intanm/indonesian_financial_statements
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How to use intanm/mlm-20230405-002-4 with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("fill-mask", model="intanm/mlm-20230405-002-4") # Load model directly
from transformers import AutoTokenizer, AutoModelForMaskedLM
tokenizer = AutoTokenizer.from_pretrained("intanm/mlm-20230405-002-4")
model = AutoModelForMaskedLM.from_pretrained("intanm/mlm-20230405-002-4")This model is a fine-tuned version of indobenchmark/indobert-base-p1 on the None 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 |
|---|---|---|---|
| No log | 1.0 | 284 | 4.0646 |
| 4.7247 | 2.0 | 568 | 3.3108 |
| 4.7247 | 3.0 | 852 | 3.0008 |
| 3.1652 | 4.0 | 1136 | 2.7421 |
| 3.1652 | 5.0 | 1420 | 2.5398 |
| 2.6664 | 6.0 | 1704 | 2.4601 |
| 2.6664 | 7.0 | 1988 | 2.3281 |
| 2.4079 | 8.0 | 2272 | 2.2595 |
| 2.235 | 9.0 | 2556 | 2.2096 |
| 2.235 | 10.0 | 2840 | 2.1656 |
| 2.1012 | 11.0 | 3124 | 2.1208 |
| 2.1012 | 12.0 | 3408 | 2.0601 |
| 1.9958 | 13.0 | 3692 | 2.0032 |
| 1.9958 | 14.0 | 3976 | 2.0479 |
| 1.9279 | 15.0 | 4260 | 1.9541 |
| 1.8739 | 16.0 | 4544 | 1.9563 |
| 1.8739 | 17.0 | 4828 | 1.9444 |
| 1.8358 | 18.0 | 5112 | 1.9108 |
| 1.8358 | 19.0 | 5396 | 1.9408 |
| 1.8018 | 20.0 | 5680 | 1.9278 |