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012-microsoft-deberta-v3-base-finetuned-yahoo-8000_2000

Text Classification
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deberta-v2
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+ ---
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+ license: mit
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+ base_model: microsoft/deberta-v3-base
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+ tags:
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+ - generated_from_trainer
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+ metrics:
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+ - f1
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+ - accuracy
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+ - precision
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+ - recall
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+ model-index:
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+ - name: 012-microsoft-deberta-v3-base-finetuned-yahoo-8000_2000
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+ results: []
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+ ---
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+
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+ <!-- This model card has been generated automatically according to the information the Trainer had access to. You
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+ should probably proofread and complete it, then remove this comment. -->
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+
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+ # 012-microsoft-deberta-v3-base-finetuned-yahoo-8000_2000
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+
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+ This model is a fine-tuned version of [microsoft/deberta-v3-base](https://huggingface.co/microsoft/deberta-v3-base) on the None dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 0.9425
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+ - F1: 0.7138
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+ - Accuracy: 0.718
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+ - Precision: 0.7184
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+ - Recall: 0.718
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+ - System Ram Used: 4.1370
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+ - System Ram Total: 83.4807
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+ - Gpu Ram Allocated: 2.0897
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+ - Gpu Ram Cached: 25.8555
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+ - Gpu Ram Total: 39.5640
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+ - Gpu Utilization: 46
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+ - Disk Space Used: 29.6434
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+ - Disk Space Total: 78.1898
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+
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+ ## Model description
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+
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+ More information needed
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+
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+ ## Intended uses & limitations
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+
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+ More information needed
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+
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+ ## Training and evaluation data
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+
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+ More information needed
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+
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+ ## Training procedure
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+
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+ ### Training hyperparameters
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+
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+ The following hyperparameters were used during training:
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+ - learning_rate: 2e-05
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+ - train_batch_size: 32
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+ - eval_batch_size: 32
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+ - seed: 42
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+ - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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+ - lr_scheduler_type: linear
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+ - num_epochs: 5
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+
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+ ### Training results
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+
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+ | 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 |
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+ |:-------------:|:-----:|:----:|:---------------:|:------:|:--------:|:---------:|:------:|:---------------:|:----------------:|:-----------------:|:--------------:|:-------------:|:---------------:|:---------------:|:----------------:|
