indonlp/indonlu
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How to use LazarusNLP/NusaBERT-base-POSP with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("token-classification", model="LazarusNLP/NusaBERT-base-POSP") # Load model directly
from transformers import AutoTokenizer, AutoModelForTokenClassification
tokenizer = AutoTokenizer.from_pretrained("LazarusNLP/NusaBERT-base-POSP")
model = AutoModelForTokenClassification.from_pretrained("LazarusNLP/NusaBERT-base-POSP")This model is a fine-tuned version of LazarusNLP/NusaBERT-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 | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| No log | 1.0 | 420 | 0.2680 | 0.9203 | 0.9203 | 0.9203 | 0.9203 |
| 0.6283 | 2.0 | 840 | 0.2017 | 0.9379 | 0.9379 | 0.9379 | 0.9379 |
| 0.218 | 3.0 | 1260 | 0.1785 | 0.9449 | 0.9449 | 0.9449 | 0.9449 |
| 0.1612 | 4.0 | 1680 | 0.1692 | 0.9490 | 0.9490 | 0.9490 | 0.9490 |
| 0.1393 | 5.0 | 2100 | 0.1577 | 0.9511 | 0.9511 | 0.9511 | 0.9511 |
| 0.1119 | 6.0 | 2520 | 0.1503 | 0.9539 | 0.9539 | 0.9539 | 0.9539 |
| 0.1119 | 7.0 | 2940 | 0.1499 | 0.9549 | 0.9549 | 0.9549 | 0.9549 |
| 0.0943 | 8.0 | 3360 | 0.1542 | 0.9547 | 0.9547 | 0.9547 | 0.9547 |
| 0.0824 | 9.0 | 3780 | 0.1517 | 0.9558 | 0.9558 | 0.9558 | 0.9558 |
| 0.0785 | 10.0 | 4200 | 0.1519 | 0.9557 | 0.9557 | 0.9557 | 0.9557 |
Base model
LazarusNLP/NusaBERT-base