---
license: mit
base_model: indobenchmark/indobert-base-p2
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: performa_model
  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. -->

# performa_model

This model is a fine-tuned version of [indobenchmark/indobert-base-p2](https://huggingface.co/indobenchmark/indobert-base-p2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5465
- Accuracy: 0.8122
- F1: 0.8102
- Precision: 0.8105
- Recall: 0.8100

## 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: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| No log        | 0.14  | 50   | 0.4595          | 0.7946   | 0.7926 | 0.7926    | 0.7926 |
| No log        | 0.27  | 100  | 0.4523          | 0.7946   | 0.7946 | 0.7995    | 0.8009 |
| No log        | 0.41  | 150  | 0.4501          | 0.8122   | 0.8098 | 0.8110    | 0.8089 |
| No log        | 0.54  | 200  | 0.4676          | 0.7811   | 0.7709 | 0.7965    | 0.7678 |
| No log        | 0.68  | 250  | 0.4551          | 0.8135   | 0.8099 | 0.8149    | 0.8077 |
| No log        | 0.81  | 300  | 0.4422          | 0.8162   | 0.8152 | 0.8146    | 0.8168 |
| No log        | 0.95  | 350  | 0.4336          | 0.8162   | 0.8137 | 0.8154    | 0.8126 |
| No log        | 1.08  | 400  | 0.4645          | 0.8189   | 0.8164 | 0.8182    | 0.8153 |
| No log        | 1.22  | 450  | 0.4805          | 0.8243   | 0.8236 | 0.8231    | 0.8258 |
| 0.4139        | 1.35  | 500  | 0.4984          | 0.8068   | 0.8053 | 0.8048    | 0.8061 |
| 0.4139        | 1.49  | 550  | 0.4506          | 0.8149   | 0.8137 | 0.8131    | 0.8148 |
| 0.4139        | 1.62  | 600  | 0.4364          | 0.8216   | 0.8201 | 0.8198    | 0.8204 |
| 0.4139        | 1.76  | 650  | 0.4889          | 0.7892   | 0.7892 | 0.7992    | 0.7978 |
| 0.4139        | 1.89  | 700  | 0.4348          | 0.8108   | 0.8105 | 0.8114    | 0.8143 |
| 0.4139        | 2.03  | 750  | 0.4537          | 0.8068   | 0.8056 | 0.8050    | 0.8069 |
| 0.4139        | 2.16  | 800  | 0.5296          | 0.7905   | 0.7905 | 0.7947    | 0.7964 |
| 0.4139        | 2.3   | 850  | 0.5819          | 0.7946   | 0.7943 | 0.7955    | 0.7982 |
| 0.4139        | 2.43  | 900  | 0.5868          | 0.8122   | 0.8110 | 0.8104    | 0.8124 |
| 0.4139        | 2.57  | 950  | 0.5613          | 0.8081   | 0.8050 | 0.8081    | 0.8034 |
| 0.2978        | 2.7   | 1000 | 0.5465          | 0.8122   | 0.8102 | 0.8105    | 0.8100 |
| 0.2978        | 2.84  | 1050 | 0.5665          | 0.8041   | 0.8022 | 0.8022    | 0.8023 |
| 0.2978        | 2.97  | 1100 | 0.5876          | 0.7932   | 0.7924 | 0.7921    | 0.7946 |
| 0.2978        | 3.11  | 1150 | 0.7388          | 0.8014   | 0.8000 | 0.7994    | 0.8009 |


### Framework versions

- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0