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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: 008-microsoft-deberta-v3-base-finetuned-yahoo-800_200 |
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results: [] |
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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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# 008-microsoft-deberta-v3-base-finetuned-yahoo-800_200 |
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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: 1.1139 |
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- F1: 0.6463 |
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- Accuracy: 0.65 |
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- Precision: 0.6514 |
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- Recall: 0.65 |
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- System Ram Used: 4.2190 |
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- System Ram Total: 83.4807 |
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- Gpu Ram Allocated: 2.0914 |
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- Gpu Ram Cached: 24.6602 |
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- Gpu Ram Total: 39.5640 |
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- Gpu Utilization: 33 |
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- Disk Space Used: 31.6928 |
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- Disk Space Total: 78.1898 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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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: 10 |
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### Training results |
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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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| 2.2992 | 0.52 | 13 | 2.3031 | 0.0182 | 0.1 | 0.01 | 0.1 | 3.9340 | 83.4807 | 2.0915 | 24.6484 | 39.5640 | 50 | 24.7853 | 78.1898 | |
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| 2.3096 | 1.04 | 26 | 2.2984 | 0.0182 | 0.1 | 0.01 | 0.1 | 4.1195 | 83.4807 | 2.0915 | 24.6602 | 39.5640 | 43 | 29.6206 | 78.1898 | |
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| 2.2906 | 1.56 | 39 | 2.2852 | 0.0648 | 0.145 | 0.0525 | 0.145 | 4.2050 | 83.4807 | 2.0915 | 24.6602 | 39.5640 | 51 | 29.6206 | 78.1898 | |
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| 2.2723 | 2.08 | 52 | 2.2198 | 0.1283 | 0.225 | 0.1625 | 0.225 | 4.2165 | 83.4807 | 2.0915 | 24.6602 | 39.5640 | 43 | 31.6924 | 78.1898 | |
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| 2.1387 | 2.6 | 65 | 2.0293 | 0.2580 | 0.335 | 0.2655 | 0.335 | 4.2218 | 83.4807 | 2.0916 | 24.6602 | 39.5640 | 56 | 31.6925 | 78.1898 | |
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| 1.9534 | 3.12 | 78 | 1.8757 | 0.3730 | 0.4 | 0.4419 | 0.4 | 4.2092 | 83.4807 | 2.0915 | 24.6602 | 39.5640 | 41 | 31.6925 | 78.1898 | |
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| 1.7689 | 3.64 | 91 | 1.7209 | 0.4443 | 0.48 | 0.5198 | 0.48 | 4.2303 | 83.4807 | 2.0915 | 24.6602 | 39.5640 | 46 | 31.6925 | 78.1898 | |
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| 1.6052 | 4.16 | 104 | 1.6318 | 0.5044 | 0.525 | 0.5139 | 0.525 | 4.2297 | 83.4807 | 2.0915 | 24.6602 | 39.5640 | 45 | 31.6926 | 78.1898 | |
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| 1.4606 | 4.68 | 117 | 1.4969 | 0.5539 | 0.575 | 0.5788 | 0.575 | 4.2315 | 83.4807 | 2.0915 | 24.6602 | 39.5640 | 47 | 31.6926 | 78.1898 | |
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| 1.2963 | 5.2 | 130 | 1.3920 | 0.6037 | 0.61 | 0.6063 | 0.61 | 4.2420 | 83.4807 | 2.0916 | 24.6602 | 39.5640 | 43 | 31.6926 | 78.1898 | |
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| 1.1948 | 5.72 | 143 | 1.3030 | 0.6251 | 0.63 | 0.6292 | 0.63 | 4.2687 | 83.4807 | 2.0915 | 24.6602 | 39.5640 | 48 | 31.6926 | 78.1898 | |
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| 1.0248 | 6.24 | 156 | 1.2568 | 0.6184 | 0.625 | 0.6354 | 0.625 | 4.2596 | 83.4807 | 2.0915 | 24.6602 | 39.5640 | 50 | 31.6927 | 78.1898 | |
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| 0.9509 | 6.76 | 169 | 1.1911 | 0.6448 | 0.65 | 0.6552 | 0.65 | 4.2625 | 83.4807 | 2.0915 | 24.6602 | 39.5640 | 44 | 31.6927 | 78.1898 | |
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| 0.9081 | 7.28 | 182 | 1.1784 | 0.6441 | 0.655 | 0.6450 | 0.655 | 4.1955 | 83.4807 | 2.0915 | 24.6602 | 39.5640 | 50 | 31.6927 | 78.1898 | |
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| 0.7629 | 7.8 | 195 | 1.1354 | 0.6598 | 0.655 | 0.6737 | 0.655 | 4.1868 | 83.4807 | 2.0915 | 24.6602 | 39.5640 | 44 | 31.6927 | 78.1898 | |
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| 0.7348 | 8.32 | 208 | 1.1369 | 0.6430 | 0.65 | 0.6483 | 0.65 | 4.2168 | 83.4807 | 2.0915 | 24.6602 | 39.5640 | 43 | 31.6927 | 78.1898 | |
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| 0.7443 | 8.84 | 221 | 1.1274 | 0.6531 | 0.66 | 0.6576 | 0.66 | 4.2273 | 83.4807 | 2.0915 | 24.6602 | 39.5640 | 51 | 31.6927 | 78.1898 | |
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| 0.5945 | 9.36 | 234 | 1.1228 | 0.6640 | 0.67 | 0.6694 | 0.67 | 4.1791 | 83.4807 | 2.0915 | 24.6602 | 39.5640 | 44 | 31.6928 | 78.1898 | |
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| 0.6885 | 9.88 | 247 | 1.1145 | 0.6463 | 0.65 | 0.6514 | 0.65 | 4.1849 | 83.4807 | 2.0915 | 24.6602 | 39.5640 | 48 | 31.6928 | 78.1898 | |
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### Framework versions |
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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 |
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