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---
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
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