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---
license: mit
base_model: xlm-roberta-base
tags:
- generated_from_trainer
datasets:
- indolem_sentiment
metrics:
- accuracy
- f1
model-index:
- name: scenario-normal-finetune-clf-data-indolem_sentiment-model-xlm-roberta-base
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: indolem_sentiment
type: indolem_sentiment
config: indolem_sentiment_nusantara_text
split: validation
args: indolem_sentiment_nusantara_text
metrics:
- name: Accuracy
type: accuracy
value: 0.9147869674185464
- name: F1
type: f1
value: 0.8629032258064516
---
<!-- 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-indolem_sentiment-model-xlm-roberta-base
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the indolem_sentiment dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5769
- Accuracy: 0.9148
- F1: 0.8629
## 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-06
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 30
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| No log | 0.44 | 200 | 0.4983 | 0.7068 | 0.0 |
| No log | 0.88 | 400 | 0.4663 | 0.7995 | 0.7059 |
| 0.5119 | 1.32 | 600 | 0.4746 | 0.8722 | 0.7792 |
| 0.5119 | 1.76 | 800 | 0.4463 | 0.8797 | 0.7949 |
| 0.3523 | 2.2 | 1000 | 0.5374 | 0.8772 | 0.7984 |
| 0.3523 | 2.64 | 1200 | 0.4591 | 0.8897 | 0.8087 |
| 0.3523 | 3.08 | 1400 | 0.4909 | 0.8872 | 0.8148 |
| 0.2978 | 3.52 | 1600 | 0.5236 | 0.8872 | 0.8263 |
| 0.2978 | 3.96 | 1800 | 0.4410 | 0.9148 | 0.8559 |
| 0.2623 | 4.4 | 2000 | 0.4655 | 0.8997 | 0.8347 |
| 0.2623 | 4.84 | 2200 | 0.6111 | 0.8772 | 0.8231 |
| 0.2623 | 5.27 | 2400 | 0.4194 | 0.9198 | 0.8667 |
| 0.1863 | 5.71 | 2600 | 0.5278 | 0.8972 | 0.8392 |
| 0.1863 | 6.15 | 2800 | 0.4805 | 0.9173 | 0.8559 |
| 0.1332 | 6.59 | 3000 | 0.5610 | 0.9098 | 0.8548 |
| 0.1332 | 7.03 | 3200 | 0.4435 | 0.9248 | 0.875 |
| 0.1332 | 7.47 | 3400 | 0.5367 | 0.9148 | 0.8651 |
| 0.1143 | 7.91 | 3600 | 0.5159 | 0.9148 | 0.8618 |
| 0.1143 | 8.35 | 3800 | 0.5945 | 0.9098 | 0.8487 |
| 0.0836 | 8.79 | 4000 | 0.7401 | 0.8947 | 0.8421 |
| 0.0836 | 9.23 | 4200 | 0.5591 | 0.9148 | 0.8618 |
| 0.0836 | 9.67 | 4400 | 0.6025 | 0.9123 | 0.8511 |
| 0.0899 | 10.11 | 4600 | 0.5769 | 0.9148 | 0.8629 |
### Framework versions
- Transformers 4.33.3
- Pytorch 2.0.1
- Datasets 2.14.5
- Tokenizers 0.13.3
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