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---
tags:
- generated_from_trainer
datasets:
- wisesight_sentiment
base_model: airesearch/wangchanberta-base-att-spm-uncased
model-index:
- name: Wangchanberta-Depress-Finetuned
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. -->
# Wangchanberta-Depress-Finetuned
This model is a fine-tuned version of [airesearch/wangchanberta-base-att-spm-uncased](https://huggingface.co/airesearch/wangchanberta-base-att-spm-uncased) on the wisesight_sentiment dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5910
## 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
- lr_scheduler_warmup_steps: 400
- num_epochs: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 1.0114 | 0.08 | 200 | 0.9538 |
| 0.8617 | 0.15 | 400 | 0.8280 |
| 0.7882 | 0.23 | 600 | 0.7472 |
| 0.7132 | 0.3 | 800 | 0.7264 |
| 0.7226 | 0.38 | 1000 | 0.7265 |
| 0.6854 | 0.45 | 1200 | 0.6792 |
| 0.621 | 0.53 | 1400 | 0.6451 |
| 0.6093 | 0.61 | 1600 | 0.6364 |
| 0.6099 | 0.68 | 1800 | 0.6128 |
| 0.5766 | 0.76 | 2000 | 0.6388 |
| 0.6033 | 0.83 | 2200 | 0.6148 |
| 0.5966 | 0.91 | 2400 | 0.6440 |
| 0.6208 | 0.98 | 2600 | 0.5910 |
| 0.5178 | 1.06 | 2800 | 0.6340 |
| 0.4863 | 1.13 | 3000 | 0.7177 |
| 0.4852 | 1.21 | 3200 | 0.6766 |
| 0.4711 | 1.29 | 3400 | 0.6739 |
| 0.5203 | 1.36 | 3600 | 0.6429 |
| 0.5167 | 1.44 | 3800 | 0.6539 |
| 0.5053 | 1.51 | 4000 | 0.6172 |
| 0.5076 | 1.59 | 4200 | 0.6053 |
| 0.4704 | 1.66 | 4400 | 0.6474 |
| 0.4807 | 1.74 | 4600 | 0.6225 |
| 0.4792 | 1.82 | 4800 | 0.6282 |
| 0.5177 | 1.89 | 5000 | 0.6011 |
| 0.4839 | 1.97 | 5200 | 0.6231 |
| 0.4155 | 2.04 | 5400 | 0.6668 |
| 0.3923 | 2.12 | 5600 | 0.6886 |
| 0.3713 | 2.19 | 5800 | 0.6895 |
| 0.364 | 2.27 | 6000 | 0.6886 |
| 0.3774 | 2.34 | 6200 | 0.7117 |
| 0.4001 | 2.42 | 6400 | 0.7081 |
| 0.3531 | 2.5 | 6600 | 0.7465 |
| 0.3768 | 2.57 | 6800 | 0.7706 |
| 0.3324 | 2.65 | 7000 | 0.7456 |
| 0.3597 | 2.72 | 7200 | 0.7507 |
| 0.3868 | 2.8 | 7400 | 0.7542 |
| 0.4141 | 2.87 | 7600 | 0.7223 |
| 0.3701 | 2.95 | 7800 | 0.7374 |
| 0.3175 | 3.03 | 8000 | 0.7615 |
| 0.2951 | 3.1 | 8200 | 0.7880 |
| 0.2885 | 3.18 | 8400 | 0.8158 |
| 0.2913 | 3.25 | 8600 | 0.8565 |
| 0.2815 | 3.33 | 8800 | 0.8649 |
| 0.2748 | 3.4 | 9000 | 0.8783 |
| 0.2776 | 3.48 | 9200 | 0.8851 |
| 0.2982 | 3.56 | 9400 | 0.8922 |
| 0.2939 | 3.63 | 9600 | 0.8796 |
| 0.2712 | 3.71 | 9800 | 0.8873 |
| 0.2918 | 3.78 | 10000 | 0.8973 |
| 0.3144 | 3.86 | 10200 | 0.8978 |
| 0.2988 | 3.93 | 10400 | 0.8951 |
### Framework versions
- Transformers 4.11.2
- Pytorch 1.11.0+cu113
- Datasets 2.1.0
- Tokenizers 0.10.3
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