---
license: mit
base_model: LazarusNLP/NusaBERT-base
tags:
- generated_from_trainer
datasets:
- indonlu
metrics:
- f1
model-index:
- name: NusaBERT-base-EmoT
  results:
  - task:
      name: Text Classification
      type: text-classification
    dataset:
      name: indonlu
      type: indonlu
      config: emot
      split: validation
      args: emot
    metrics:
    - name: F1
      type: f1
      value: 0.761
---

<!-- 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. -->

# NusaBERT-base-EmoT

This model is a fine-tuned version of [LazarusNLP/NusaBERT-base](https://huggingface.co./LazarusNLP/NusaBERT-base) on the indonlu dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7778
- F1: 0.761

## 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: 1e-05
- train_batch_size: 32
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 100

### Training results

| Training Loss | Epoch | Step | Validation Loss | F1     |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| No log        | 1.0   | 111  | 1.2158          | 0.5511 |
| No log        | 2.0   | 222  | 0.8299          | 0.6929 |
| No log        | 3.0   | 333  | 0.7713          | 0.7324 |
| No log        | 4.0   | 444  | 0.7383          | 0.7431 |
| 0.8834        | 5.0   | 555  | 0.7813          | 0.7261 |
| 0.8834        | 6.0   | 666  | 0.7781          | 0.7591 |
| 0.8834        | 7.0   | 777  | 0.8360          | 0.7375 |
| 0.8834        | 8.0   | 888  | 0.8802          | 0.7287 |
| 0.8834        | 9.0   | 999  | 0.9192          | 0.7474 |
| 0.2881        | 10.0  | 1110 | 1.0433          | 0.733  |
| 0.2881        | 11.0  | 1221 | 1.0686          | 0.7277 |


### Framework versions

- Transformers 4.37.2
- Pytorch 2.2.0+cu118
- Datasets 2.17.1
- Tokenizers 0.15.1