---
language:
- id
license: mit
base_model: indolem/indobert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: sentiment-lora-r4a2d0.05-0
  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-r4a2d0.05-0

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: 0.3486
- Accuracy: 0.8396
- Precision: 0.8055
- Recall: 0.8115
- F1: 0.8084

## 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     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.5619        | 1.0   | 122  | 0.5127          | 0.7168   | 0.6536    | 0.6446 | 0.6484 |
| 0.5059        | 2.0   | 244  | 0.4967          | 0.7343   | 0.6956    | 0.7220 | 0.7022 |
| 0.4822        | 3.0   | 366  | 0.4506          | 0.7469   | 0.7006    | 0.7159 | 0.7065 |
| 0.4402        | 4.0   | 488  | 0.3984          | 0.8195   | 0.7876    | 0.7623 | 0.7728 |
| 0.4068        | 5.0   | 610  | 0.4136          | 0.7870   | 0.7473    | 0.7718 | 0.7561 |
| 0.3791        | 6.0   | 732  | 0.3771          | 0.8321   | 0.7972    | 0.7987 | 0.7979 |
| 0.3635        | 7.0   | 854  | 0.3916          | 0.8195   | 0.7822    | 0.8048 | 0.7912 |
| 0.3433        | 8.0   | 976  | 0.3799          | 0.8296   | 0.7934    | 0.8019 | 0.7974 |
| 0.3379        | 9.0   | 1098 | 0.3714          | 0.8271   | 0.7903    | 0.8026 | 0.7959 |
| 0.3296        | 10.0  | 1220 | 0.3635          | 0.8371   | 0.8032    | 0.8047 | 0.8040 |
| 0.3105        | 11.0  | 1342 | 0.3652          | 0.8296   | 0.7933    | 0.8044 | 0.7984 |
| 0.3024        | 12.0  | 1464 | 0.3702          | 0.8346   | 0.7988    | 0.8180 | 0.8069 |
| 0.309         | 13.0  | 1586 | 0.3512          | 0.8371   | 0.8032    | 0.8047 | 0.8040 |
| 0.3021        | 14.0  | 1708 | 0.3505          | 0.8396   | 0.8060    | 0.8090 | 0.8075 |
| 0.2903        | 15.0  | 1830 | 0.3553          | 0.8421   | 0.8077    | 0.8208 | 0.8136 |
| 0.2834        | 16.0  | 1952 | 0.3530          | 0.8396   | 0.8046    | 0.8215 | 0.8119 |
| 0.2811        | 17.0  | 2074 | 0.3471          | 0.8446   | 0.8120    | 0.8151 | 0.8135 |
| 0.288         | 18.0  | 2196 | 0.3505          | 0.8446   | 0.8107    | 0.8226 | 0.8161 |
| 0.277         | 19.0  | 2318 | 0.3479          | 0.8396   | 0.8055    | 0.8115 | 0.8084 |
| 0.2775        | 20.0  | 2440 | 0.3486          | 0.8396   | 0.8055    | 0.8115 | 0.8084 |


### Framework versions

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