---
library_name: transformers
license: agpl-3.0
base_model: vinai/phobert-base-v2
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: model_segment_content_DEFI
  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. -->

# model_segment_content_DEFI

This model is a fine-tuned version of [vinai/phobert-base-v2](https://huggingface.co/vinai/phobert-base-v2) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1130
- Accuracy: 0.9707
- F1: 0.9659

## 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: 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
- num_epochs: 10

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Accuracy | F1     |
|:-------------:|:------:|:----:|:---------------:|:--------:|:------:|
| No log        | 0.1419 | 150  | 0.1087          | 0.9636   | 0.9580 |
| No log        | 0.2838 | 300  | 0.0979          | 0.9671   | 0.9620 |
| No log        | 0.4257 | 450  | 0.0967          | 0.9614   | 0.9559 |
| No log        | 0.5676 | 600  | 0.0894          | 0.9688   | 0.9639 |
| No log        | 0.7096 | 750  | 0.0917          | 0.9693   | 0.9644 |
| No log        | 0.8515 | 900  | 0.0967          | 0.9672   | 0.9617 |
| No log        | 0.9934 | 1050 | 0.0939          | 0.9693   | 0.9643 |
| 0.1071        | 1.1353 | 1200 | 0.0852          | 0.9697   | 0.9651 |
| 0.1071        | 1.2772 | 1350 | 0.0914          | 0.9705   | 0.9659 |
| 0.1071        | 1.4191 | 1500 | 0.0870          | 0.9706   | 0.9658 |
| 0.1071        | 1.5610 | 1650 | 0.0908          | 0.9707   | 0.9659 |
| 0.1071        | 1.7029 | 1800 | 0.0873          | 0.9707   | 0.9662 |
| 0.1071        | 1.8448 | 1950 | 0.1037          | 0.9699   | 0.9654 |
| 0.1071        | 1.9868 | 2100 | 0.0829          | 0.9716   | 0.9673 |
| 0.0675        | 2.1287 | 2250 | 0.1106          | 0.9686   | 0.9640 |
| 0.0675        | 2.2706 | 2400 | 0.0950          | 0.9716   | 0.9672 |
| 0.0675        | 2.4125 | 2550 | 0.0900          | 0.9712   | 0.9666 |
| 0.0675        | 2.5544 | 2700 | 0.0904          | 0.9723   | 0.9678 |
| 0.0675        | 2.6963 | 2850 | 0.1167          | 0.9669   | 0.9621 |
| 0.0675        | 2.8382 | 3000 | 0.0909          | 0.9694   | 0.9649 |
| 0.0675        | 2.9801 | 3150 | 0.1014          | 0.9687   | 0.9640 |
| 0.0506        | 3.1220 | 3300 | 0.1003          | 0.9720   | 0.9675 |
| 0.0506        | 3.2640 | 3450 | 0.1130          | 0.9707   | 0.9659 |


### Framework versions

- Transformers 5.12.1
- Pytorch 2.7.1+cu118
- Datasets 5.0.0
- Tokenizers 0.22.2