metadata

language:
  - id
tags:
  - generated_from_trainer
datasets:
  - indonlp/indonlu
metrics:
  - accuracy
widget:
  - text: Entah mengapa saya merasakan ada sesuatu yang janggal di produk ini
base_model: cahya/roberta-base-indonesian-1.5G
model-index:
  - name: roberta-base-indonesian-1.5G-sentiment-analysis-smsa
    results:
      - task:
          type: text-classification
          name: Text Classification
        dataset:
          name: indonlu
          type: indonlu
          args: smsa
        metrics:
          - type: accuracy
            value: 0.9261904761904762
            name: Accuracy

roberta-base-indonesian-1.5G-sentiment-analysis-smsa

This model is a fine-tuned version of cahya/roberta-base-indonesian-1.5G on the indonlu dataset. It achieves the following results on the evaluation set:

Loss: 0.4294
Accuracy: 0.9262

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_steps: 1500
num_epochs: 10

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
0.6461	1.0	688	0.2620	0.9087
0.2627	2.0	1376	0.2291	0.9151
0.1784	3.0	2064	0.2891	0.9167
0.1099	4.0	2752	0.3317	0.9230
0.0857	5.0	3440	0.4294	0.9262
0.0346	6.0	4128	0.4759	0.9246
0.0221	7.0	4816	0.4946	0.9206
0.006	8.0	5504	0.5823	0.9175
0.0047	9.0	6192	0.5777	0.9159
0.004	10.0	6880	0.5800	0.9175

How to use this model in Transformers Library

from transformers import pipeline

pipe = pipeline(
"text-classification",
model="ayameRushia/roberta-base-indonesian-1.5G-sentiment-analysis-smsa"
)

pipe("Terima kasih atas bantuannya ya!")

Framework versions

Transformers 4.14.1
Pytorch 1.10.0+cu111
Datasets 1.16.1
Tokenizers 0.10.3