metadata

license: apache-2.0
base_model: Toshifumi/distilbert-base-multilingual-cased-finetuned-emotion
tags:
  - generated_from_trainer
datasets:
  - indonlu
metrics:
  - accuracy
  - precision
  - recall
  - f1
model-index:
  - name: indonesian-distilbert-base-cased-finetuned-indonlu
    results:
      - task:
          name: Text Classification
          type: text-classification
        dataset:
          name: indonlu
          type: indonlu
          config: emot
          split: validation
          args: emot
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.6113636363636363
          - name: Precision
            type: precision
            value: 0.6057688190944959
          - name: Recall
            type: recall
            value: 0.6113636363636363
          - name: F1
            type: f1
            value: 0.6068671444135532

indonesian-distilbert-base-cased-finetuned-indonlu

This model is a fine-tuned version of Toshifumi/distilbert-base-multilingual-cased-finetuned-emotion on the indonlu dataset. It achieves the following results on the evaluation set:

Loss: 1.1300
Accuracy: 0.6114
Precision: 0.6058
Recall: 0.6114
F1: 0.6069

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: 16
eval_batch_size: 16
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	221	1.2623	0.475	0.4817	0.475	0.4458
No log	2.0	442	1.0937	0.55	0.5555	0.55	0.5444
1.2289	3.0	663	1.0749	0.5886	0.6003	0.5886	0.5898
1.2289	4.0	884	1.0836	0.5818	0.6019	0.5818	0.5800
0.7857	5.0	1105	1.1300	0.6114	0.6058	0.6114	0.6069
0.7857	6.0	1326	1.1595	0.6	0.5996	0.6	0.5984
0.5288	7.0	1547	1.1767	0.6	0.5986	0.6	0.5958
0.5288	8.0	1768	1.2195	0.6	0.5969	0.6	0.5952
0.5288	9.0	1989	1.2422	0.5932	0.5915	0.5932	0.5909
0.3685	10.0	2210	1.2406	0.5841	0.5842	0.5841	0.5830

Framework versions

Transformers 4.35.2
Pytorch 2.1.0+cu121
Datasets 2.17.1
Tokenizers 0.15.2