---
language:
- id
license: mit
base_model: indolem/indobert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: sentiment-lora-r8-4
  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-r8-4

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: 0.2847
- Accuracy: 0.8672
- Precision: 0.8423
- Recall: 0.8335
- F1: 0.8377

## 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     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.5577        | 1.0   | 122  | 0.5372          | 0.7193   | 0.6692    | 0.6814 | 0.6738 |
| 0.5012        | 2.0   | 244  | 0.4771          | 0.7669   | 0.7370    | 0.6501 | 0.6651 |
| 0.4626        | 3.0   | 366  | 0.4250          | 0.8070   | 0.7756    | 0.7360 | 0.7504 |
| 0.4055        | 4.0   | 488  | 0.3896          | 0.8346   | 0.7996    | 0.8055 | 0.8024 |
| 0.3709        | 5.0   | 610  | 0.3578          | 0.8296   | 0.7961    | 0.7869 | 0.7912 |
| 0.3385        | 6.0   | 732  | 0.3523          | 0.8371   | 0.8017    | 0.8172 | 0.8086 |
| 0.3276        | 7.0   | 854  | 0.3307          | 0.8521   | 0.8271    | 0.8079 | 0.8164 |
| 0.3133        | 8.0   | 976  | 0.3256          | 0.8571   | 0.8381    | 0.8064 | 0.8196 |
| 0.3039        | 9.0   | 1098 | 0.3282          | 0.8647   | 0.8491    | 0.8142 | 0.8286 |
| 0.2831        | 10.0  | 1220 | 0.3142          | 0.8596   | 0.8316    | 0.8282 | 0.8298 |
| 0.2798        | 11.0  | 1342 | 0.3034          | 0.8747   | 0.8523    | 0.8413 | 0.8465 |
| 0.269         | 12.0  | 1464 | 0.3002          | 0.8672   | 0.8479    | 0.8235 | 0.8342 |
| 0.2699        | 13.0  | 1586 | 0.2973          | 0.8697   | 0.8428    | 0.8428 | 0.8428 |
| 0.2657        | 14.0  | 1708 | 0.2985          | 0.8722   | 0.8445    | 0.8496 | 0.8470 |
| 0.2537        | 15.0  | 1830 | 0.2886          | 0.8672   | 0.8423    | 0.8335 | 0.8377 |
| 0.2529        | 16.0  | 1952 | 0.2878          | 0.8647   | 0.8387    | 0.8317 | 0.8351 |
| 0.2565        | 17.0  | 2074 | 0.2877          | 0.8722   | 0.8463    | 0.8446 | 0.8454 |
| 0.2514        | 18.0  | 2196 | 0.2857          | 0.8672   | 0.8412    | 0.8360 | 0.8385 |
| 0.2517        | 19.0  | 2318 | 0.2844          | 0.8697   | 0.8438    | 0.8403 | 0.8420 |
| 0.2512        | 20.0  | 2440 | 0.2847          | 0.8672   | 0.8423    | 0.8335 | 0.8377 |


### Framework versions

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