---
license: mit
base_model: indolem/indobert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: sentiment-lora-r2a2d0.05-1
  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-r2a2d0.05-1

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: 0.3638
- Accuracy: 0.8446
- Precision: 0.8193
- Recall: 0.7951
- F1: 0.8055

## 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     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.5663        | 1.0   | 122  | 0.5216          | 0.7293   | 0.6677    | 0.6510 | 0.6572 |
| 0.5149        | 2.0   | 244  | 0.5134          | 0.7243   | 0.6758    | 0.6899 | 0.6810 |
| 0.4925        | 3.0   | 366  | 0.4821          | 0.7569   | 0.7055    | 0.6980 | 0.7014 |
| 0.4608        | 4.0   | 488  | 0.4654          | 0.7644   | 0.7150    | 0.7083 | 0.7114 |
| 0.4493        | 5.0   | 610  | 0.4600          | 0.7569   | 0.7126    | 0.7305 | 0.7193 |
| 0.4257        | 6.0   | 732  | 0.4307          | 0.7870   | 0.7433    | 0.7318 | 0.7369 |
| 0.4178        | 7.0   | 854  | 0.4181          | 0.7970   | 0.7552    | 0.7614 | 0.7581 |
| 0.3977        | 8.0   | 976  | 0.3972          | 0.8070   | 0.7687    | 0.7560 | 0.7617 |
| 0.3946        | 9.0   | 1098 | 0.3937          | 0.8145   | 0.7779    | 0.7663 | 0.7716 |
| 0.3762        | 10.0  | 1220 | 0.3874          | 0.8246   | 0.7995    | 0.7584 | 0.7738 |
| 0.3727        | 11.0  | 1342 | 0.3787          | 0.8321   | 0.8014    | 0.7837 | 0.7915 |
| 0.3626        | 12.0  | 1464 | 0.3750          | 0.8371   | 0.8059    | 0.7947 | 0.7999 |
| 0.359         | 13.0  | 1586 | 0.3728          | 0.8296   | 0.8066    | 0.7644 | 0.7803 |
| 0.3488        | 14.0  | 1708 | 0.3709          | 0.8296   | 0.8049    | 0.7669 | 0.7816 |
| 0.3445        | 15.0  | 1830 | 0.3667          | 0.8421   | 0.8131    | 0.7983 | 0.8050 |
| 0.3344        | 16.0  | 1952 | 0.3656          | 0.8421   | 0.8142    | 0.7958 | 0.8040 |
| 0.3339        | 17.0  | 2074 | 0.3654          | 0.8396   | 0.8128    | 0.7890 | 0.7992 |
| 0.3357        | 18.0  | 2196 | 0.3638          | 0.8421   | 0.8154    | 0.7933 | 0.8029 |
| 0.3357        | 19.0  | 2318 | 0.3646          | 0.8421   | 0.8154    | 0.7933 | 0.8029 |
| 0.3359        | 20.0  | 2440 | 0.3638          | 0.8446   | 0.8193    | 0.7951 | 0.8055 |


### Framework versions

- Transformers 4.39.3
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.15.2