---
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