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---
base_model: vinai/phobert-base-v2
tags:
- generated_from_trainer
model-index:
- name: CS505-Classifier-T4_predictLabel_a1_v8
  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. -->

# CS505-Classifier-T4_predictLabel_a1_v8

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: 0.0078

## 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: 5e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 50

### Training results

| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log        | 0.99  | 97   | 0.5015          |
| No log        | 1.98  | 194  | 0.3145          |
| No log        | 2.97  | 291  | 0.2217          |
| No log        | 3.96  | 388  | 0.1995          |
| No log        | 4.95  | 485  | 0.1427          |
| 0.4489        | 5.94  | 582  | 0.1056          |
| 0.4489        | 6.93  | 679  | 0.0765          |
| 0.4489        | 7.92  | 776  | 0.0530          |
| 0.4489        | 8.91  | 873  | 0.0605          |
| 0.4489        | 9.9   | 970  | 0.0387          |
| 0.1098        | 10.89 | 1067 | 0.0360          |
| 0.1098        | 11.88 | 1164 | 0.0179          |
| 0.1098        | 12.87 | 1261 | 0.0104          |
| 0.1098        | 13.86 | 1358 | 0.0135          |
| 0.1098        | 14.85 | 1455 | 0.0066          |
| 0.0301        | 15.84 | 1552 | 0.0137          |
| 0.0301        | 16.83 | 1649 | 0.0078          |


### Framework versions

- Transformers 4.38.2
- Pytorch 2.1.0+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2