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+ | 2.2963 | 0.2 | 50 | 2.2150 | 0.1298 | 0.2015 | 0.2090 | 0.2015 | 3.9807 | 83.4807 | 2.0898 | 25.8457 | 39.5640 | 48 | 24.8073 | 78.1898 |
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+ | 1.8843 | 0.4 | 100 | 1.4590 | 0.5588 | 0.592 | 0.6418 | 0.592 | 3.9979 | 83.4807 | 2.0898 | 25.8477 | 39.5640 | 49 | 24.8074 | 78.1898 |
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+ | 1.3348 | 0.6 | 150 | 1.1809 | 0.6613 | 0.668 | 0.6736 | 0.668 | 3.9836 | 83.4807 | 2.0898 | 25.8555 | 39.5640 | 49 | 24.8074 | 78.1898 |
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+ | 1.1501 | 0.8 | 200 | 1.0484 | 0.6929 | 0.695 | 0.6981 | 0.695 | 3.9695 | 83.4807 | 2.0898 | 25.8555 | 39.5640 | 51 | 24.8074 | 78.1898 |
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+ | 1.0842 | 1.0 | 250 | 1.0265 | 0.6825 | 0.6905 | 0.6894 | 0.6905 | 3.9755 | 83.4807 | 2.0898 | 25.8555 | 39.5640 | 50 | 24.8075 | 78.1898 |
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+ | 0.8618 | 1.2 | 300 | 0.9904 | 0.7024 | 0.704 | 0.7048 | 0.704 | 3.9708 | 83.4807 | 2.0898 | 25.8555 | 39.5640 | 50 | 24.8075 | 78.1898 |
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+ | 0.9329 | 1.4 | 350 | 0.9927 | 0.6825 | 0.686 | 0.6939 | 0.686 | 3.9595 | 83.4807 | 2.0898 | 25.8555 | 39.5640 | 48 | 24.8076 | 78.1898 |
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+ | 0.9053 | 1.6 | 400 | 0.9795 | 0.7021 | 0.705 | 0.7048 | 0.705 | 3.9837 | 83.4807 | 2.0898 | 25.8555 | 39.5640 | 48 | 24.8076 | 78.1898 |
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+ | 0.9173 | 1.8 | 450 | 0.9749 | 0.7024 | 0.709 | 0.7140 | 0.709 | 3.9851 | 83.4807 | 2.0898 | 25.8555 | 39.5640 | 48 | 24.8077 | 78.1898 |
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+ | 0.9189 | 2.0 | 500 | 0.9425 | 0.7138 | 0.718 | 0.7184 | 0.718 | 3.9949 | 83.4807 | 2.0898 | 25.8555 | 39.5640 | 48 | 24.8077 | 78.1898 |
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+ | 0.7727 | 2.2 | 550 | 0.9590 | 0.7101 | 0.7155 | 0.7150 | 0.7155 | 4.1847 | 83.4807 | 2.0898 | 25.8555 | 39.5640 | 45 | 29.6429 | 78.1898 |
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+ | 0.7092 | 2.4 | 600 | 0.9389 | 0.7180 | 0.7215 | 0.7177 | 0.7215 | 4.1798 | 83.4807 | 2.0901 | 25.8555 | 39.5640 | 47 | 29.6429 | 78.1898 |
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+ | 0.737 | 2.6 | 650 | 0.9606 | 0.7074 | 0.715 | 0.7144 | 0.715 | 4.1766 | 83.4807 | 2.0898 | 25.8555 | 39.5640 | 51 | 29.6430 | 78.1898 |
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+ | 0.7334 | 2.8 | 700 | 0.9348 | 0.7175 | 0.72 | 0.7180 | 0.72 | 4.1699 | 83.4807 | 2.0898 | 25.8555 | 39.5640 | 50 | 29.6430 | 78.1898 |
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+ | 0.7316 | 3.0 | 750 | 0.9407 | 0.7230 | 0.7275 | 0.7238 | 0.7275 | 4.1785 | 83.4807 | 2.0898 | 25.8555 | 39.5640 | 50 | 29.6431 | 78.1898 |
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+ | 0.6045 | 3.2 | 800 | 0.9300 | 0.7208 | 0.721 | 0.7253 | 0.721 | 4.1864 | 83.4807 | 2.0898 | 25.8555 | 39.5640 | 48 | 29.6431 | 78.1898 |
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+ | 0.6262 | 3.4 | 850 | 0.9416 | 0.7165 | 0.7175 | 0.7184 | 0.7175 | 4.1847 | 83.4807 | 2.0898 | 25.8555 | 39.5640 | 49 | 29.6431 | 78.1898 |
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+ | 0.5999 | 3.6 | 900 | 0.9542 | 0.7155 | 0.718 | 0.7156 | 0.718 | 4.1891 | 83.4807 | 2.0898 | 25.8555 | 39.5640 | 47 | 29.6431 | 78.1898 |
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+ | 0.6436 | 3.8 | 950 | 0.9580 | 0.7085 | 0.7115 | 0.7127 | 0.7115 | 4.1644 | 83.4807 | 2.0898 | 25.8555 | 39.5640 | 49 | 29.6431 | 78.1898 |
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+ | 0.59 | 4.0 | 1000 | 0.9476 | 0.7209 | 0.723 | 0.7208 | 0.723 | 4.1608 | 83.4807 | 2.0898 | 25.8555 | 39.5640 | 47 | 29.6432 | 78.1898 |
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+ | 0.5422 | 4.2 | 1050 | 0.9658 | 0.7201 | 0.7205 | 0.7224 | 0.7205 | 4.1462 | 83.4807 | 2.0898 | 25.8555 | 39.5640 | 46 | 31.7150 | 78.1898 |
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+ | 0.5205 | 4.4 | 1100 | 0.9674 | 0.7122 | 0.7155 | 0.7128 | 0.7155 | 4.1598 | 83.4807 | 2.0898 | 25.8555 | 39.5640 | 49 | 31.7151 | 78.1898 |
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+ | 0.5253 | 4.6 | 1150 | 0.9563 | 0.7175 | 0.7195 | 0.7185 | 0.7195 | 4.1854 | 83.4807 | 2.0898 | 25.8555 | 39.5640 | 49 | 31.7151 | 78.1898 |
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+ | 0.5109 | 4.8 | 1200 | 0.9621 | 0.7201 | 0.722 | 0.7192 | 0.722 | 4.1908 | 83.4807 | 2.0898 | 25.8555 | 39.5640 | 49 | 31.7151 | 78.1898 |
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+ | 0.5216 | 5.0 | 1250 | 0.9635 | 0.7190 | 0.7215 | 0.7189 | 0.7215 | 4.1862 | 83.4807 | 2.0898 | 25.8555 | 39.5640 | 50 | 31.7151 | 78.1898 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.31.0
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+ - Pytorch 2.0.1+cu118
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+ - Datasets 2.13.1
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+ - Tokenizers 0.13.3