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---
license: mit
base_model: microsoft/deberta-v3-base
tags:
- generated_from_trainer
metrics:
- f1
- accuracy
- precision
- recall
model-index:
- name: 010-microsoft-deberta-v3-base-finetuned-yahoo-800_200
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. -->
# 010-microsoft-deberta-v3-base-finetuned-yahoo-800_200
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: 1.1327
- F1: 0.6339
- Accuracy: 0.64
- Precision: 0.6436
- Recall: 0.64
- System Ram Used: 4.1191
- System Ram Total: 83.4807
- Gpu Ram Allocated: 2.0916
- Gpu Ram Cached: 24.6602
- Gpu Ram Total: 39.5640
- Gpu Utilization: 46
- Disk Space Used: 42.7346
- 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: 10
### 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 |
|:-------------:|:-----:|:----:|:---------------:|:------:|:--------:|:---------:|:------:|:---------------:|:----------------:|:-----------------:|:--------------:|:-------------:|:---------------:|:---------------:|:----------------:|
| 2.3122 | 0.4 | 10 | 2.3038 | 0.0182 | 0.1 | 0.01 | 0.1 | 3.9481 | 83.4807 | 2.0915 | 24.6484 | 39.5640 | 44 | 42.7345 | 78.1898 |
| 2.3122 | 0.8 | 20 | 2.3008 | 0.0182 | 0.1 | 0.01 | 0.1 | 3.9500 | 83.4807 | 2.0916 | 24.6602 | 39.5640 | 64 | 42.7345 | 78.1898 |
| 2.3122 | 1.2 | 30 | 2.2951 | 0.0182 | 0.1 | 0.01 | 0.1 | 3.9885 | 83.4807 | 2.0915 | 24.6602 | 39.5640 | 44 | 42.7345 | 78.1898 |
| 2.3122 | 1.6 | 40 | 2.2860 | 0.0830 | 0.15 | 0.0948 | 0.15 | 4.0161 | 83.4807 | 2.0915 | 24.6602 | 39.5640 | 43 | 42.7345 | 78.1898 |
| 2.3122 | 2.0 | 50 | 2.2335 | 0.0916 | 0.195 | 0.1010 | 0.195 | 4.0651 | 83.4807 | 2.0916 | 24.6602 | 39.5640 | 43 | 42.7345 | 78.1898 |
| 2.3122 | 2.4 | 60 | 2.1085 | 0.2197 | 0.295 | 0.2090 | 0.295 | 4.0829 | 83.4807 | 2.0915 | 24.6602 | 39.5640 | 42 | 42.7345 | 78.1898 |
| 2.3122 | 2.8 | 70 | 1.9703 | 0.2923 | 0.33 | 0.3951 | 0.33 | 4.1017 | 83.4807 | 2.0915 | 24.6602 | 39.5640 | 47 | 42.7345 | 78.1898 |
| 2.3122 | 3.2 | 80 | 1.8818 | 0.3441 | 0.395 | 0.4073 | 0.395 | 4.1170 | 83.4807 | 2.0915 | 24.6602 | 39.5640 | 49 | 42.7345 | 78.1898 |
| 2.3122 | 3.6 | 90 | 1.7649 | 0.4158 | 0.44 | 0.4853 | 0.44 | 4.1182 | 83.4807 | 2.0915 | 24.6602 | 39.5640 | 45 | 42.7345 | 78.1898 |
| 2.3122 | 4.0 | 100 | 1.6408 | 0.5143 | 0.53 | 0.5429 | 0.53 | 4.1156 | 83.4807 | 2.0916 | 24.6602 | 39.5640 | 48 | 42.7345 | 78.1898 |
| 2.3122 | 4.4 | 110 | 1.5896 | 0.5167 | 0.535 | 0.5320 | 0.535 | 4.1162 | 83.4807 | 2.0915 | 24.6602 | 39.5640 | 46 | 42.7345 | 78.1898 |
| 2.3122 | 4.8 | 120 | 1.4783 | 0.5627 | 0.575 | 0.5692 | 0.575 | 4.1160 | 83.4807 | 2.0915 | 24.6602 | 39.5640 | 51 | 42.7345 | 78.1898 |
| 2.3122 | 5.2 | 130 | 1.3900 | 0.5844 | 0.595 | 0.6033 | 0.595 | 4.1169 | 83.4807 | 2.0915 | 24.6602 | 39.5640 | 57 | 42.7345 | 78.1898 |
| 2.3122 | 5.6 | 140 | 1.3547 | 0.6052 | 0.625 | 0.6127 | 0.625 | 4.1181 | 83.4807 | 2.0915 | 24.6602 | 39.5640 | 46 | 42.7345 | 78.1898 |
| 2.3122 | 6.0 | 150 | 1.2983 | 0.6032 | 0.6 | 0.6455 | 0.6 | 4.0997 | 83.4807 | 2.0915 | 24.6602 | 39.5640 | 48 | 42.7345 | 78.1898 |
| 2.3122 | 6.4 | 160 | 1.2805 | 0.5972 | 0.615 | 0.6058 | 0.615 | 4.1017 | 83.4807 | 2.0915 | 24.6602 | 39.5640 | 55 | 42.7345 | 78.1898 |
| 2.3122 | 6.8 | 170 | 1.2105 | 0.6213 | 0.62 | 0.6325 | 0.62 | 4.1238 | 83.4807 | 2.0915 | 24.6602 | 39.5640 | 50 | 42.7345 | 78.1898 |
| 2.3122 | 7.2 | 180 | 1.2458 | 0.5944 | 0.615 | 0.5958 | 0.615 | 4.1257 | 83.4807 | 2.0915 | 24.6602 | 39.5640 | 45 | 42.7345 | 78.1898 |
| 2.3122 | 7.6 | 190 | 1.1695 | 0.6629 | 0.665 | 0.6736 | 0.665 | 4.1261 | 83.4807 | 2.0915 | 24.6602 | 39.5640 | 52 | 42.7345 | 78.1898 |
| 2.3122 | 8.0 | 200 | 1.1737 | 0.6383 | 0.645 | 0.6425 | 0.645 | 4.1259 | 83.4807 | 2.0915 | 24.6602 | 39.5640 | 54 | 42.7345 | 78.1898 |
| 2.3122 | 8.4 | 210 | 1.1540 | 0.6347 | 0.635 | 0.6418 | 0.635 | 4.1258 | 83.4807 | 2.0915 | 24.6602 | 39.5640 | 47 | 42.7345 | 78.1898 |
| 2.3122 | 8.8 | 220 | 1.1422 | 0.6322 | 0.64 | 0.6413 | 0.64 | 4.1251 | 83.4807 | 2.0915 | 24.6602 | 39.5640 | 50 | 42.7346 | 78.1898 |
| 2.3122 | 9.2 | 230 | 1.1422 | 0.6443 | 0.65 | 0.6575 | 0.65 | 4.1251 | 83.4807 | 2.0916 | 24.6602 | 39.5640 | 47 | 42.7346 | 78.1898 |
| 2.3122 | 9.6 | 240 | 1.1345 | 0.6345 | 0.64 | 0.6483 | 0.64 | 4.1032 | 83.4807 | 2.0915 | 24.6602 | 39.5640 | 44 | 42.7346 | 78.1898 |
| 2.3122 | 10.0 | 250 | 1.1327 | 0.6339 | 0.64 | 0.6436 | 0.64 | 4.1084 | 83.4807 | 2.0915 | 24.6602 | 39.5640 | 44 | 42.7346 | 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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