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library_name: transformers
license: agpl-3.0
base_model: vinai/phobert-base-v2
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: model_content_V2_test
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. -->
# model_content_V2_test
This model is a fine-tuned version of [vinai/phobert-base-v2](https://huggingface.co/vinai/phobert-base-v2) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2218
- Accuracy: 0.9696
- F1: 0.9647
## 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: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:------:|:-----:|:---------------:|:--------:|:------:|
| No log | 0.1419 | 150 | 0.1158 | 0.9649 | 0.9590 |
| No log | 0.2838 | 300 | 0.1143 | 0.9619 | 0.9551 |
| No log | 0.4257 | 450 | 0.1021 | 0.9589 | 0.9532 |
| No log | 0.5676 | 600 | 0.1081 | 0.9674 | 0.9621 |
| No log | 0.7096 | 750 | 0.0905 | 0.9659 | 0.9608 |
| No log | 0.8515 | 900 | 0.0891 | 0.9685 | 0.9635 |
| No log | 0.9934 | 1050 | 0.1108 | 0.9676 | 0.9623 |
| 0.111 | 1.1353 | 1200 | 0.0890 | 0.9690 | 0.9643 |
| 0.111 | 1.2772 | 1350 | 0.0882 | 0.9700 | 0.9654 |
| 0.111 | 1.4191 | 1500 | 0.0890 | 0.9708 | 0.9661 |
| 0.111 | 1.5610 | 1650 | 0.0946 | 0.9688 | 0.9639 |
| 0.111 | 1.7029 | 1800 | 0.0936 | 0.9703 | 0.9656 |
| 0.111 | 1.8448 | 1950 | 0.0982 | 0.9712 | 0.9667 |
| 0.111 | 1.9868 | 2100 | 0.1060 | 0.9614 | 0.9560 |
| 0.0717 | 2.1287 | 2250 | 0.1264 | 0.9658 | 0.9609 |
| 0.0717 | 2.2706 | 2400 | 0.0902 | 0.9691 | 0.9643 |
| 0.0717 | 2.4125 | 2550 | 0.0869 | 0.9699 | 0.9653 |
| 0.0717 | 2.5544 | 2700 | 0.1086 | 0.9689 | 0.9638 |
| 0.0717 | 2.6963 | 2850 | 0.1122 | 0.9683 | 0.9638 |
| 0.0717 | 2.8382 | 3000 | 0.0945 | 0.9698 | 0.9651 |
| 0.0717 | 2.9801 | 3150 | 0.1068 | 0.9692 | 0.9647 |
| 0.0555 | 3.1220 | 3300 | 0.1041 | 0.9713 | 0.9668 |
| 0.0555 | 3.2640 | 3450 | 0.1022 | 0.9710 | 0.9664 |
| 0.0555 | 3.4059 | 3600 | 0.1292 | 0.9684 | 0.9637 |
| 0.0555 | 3.5478 | 3750 | 0.1135 | 0.9718 | 0.9673 |
| 0.0555 | 3.6897 | 3900 | 0.1114 | 0.9711 | 0.9664 |
| 0.0555 | 3.8316 | 4050 | 0.1205 | 0.9704 | 0.9656 |
| 0.0555 | 3.9735 | 4200 | 0.1136 | 0.9692 | 0.9646 |
| 0.0429 | 4.1154 | 4350 | 0.1356 | 0.9688 | 0.9641 |
| 0.0429 | 4.2573 | 4500 | 0.1547 | 0.9668 | 0.9619 |
| 0.0429 | 4.3992 | 4650 | 0.1360 | 0.9687 | 0.9640 |
