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

# absa_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: 0.6475
- Accuracy: 0.8392
- F1: 0.8409
- Precision: 0.8444
- Recall: 0.8431

## 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: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| No log        | 1.0   | 72   | 0.6475          | 0.8392   | 0.8409 | 0.8444    | 0.8431 |
| No log        | 2.0   | 144  | 0.8639          | 0.8252   | 0.8156 | 0.8261    | 0.8177 |
| No log        | 3.0   | 216  | 0.9170          | 0.7832   | 0.7800 | 0.8112    | 0.7676 |
| No log        | 4.0   | 288  | 0.8206          | 0.8322   | 0.8359 | 0.8346    | 0.8405 |
| No log        | 5.0   | 360  | 0.8318          | 0.8392   | 0.8417 | 0.8434    | 0.8404 |
| No log        | 6.0   | 432  | 0.9578          | 0.8252   | 0.8255 | 0.8243    | 0.8325 |
| 0.0684        | 7.0   | 504  | 0.9713          | 0.8112   | 0.8027 | 0.8143    | 0.7967 |
| 0.0684        | 8.0   | 576  | 0.9850          | 0.8252   | 0.8137 | 0.8236    | 0.8089 |
| 0.0684        | 9.0   | 648  | 0.9955          | 0.8392   | 0.8258 | 0.8347    | 0.8203 |
| 0.0684        | 10.0  | 720  | 0.9964          | 0.8392   | 0.8258 | 0.8347    | 0.8203 |


### Framework versions

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