---
library_name: transformers
license: mit
base_model: vinai/phobert-base
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: phobert-human-finetune-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-finetune-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: 0.3980
- Accuracy: 0.8683
- Precision: 0.6929
- Recall: 0.6500
- F1: 0.6658

## 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  | 0.4182          | 0.8406   | 0.6133    | 0.6124 | 0.5936 |
| 0.4634        | 2.0   | 692  | 0.4766          | 0.8353   | 0.6370    | 0.5809 | 0.5147 |
| 0.291         | 3.0   | 1038 | 0.3980          | 0.8683   | 0.6929    | 0.6500 | 0.6658 |
| 0.291         | 4.0   | 1384 | 0.5448          | 0.8537   | 0.6428    | 0.6282 | 0.6269 |
| 0.1759        | 5.0   | 1730 | 0.5188          | 0.8372   | 0.6457    | 0.6572 | 0.6379 |
| 0.1243        | 6.0   | 2076 | 0.5866          | 0.8518   | 0.6448    | 0.6292 | 0.6357 |
| 0.1243        | 7.0   | 2422 | 0.6440          | 0.8630   | 0.6778    | 0.6128 | 0.6404 |
| 0.1005        | 8.0   | 2768 | 0.6986          | 0.8593   | 0.6667    | 0.6217 | 0.6341 |


### Framework versions

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