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--- |
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language: |
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- eng |
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license: wtfpl |
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tags: |
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- multilabel-image-classification |
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- multilabel |
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- generated_from_trainer |
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base_model: facebook/dinov2-large |
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model-index: |
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- name: drone-DinoVdeau-large-2024_09_17-batch-size64_epochs100_freeze |
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results: [] |
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--- |
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DinoVd'eau is a fine-tuned version of [facebook/dinov2-large](https://huggingface.co/facebook/dinov2-large). It achieves the following results on the test set: |
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- Explained variance: 0.4014 |
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- Loss: 0.3578 |
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- MAE: 0.1288 |
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- MSE: 0.0378 |
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- R2: 0.4008 |
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- RMSE: 0.1943 |
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--- |
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# Model description |
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DinoVd'eau is a model built on top of dinov2 model for underwater multilabel image classification.The classification head is a combination of linear, ReLU, batch normalization, and dropout layers. |
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The source code for training the model can be found in this [Git repository](https://github.com/SeatizenDOI/DinoVdeau). |
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- **Developed by:** [lombardata](https://huggingface.co/lombardata), credits to [César Leblanc](https://huggingface.co/CesarLeblanc) and [Victor Illien](https://huggingface.co/groderg) |
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# Intended uses & limitations |
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You can use the raw model for classify diverse marine species, encompassing coral morphotypes classes taken from the Global Coral Reef Monitoring Network (GCRMN), habitats classes and seagrass species. |
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--- |
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# Training and evaluation data |
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Details on the number of images for each class are given in the following table: |
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| Class | train | val | test | Total | |
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|:------------------------|--------:|------:|-------:|--------:| |
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| Acropore_branched | 1956 | 651 | 652 | 3259 | |
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| Acropore_digitised | 1717 | 576 | 576 | 2869 | |
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| Acropore_tabular | 1105 | 384 | 379 | 1868 | |
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| Algae | 11092 | 3677 | 3674 | 18443 | |
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| Dead_coral | 5888 | 1952 | 1959 | 9799 | |
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| Fish | 3453 | 1157 | 1157 | 5767 | |
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| Millepore | 1760 | 690 | 693 | 3143 | |
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| No_acropore_encrusting | 2707 | 974 | 999 | 4680 | |
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| No_acropore_massive | 6487 | 2158 | 2167 | 10812 | |
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| No_acropore_sub_massive | 5015 | 1776 | 1776 | 8567 | |
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| Rock | 11176 | 3725 | 3725 | 18626 | |
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| Rubble | 10689 | 3563 | 3563 | 17815 | |
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| Sand | 11168 | 3723 | 3723 | 18614 | |
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--- |
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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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- **Number of Epochs**: 100 |
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- **Learning Rate**: 0.001 |
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- **Train Batch Size**: 64 |
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- **Eval Batch Size**: 64 |
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- **Optimizer**: Adam |
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- **LR Scheduler Type**: ReduceLROnPlateau with a patience of 5 epochs and a factor of 0.1 |
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- **Freeze Encoder**: Yes |
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- **Data Augmentation**: Yes |
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## Data Augmentation |
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Data were augmented using the following transformations : |
