 0.9717    | 0.971  | 0.9710 | 0.481 |
| 0.3348        | 0.5867 | 110  | 0.3502          | 0.976    | 0.9760    | 0.976  | 0.9760 | 0.496 |
| 0.3393        | 0.64   | 120  | 0.3441          | 0.978    | 0.9780    | 0.978  | 0.9780 | 0.496 |
| 0.3421        | 0.6933 | 130  | 0.3397          | 0.979    | 0.9791    | 0.979  | 0.9790 | 0.493 |
| 0.3319        | 0.7467 | 140  | 0.3412          | 0.979    | 0.9791    | 0.979  | 0.9790 | 0.493 |
| 0.3554        | 0.8    | 150  | 0.3416          | 0.977    | 0.9772    | 0.977  | 0.9770 | 0.489 |
| 0.3829        | 0.8533 | 160  | 0.3428          | 0.978    | 0.9785    | 0.978  | 0.9780 | 0.484 |
| 0.3631        | 0.9067 | 170  | 0.3396          | 0.979    | 0.9793    | 0.979  | 0.9790 | 0.487 |
| 0.3362        | 0.96   | 180  | 0.3376          | 0.98     | 0.9803    | 0.98   | 0.9800 | 0.488 |


### Framework versions

- Transformers 4.41.1
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1