indonlp/indonlu
Updated • 1.02k • 41
How to use LazarusNLP/NusaBERT-base-SmSA with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-classification", model="LazarusNLP/NusaBERT-base-SmSA") # Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("LazarusNLP/NusaBERT-base-SmSA")
model = AutoModelForSequenceClassification.from_pretrained("LazarusNLP/NusaBERT-base-SmSA")This model is a fine-tuned version of LazarusNLP/NusaBERT-base on the indonlu dataset. It achieves the following results on the evaluation set:
More information needed
More information needed
More information needed
The following hyperparameters were used during training:
| Training Loss | Epoch | Step | Validation Loss | F1 |
|---|---|---|---|---|
| No log | 1.0 | 344 | 0.2119 | 0.895 |
| 0.3517 | 2.0 | 688 | 0.1745 | 0.9193 |
| 0.1543 | 3.0 | 1032 | 0.1945 | 0.9135 |
| 0.1543 | 4.0 | 1376 | 0.1901 | 0.9149 |
| 0.1006 | 5.0 | 1720 | 0.2158 | 0.9172 |
| 0.0652 | 6.0 | 2064 | 0.2796 | 0.9151 |
| 0.0652 | 7.0 | 2408 | 0.3146 | 0.9164 |
Base model
LazarusNLP/NusaBERT-base