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---
tags:
- generated_from_trainer
model-index:
- name: scideberta-cs-tdm-pretrained
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. -->
# scideberta-cs-tdm-pretrained
This model is a fine-tuned version of [KISTI-AI/scideberta-cs](https://huggingface.co/KISTI-AI/scideberta-cs) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8211
- Overall Precision: 0.6247
- Overall Recall: 0.7665
- Overall F1: 0.6884
- Overall Accuracy: 0.9288
- Datasetname F1: 0.6345
- Metricname F1: 0.8177
- Taskname F1: 0.6622
## 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
- num_epochs: 100
### Training results
| Training Loss | Epoch | Step | Validation Loss | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy | Datasetname F1 | Metricname F1 | Taskname F1 |
|:-------------:|:-----:|:----:|:---------------:|:-----------------:|:--------------:|:----------:|:----------------:|:--------------:|:-------------:|:-----------:|
| No log | 1.0 | 191 | 0.3160 | 0.4684 | 0.6953 | 0.5597 | 0.9187 | 0.5439 | 0.7008 | 0.5135 |
| No log | 2.0 | 382 | 0.3152 | 0.4370 | 0.6544 | 0.5240 | 0.9084 | 0.3623 | 0.7527 | 0.5303 |
| 0.4334 | 3.0 | 573 | 0.3713 | 0.4900 | 0.6768 | 0.5684 | 0.9116 | 0.5525 | 0.7692 | 0.5025 |
| 0.4334 | 4.0 | 764 | 0.4347 | 0.5554 | 0.6807 | 0.6117 | 0.9253 | 0.5943 | 0.7074 | 0.5795 |
| 0.4334 | 5.0 | 955 | 0.5098 | 0.5777 | 0.7309 | 0.6453 | 0.9258 | 0.6478 | 0.7902 | 0.5868 |
| 0.1097 | 6.0 | 1146 | 0.5453 | 0.5784 | 0.7401 | 0.6493 | 0.9265 | 0.5782 | 0.7642 | 0.6390 |
| 0.1097 | 7.0 | 1337 | 0.6200 | 0.6264 | 0.7586 | 0.6862 | 0.9349 | 0.6513 | 0.7826 | 0.6629 |
| 0.0499 | 8.0 | 1528 | 0.6072 | 0.6448 | 0.7401 | 0.6892 | 0.9380 | 0.6783 | 0.7935 | 0.6496 |
| 0.0499 | 9.0 | 1719 | 0.6568 | 0.6329 | 0.7414 | 0.6829 | 0.9347 | 0.6413 | 0.8086 | 0.6487 |
| 0.0499 | 10.0 | 1910 | 0.6726 | 0.6264 | 0.7520 | 0.6835 | 0.9312 | 0.6618 | 0.7967 | 0.6472 |
| 0.0247 | 11.0 | 2101 | 0.8104 | 0.6635 | 0.7282 | 0.6943 | 0.9395 | 0.6514 | 0.8159 | 0.6635 |
| 0.0247 | 12.0 | 2292 | 0.7022 | 0.6320 | 0.7704 | 0.6944 | 0.9376 | 0.6452 | 0.8122 | 0.6704 |
| 0.0247 | 13.0 | 2483 | 0.8143 | 0.6655 | 0.7216 | 0.6924 | 0.9366 | 0.6321 | 0.8122 | 0.6700 |
| 0.0176 | 14.0 | 2674 | 0.7723 | 0.6434 | 0.7309 | 0.6844 | 0.9329 | 0.6190 | 0.7934 | 0.6699 |
| 0.0176 | 15.0 | 2865 | 0.7726 | 0.6071 | 0.7480 | 0.6702 | 0.9320 | 0.6174 | 0.8122 | 0.6391 |
| 0.0132 | 16.0 | 3056 | 0.8124 | 0.6404 | 0.7493 | 0.6906 | 0.9329 | 0.6326 | 0.8098 | 0.6682 |
| 0.0132 | 17.0 | 3247 | 0.8269 | 0.6374 | 0.7467 | 0.6877 | 0.9336 | 0.6071 | 0.8268 | 0.6714 |
| 0.0132 | 18.0 | 3438 | 0.8826 | 0.6315 | 0.7573 | 0.6887 | 0.9343 | 0.6456 | 0.8142 | 0.6573 |
| 0.0125 | 19.0 | 3629 | 0.8602 | 0.6446 | 0.7467 | 0.6919 | 0.9320 | 0.6190 | 0.8156 | 0.6760 |
| 0.0125 | 20.0 | 3820 | 1.0048 | 0.6679 | 0.7216 | 0.6937 | 0.9350 | 0.6683 | 0.7932 | 0.6634 |
| 0.0093 | 21.0 | 4011 | 0.8211 | 0.6247 | 0.7665 | 0.6884 | 0.9288 | 0.6345 | 0.8177 | 0.6622 |
### Framework versions
- Transformers 4.23.1
- Pytorch 1.12.1+cu102
- Datasets 2.6.1
- Tokenizers 0.13.1
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