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---
language:
- id
license: mit
base_model: indolem/indobert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: sentiment-lora-r8a1d0.15-1
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. -->
# sentiment-lora-r8a1d0.15-1
This model is a fine-tuned version of [indolem/indobert-base-uncased](https://huggingface.co/indolem/indobert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3168
- Accuracy: 0.8722
- Precision: 0.8528
- Recall: 0.8321
- F1: 0.8413
## 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: 30
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.5647 | 1.0 | 122 | 0.5166 | 0.7068 | 0.6380 | 0.6250 | 0.6297 |
| 0.5067 | 2.0 | 244 | 0.4954 | 0.7343 | 0.6870 | 0.7020 | 0.6926 |
| 0.4617 | 3.0 | 366 | 0.4391 | 0.7920 | 0.7491 | 0.7503 | 0.7497 |
| 0.4044 | 4.0 | 488 | 0.3911 | 0.8145 | 0.7761 | 0.7788 | 0.7774 |
| 0.382 | 5.0 | 610 | 0.3827 | 0.8195 | 0.7849 | 0.8198 | 0.7962 |
| 0.3494 | 6.0 | 732 | 0.3528 | 0.8421 | 0.8092 | 0.8108 | 0.8100 |
| 0.3423 | 7.0 | 854 | 0.3442 | 0.8546 | 0.8239 | 0.8272 | 0.8255 |
| 0.33 | 8.0 | 976 | 0.3400 | 0.8672 | 0.8479 | 0.8235 | 0.8342 |
| 0.3296 | 9.0 | 1098 | 0.3349 | 0.8496 | 0.8245 | 0.8036 | 0.8128 |
| 0.3074 | 10.0 | 1220 | 0.3349 | 0.8622 | 0.8467 | 0.8100 | 0.8249 |
| 0.2911 | 11.0 | 1342 | 0.3240 | 0.8697 | 0.8503 | 0.8278 | 0.8377 |
| 0.2855 | 12.0 | 1464 | 0.3273 | 0.8722 | 0.8463 | 0.8446 | 0.8454 |
| 0.2903 | 13.0 | 1586 | 0.3285 | 0.8647 | 0.8472 | 0.8167 | 0.8296 |
| 0.2896 | 14.0 | 1708 | 0.3254 | 0.8672 | 0.8479 | 0.8235 | 0.8342 |
| 0.2744 | 15.0 | 1830 | 0.3241 | 0.8647 | 0.8377 | 0.8342 | 0.8359 |
| 0.2691 | 16.0 | 1952 | 0.3210 | 0.8571 | 0.8289 | 0.8239 | 0.8264 |
| 0.2671 | 17.0 | 2074 | 0.3208 | 0.8697 | 0.8503 | 0.8278 | 0.8377 |
| 0.2736 | 18.0 | 2196 | 0.3179 | 0.8722 | 0.8512 | 0.8346 | 0.8422 |
| 0.2662 | 19.0 | 2318 | 0.3180 | 0.8722 | 0.8544 | 0.8296 | 0.8404 |
| 0.2664 | 20.0 | 2440 | 0.3168 | 0.8722 | 0.8528 | 0.8321 | 0.8413 |
### Framework versions
- Transformers 4.39.3
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.15.2
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