---
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-42
  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-42

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: 0.4535
- 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  | 0.5155          | 0.8230   | 0.5189    | 0.3466 | 0.3267 |
| 0.5915        | 2.0   | 692  | 0.4712          | 0.8305   | 0.6068    | 0.4030 | 0.4152 |
| 0.4672        | 3.0   | 1038 | 0.4644          | 0.8327   | 0.6736    | 0.4060 | 0.4224 |
| 0.4672        | 4.0   | 1384 | 0.4667          | 0.8361   | 0.6805    | 0.4135 | 0.4321 |
| 0.4614        | 5.0   | 1730 | 0.4633          | 0.8361   | 0.6715    | 0.4145 | 0.4368 |
| 0.4576        | 6.0   | 2076 | 0.4599          | 0.8357   | 0.6605    | 0.4245 | 0.4437 |
| 0.4576        | 7.0   | 2422 | 0.4622          | 0.8365   | 0.6768    | 0.4147 | 0.4375 |
| 0.4553        | 8.0   | 2768 | 0.4527          | 0.8383   | 0.6430    | 0.4410 | 0.4643 |
| 0.4542        | 9.0   | 3114 | 0.4601          | 0.8335   | 0.6428    | 0.4059 | 0.4254 |
| 0.4542        | 10.0  | 3460 | 0.4600          | 0.8342   | 0.6642    | 0.4372 | 0.4537 |
| 0.4588        | 11.0  | 3806 | 0.4585          | 0.8353   | 0.6504    | 0.4105 | 0.4342 |
| 0.4487        | 12.0  | 4152 | 0.4535          | 0.8391   | 0.6540    | 0.4704 | 0.4947 |
| 0.4487        | 13.0  | 4498 | 0.4552          | 0.8361   | 0.6410    | 0.4205 | 0.4471 |
| 0.4577        | 14.0  | 4844 | 0.4558          | 0.8353   | 0.6467    | 0.4113 | 0.4336 |
| 0.4487        | 15.0  | 5190 | 0.4589          | 0.8335   | 0.6571    | 0.4019 | 0.4216 |
| 0.4586        | 16.0  | 5536 | 0.4665          | 0.8327   | 0.6580    | 0.3987 | 0.4161 |
| 0.4586        | 17.0  | 5882 | 0.4513          | 0.8379   | 0.6188    | 0.4343 | 0.4590 |


### Framework versions

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