indonlp/indonlu
Updated • 614 • 42
How to use AptaArkana/indonesian_sentiment_sbert_base with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-classification", model="AptaArkana/indonesian_sentiment_sbert_base") # Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("AptaArkana/indonesian_sentiment_sbert_base")
model = AutoModelForSequenceClassification.from_pretrained("AptaArkana/indonesian_sentiment_sbert_base", device_map="auto")This model is a fine-tuned version of naufalihsan/indonesian-sbert-large 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 | Precision | Recall | F1 |
|---|---|---|---|---|---|---|---|
| No log | 1.0 | 275 | 0.2837 | 0.9405 | 0.9427 | 0.9405 | 0.9396 |
| 0.0501 | 2.0 | 550 | 0.1966 | 0.9460 | 0.9468 | 0.9460 | 0.9458 |
| 0.0501 | 3.0 | 825 | 0.2927 | 0.9437 | 0.9435 | 0.9437 | 0.9427 |
| 0.0369 | 4.0 | 1100 | 0.3666 | 0.9460 | 0.9459 | 0.9460 | 0.9456 |
| 0.0369 | 5.0 | 1375 | 0.3579 | 0.9468 | 0.9465 | 0.9468 | 0.9465 |
| 0.0098 | 6.0 | 1650 | 0.4497 | 0.9476 | 0.9479 | 0.9476 | 0.9471 |
| 0.0098 | 7.0 | 1925 | 0.4308 | 0.95 | 0.9501 | 0.95 | 0.9496 |
| 0.0012 | 8.0 | 2200 | 0.4402 | 0.95 | 0.9499 | 0.95 | 0.9496 |
| 0.0012 | 9.0 | 2475 | 0.4429 | 0.95 | 0.9500 | 0.95 | 0.9496 |
| 0.0007 | 10.0 | 2750 | 0.4450 | 0.95 | 0.9500 | 0.95 | 0.9496 |
Base model
naufalihsan/indonesian-sbert-large