---
license: mit
base_model: indolem/indobert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: sentiment-lora-r8a2d0.1-1
  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-r8a2d0.1-1

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