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---
base_model: vinai/phobert-base-v2
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: PhoBert_70KURL
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. -->
# PhoBert_70KURL
This model is a fine-tuned version of [vinai/phobert-base-v2](https://huggingface.co/vinai/phobert-base-v2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3781
- Accuracy: 0.9371
- F1: 0.9417
## 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
- gradient_accumulation_steps: 2
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 2150
- num_epochs: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-------:|:----:|:---------------:|:--------:|:------:|
| No log | 0.4651 | 200 | 0.3374 | 0.9302 | 0.9233 |
| No log | 0.9302 | 400 | 0.1954 | 0.9353 | 0.9383 |
| No log | 1.3953 | 600 | 0.1273 | 0.9598 | 0.9609 |
| No log | 1.8605 | 800 | 0.1601 | 0.9423 | 0.9461 |
| 0.3263 | 2.3256 | 1000 | 0.1059 | 0.9666 | 0.9675 |
| 0.3263 | 2.7907 | 1200 | 0.0832 | 0.9742 | 0.9746 |
| 0.3263 | 3.2558 | 1400 | 0.1073 | 0.9621 | 0.9637 |
| 0.3263 | 3.7209 | 1600 | 0.1053 | 0.9644 | 0.9658 |
| 0.1315 | 4.1860 | 1800 | 0.0994 | 0.9653 | 0.9663 |
| 0.1315 | 4.6512 | 2000 | 0.1109 | 0.9617 | 0.9633 |
| 0.1315 | 5.1163 | 2200 | 0.1350 | 0.9491 | 0.9523 |
| 0.1315 | 5.5814 | 2400 | 0.1204 | 0.9543 | 0.9567 |
| 0.0959 | 6.0465 | 2600 | 0.2663 | 0.8906 | 0.9034 |
| 0.0959 | 6.5116 | 2800 | 0.1309 | 0.9510 | 0.9538 |
| 0.0959 | 6.9767 | 3000 | 0.2027 | 0.9279 | 0.9340 |
| 0.0959 | 7.4419 | 3200 | 0.1277 | 0.9509 | 0.9535 |
| 0.0959 | 7.9070 | 3400 | 0.1892 | 0.9473 | 0.9507 |
| 0.0689 | 8.3721 | 3600 | 0.2090 | 0.9350 | 0.9396 |
| 0.0689 | 8.8372 | 3800 | 0.1247 | 0.9579 | 0.9596 |
| 0.0689 | 9.3023 | 4000 | 0.2984 | 0.9132 | 0.9216 |
| 0.0689 | 9.7674 | 4200 | 0.2277 | 0.9352 | 0.9401 |
| 0.0483 | 10.2326 | 4400 | 0.2595 | 0.9266 | 0.9328 |
| 0.0483 | 10.6977 | 4600 | 0.2725 | 0.9249 | 0.9313 |
| 0.0483 | 11.1628 | 4800 | 0.2483 | 0.9341 | 0.9391 |
| 0.0483 | 11.6279 | 5000 | 0.2195 | 0.9442 | 0.9478 |
| 0.0379 | 12.0930 | 5200 | 0.7268 | 0.8450 | 0.8672 |
| 0.0379 | 12.5581 | 5400 | 0.2236 | 0.9503 | 0.9530 |
| 0.0379 | 13.0233 | 5600 | 0.2610 | 0.9438 | 0.9474 |
| 0.0379 | 13.4884 | 5800 | 0.3155 | 0.9366 | 0.9412 |
| 0.0379 | 13.9535 | 6000 | 0.3225 | 0.9374 | 0.9418 |
| 0.0284 | 14.4186 | 6200 | 0.4604 | 0.9158 | 0.9238 |
| 0.0284 | 14.8837 | 6400 | 0.3269 | 0.9372 | 0.9417 |
| 0.0284 | 15.3488 | 6600 | 0.3419 | 0.9401 | 0.9442 |
| 0.0284 | 15.8140 | 6800 | 0.2869 | 0.9465 | 0.9497 |
| 0.0213 | 16.2791 | 7000 | 0.3623 | 0.9364 | 0.9410 |
| 0.0213 | 16.7442 | 7200 | 0.2880 | 0.9433 | 0.9466 |
| 0.0213 | 17.2093 | 7400 | 0.3010 | 0.9454 | 0.9484 |
| 0.0213 | 17.6744 | 7600 | 0.3719 | 0.9344 | 0.9391 |
| 0.0165 | 18.1395 | 7800 | 0.3160 | 0.9448 | 0.9481 |
| 0.0165 | 18.6047 | 8000 | 0.3767 | 0.9388 | 0.9431 |
| 0.0165 | 19.0698 | 8200 | 0.3702 | 0.9383 | 0.9426 |
| 0.0165 | 19.5349 | 8400 | 0.3998 | 0.9343 | 0.9393 |
| 0.0121 | 20.0 | 8600 | 0.3781 | 0.9371 | 0.9417 |
### Framework versions
- Transformers 4.41.2
- Pytorch 2.1.2
- Datasets 2.19.2
- Tokenizers 0.19.1
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