---
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-seg-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-seg-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: 0.4485
- Accuracy: 0.8432
- Precision: 0.6562
- Recall: 0.4696
- F1: 0.4951

## 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  | 0.5115          | 0.8252   | 0.5391    | 0.3562 | 0.3443 |
| 0.5974        | 2.0   | 692  | 0.4692          | 0.8387   | 0.6396    | 0.4344 | 0.4514 |
| 0.4599        | 3.0   | 1038 | 0.4663          | 0.8361   | 0.6226    | 0.4128 | 0.4293 |
| 0.4599        | 4.0   | 1384 | 0.4646          | 0.8379   | 0.6699    | 0.4218 | 0.4423 |
| 0.4571        | 5.0   | 1730 | 0.4580          | 0.8383   | 0.6521    | 0.4285 | 0.4490 |
| 0.4498        | 6.0   | 2076 | 0.4584          | 0.8402   | 0.6690    | 0.4422 | 0.4610 |
| 0.4498        | 7.0   | 2422 | 0.4629          | 0.8372   | 0.6626    | 0.4118 | 0.4318 |
| 0.4515        | 8.0   | 2768 | 0.4522          | 0.8409   | 0.6320    | 0.4529 | 0.4744 |
| 0.4474        | 9.0   | 3114 | 0.4536          | 0.8387   | 0.6573    | 0.4217 | 0.4438 |
| 0.4474        | 10.0  | 3460 | 0.4561          | 0.8439   | 0.6656    | 0.4647 | 0.4816 |
| 0.4509        | 11.0  | 3806 | 0.4530          | 0.8379   | 0.6461    | 0.4221 | 0.4468 |
| 0.4438        | 12.0  | 4152 | 0.4485          | 0.8432   | 0.6562    | 0.4696 | 0.4951 |
| 0.4438        | 13.0  | 4498 | 0.4501          | 0.8387   | 0.6479    | 0.4342 | 0.4618 |
| 0.4505        | 14.0  | 4844 | 0.4528          | 0.8387   | 0.6406    | 0.4239 | 0.4471 |
| 0.4421        | 15.0  | 5190 | 0.4529          | 0.8391   | 0.6485    | 0.4233 | 0.4478 |
| 0.45          | 16.0  | 5536 | 0.4665          | 0.8372   | 0.6692    | 0.4110 | 0.4320 |
| 0.45          | 17.0  | 5882 | 0.4475          | 0.8417   | 0.6431    | 0.4438 | 0.4686 |


### Framework versions

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