metadata

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: 0.8922305764411027
          - name: F1
            type: f1
            value: 0.8154506437768241

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

This model is a fine-tuned version of indolem/indobert-base-uncased on the indolem_sentiment dataset. It achieves the following results on the evaluation set:

Loss: 0.7311
Accuracy: 0.8922
F1: 0.8155

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	0.5133	0.7544	0.3718
No log	0.88	400	0.4239	0.7995	0.6875
0.4818	1.32	600	0.3889	0.8647	0.7523
0.4818	1.76	800	0.3263	0.8872	0.8069
0.291	2.2	1000	0.3933	0.8847	0.8067
0.291	2.64	1200	0.4703	0.8847	0.7982
0.291	3.08	1400	0.5284	0.8622	0.7843
0.2432	3.52	1600	0.4924	0.8897	0.8136
0.2432	3.96	1800	0.4952	0.9023	0.8219
0.1982	4.4	2000	0.5157	0.9098	0.8421
0.1982	4.84	2200	0.6454	0.8847	0.8099
0.1982	5.27	2400	0.5636	0.9048	0.8348
0.1441	5.71	2600	0.6147	0.8872	0.8193
0.1441	6.15	2800	0.6280	0.8997	0.8198
0.1147	6.59	3000	0.6505	0.8947	0.8205
0.1147	7.03	3200	0.6547	0.8972	0.8285
0.1147	7.47	3400	0.7311	0.8922	0.8155

Framework versions

Transformers 4.33.3
Pytorch 2.0.1
Datasets 2.14.5
Tokenizers 0.13.3