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---
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
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