---
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-69
  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-69

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: 0.8398
- Precision: 0.6509
- Recall: 0.4718
- F1: 0.4965

## 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  | 0.5169          | 0.8237   | 0.5413    | 0.3480 | 0.3291 |
| 0.5883        | 2.0   | 692  | 0.4718          | 0.8320   | 0.6263    | 0.4068 | 0.4201 |
| 0.4671        | 3.0   | 1038 | 0.4658          | 0.8335   | 0.6649    | 0.4074 | 0.4246 |
| 0.4671        | 4.0   | 1384 | 0.4671          | 0.8368   | 0.6924    | 0.4163 | 0.4365 |
| 0.4618        | 5.0   | 1730 | 0.4639          | 0.8357   | 0.6689    | 0.4144 | 0.4365 |
| 0.4577        | 6.0   | 2076 | 0.4606          | 0.8357   | 0.6478    | 0.4245 | 0.4437 |
| 0.4577        | 7.0   | 2422 | 0.4627          | 0.8357   | 0.6907    | 0.4133 | 0.4353 |
| 0.4555        | 8.0   | 2768 | 0.4532          | 0.8394   | 0.6546    | 0.4429 | 0.4681 |
| 0.4542        | 9.0   | 3114 | 0.4600          | 0.8338   | 0.6444    | 0.4071 | 0.4271 |
| 0.4542        | 10.0  | 3460 | 0.4604          | 0.8342   | 0.6652    | 0.4372 | 0.4540 |
| 0.4587        | 11.0  | 3806 | 0.4590          | 0.8357   | 0.6626    | 0.4125 | 0.4378 |
| 0.4489        | 12.0  | 4152 | 0.4535          | 0.8398   | 0.6509    | 0.4718 | 0.4965 |
| 0.4489        | 13.0  | 4498 | 0.4556          | 0.8353   | 0.6360    | 0.4202 | 0.4464 |
| 0.4575        | 14.0  | 4844 | 0.4565          | 0.8361   | 0.6533    | 0.4141 | 0.4378 |
| 0.449         | 15.0  | 5190 | 0.4597          | 0.8335   | 0.6578    | 0.4008 | 0.4203 |
| 0.4588        | 16.0  | 5536 | 0.4663          | 0.8323   | 0.6494    | 0.3975 | 0.4143 |
| 0.4588        | 17.0  | 5882 | 0.4515          | 0.8368   | 0.6142    | 0.4328 | 0.4568 |


### Framework versions

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