| 0.0429 | 4.5412 | 4800 | 0.1505 | 0.9686 | 0.9633 |
| 0.0429 | 4.6831 | 4950 | 0.1401 | 0.9677 | 0.9629 |
| 0.0429 | 4.8250 | 5100 | 0.1359 | 0.9710 | 0.9664 |
| 0.0429 | 4.9669 | 5250 | 0.1400 | 0.9711 | 0.9664 |
| 0.0311 | 5.1088 | 5400 | 0.1545 | 0.9690 | 0.9643 |
| 0.0311 | 5.2507 | 5550 | 0.1638 | 0.9689 | 0.9641 |
| 0.0311 | 5.3926 | 5700 | 0.1801 | 0.9692 | 0.9645 |
| 0.0311 | 5.5345 | 5850 | 0.1618 | 0.9698 | 0.9649 |
| 0.0311 | 5.6764 | 6000 | 0.1612 | 0.9640 | 0.9575 |
| 0.0311 | 5.8184 | 6150 | 0.1831 | 0.9681 | 0.9628 |
| 0.0311 | 5.9603 | 6300 | 0.1496 | 0.9700 | 0.9651 |
| 0.0229 | 6.1022 | 6450 | 0.1788 | 0.9697 | 0.9648 |
| 0.0229 | 6.2441 | 6600 | 0.1743 | 0.9700 | 0.9650 |
| 0.0229 | 6.3860 | 6750 | 0.1856 | 0.9701 | 0.9652 |
| 0.0229 | 6.5279 | 6900 | 0.1718 | 0.9702 | 0.9654 |
| 0.0229 | 6.6698 | 7050 | 0.1668 | 0.9695 | 0.9645 |
| 0.0229 | 6.8117 | 7200 | 0.1705 | 0.9697 | 0.9647 |
| 0.0229 | 6.9536 | 7350 | 0.1758 | 0.9701 | 0.9652 |
| 0.0178 | 7.0956 | 7500 | 0.1803 | 0.9679 | 0.9631 |
| 0.0178 | 7.2375 | 7650 | 0.1744 | 0.9701 | 0.9651 |
| 0.0178 | 7.3794 | 7800 | 0.1708 | 0.9693 | 0.9644 |
| 0.0178 | 7.5213 | 7950 | 0.1663 | 0.9692 | 0.9643 |
| 0.0178 | 7.6632 | 8100 | 0.1895 | 0.9692 | 0.9644 |
| 0.0178 | 7.8051 | 8250 | 0.1877 | 0.9701 | 0.9653 |
| 0.0178 | 7.9470 | 8400 | 0.1864 | 0.9692 | 0.9644 |
| 0.0125 | 8.0889 | 8550 | 0.1953 | 0.9702 | 0.9655 |
| 0.0125 | 8.2308 | 8700 | 0.2072 | 0.9692 | 0.9642 |
| 0.0125 | 8.3728 | 8850 | 0.1991 | 0.9686 | 0.9636 |
| 0.0125 | 8.5147 | 9000 | 0.2083 | 0.9697 | 0.9647 |
| 0.0125 | 8.6566 | 9150 | 0.2085 | 0.9697 | 0.9648 |
| 0.0125 | 8.7985 | 9300 | 0.2087 | 0.9699 | 0.9651 |
| 0.0125 | 8.9404 | 9450 | 0.2128 | 0.9688 | 0.9639 |
| 0.0076 | 9.0823 | 9600 | 0.2150 | 0.9692 | 0.9642 |
| 0.0076 | 9.2242 | 9750 | 0.2133 | 0.9692 | 0.9643 |
| 0.0076 | 9.3661 | 9900 | 0.2121 | 0.9692 | 0.9642 |
| 0.0076 | 9.5080 | 10050 | 0.2220 | 0.9694 | 0.9645 |
| 0.0076 | 9.6500 | 10200 | 0.2218 | 0.9692 | 0.9643 |
| 0.0076 | 9.7919 | 10350 | 0.2201 | 0.9696 | 0.9647 |
| 0.0076 | 9.9338 | 10500 | 0.2218 | 0.9696 | 0.9647 |
### Framework versions
- Transformers 4.56.1
- Pytorch 2.6.0+cu124
- Datasets 3.6.0
- Tokenizers 0.22.0
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