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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: 0.1315
- F1: 0.7476
- Precision: 0.7075
- Recall: 0.7924
- Accuracy: 0.9782
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:------:|:---------:|:------:|:--------:|
| 0.4058 | 0.8 | 20 | 0.1934 | 0.055 | 0.12 | 0.0357 | 0.9433 |
| 0.1639 | 1.6 | 40 | 0.1266 | 0.3309 | 0.2967 | 0.3741 | 0.9582 |
| 0.0579 | 2.4 | 60 | 0.1016 | 0.4844 | 0.4477 | 0.5276 | 0.9669 |
| 0.062 | 3.2 | 80 | 0.0864 | 0.6686 | 0.6147 | 0.7330 | 0.9747 |
| 0.0521 | 4.0 | 100 | 0.0919 | 0.7018 | 0.6788 | 0.7265 | 0.9766 |
| 0.015 | 4.8 | 120 | 0.0922 | 0.7314 | 0.6931 | 0.7741 | 0.9767 |
| 0.0732 | 5.6 | 140 | 0.0931 | 0.7448 | 0.7068 | 0.7870 | 0.9779 |
| 0.0123 | 6.4 | 160 | 0.0965 | 0.7244 | 0.6710 | 0.7870 | 0.9764 |
| 0.019 | 7.2 | 180 | 0.0975 | 0.7285 | 0.6821 | 0.7816 | 0.9771 |
| 0.0314 | 8.0 | 200 | 0.0982 | 0.7396 | 0.6984 | 0.7859 | 0.9774 |
| 0.0125 | 8.8 | 220 | 0.1071 | 0.7488 | 0.7214 | 0.7784 | 0.9786 |
| 0.003 | 9.6 | 240 | 0.1129 | 0.7467 | 0.7368 | 0.7568 | 0.9784 |
| 0.0114 | 10.4 | 260 | 0.1137 | 0.7479 | 0.7361 | 0.76 | 0.9790 |
| 0.0029 | 11.2 | 280 | 0.1153 | 0.7417 | 0.7039 | 0.7838 | 0.9783 |
| 0.0159 | 12.0 | 300 | 0.1171 | 0.7422 | 0.6922 | 0.8 | 0.9775 |
| 0.0077 | 12.8 | 320 | 0.1167 | 0.7585 | 0.7466 | 0.7708 | 0.9792 |
| 0.002 | 13.6 | 340 | 0.1138 | 0.7400 | 0.6907 | 0.7968 | 0.9772 |
| 0.001 | 14.4 | 360 | 0.1171 | 0.7505 | 0.7172 | 0.7870 | 0.9785 |
| 0.0035 | 15.2 | 380 | 0.1202 | 0.7467 | 0.7102 | 0.7870 | 0.9781 |
| 0.0016 | 16.0 | 400 | 0.1212 | 0.7507 | 0.7296 | 0.7730 | 0.9786 |
| 0.0342 | 16.8 | 420 | 0.1221 | 0.7481 | 0.7042 | 0.7978 | 0.9779 |
| 0.0015 | 17.6 | 440 | 0.1216 | 0.7438 | 0.6983 | 0.7957 | 0.9782 |
| 0.0008 | 18.4 | 460 | 0.1230 | 0.7439 | 0.6993 | 0.7946 | 0.9780 |
| 0.001 | 19.2 | 480 | 0.1261 | 0.7463 | 0.7096 | 0.7870 | 0.9783 |
| 0.0008 | 20.0 | 500 | 0.1262 | 0.7427 | 0.6988 | 0.7924 | 0.9779 |
| 0.0008 | 20.8 | 520 | 0.1269 | 0.7386 | 0.6891 | 0.7957 | 0.9773 |
| 0.0009 | 21.6 | 540 | 0.1276 | 0.7418 | 0.6964 | 0.7935 | 0.9776 |
| 0.0009 | 22.4 | 560 | 0.1279 | 0.7420 | 0.7010 | 0.7881 | 0.9778 |
| 0.0006 | 23.2 | 580 | 0.1304 | 0.7397 | 0.6911 | 0.7957 | 0.9773 |
| 0.001 | 24.0 | 600 | 0.1307 | 0.7402 | 0.6904 | 0.7978 | 0.9772 |
| 0.0007 | 24.8 | 620 | 0.1308 | 0.7417 | 0.6930 | 0.7978 | 0.9774 |
| 0.0015 | 25.6 | 640 | 0.1305 | 0.7452 | 0.7024 | 0.7935 | 0.9780 |
| 0.0006 | 26.4 | 660 | 0.1305 | 0.7454 | 0.7071 | 0.7881 | 0.9782 |
| 0.0007 | 27.2 | 680 | 0.1308 | 0.7458 | 0.7078 | 0.7881 | 0.9782 |
| 0.0016 | 28.0 | 700 | 0.1311 | 0.7469 | 0.7072 | 0.7914 | 0.9782 |
| 0.0011 | 28.8 | 720 | 0.1314 | 0.7476 | 0.7075 | 0.7924 | 0.9782 |
| 0.0011 | 29.6 | 740 | 0.1315 | 0.7476 | 0.7075 | 0.7924 | 0.9782 |
### Framework versions
- Transformers 4.57.1
- Pytorch 2.8.0+cu128
- Datasets 4.3.0
- Tokenizers 0.22.1
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