---
library_name: transformers
license: mit
base_model: vinai/phobert-base
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: phobert-human-tl-seed-24
  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-human-tl-seed-24

This model is a fine-tuned version of [vinai/phobert-base](https://huggingface.co/vinai/phobert-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4536
- Accuracy: 0.8391
- Precision: 0.6540
- Recall: 0.4704
- F1: 0.4947

## 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: 0.0001
- 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
- lr_scheduler_warmup_steps: 500
- num_epochs: 50

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| No log        | 1.0   | 346  | 0.5144          | 0.8237   | 0.5296    | 0.3491 | 0.3313 |
| 0.5988        | 2.0   | 692  | 0.4708          | 0.8305   | 0.6087    | 0.4030 | 0.4154 |
| 0.4669        | 3.0   | 1038 | 0.4640          | 0.8316   | 0.6565    | 0.4041 | 0.4188 |
| 0.4669        | 4.0   | 1384 | 0.4664          | 0.8376   | 0.7006    | 0.4177 | 0.4389 |
| 0.4619        | 5.0   | 1730 | 0.4635          | 0.8365   | 0.6788    | 0.4147 | 0.4368 |
| 0.4576        | 6.0   | 2076 | 0.4599          | 0.8365   | 0.6599    | 0.4270 | 0.4462 |
| 0.4576        | 7.0   | 2422 | 0.4623          | 0.8357   | 0.6823    | 0.4133 | 0.4355 |
| 0.4552        | 8.0   | 2768 | 0.4529          | 0.8398   | 0.6587    | 0.4438 | 0.4684 |
| 0.4543        | 9.0   | 3114 | 0.4595          | 0.8342   | 0.6396    | 0.4083 | 0.4288 |
| 0.4543        | 10.0  | 3460 | 0.4604          | 0.8346   | 0.6654    | 0.4374 | 0.4541 |
| 0.4586        | 11.0  | 3806 | 0.4589          | 0.8353   | 0.6563    | 0.4105 | 0.4341 |
| 0.449         | 12.0  | 4152 | 0.4536          | 0.8391   | 0.6540    | 0.4704 | 0.4947 |
| 0.449         | 13.0  | 4498 | 0.4552          | 0.8353   | 0.6381    | 0.4202 | 0.4463 |
| 0.4579        | 14.0  | 4844 | 0.4561          | 0.8353   | 0.6440    | 0.4113 | 0.4333 |
| 0.4489        | 15.0  | 5190 | 0.4596          | 0.8338   | 0.6609    | 0.4020 | 0.4219 |
| 0.459         | 16.0  | 5536 | 0.4667          | 0.8323   | 0.6563    | 0.3975 | 0.4143 |
| 0.459         | 17.0  | 5882 | 0.4513          | 0.8379   | 0.6220    | 0.4343 | 0.4589 |


### Framework versions

- Transformers 4.51.1
- Pytorch 2.5.1+cu124
- Datasets 3.5.0
- Tokenizers 0.21.0