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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-r8-4
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-r8-4
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: 0.2847
- Accuracy: 0.8672
- Precision: 0.8423
- Recall: 0.8335
- F1: 0.8377
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.5577 | 1.0 | 122 | 0.5372 | 0.7193 | 0.6692 | 0.6814 | 0.6738 |
| 0.5012 | 2.0 | 244 | 0.4771 | 0.7669 | 0.7370 | 0.6501 | 0.6651 |
| 0.4626 | 3.0 | 366 | 0.4250 | 0.8070 | 0.7756 | 0.7360 | 0.7504 |
| 0.4055 | 4.0 | 488 | 0.3896 | 0.8346 | 0.7996 | 0.8055 | 0.8024 |
| 0.3709 | 5.0 | 610 | 0.3578 | 0.8296 | 0.7961 | 0.7869 | 0.7912 |
| 0.3385 | 6.0 | 732 | 0.3523 | 0.8371 | 0.8017 | 0.8172 | 0.8086 |
| 0.3276 | 7.0 | 854 | 0.3307 | 0.8521 | 0.8271 | 0.8079 | 0.8164 |
| 0.3133 | 8.0 | 976 | 0.3256 | 0.8571 | 0.8381 | 0.8064 | 0.8196 |
| 0.3039 | 9.0 | 1098 | 0.3282 | 0.8647 | 0.8491 | 0.8142 | 0.8286 |
| 0.2831 | 10.0 | 1220 | 0.3142 | 0.8596 | 0.8316 | 0.8282 | 0.8298 |
| 0.2798 | 11.0 | 1342 | 0.3034 | 0.8747 | 0.8523 | 0.8413 | 0.8465 |
| 0.269 | 12.0 | 1464 | 0.3002 | 0.8672 | 0.8479 | 0.8235 | 0.8342 |
| 0.2699 | 13.0 | 1586 | 0.2973 | 0.8697 | 0.8428 | 0.8428 | 0.8428 |
| 0.2657 | 14.0 | 1708 | 0.2985 | 0.8722 | 0.8445 | 0.8496 | 0.8470 |
| 0.2537 | 15.0 | 1830 | 0.2886 | 0.8672 | 0.8423 | 0.8335 | 0.8377 |
| 0.2529 | 16.0 | 1952 | 0.2878 | 0.8647 | 0.8387 | 0.8317 | 0.8351 |
| 0.2565 | 17.0 | 2074 | 0.2877 | 0.8722 | 0.8463 | 0.8446 | 0.8454 |
| 0.2514 | 18.0 | 2196 | 0.2857 | 0.8672 | 0.8412 | 0.8360 | 0.8385 |
| 0.2517 | 19.0 | 2318 | 0.2844 | 0.8697 | 0.8438 | 0.8403 | 0.8420 |
| 0.2512 | 20.0 | 2440 | 0.2847 | 0.8672 | 0.8423 | 0.8335 | 0.8377 |
### Framework versions
- Transformers 4.39.3
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.15.2
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