|
--- |
|
language: |
|
- id |
|
license: mit |
|
base_model: indolem/indobert-base-uncased |
|
tags: |
|
- generated_from_trainer |
|
metrics: |
|
- accuracy |
|
- precision |
|
- recall |
|
- f1 |
|
model-index: |
|
- name: sentiment-lora-r8a0d0.05-1 |
|
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-r8a0d0.05-1 |
|
|
|
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: 0.3148 |
|
- Accuracy: 0.8697 |
|
- Precision: 0.8474 |
|
- Recall: 0.8328 |
|
- F1: 0.8395 |
|
|
|
## 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 | |
|
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:| |
|
| 0.5657 | 1.0 | 122 | 0.5161 | 0.7243 | 0.6616 | 0.6474 | 0.6529 | |
|
| 0.5088 | 2.0 | 244 | 0.4913 | 0.7393 | 0.6917 | 0.7056 | 0.6971 | |
|
| 0.4682 | 3.0 | 366 | 0.4424 | 0.7845 | 0.7401 | 0.7425 | 0.7413 | |
|
| 0.4114 | 4.0 | 488 | 0.3980 | 0.8095 | 0.7702 | 0.7702 | 0.7702 | |
|
| 0.3862 | 5.0 | 610 | 0.3890 | 0.8145 | 0.7783 | 0.8088 | 0.7889 | |
|
| 0.3512 | 6.0 | 732 | 0.3583 | 0.8496 | 0.8245 | 0.8036 | 0.8128 | |
|
| 0.3428 | 7.0 | 854 | 0.3496 | 0.8521 | 0.8207 | 0.8254 | 0.8229 | |
|
| 0.3254 | 8.0 | 976 | 0.3425 | 0.8496 | 0.8245 | 0.8036 | 0.8128 | |
|
| 0.3226 | 9.0 | 1098 | 0.3388 | 0.8571 | 0.8310 | 0.8189 | 0.8245 | |
|
| 0.3063 | 10.0 | 1220 | 0.3376 | 0.8647 | 0.8439 | 0.8217 | 0.8315 | |
|
| 0.2939 | 11.0 | 1342 | 0.3319 | 0.8672 | 0.8463 | 0.8260 | 0.8351 | |
|
| 0.2838 | 12.0 | 1464 | 0.3323 | 0.8546 | 0.8263 | 0.8196 | 0.8229 | |
|
| 0.2916 | 13.0 | 1586 | 0.3283 | 0.8647 | 0.8472 | 0.8167 | 0.8296 | |
|
| 0.2826 | 14.0 | 1708 | 0.3244 | 0.8672 | 0.8463 | 0.8260 | 0.8351 | |
|
| 0.2739 | 15.0 | 1830 | 0.3231 | 0.8697 | 0.8449 | 0.8378 | 0.8412 | |
|
| 0.2674 | 16.0 | 1952 | 0.3221 | 0.8697 | 0.8449 | 0.8378 | 0.8412 | |
|
| 0.2648 | 17.0 | 2074 | 0.3193 | 0.8722 | 0.8528 | 0.8321 | 0.8413 | |
|
| 0.2687 | 18.0 | 2196 | 0.3172 | 0.8697 | 0.8460 | 0.8353 | 0.8404 | |
|
| 0.264 | 19.0 | 2318 | 0.3170 | 0.8747 | 0.8552 | 0.8363 | 0.8448 | |
|
| 0.2637 | 20.0 | 2440 | 0.3148 | 0.8697 | 0.8474 | 0.8328 | 0.8395 | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.39.3 |
|
- Pytorch 2.3.0+cu121 |
|
- Datasets 2.19.1 |
|
- Tokenizers 0.15.2 |
|
|
