---
base_model: vinai/phobert-base-v2
tags:
- generated_from_trainer
model-index:
- name: CS505-Classifier-T4_predictLabel_a1_v11
  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_v11

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: 0.0012

## 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: 32
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 70

### Training results

| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log        | 0.98  | 48   | 0.6966          |
| No log        | 1.96  | 96   | 0.3265          |
| No log        | 2.94  | 144  | 0.2746          |
| No log        | 3.92  | 192  | 0.1899          |
| No log        | 4.9   | 240  | 0.1671          |
| No log        | 5.88  | 288  | 0.1193          |
| No log        | 6.86  | 336  | 0.1259          |
| No log        | 7.84  | 384  | 0.0737          |
| No log        | 8.82  | 432  | 0.0461          |
| No log        | 9.8   | 480  | 0.0490          |
| 0.3023        | 10.78 | 528  | 0.0293          |
| 0.3023        | 11.76 | 576  | 0.0324          |
| 0.3023        | 12.73 | 624  | 0.0355          |
| 0.3023        | 13.71 | 672  | 0.0331          |
| 0.3023        | 14.69 | 720  | 0.0158          |
| 0.3023        | 15.67 | 768  | 0.0108          |
| 0.3023        | 16.65 | 816  | 0.0062          |
| 0.3023        | 17.63 | 864  | 0.0048          |
| 0.3023        | 18.61 | 912  | 0.0038          |
| 0.3023        | 19.59 | 960  | 0.0053          |
| 0.0241        | 20.57 | 1008 | 0.0077          |
| 0.0241        | 21.55 | 1056 | 0.0027          |
| 0.0241        | 22.53 | 1104 | 0.0025          |
| 0.0241        | 23.51 | 1152 | 0.0051          |
| 0.0241        | 24.49 | 1200 | 0.0088          |
| 0.0241        | 25.47 | 1248 | 0.0023          |
| 0.0241        | 26.45 | 1296 | 0.0023          |
| 0.0241        | 27.43 | 1344 | 0.0022          |
| 0.0241        | 28.41 | 1392 | 0.0018          |
| 0.0241        | 29.39 | 1440 | 0.0019          |
| 0.0241        | 30.37 | 1488 | 0.0018          |
| 0.0065        | 31.35 | 1536 | 0.0017          |
| 0.0065        | 32.33 | 1584 | 0.0033          |
| 0.0065        | 33.31 | 1632 | 0.0016          |
| 0.0065        | 34.29 | 1680 | 0.0017          |
| 0.0065        | 35.27 | 1728 | 0.0015          |
| 0.0065        | 36.24 | 1776 | 0.0017          |
| 0.0065        | 37.22 | 1824 | 0.0015          |
| 0.0065        | 38.2  | 1872 | 0.0015          |
| 0.0065        | 39.18 | 1920 | 0.0014          |
| 0.0065        | 40.16 | 1968 | 0.0014          |
| 0.0028        | 41.14 | 2016 | 0.0014          |
| 0.0028        | 42.12 | 2064 | 0.0026          |
| 0.0028        | 43.1  | 2112 | 0.0015          |
| 0.0028        | 44.08 | 2160 | 0.0014          |
| 0.0028        | 45.06 | 2208 | 0.0013          |
| 0.0028        | 46.04 | 2256 | 0.0013          |
| 0.0028        | 47.02 | 2304 | 0.0013          |
| 0.0028        | 48.0  | 2352 | 0.0013          |
| 0.0028        | 48.98 | 2400 | 0.0013          |
| 0.0028        | 49.96 | 2448 | 0.0013          |
| 0.0028        | 50.94 | 2496 | 0.0013          |
| 0.002         | 51.92 | 2544 | 0.0012          |
| 0.002         | 52.9  | 2592 | 0.0012          |
| 0.002         | 53.88 | 2640 | 0.0012          |
| 0.002         | 54.86 | 2688 | 0.0012          |
| 0.002         | 55.84 | 2736 | 0.0013          |
| 0.002         | 56.82 | 2784 | 0.0012          |
| 0.002         | 57.8  | 2832 | 0.0012          |
| 0.002         | 58.78 | 2880 | 0.0012          |
| 0.002         | 59.76 | 2928 | 0.0012          |
| 0.002         | 60.73 | 2976 | 0.0012          |
| 0.0016        | 61.71 | 3024 | 0.0012          |
| 0.0016        | 62.69 | 3072 | 0.0012          |
| 0.0016        | 63.67 | 3120 | 0.0012          |
| 0.0016        | 64.65 | 3168 | 0.0012          |
| 0.0016        | 65.63 | 3216 | 0.0012          |
| 0.0016        | 66.61 | 3264 | 0.0012          |
| 0.0016        | 67.59 | 3312 | 0.0012          |
| 0.0016        | 68.57 | 3360 | 0.0012          |
| 0.0016        | 69.55 | 3408 | 0.0012          |


### Framework versions

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