---
license: mit
base_model: xlm-roberta-base
tags:
- generated_from_trainer
datasets:
- smsa
metrics:
- accuracy
- f1
model-index:
- name: scenario-normal-finetune-clf-data-smsa-model-xlm-roberta-base
  results:
  - task:
      name: Text Classification
      type: text-classification
    dataset:
      name: smsa
      type: smsa
      config: smsa_nusantara_text
      split: validation
      args: smsa_nusantara_text
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.9222222222222223
    - name: F1
      type: f1
      value: 0.9010725836501758
---

<!-- 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. -->

# scenario-normal-finetune-clf-data-smsa-model-xlm-roberta-base

This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the smsa dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3511
- Accuracy: 0.9222
- F1: 0.9011

## 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: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 6969

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| No log        | 0.29  | 100  | 0.4204          | 0.8397   | 0.6487 |
| No log        | 0.58  | 200  | 0.3298          | 0.9095   | 0.8696 |
| No log        | 0.87  | 300  | 0.2664          | 0.9214   | 0.8843 |
| No log        | 1.16  | 400  | 0.2882          | 0.9151   | 0.8849 |
| 0.3642        | 1.45  | 500  | 0.2531          | 0.9175   | 0.8808 |
| 0.3642        | 1.74  | 600  | 0.2847          | 0.9175   | 0.8820 |
| 0.3642        | 2.03  | 700  | 0.2889          | 0.9294   | 0.9060 |
| 0.3642        | 2.33  | 800  | 0.3066          | 0.9270   | 0.8996 |
| 0.3642        | 2.62  | 900  | 0.3736          | 0.9190   | 0.8914 |
| 0.2064        | 2.91  | 1000 | 0.2706          | 0.9214   | 0.8853 |
| 0.2064        | 3.2   | 1100 | 0.3201          | 0.9190   | 0.8878 |
| 0.2064        | 3.49  | 1200 | 0.2372          | 0.9254   | 0.9007 |
| 0.2064        | 3.78  | 1300 | 0.2534          | 0.9190   | 0.8904 |
| 0.2064        | 4.07  | 1400 | 0.3266          | 0.9214   | 0.8939 |
| 0.1543        | 4.36  | 1500 | 0.3405          | 0.9135   | 0.8815 |
| 0.1543        | 4.65  | 1600 | 0.3485          | 0.9238   | 0.8988 |
| 0.1543        | 4.94  | 1700 | 0.3287          | 0.9270   | 0.9011 |
| 0.1543        | 5.23  | 1800 | 0.3631          | 0.9167   | 0.8866 |
| 0.1543        | 5.52  | 1900 | 0.3714          | 0.9167   | 0.8922 |
| 0.1227        | 5.81  | 2000 | 0.3030          | 0.9119   | 0.8794 |
| 0.1227        | 6.1   | 2100 | 0.3363          | 0.9286   | 0.9046 |
| 0.1227        | 6.4   | 2200 | 0.3511          | 0.9222   | 0.9011 |


### Framework versions

- Transformers 4.33.3
- Pytorch 2.0.1
- Datasets 2.14.5
- Tokenizers 0.13.3