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library_name: transformers
license: mit
base_model: microsoft/deberta-v3-base
tags:
- generated_from_trainer
metrics:
- f1
- precision
- recall
- accuracy
model-index:
- name: deberta-v3-base-uner-down-synth400
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. -->
# deberta-v3-base-uner-down-synth400
This model is a fine-tuned version of [microsoft/deberta-v3-base](https://huggingface.co/microsoft/deberta-v3-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1225
- F1: 0.7274
- Precision: 0.6706
- Recall: 0.7946
- Accuracy: 0.9784
## 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: 2.5e-05
- train_batch_size: 16
- eval_batch_size: 32
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 30
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 | Precision | Recall | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:------:|:---------:|:------:|:--------:|
| 0.3736 | 0.8 | 20 | 0.2035 | 0.0482 | 0.1176 | 0.0303 | 0.9431 |
| 0.2416 | 1.6 | 40 | 0.1315 | 0.3044 | 0.2724 | 0.3449 | 0.9559 |
| 0.0684 | 2.4 | 60 | 0.1027 | 0.4766 | 0.4303 | 0.5341 | 0.9668 |
| 0.0588 | 3.2 | 80 | 0.0881 | 0.6067 | 0.5490 | 0.6778 | 0.9733 |
| 0.1194 | 4.0 | 100 | 0.0851 | 0.6805 | 0.6521 | 0.7114 | 0.9766 |
| 0.014 | 4.8 | 120 | 0.0800 | 0.7078 | 0.6512 | 0.7751 | 0.9769 |
| 0.0572 | 5.6 | 140 | 0.0815 | 0.7228 | 0.6882 | 0.7611 | 0.9788 |
| 0.0059 | 6.4 | 160 | 0.0910 | 0.7016 | 0.6408 | 0.7751 | 0.9767 |
| 0.0079 | 7.2 | 180 | 0.0946 | 0.6864 | 0.6237 | 0.7632 | 0.9760 |
| 0.0152 | 8.0 | 200 | 0.0981 | 0.7107 | 0.6494 | 0.7849 | 0.9760 |
| 0.0169 | 8.8 | 220 | 0.0954 | 0.702 | 0.6530 | 0.7589 | 0.9771 |
| 0.0027 | 9.6 | 240 | 0.0983 | 0.7214 | 0.6984 | 0.7459 | 0.9775 |
| 0.0107 | 10.4 | 260 | 0.1050 | 0.7141 | 0.6544 | 0.7859 | 0.9773 |
| 0.0029 | 11.2 | 280 | 0.1072 | 0.7139 | 0.6555 | 0.7838 | 0.9773 |
| 0.0056 | 12.0 | 300 | 0.1075 | 0.7216 | 0.6670 | 0.7859 | 0.9777 |
| 0.0048 | 12.8 | 320 | 0.1109 | 0.7245 | 0.6628 | 0.7989 | 0.9775 |
| 0.0033 | 13.6 | 340 | 0.1133 | 0.7242 | 0.6691 | 0.7892 | 0.9773 |
| 0.0021 | 14.4 | 360 | 0.1098 | 0.7247 | 0.6916 | 0.7611 | 0.9784 |
| 0.0057 | 15.2 | 380 | 0.1131 | 0.7223 | 0.6652 | 0.7903 | 0.9779 |
| 0.0014 | 16.0 | 400 | 0.1113 | 0.7319 | 0.6889 | 0.7805 | 0.9789 |
| 0.0098 | 16.8 | 420 | 0.1140 | 0.7207 | 0.6670 | 0.7838 | 0.9778 |
| 0.0019 | 17.6 | 440 | 0.1162 | 0.7177 | 0.6612 | 0.7849 | 0.9778 |
| 0.001 | 18.4 | 460 | 0.1194 | 0.7259 | 0.6682 | 0.7946 | 0.9780 |
| 0.0009 | 19.2 | 480 | 0.1174 | 0.7307 | 0.6860 | 0.7816 | 0.9784 |
| 0.001 | 20.0 | 500 | 0.1219 | 0.7267 | 0.6673 | 0.7978 | 0.9779 |
| 0.0016 | 20.8 | 520 | 0.1183 | 0.7312 | 0.6819 | 0.7881 | 0.9784 |
| 0.001 | 21.6 | 540 | 0.1187 | 0.7306 | 0.6850 | 0.7827 | 0.9784 |
| 0.0012 | 22.4 | 560 | 0.1227 | 0.7323 | 0.6752 | 0.8 | 0.9784 |
| 0.0011 | 23.2 | 580 | 0.1218 | 0.7212 | 0.6624 | 0.7914 | 0.9777 |
| 0.0012 | 24.0 | 600 | 0.1217 | 0.7243 | 0.6700 | 0.7881 | 0.9780 |
| 0.0006 | 24.8 | 620 | 0.1217 | 0.7296 | 0.6807 | 0.7859 | 0.9784 |
| 0.0006 | 25.6 | 640 | 0.1233 | 0.7272 | 0.6688 | 0.7968 | 0.9782 |
| 0.001 | 26.4 | 660 | 0.1206 | 0.7305 | 0.6807 | 0.7881 | 0.9787 |
| 0.0005 | 27.2 | 680 | 0.1207 | 0.7329 | 0.6842 | 0.7892 | 0.9786 |
| 0.0012 | 28.0 | 700 | 0.1211 | 0.7318 | 0.6822 | 0.7892 | 0.9786 |
| 0.0017 | 28.8 | 720 | 0.1225 | 0.7274 | 0.6706 | 0.7946 | 0.9784 |
| 0.001 | 29.6 | 740 | 0.1225 | 0.7274 | 0.6706 | 0.7946 | 0.9784 |
### Framework versions
- Transformers 4.57.1
- Pytorch 2.8.0+cu128
- Datasets 4.3.0
- Tokenizers 0.22.1
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