---
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