indonlp/indonlu
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How to use ayameRushia/roberta-base-indonesian-sentiment-analysis-smsa with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-classification", model="ayameRushia/roberta-base-indonesian-sentiment-analysis-smsa") # Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("ayameRushia/roberta-base-indonesian-sentiment-analysis-smsa")
model = AutoModelForSequenceClassification.from_pretrained("ayameRushia/roberta-base-indonesian-sentiment-analysis-smsa")This model is a fine-tuned version of flax-community/indonesian-roberta-base on the indonlu dataset. It achieves the following results on the evaluation set:
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The following hyperparameters were used during training:
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.7582 | 1.0 | 688 | 0.3280 | 0.8786 |
| 0.3225 | 2.0 | 1376 | 0.2398 | 0.9206 |
| 0.2057 | 3.0 | 2064 | 0.2574 | 0.9230 |
| 0.1642 | 4.0 | 2752 | 0.2820 | 0.9302 |
| 0.1266 | 5.0 | 3440 | 0.3344 | 0.9317 |
| 0.0608 | 6.0 | 4128 | 0.3543 | 0.9341 |
| 0.058 | 7.0 | 4816 | 0.4252 | 0.9349 |
| 0.0315 | 8.0 | 5504 | 0.4736 | 0.9310 |
| 0.0166 | 9.0 | 6192 | 0.4649 | 0.9349 |
| 0.0143 | 10.0 | 6880 | 0.4648 | 0.9341 |