---
base_model: FPTAI/vibert-base-cased
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: vibert-base-cased-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. -->

# vibert-base-cased-ed

This model is a fine-tuned version of [FPTAI/vibert-base-cased](https://huggingface.co/FPTAI/vibert-base-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0595
- F1 Micro: 0.7034
- F1 Macro: 0.0430
- Accuracy: 0.6374
- Recall Micro: 0.6094
- Precision Micro: 0.8317
- Recall Macro: 0.0392
- Precision Macro: 0.0621
- F1: 0.5913

## 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 | Accuracy | Recall Micro | Precision Micro | Recall Macro | Precision Macro | F1     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:--------:|:------------:|:---------------:|:------------:|:---------------:|:------:|
| 0.0696        | 1.0   | 1526 | 0.0711          | 0.6892   | 0.0243   | 0.7054   | 0.6737       | 0.7054          | 0.0294       | 0.0207          | 0.5573 |
| 0.055         | 2.0   | 3052 | 0.0622          | 0.6965   | 0.0252   | 0.6345   | 0.6060       | 0.8187          | 0.0265       | 0.0241          | 0.5775 |
| 0.0631        | 3.0   | 4578 | 0.0598          | 0.7054   | 0.0255   | 0.6436   | 0.6147       | 0.8274          | 0.0268       | 0.0243          | 0.5847 |
| 0.0534        | 4.0   | 6104 | 0.0591          | 0.6980   | 0.0260   | 0.6268   | 0.5989       | 0.8362          | 0.0265       | 0.0540          | 0.5809 |
| 0.0296        | 5.0   | 7630 | 0.0595          | 0.7034   | 0.0430   | 0.6374   | 0.6094       | 0.8317          | 0.0392       | 0.0621          | 0.5913 |


### Framework versions

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