---
license: mit
base_model: indolem/indobert-base-uncased
tags:
- generated_from_trainer
datasets:
- indolem_sentiment
metrics:
- accuracy
- f1
model-index:
- name: scenario-normal-finetune-clf-data-indolem_sentiment-model-indolem-indobert-base-uncased
  results:
  - task:
      name: Text Classification
      type: text-classification
    dataset:
      name: indolem_sentiment
      type: indolem_sentiment
      config: indolem_sentiment_nusantara_text
      split: validation
      args: indolem_sentiment_nusantara_text
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.899749373433584
    - name: F1
      type: f1
      value: 0.8181818181818181
---

<!-- 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. -->

# scenario-normal-finetune-clf-data-indolem_sentiment-model-indolem-indobert-base-uncased

This model is a fine-tuned version of [indolem/indobert-base-uncased](https://huggingface.co./indolem/indobert-base-uncased) on the indolem_sentiment dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7320
- Accuracy: 0.8997
- F1: 0.8182

## 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-06
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 30

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| No log        | 0.44  | 200  | 0.5218          | 0.7343   | 0.2838 |
| No log        | 0.88  | 400  | 0.4318          | 0.8070   | 0.7138 |
| 0.4843        | 1.32  | 600  | 0.4092          | 0.8521   | 0.7281 |
| 0.4843        | 1.76  | 800  | 0.3515          | 0.8772   | 0.7803 |
| 0.2912        | 2.2   | 1000 | 0.4582          | 0.8697   | 0.7833 |
| 0.2912        | 2.64  | 1200 | 0.5148          | 0.8747   | 0.7881 |
| 0.2912        | 3.08  | 1400 | 0.5736          | 0.8672   | 0.7837 |
| 0.2526        | 3.52  | 1600 | 0.5119          | 0.8797   | 0.7983 |
| 0.2526        | 3.96  | 1800 | 0.5242          | 0.8997   | 0.8095 |
| 0.1974        | 4.4   | 2000 | 0.5311          | 0.8997   | 0.8182 |
| 0.1974        | 4.84  | 2200 | 0.6478          | 0.8797   | 0.7983 |
| 0.1974        | 5.27  | 2400 | 0.6219          | 0.8822   | 0.8000 |
| 0.1526        | 5.71  | 2600 | 0.6591          | 0.8872   | 0.8178 |
| 0.1526        | 6.15  | 2800 | 0.6483          | 0.8947   | 0.8056 |
| 0.1159        | 6.59  | 3000 | 0.7075          | 0.8847   | 0.8099 |
| 0.1159        | 7.03  | 3200 | 0.7157          | 0.8872   | 0.8    |
| 0.1159        | 7.47  | 3400 | 0.7320          | 0.8997   | 0.8182 |


### Framework versions

- Transformers 4.33.3
- Pytorch 2.0.1
- Datasets 2.14.5
- Tokenizers 0.13.3