|
--- |
|
language: |
|
- id |
|
license: mit |
|
base_model: indolem/indobert-base-uncased |
|
tags: |
|
- generated_from_trainer |
|
metrics: |
|
- accuracy |
|
- precision |
|
- recall |
|
- f1 |
|
model-index: |
|
- name: sentiment-lora-r8a0d0.1-1 |
|
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-r8a0d0.1-1 |
|
|
|
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: 0.3035 |
|
- Accuracy: 0.8747 |
|
- Precision: 0.8523 |
|
- Recall: 0.8413 |
|
- F1: 0.8465 |
|
|
|
## 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 | |
|
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:| |
|
| 0.5655 | 1.0 | 122 | 0.5179 | 0.7243 | 0.6623 | 0.6499 | 0.6548 | |
|
| 0.5048 | 2.0 | 244 | 0.4926 | 0.7519 | 0.7079 | 0.7270 | 0.7147 | |
|
| 0.4529 | 3.0 | 366 | 0.4301 | 0.7995 | 0.7581 | 0.7606 | 0.7593 | |
|
| 0.393 | 4.0 | 488 | 0.3863 | 0.8221 | 0.7871 | 0.7766 | 0.7814 | |
|
| 0.3754 | 5.0 | 610 | 0.3868 | 0.8246 | 0.7892 | 0.8209 | 0.8003 | |
|
| 0.3455 | 6.0 | 732 | 0.3605 | 0.8446 | 0.8126 | 0.8126 | 0.8126 | |
|
| 0.3344 | 7.0 | 854 | 0.3396 | 0.8546 | 0.8263 | 0.8196 | 0.8229 | |
|
| 0.3157 | 8.0 | 976 | 0.3319 | 0.8672 | 0.8436 | 0.8310 | 0.8369 | |
|
| 0.3076 | 9.0 | 1098 | 0.3273 | 0.8546 | 0.8284 | 0.8146 | 0.8210 | |
|
| 0.2948 | 10.0 | 1220 | 0.3238 | 0.8747 | 0.8552 | 0.8363 | 0.8448 | |
|
| 0.2737 | 11.0 | 1342 | 0.3199 | 0.8697 | 0.8474 | 0.8328 | 0.8395 | |
|
| 0.2741 | 12.0 | 1464 | 0.3190 | 0.8596 | 0.8299 | 0.8332 | 0.8315 | |
|
| 0.275 | 13.0 | 1586 | 0.3146 | 0.8772 | 0.8628 | 0.8331 | 0.8458 | |
|
| 0.2736 | 14.0 | 1708 | 0.3104 | 0.8697 | 0.8460 | 0.8353 | 0.8404 | |
|
| 0.263 | 15.0 | 1830 | 0.3112 | 0.8672 | 0.8393 | 0.8410 | 0.8402 | |
|
| 0.2583 | 16.0 | 1952 | 0.3086 | 0.8722 | 0.8453 | 0.8471 | 0.8462 | |
|
| 0.2544 | 17.0 | 2074 | 0.3065 | 0.8722 | 0.8512 | 0.8346 | 0.8422 | |
|
| 0.2594 | 18.0 | 2196 | 0.3056 | 0.8697 | 0.8449 | 0.8378 | 0.8412 | |
|
| 0.256 | 19.0 | 2318 | 0.3043 | 0.8722 | 0.8512 | 0.8346 | 0.8422 | |
|
| 0.2515 | 20.0 | 2440 | 0.3035 | 0.8747 | 0.8523 | 0.8413 | 0.8465 | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.39.3 |
|
- Pytorch 2.3.0+cu121 |
|
- Datasets 2.19.1 |
|
- Tokenizers 0.15.2 |
|
|
