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---
license: apache-2.0
base_model: bert-base-multilingual-cased
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: bg
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bg
This model is an adapter fine-tuned on top of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) on the Bulgarian ConceptNet dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4640
- Accuracy: 0.8875
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- distributed_type: multi-GPU
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 50000
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.5057 | 0.15 | 500 | 0.9846 | 0.8149 |
| 1.0172 | 0.31 | 1000 | 0.8395 | 0.8259 |
| 0.8814 | 0.46 | 1500 | 0.7823 | 0.8368 |
| 0.8405 | 0.61 | 2000 | 0.7437 | 0.8449 |
| 0.7773 | 0.77 | 2500 | 0.7247 | 0.8387 |
| 0.7762 | 0.92 | 3000 | 0.6521 | 0.8513 |
| 0.7186 | 1.07 | 3500 | 0.6834 | 0.8492 |
| 0.7033 | 1.22 | 4000 | 0.6715 | 0.8523 |
| 0.672 | 1.38 | 4500 | 0.6539 | 0.8560 |
| 0.6613 | 1.53 | 5000 | 0.6387 | 0.8567 |
| 0.6712 | 1.68 | 5500 | 0.6180 | 0.8624 |
| 0.6776 | 1.84 | 6000 | 0.6635 | 0.8537 |
| 0.6484 | 1.99 | 6500 | 0.5946 | 0.8661 |
| 0.6817 | 2.14 | 7000 | 0.6126 | 0.8655 |
| 0.6392 | 2.3 | 7500 | 0.6136 | 0.8613 |
| 0.6394 | 2.45 | 8000 | 0.6321 | 0.8621 |
| 0.6273 | 2.6 | 8500 | 0.5997 | 0.8629 |
| 0.5993 | 2.76 | 9000 | 0.6028 | 0.8646 |
| 0.6527 | 2.91 | 9500 | 0.6584 | 0.8510 |
| 0.5897 | 3.06 | 10000 | 0.5728 | 0.8676 |
| 0.574 | 3.21 | 10500 | 0.5870 | 0.8671 |
| 0.6026 | 3.37 | 11000 | 0.6067 | 0.8677 |
| 0.5896 | 3.52 | 11500 | 0.6000 | 0.8638 |
| 0.566 | 3.67 | 12000 | 0.5566 | 0.8712 |
| 0.5928 | 3.83 | 12500 | 0.5621 | 0.8675 |
| 0.597 | 3.98 | 13000 | 0.5162 | 0.8771 |
| 0.5836 | 4.13 | 13500 | 0.5498 | 0.8696 |
| 0.5864 | 4.29 | 14000 | 0.5728 | 0.8640 |
| 0.5562 | 4.44 | 14500 | 0.6000 | 0.8623 |
| 0.5999 | 4.59 | 15000 | 0.5589 | 0.8679 |
| 0.5767 | 4.75 | 15500 | 0.5713 | 0.8681 |
| 0.5574 | 4.9 | 16000 | 0.5338 | 0.8739 |
| 0.568 | 5.05 | 16500 | 0.5527 | 0.8725 |
| 0.5568 | 5.21 | 17000 | 0.5058 | 0.8777 |
| 0.5369 | 5.36 | 17500 | 0.5599 | 0.8720 |
| 0.518 | 5.51 | 18000 | 0.5610 | 0.8720 |
| 0.5637 | 5.66 | 18500 | 0.5467 | 0.8728 |
| 0.557 | 5.82 | 19000 | 0.5349 | 0.8714 |
| 0.5499 | 5.97 | 19500 | 0.5468 | 0.8724 |
| 0.5304 | 6.12 | 20000 | 0.5243 | 0.8741 |
| 0.5431 | 6.28 | 20500 | 0.4998 | 0.8784 |
| 0.5508 | 6.43 | 21000 | 0.5367 | 0.8764 |
| 0.5701 | 6.58 | 21500 | 0.5365 | 0.8734 |
| 0.521 | 6.74 | 22000 | 0.4879 | 0.8819 |
| 0.5514 | 6.89 | 22500 | 0.5106 | 0.8787 |
