---
base_model: naufalihsan/indonesian-sbert-large
tags:
- generated_from_trainer
datasets:
- indonlu
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: sentiment
  results:
  - task:
      name: Text Classification
      type: text-classification
    dataset:
      name: indonlu
      type: indonlu
      config: smsa
      split: validation
      args: smsa
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.95
    - name: Precision
      type: precision
      value: 0.9499758037063356
    - name: Recall
      type: recall
      value: 0.95
    - name: F1
      type: f1
      value: 0.9496487652420723
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# sentiment

This model is a fine-tuned version of [naufalihsan/indonesian-sbert-large](https://huggingface.co/naufalihsan/indonesian-sbert-large) on the indonlu dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4450
- Accuracy: 0.95
- Precision: 0.9500
- Recall: 0.95
- F1: 0.9496

## 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: 40
- eval_batch_size: 40
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.01
- num_epochs: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| No log        | 1.0   | 275  | 0.2837          | 0.9405   | 0.9427    | 0.9405 | 0.9396 |
| 0.0501        | 2.0   | 550  | 0.1966          | 0.9460   | 0.9468    | 0.9460 | 0.9458 |
| 0.0501        | 3.0   | 825  | 0.2927          | 0.9437   | 0.9435    | 0.9437 | 0.9427 |
| 0.0369        | 4.0   | 1100 | 0.3666          | 0.9460   | 0.9459    | 0.9460 | 0.9456 |
| 0.0369        | 5.0   | 1375 | 0.3579          | 0.9468   | 0.9465    | 0.9468 | 0.9465 |
| 0.0098        | 6.0   | 1650 | 0.4497          | 0.9476   | 0.9479    | 0.9476 | 0.9471 |
| 0.0098        | 7.0   | 1925 | 0.4308          | 0.95     | 0.9501    | 0.95   | 0.9496 |
| 0.0012        | 8.0   | 2200 | 0.4402          | 0.95     | 0.9499    | 0.95   | 0.9496 |
| 0.0012        | 9.0   | 2475 | 0.4429          | 0.95     | 0.9500    | 0.95   | 0.9496 |
| 0.0007        | 10.0  | 2750 | 0.4450          | 0.95     | 0.9500    | 0.95   | 0.9496 |


### Framework versions

- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.17.1
- Tokenizers 0.15.2