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--- |
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tags: |
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- generated_from_trainer |
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metrics: |
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- accuracy |
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model-index: |
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- name: sdss-cnn |
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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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# sdss-cnn |
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This model is a fine-tuned version of [](https://huggingface.co/) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.1573 |
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- Accuracy: 0.9505 |
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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: 0.0001 |
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- train_batch_size: 100 |
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- eval_batch_size: 100 |
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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: 40 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:| |
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| No log | 1.0 | 80 | 0.4954 | 0.8635 | |
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| No log | 2.0 | 160 | 0.2788 | 0.9055 | |
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| No log | 3.0 | 240 | 0.2239 | 0.9085 | |
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| No log | 4.0 | 320 | 0.1991 | 0.9325 | |
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| No log | 5.0 | 400 | 0.1954 | 0.94 | |
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| No log | 6.0 | 480 | 0.1854 | 0.9445 | |
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| 0.3543 | 7.0 | 560 | 0.1891 | 0.9375 | |
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| 0.3543 | 8.0 | 640 | 0.1777 | 0.943 | |
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| 0.3543 | 9.0 | 720 | 0.1780 | 0.9415 | |
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| 0.3543 | 10.0 | 800 | 0.1804 | 0.942 | |
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| 0.3543 | 11.0 | 880 | 0.1734 | 0.9475 | |
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| 0.3543 | 12.0 | 960 | 0.1689 | 0.947 | |
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| 0.2022 | 13.0 | 1040 | 0.1698 | 0.9445 | |
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| 0.2022 | 14.0 | 1120 | 0.1689 | 0.9405 | |
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| 0.2022 | 15.0 | 1200 | 0.1650 | 0.9475 | |
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| 0.2022 | 16.0 | 1280 | 0.1755 | 0.934 | |
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| 0.2022 | 17.0 | 1360 | 0.1635 | 0.944 | |
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| 0.2022 | 18.0 | 1440 | 0.1711 | 0.942 | |
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| 0.1836 | 19.0 | 1520 | 0.1604 | 0.9485 | |
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| 0.1836 | 20.0 | 1600 | 0.1595 | 0.95 | |
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| 0.1836 | 21.0 | 1680 | 0.1613 | 0.9475 | |
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| 0.1836 | 22.0 | 1760 | 0.1579 | 0.949 | |
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| 0.1836 | 23.0 | 1840 | 0.1593 | 0.946 | |
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| 0.1836 | 24.0 | 1920 | 0.1579 | 0.945 | |
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| 0.167 | 25.0 | 2000 | 0.1584 | 0.9495 | |
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| 0.167 | 26.0 | 2080 | 0.1573 | 0.9505 | |
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| 0.167 | 27.0 | 2160 | 0.1596 | 0.945 | |
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| 0.167 | 28.0 | 2240 | 0.1599 | 0.9435 | |
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| 0.167 | 29.0 | 2320 | 0.1565 | 0.9485 | |
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| 0.167 | 30.0 | 2400 | 0.1582 | 0.946 | |
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| 0.167 | 31.0 | 2480 | 0.1563 | 0.95 | |
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| 0.1568 | 32.0 | 2560 | 0.1563 | 0.95 | |
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| 0.1568 | 33.0 | 2640 | 0.1573 | 0.9495 | |
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| 0.1568 | 34.0 | 2720 | 0.1564 | 0.9465 | |
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| 0.1568 | 35.0 | 2800 | 0.1557 | 0.95 | |
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| 0.1568 | 36.0 | 2880 | 0.1554 | 0.949 | |
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| 0.1568 | 37.0 | 2960 | 0.1562 | 0.948 | |
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| 0.1515 | 38.0 | 3040 | 0.1555 | 0.948 | |
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| 0.1515 | 39.0 | 3120 | 0.1557 | 0.95 | |
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| 0.1515 | 40.0 | 3200 | 0.1559 | 0.9485 | |
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### Framework versions |
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- Transformers 4.33.3 |
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- Pytorch 2.0.1+cu118 |
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- Datasets 2.14.5 |
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- Tokenizers 0.13.3 |
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