---
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-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-seg-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: 0.4484
- Accuracy: 0.8428
- Precision: 0.6491
- Recall: 0.4691
- F1: 0.4932

## 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  | 0.5133          | 0.8256   | 0.5419    | 0.3574 | 0.3465 |
| 0.5919        | 2.0   | 692  | 0.4692          | 0.8372   | 0.6180    | 0.4317 | 0.4475 |
| 0.4602        | 3.0   | 1038 | 0.4660          | 0.8361   | 0.6390    | 0.4132 | 0.4307 |
| 0.4602        | 4.0   | 1384 | 0.4649          | 0.8394   | 0.6767    | 0.4268 | 0.4487 |
| 0.4568        | 5.0   | 1730 | 0.4581          | 0.8372   | 0.6211    | 0.4259 | 0.4459 |
| 0.4498        | 6.0   | 2076 | 0.4586          | 0.8402   | 0.6572    | 0.4411 | 0.4606 |
| 0.4498        | 7.0   | 2422 | 0.4628          | 0.8372   | 0.6626    | 0.4118 | 0.4318 |
| 0.4516        | 8.0   | 2768 | 0.4520          | 0.8417   | 0.6340    | 0.4522 | 0.4741 |
| 0.4473        | 9.0   | 3114 | 0.4540          | 0.8387   | 0.6594    | 0.4217 | 0.4441 |
| 0.4473        | 10.0  | 3460 | 0.4559          | 0.8447   | 0.6911    | 0.4675 | 0.4855 |
| 0.451         | 11.0  | 3806 | 0.4530          | 0.8379   | 0.6461    | 0.4221 | 0.4468 |
| 0.4436        | 12.0  | 4152 | 0.4484          | 0.8428   | 0.6491    | 0.4691 | 0.4932 |
| 0.4436        | 13.0  | 4498 | 0.4499          | 0.8394   | 0.6503    | 0.4367 | 0.4648 |
| 0.4506        | 14.0  | 4844 | 0.4526          | 0.8391   | 0.6419    | 0.4251 | 0.4486 |
| 0.4418        | 15.0  | 5190 | 0.4523          | 0.8398   | 0.6584    | 0.4261 | 0.4522 |
| 0.4497        | 16.0  | 5536 | 0.4667          | 0.8372   | 0.6662    | 0.4110 | 0.4317 |
| 0.4497        | 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