---
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-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-seg-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: 0.4484
- Accuracy: 0.8428
- Precision: 0.6472
- Recall: 0.4680
- F1: 0.4918

## 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  | 0.5132          | 0.8245   | 0.5325    | 0.3537 | 0.3399 |
| 0.5879        | 2.0   | 692  | 0.4699          | 0.8372   | 0.6490    | 0.4338 | 0.4492 |
| 0.4602        | 3.0   | 1038 | 0.4677          | 0.8365   | 0.6414    | 0.4133 | 0.4311 |
| 0.4602        | 4.0   | 1384 | 0.4650          | 0.8387   | 0.6755    | 0.4232 | 0.4445 |
| 0.4569        | 5.0   | 1730 | 0.4584          | 0.8372   | 0.6211    | 0.4259 | 0.4459 |
| 0.4498        | 6.0   | 2076 | 0.4587          | 0.8398   | 0.6538    | 0.4421 | 0.4608 |
| 0.4498        | 7.0   | 2422 | 0.4631          | 0.8372   | 0.6655    | 0.4118 | 0.4320 |
| 0.4517        | 8.0   | 2768 | 0.4523          | 0.8409   | 0.6333    | 0.4497 | 0.4717 |
| 0.4471        | 9.0   | 3114 | 0.4536          | 0.8391   | 0.6607    | 0.4230 | 0.4457 |
| 0.4471        | 10.0  | 3460 | 0.4559          | 0.8439   | 0.6747    | 0.4650 | 0.4830 |
| 0.4509        | 11.0  | 3806 | 0.4531          | 0.8383   | 0.6474    | 0.4233 | 0.4484 |
| 0.4438        | 12.0  | 4152 | 0.4484          | 0.8428   | 0.6472    | 0.4680 | 0.4918 |
| 0.4438        | 13.0  | 4498 | 0.4502          | 0.8383   | 0.6414    | 0.4330 | 0.4603 |
| 0.4503        | 14.0  | 4844 | 0.4528          | 0.8398   | 0.6454    | 0.4265 | 0.4506 |
| 0.4423        | 15.0  | 5190 | 0.4527          | 0.8387   | 0.6390    | 0.4217 | 0.4450 |
| 0.45          | 16.0  | 5536 | 0.4661          | 0.8372   | 0.6662    | 0.4110 | 0.4317 |
| 0.45          | 17.0  | 5882 | 0.4474          | 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