---
license: mit
base_model: xlm-roberta-base
tags:
- generated_from_trainer
datasets:
- smsa
metrics:
- accuracy
- f1
model-index:
- name: scenario-non-kd-from-scratch-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: 0.8626984126984127
    - name: F1
      type: f1
      value: 0.8160944657671786
---

<!-- 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-non-kd-from-scratch-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: 0.7791
- Accuracy: 0.8627
- F1: 0.8161

## 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  | 0.7374          | 0.7008   | 0.4858 |
| No log        | 0.58  | 200  | 0.5012          | 0.7929   | 0.6973 |
| No log        | 0.87  | 300  | 0.4802          | 0.8302   | 0.7562 |
| No log        | 1.16  | 400  | 0.5320          | 0.8016   | 0.7363 |
| 0.5388        | 1.45  | 500  | 0.3564          | 0.8571   | 0.8186 |
| 0.5388        | 1.74  | 600  | 0.3728          | 0.8706   | 0.8283 |
| 0.5388        | 2.03  | 700  | 0.4158          | 0.8595   | 0.8271 |
| 0.5388        | 2.33  | 800  | 0.3882          | 0.8659   | 0.8281 |
| 0.5388        | 2.62  | 900  | 0.3844          | 0.8595   | 0.8236 |
| 0.2836        | 2.91  | 1000 | 0.4190          | 0.8675   | 0.8208 |
| 0.2836        | 3.2   | 1100 | 0.4827          | 0.8627   | 0.8247 |
| 0.2836        | 3.49  | 1200 | 0.4237          | 0.8706   | 0.8356 |
| 0.2836        | 3.78  | 1300 | 0.4066          | 0.8651   | 0.8288 |
| 0.2836        | 4.07  | 1400 | 0.4248          | 0.8651   | 0.8367 |
| 0.1908        | 4.36  | 1500 | 0.4304          | 0.8611   | 0.8251 |
| 0.1908        | 4.65  | 1600 | 0.6591          | 0.8413   | 0.8115 |
| 0.1908        | 4.94  | 1700 | 0.4593          | 0.8714   | 0.8421 |
| 0.1908        | 5.23  | 1800 | 0.5588          | 0.8587   | 0.8255 |
| 0.1908        | 5.52  | 1900 | 0.5687          | 0.8571   | 0.8120 |
| 0.1446        | 5.81  | 2000 | 0.5971          | 0.8635   | 0.8282 |
| 0.1446        | 6.1   | 2100 | 0.7238          | 0.8460   | 0.8033 |
| 0.1446        | 6.4   | 2200 | 0.6470          | 0.8563   | 0.8095 |
| 0.1446        | 6.69  | 2300 | 0.6291          | 0.8659   | 0.8243 |
| 0.1446        | 6.98  | 2400 | 0.7162          | 0.8667   | 0.8233 |
| 0.1092        | 7.27  | 2500 | 0.7199          | 0.8643   | 0.8344 |
| 0.1092        | 7.56  | 2600 | 0.7302          | 0.85     | 0.8207 |
| 0.1092        | 7.85  | 2700 | 0.6520          | 0.8627   | 0.8235 |
| 0.1092        | 8.14  | 2800 | 0.7624          | 0.8548   | 0.7925 |
| 0.1092        | 8.43  | 2900 | 0.9006          | 0.8556   | 0.8003 |
| 0.0807        | 8.72  | 3000 | 0.8713          | 0.8635   | 0.8258 |
| 0.0807        | 9.01  | 3100 | 0.7922          | 0.8667   | 0.8263 |
| 0.0807        | 9.3   | 3200 | 0.7791          | 0.8627   | 0.8161 |


### Framework versions

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