indonlp/indonlu
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How to use dhanikitkat/indo_smsa-1.5G_sentiment_analysis with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-classification", model="dhanikitkat/indo_smsa-1.5G_sentiment_analysis") # Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("dhanikitkat/indo_smsa-1.5G_sentiment_analysis")
model = AutoModelForSequenceClassification.from_pretrained("dhanikitkat/indo_smsa-1.5G_sentiment_analysis")This model is a fine-tuned version of cahya/bert-base-indonesian-1.5G 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 |
|---|---|---|---|---|
| 0.2864 | 1.0 | 688 | 0.2154 | 0.9286 |
| 0.1648 | 2.0 | 1376 | 0.2238 | 0.9357 |
| 0.0759 | 3.0 | 2064 | 0.3351 | 0.9365 |
| 0.044 | 4.0 | 2752 | 0.3390 | 0.9373 |
| 0.0308 | 5.0 | 3440 | 0.4346 | 0.9365 |
| 0.0113 | 6.0 | 4128 | 0.4708 | 0.9365 |
| 0.006 | 7.0 | 4816 | 0.5533 | 0.9325 |
| 0.0047 | 8.0 | 5504 | 0.5888 | 0.9310 |
| 0.0001 | 9.0 | 6192 | 0.5961 | 0.9333 |
| 0.0 | 10.0 | 6880 | 0.5992 | 0.9357 |