---
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-6969
  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-6969

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: 0.4483
- Accuracy: 0.8432
- Precision: 0.6507
- Recall: 0.4682
- F1: 0.4928

## 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  | 0.5129          | 0.8245   | 0.5325    | 0.3537 | 0.3399 |
| 0.5757        | 2.0   | 692  | 0.4692          | 0.8383   | 0.6238    | 0.4332 | 0.4500 |
| 0.4598        | 3.0   | 1038 | 0.4671          | 0.8357   | 0.6235    | 0.4116 | 0.4280 |
| 0.4598        | 4.0   | 1384 | 0.4641          | 0.8394   | 0.6756    | 0.4279 | 0.4498 |
| 0.4567        | 5.0   | 1730 | 0.4582          | 0.8379   | 0.6325    | 0.4272 | 0.4478 |
| 0.4499        | 6.0   | 2076 | 0.4588          | 0.8406   | 0.6718    | 0.4424 | 0.4617 |
| 0.4499        | 7.0   | 2422 | 0.4633          | 0.8372   | 0.6655    | 0.4118 | 0.4320 |
| 0.4517        | 8.0   | 2768 | 0.4522          | 0.8417   | 0.6340    | 0.4522 | 0.4741 |
| 0.4477        | 9.0   | 3114 | 0.4539          | 0.8402   | 0.6644    | 0.4267 | 0.4503 |
| 0.4477        | 10.0  | 3460 | 0.4560          | 0.8439   | 0.6645    | 0.4647 | 0.4813 |
| 0.4508        | 11.0  | 3806 | 0.4534          | 0.8387   | 0.6499    | 0.4235 | 0.4488 |
| 0.444         | 12.0  | 4152 | 0.4483          | 0.8432   | 0.6507    | 0.4682 | 0.4928 |
| 0.444         | 13.0  | 4498 | 0.4501          | 0.8391   | 0.6491    | 0.4355 | 0.4633 |
| 0.4506        | 14.0  | 4844 | 0.4532          | 0.8394   | 0.6442    | 0.4253 | 0.4490 |
| 0.4422        | 15.0  | 5190 | 0.4528          | 0.8394   | 0.6497    | 0.4245 | 0.4494 |
| 0.4498        | 16.0  | 5536 | 0.4663          | 0.8376   | 0.6743    | 0.4123 | 0.4332 |
| 0.4498        | 17.0  | 5882 | 0.4476          | 0.8417   | 0.6345    | 0.4435 | 0.4672 |


### Framework versions

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