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

# story_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: 0.2923
- Accuracy: 0.9409
- F1: 0.9043
- Precision: 0.9087
- Recall: 0.9

## 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        | 0.53  | 50   | 0.5324          | 0.8978   | 0.8032 | 0.9009    | 0.7572 |
| No log        | 1.06  | 100  | 0.2795          | 0.9355   | 0.8967 | 0.8967    | 0.8967 |
| No log        | 1.6   | 150  | 0.2561          | 0.9194   | 0.8772 | 0.8608    | 0.8972 |
| No log        | 2.13  | 200  | 0.3274          | 0.9194   | 0.8635 | 0.8871    | 0.8444 |
| No log        | 2.66  | 250  | 0.2756          | 0.9247   | 0.8819 | 0.8745    | 0.89   |
| No log        | 3.19  | 300  | 0.4554          | 0.9032   | 0.8302 | 0.8696    | 0.8028 |
| No log        | 3.72  | 350  | 0.2333          | 0.9462   | 0.9157 | 0.9075    | 0.9244 |
| No log        | 4.26  | 400  | 0.4101          | 0.9247   | 0.8711 | 0.9013    | 0.8478 |
| No log        | 4.79  | 450  | 0.2826          | 0.9409   | 0.9063 | 0.9021    | 0.9106 |


### Framework versions

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