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---
license: mit
base_model: microsoft/deberta-v3-base
tags:
- generated_from_trainer
metrics:
- f1
- accuracy
- precision
- recall
model-index:
- name: 011-microsoft-deberta-v3-base-finetuned-yahoo-8000_2000
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. -->
# 011-microsoft-deberta-v3-base-finetuned-yahoo-8000_2000
This model is a fine-tuned version of [microsoft/deberta-v3-base](https://huggingface.co/microsoft/deberta-v3-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.8660
- F1: 0.7055
- Accuracy: 0.7045
- Precision: 0.7076
- Recall: 0.7045
- System Ram Used: 4.2773
- System Ram Total: 83.4807
- Gpu Ram Allocated: 2.0897
- Gpu Ram Cached: 25.8555
- Gpu Ram Total: 39.5640
- Gpu Utilization: 48
- Disk Space Used: 35.8287
- Disk Space Total: 78.1898
## 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: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 15
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 | Accuracy | Precision | Recall | System Ram Used | System Ram Total | Gpu Ram Allocated | Gpu Ram Cached | Gpu Ram Total | Gpu Utilization | Disk Space Used | Disk Space Total |
|:-------------:|:-----:|:----:|:---------------:|:------:|:--------:|:---------:|:------:|:---------------:|:----------------:|:-----------------:|:--------------:|:-------------:|:---------------:|:---------------:|:----------------:|
| 1.6916 | 0.75 | 188 | 1.1063 | 0.6708 | 0.6755 | 0.6900 | 0.6755 | 4.0191 | 83.4807 | 2.0898 | 25.8555 | 39.5640 | 50 | 24.8064 | 78.1898 |
| 0.9694 | 1.5 | 376 | 0.9586 | 0.7181 | 0.7195 | 0.7198 | 0.7195 | 4.2536 | 83.4807 | 2.0898 | 25.8555 | 39.5640 | 50 | 29.6418 | 78.1898 |
| 0.8509 | 2.26 | 564 | 0.9748 | 0.7070 | 0.712 | 0.7161 | 0.712 | 4.1602 | 83.4807 | 2.0898 | 25.8555 | 39.5640 | 46 | 29.6418 | 78.1898 |
| 0.7475 | 3.01 | 752 | 0.9447 | 0.7122 | 0.714 | 0.7148 | 0.714 | 4.1607 | 83.4807 | 2.0898 | 25.8555 | 39.5640 | 50 | 29.6420 | 78.1898 |
| 0.5841 | 3.76 | 940 | 1.0064 | 0.7077 | 0.711 | 0.7225 | 0.711 | 4.1889 | 83.4807 | 2.0898 | 25.8555 | 39.5640 | 47 | 29.6420 | 78.1898 |
| 0.4972 | 4.51 | 1128 | 1.0585 | 0.7110 | 0.714 | 0.7129 | 0.714 | 4.1766 | 83.4807 | 2.0898 | 25.8555 | 39.5640 | 47 | 29.6421 | 78.1898 |
| 0.4555 | 5.26 | 1316 | 1.1175 | 0.7086 | 0.7075 | 0.7151 | 0.7075 | 4.2257 | 83.4807 | 2.0898 | 25.8555 | 39.5640 | 46 | 33.7652 | 78.1898 |
| 0.3535 | 6.02 | 1504 | 1.1749 | 0.7032 | 0.708 | 0.7077 | 0.708 | 4.2302 | 83.4807 | 2.0898 | 25.8555 | 39.5640 | 50 | 33.7653 | 78.1898 |
| 0.2614 | 6.77 | 1692 | 1.2028 | 0.7056 | 0.709 | 0.7079 | 0.709 | 4.2376 | 83.4807 | 2.0898 | 25.8555 | 39.5640 | 49 | 33.7654 | 78.1898 |
| 0.2321 | 7.52 | 1880 | 1.2961 | 0.7019 | 0.698 | 0.7085 | 0.698 | 4.2248 | 83.4807 | 2.0898 | 25.8555 | 39.5640 | 49 | 33.7656 | 78.1898 |
| 0.197 | 8.27 | 2068 | 1.3960 | 0.7098 | 0.712 | 0.7137 | 0.712 | 4.2194 | 83.4807 | 2.0898 | 25.8555 | 39.5640 | 45 | 33.7657 | 78.1898 |
| 0.1505 | 9.02 | 2256 | 1.4310 | 0.7093 | 0.7075 | 0.7133 | 0.7075 | 4.2418 | 83.4807 | 2.0898 | 25.8555 | 39.5640 | 48 | 35.8277 | 78.1898 |
| 0.1132 | 9.78 | 2444 | 1.5454 | 0.7053 | 0.7045 | 0.7097 | 0.7045 | 4.2931 | 83.4807 | 2.0898 | 25.8555 | 39.5640 | 48 | 35.8278 | 78.1898 |
| 0.0979 | 10.53 | 2632 | 1.6420 | 0.7090 | 0.708 | 0.7171 | 0.708 | 4.2793 | 83.4807 | 2.0898 | 25.8555 | 39.5640 | 45 | 35.8281 | 78.1898 |
| 0.0818 | 11.28 | 2820 | 1.6869 | 0.7062 | 0.7065 | 0.7102 | 0.7065 | 4.2822 | 83.4807 | 2.0898 | 25.8555 | 39.5640 | 49 | 35.8281 | 78.1898 |
| 0.062 | 12.03 | 3008 | 1.7818 | 0.7043 | 0.701 | 0.7123 | 0.701 | 4.2864 | 83.4807 | 2.0901 | 25.8555 | 39.5640 | 50 | 35.8282 | 78.1898 |
| 0.0433 | 12.78 | 3196 | 1.7981 | 0.7080 | 0.707 | 0.7110 | 0.707 | 4.2666 | 83.4807 | 2.0898 | 25.8555 | 39.5640 | 49 | 35.8282 | 78.1898 |
| 0.0368 | 13.54 | 3384 | 1.8403 | 0.7079 | 0.7055 | 0.7131 | 0.7055 | 4.2783 | 83.4807 | 2.0898 | 25.8555 | 39.5640 | 47 | 35.8285 | 78.1898 |
| 0.0379 | 14.29 | 3572 | 1.8536 | 0.7052 | 0.705 | 0.7074 | 0.705 | 4.3013 | 83.4807 | 2.0898 | 25.8555 | 39.5640 | 47 | 35.8286 | 78.1898 |
### Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
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