|
--- |
|
language: |
|
- id |
|
license: mit |
|
base_model: indolem/indobert-base-uncased |
|
tags: |
|
- generated_from_trainer |
|
metrics: |
|
- precision |
|
- recall |
|
- f1 |
|
- accuracy |
|
model-index: |
|
- name: nerugm-lora-r2a1d0.05 |
|
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. --> |
|
|
|
# nerugm-lora-r2a1d0.05 |
|
|
|
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: 0.1346 |
|
- Precision: 0.7366 |
|
- Recall: 0.8629 |
|
- F1: 0.7948 |
|
- Accuracy: 0.9555 |
|
|
|
## 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: 16 |
|
- eval_batch_size: 64 |
|
- 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 | Precision | Recall | F1 | Accuracy | |
|
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:| |
|
| 0.7885 | 1.0 | 528 | 0.4616 | 0.3182 | 0.0813 | 0.1296 | 0.8599 | |
|
| 0.3921 | 2.0 | 1056 | 0.2524 | 0.6053 | 0.6798 | 0.6404 | 0.9273 | |
|
| 0.2392 | 3.0 | 1584 | 0.1932 | 0.6500 | 0.7844 | 0.7109 | 0.9382 | |
|
| 0.1931 | 4.0 | 2112 | 0.1676 | 0.6905 | 0.8234 | 0.7511 | 0.9444 | |
|
| 0.1719 | 5.0 | 2640 | 0.1583 | 0.7056 | 0.8396 | 0.7668 | 0.9478 | |
|
| 0.1602 | 6.0 | 3168 | 0.1539 | 0.7115 | 0.8582 | 0.7780 | 0.9502 | |
|
| 0.1533 | 7.0 | 3696 | 0.1520 | 0.7031 | 0.8629 | 0.7748 | 0.9506 | |
|
| 0.1455 | 8.0 | 4224 | 0.1456 | 0.7263 | 0.8559 | 0.7858 | 0.9525 | |
|
| 0.1398 | 9.0 | 4752 | 0.1425 | 0.7301 | 0.8536 | 0.7870 | 0.9537 | |
|
| 0.1368 | 10.0 | 5280 | 0.1395 | 0.7229 | 0.8536 | 0.7828 | 0.9533 | |
|
| 0.1331 | 11.0 | 5808 | 0.1365 | 0.7360 | 0.8536 | 0.7904 | 0.9551 | |
|
| 0.1305 | 12.0 | 6336 | 0.1377 | 0.7332 | 0.8605 | 0.7918 | 0.9549 | |
|
| 0.1279 | 13.0 | 6864 | 0.1357 | 0.7415 | 0.8582 | 0.7956 | 0.9565 | |
|
| 0.1251 | 14.0 | 7392 | 0.1355 | 0.7371 | 0.8652 | 0.7960 | 0.9555 | |
|
| 0.1239 | 15.0 | 7920 | 0.1359 | 0.7366 | 0.8629 | 0.7948 | 0.9549 | |
|
| 0.1231 | 16.0 | 8448 | 0.1347 | 0.7351 | 0.8629 | 0.7939 | 0.9551 | |
|
| 0.122 | 17.0 | 8976 | 0.1353 | 0.7351 | 0.8629 | 0.7939 | 0.9555 | |
|
| 0.1205 | 18.0 | 9504 | 0.1356 | 0.7317 | 0.8605 | 0.7909 | 0.9549 | |
|
| 0.1202 | 19.0 | 10032 | 0.1347 | 0.7351 | 0.8629 | 0.7939 | 0.9551 | |
|
| 0.1204 | 20.0 | 10560 | 0.1346 | 0.7366 | 0.8629 | 0.7948 | 0.9555 | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.39.3 |
|
- Pytorch 2.3.0+cu121 |
|
- Datasets 2.19.1 |
|
- Tokenizers 0.15.2 |
|
|