metadata

license: mit
base_model: xlm-roberta-base
tags:
  - generated_from_trainer
datasets:
  - indolem_sentiment
metrics:
  - accuracy
  - f1
model-index:
  - name: scenario-normal-finetune-clf-data-indolem_sentiment-model-xlm-roberta-base
    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: 0.9147869674185464
          - name: F1
            type: f1
            value: 0.8629032258064516

scenario-normal-finetune-clf-data-indolem_sentiment-model-xlm-roberta-base

This model is a fine-tuned version of xlm-roberta-base on the indolem_sentiment dataset. It achieves the following results on the evaluation set:

Loss: 0.5769
Accuracy: 0.9148
F1: 0.8629

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	0.4983	0.7068	0.0
No log	0.88	400	0.4663	0.7995	0.7059
0.5119	1.32	600	0.4746	0.8722	0.7792
0.5119	1.76	800	0.4463	0.8797	0.7949
0.3523	2.2	1000	0.5374	0.8772	0.7984
0.3523	2.64	1200	0.4591	0.8897	0.8087
0.3523	3.08	1400	0.4909	0.8872	0.8148
0.2978	3.52	1600	0.5236	0.8872	0.8263
0.2978	3.96	1800	0.4410	0.9148	0.8559
0.2623	4.4	2000	0.4655	0.8997	0.8347
0.2623	4.84	2200	0.6111	0.8772	0.8231
0.2623	5.27	2400	0.4194	0.9198	0.8667
0.1863	5.71	2600	0.5278	0.8972	0.8392
0.1863	6.15	2800	0.4805	0.9173	0.8559
0.1332	6.59	3000	0.5610	0.9098	0.8548
0.1332	7.03	3200	0.4435	0.9248	0.875
0.1332	7.47	3400	0.5367	0.9148	0.8651
0.1143	7.91	3600	0.5159	0.9148	0.8618
0.1143	8.35	3800	0.5945	0.9098	0.8487
0.0836	8.79	4000	0.7401	0.8947	0.8421
0.0836	9.23	4200	0.5591	0.9148	0.8618
0.0836	9.67	4400	0.6025	0.9123	0.8511
0.0899	10.11	4600	0.5769	0.9148	0.8629

Framework versions

Transformers 4.33.3
Pytorch 2.0.1
Datasets 2.14.5
Tokenizers 0.13.3