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---
base_model: vinai/phobert-base-v2
tags:
- generated_from_trainer
model-index:
- name: phobert-base-v2-ed
  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-base-v2-ed

This model is a fine-tuned version of [vinai/phobert-base-v2](https://huggingface.co/vinai/phobert-base-v2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0455
- F1 Micro: 0.7302
- F1 Macro: 0.0774
- Recall Micro: 0.6299
- Precision Micro: 0.8683
- Recall Macro: 0.0745
- Precision Macro: 0.0806

## 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: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5

### Training results

| Training Loss | Epoch | Step | Validation Loss | F1 Micro | F1 Macro | Recall Micro | Precision Micro | Recall Macro | Precision Macro |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:------------:|:---------------:|:------------:|:---------------:|
| 0.0638        | 1.0   | 1526 | 0.0622          | 0.7114   | 0.0257   | 0.6218       | 0.8312          | 0.0271       | 0.0244          |
| 0.046         | 2.0   | 3052 | 0.0543          | 0.7112   | 0.0259   | 0.6021       | 0.8684          | 0.0263       | 0.0255          |
| 0.0462        | 3.0   | 4578 | 0.0494          | 0.7049   | 0.0716   | 0.5895       | 0.8764          | 0.0685       | 0.0803          |
| 0.0472        | 4.0   | 6104 | 0.0461          | 0.7326   | 0.0762   | 0.6402       | 0.8562          | 0.0724       | 0.0812          |
| 0.0228        | 5.0   | 7630 | 0.0455          | 0.7302   | 0.0774   | 0.6299       | 0.8683          | 0.0745       | 0.0806          |


### Framework versions

- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1