| 0.547 | 7.04 | 23000 | 0.5258 | 0.8747 |
| 0.5512 | 7.2 | 23500 | 0.4975 | 0.8778 |
| 0.5407 | 7.35 | 24000 | 0.4944 | 0.8786 |
| 0.5181 | 7.5 | 24500 | 0.4912 | 0.8795 |
| 0.5493 | 7.65 | 25000 | 0.5188 | 0.8730 |
| 0.5388 | 7.81 | 25500 | 0.5000 | 0.8831 |
| 0.5284 | 7.96 | 26000 | 0.5161 | 0.8737 |
| 0.5116 | 8.11 | 26500 | 0.5263 | 0.8760 |
| 0.5161 | 8.27 | 27000 | 0.5002 | 0.8787 |
| 0.5185 | 8.42 | 27500 | 0.5127 | 0.8745 |
| 0.5291 | 8.57 | 28000 | 0.5116 | 0.8782 |
| 0.5061 | 8.73 | 28500 | 0.4972 | 0.8774 |
| 0.479 | 8.88 | 29000 | 0.4978 | 0.8798 |
| 0.5154 | 9.03 | 29500 | 0.5088 | 0.8771 |
| 0.4989 | 9.19 | 30000 | 0.5119 | 0.8744 |
| 0.5098 | 9.34 | 30500 | 0.4916 | 0.8826 |
| 0.4777 | 9.49 | 31000 | 0.4957 | 0.8824 |
| 0.5462 | 9.64 | 31500 | 0.4846 | 0.8779 |
| 0.509 | 9.8 | 32000 | 0.4873 | 0.8810 |
| 0.5181 | 9.95 | 32500 | 0.5227 | 0.8710 |
| 0.5269 | 10.1 | 33000 | 0.4929 | 0.8803 |
| 0.5094 | 10.26 | 33500 | 0.4841 | 0.8877 |
| 0.5033 | 10.41 | 34000 | 0.5129 | 0.8805 |
| 0.4913 | 10.56 | 34500 | 0.4978 | 0.8789 |
| 0.4938 | 10.72 | 35000 | 0.4640 | 0.8838 |
| 0.4954 | 10.87 | 35500 | 0.4991 | 0.8794 |
| 0.458 | 11.02 | 36000 | 0.4453 | 0.8886 |
| 0.526 | 11.18 | 36500 | 0.4863 | 0.8832 |
| 0.4809 | 11.33 | 37000 | 0.4923 | 0.8784 |
| 0.466 | 11.48 | 37500 | 0.4824 | 0.8807 |
| 0.4903 | 11.64 | 38000 | 0.4552 | 0.8848 |
| 0.4875 | 11.79 | 38500 | 0.4850 | 0.8780 |
| 0.4858 | 11.94 | 39000 | 0.4728 | 0.8833 |
| 0.4868 | 12.09 | 39500 | 0.4868 | 0.8800 |
| 0.485 | 12.25 | 40000 | 0.4935 | 0.8802 |
| 0.4823 | 12.4 | 40500 | 0.4789 | 0.8828 |
| 0.4629 | 12.55 | 41000 | 0.4834 | 0.8835 |
| 0.4915 | 12.71 | 41500 | 0.4864 | 0.8812 |
| 0.473 | 12.86 | 42000 | 0.5136 | 0.8793 |
| 0.4849 | 13.01 | 42500 | 0.4823 | 0.8815 |
| 0.4582 | 13.17 | 43000 | 0.4637 | 0.8844 |
| 0.4938 | 13.32 | 43500 | 0.4829 | 0.8842 |
| 0.4682 | 13.47 | 44000 | 0.4799 | 0.8817 |
| 0.4885 | 13.63 | 44500 | 0.4754 | 0.8858 |
| 0.4641 | 13.78 | 45000 | 0.4738 | 0.8849 |
| 0.4664 | 13.93 | 45500 | 0.4512 | 0.8869 |
| 0.4722 | 14.08 | 46000 | 0.4821 | 0.8836 |
| 0.485 | 14.24 | 46500 | 0.4735 | 0.8842 |
| 0.4784 | 14.39 | 47000 | 0.4557 | 0.8823 |
| 0.4821 | 14.54 | 47500 | 0.4707 | 0.8856 |
| 0.478 | 14.7 | 48000 | 0.4682 | 0.8846 |
| 0.451 | 14.85 | 48500 | 0.4744 | 0.8781 |
| 0.4582 | 15.0 | 49000 | 0.4617 | 0.8835 |
| 0.4949 | 15.16 | 49500 | 0.4769 | 0.8835 |
| 0.4546 | 15.31 | 50000 | 0.4677 | 0.8835 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.0.0
- Datasets 2.15.0
- Tokenizers 0.15.0
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