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Train Transforms |
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- **PreProcess**: No additional parameters |
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- **Resize**: probability=1.00 |
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- **RandomHorizontalFlip**: probability=0.25 |
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- **RandomVerticalFlip**: probability=0.25 |
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- **ColorJiggle**: probability=0.25 |
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- **RandomPerspective**: probability=0.25 |
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- **Normalize**: probability=1.00 |
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Val Transforms |
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- **PreProcess**: No additional parameters |
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- **Resize**: probability=1.00 |
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- **Normalize**: probability=1.00 |
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## Training results |
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Epoch | Explained Variance | Validation Loss | MAE | MSE | R2 | RMSE | Learning Rate |
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--- | --- | --- | --- | --- | --- | --- | --- |
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1 | 0.28 | 0.386 | 0.157 | 0.046 | 0.262 | 0.215 | 0.001 |
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2 | 0.321 | 0.376 | 0.147 | 0.044 | 0.312 | 0.21 | 0.001 |
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3 | 0.339 | 0.372 | 0.145 | 0.043 | 0.332 | 0.206 | 0.001 |
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4 | 0.357 | 0.367 | 0.14 | 0.041 | 0.355 | 0.202 | 0.001 |
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5 | 0.349 | 0.369 | 0.139 | 0.042 | 0.343 | 0.205 | 0.001 |
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6 | 0.359 | 0.367 | 0.141 | 0.041 | 0.355 | 0.202 | 0.001 |
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7 | 0.35 | 0.368 | 0.141 | 0.042 | 0.346 | 0.204 | 0.001 |
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8 | 0.364 | 0.366 | 0.139 | 0.041 | 0.36 | 0.201 | 0.001 |
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9 | 0.361 | 0.366 | 0.134 | 0.041 | 0.355 | 0.202 | 0.001 |
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10 | 0.356 | 0.367 | 0.138 | 0.041 | 0.353 | 0.202 | 0.001 |
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11 | 0.357 | 0.367 | 0.137 | 0.041 | 0.355 | 0.202 | 0.001 |
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12 | 0.36 | 0.366 | 0.14 | 0.041 | 0.359 | 0.202 | 0.001 |
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13 | 0.37 | 0.363 | 0.136 | 0.04 | 0.37 | 0.199 | 0.001 |
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14 | 0.363 | 0.367 | 0.142 | 0.041 | 0.356 | 0.202 | 0.001 |
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15 | 0.364 | 0.364 | 0.14 | 0.04 | 0.362 | 0.201 | 0.001 |
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16 | 0.372 | 0.364 | 0.136 | 0.04 | 0.369 | 0.2 | 0.001 |
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17 | 0.373 | 0.367 | 0.141 | 0.041 | 0.362 | 0.202 | 0.001 |
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18 | 0.371 | 0.363 | 0.137 | 0.04 | 0.37 | 0.2 | 0.001 |
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19 | 0.373 | 0.363 | 0.135 | 0.04 | 0.372 | 0.199 | 0.001 |
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20 | 0.362 | 0.365 | 0.135 | 0.041 | 0.359 | 0.201 | 0.001 |
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21 | 0.363 | 0.367 | 0.136 | 0.041 | 0.358 | 0.202 | 0.001 |
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22 | 0.37 | 0.365 | 0.137 | 0.04 | 0.368 | 0.2 | 0.001 |
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23 | 0.374 | 0.363 | 0.136 | 0.04 | 0.37 | 0.2 | 0.001 |
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24 | 0.376 | 0.363 | 0.139 | 0.04 | 0.373 | 0.199 | 0.001 |
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25 | 0.373 | 0.364 | 0.138 | 0.04 | 0.37 | 0.2 | 0.001 |
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26 | 0.384 | 0.361 | 0.133 | 0.039 | 0.382 | 0.198 | 0.0001 |
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27 | 0.388 | 0.36 | 0.135 | 0.039 | 0.386 | 0.197 | 0.0001 |
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28 | 0.39 | 0.359 | 0.134 | 0.038 | 0.389 | 0.196 | 0.0001 |
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29 | 0.391 | 0.36 | 0.135 | 0.038 | 0.389 | 0.196 | 0.0001 |
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30 | 0.389 | 0.36 | 0.135 | 0.039 | 0.388 | 0.197 | 0.0001 |
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31 | 0.392 | 0.359 | 0.132 | 0.038 | 0.391 | 0.196 | 0.0001 |
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32 | 0.393 | 0.358 | 0.133 | 0.038 | 0.393 | 0.196 | 0.0001 |
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33 | 0.395 | 0.358 | 0.131 | 0.038 | 0.395 | 0.195 | 0.0001 |
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34 | 0.397 | 0.358 | 0.132 | 0.038 | 0.395 | 0.195 | 0.0001 |
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35 | 0.395 | 0.358 | 0.132 | 0.038 | 0.395 | 0.195 | 0.0001 |
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36 | 0.39 | 0.359 | 0.135 | 0.039 | 0.39 | 0.196 | 0.0001 |
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37 | 0.397 | 0.358 | 0.131 | 0.038 | 0.397 | 0.195 | 0.0001 |
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38 | 0.394 | 0.358 | 0.133 | 0.038 | 0.392 | 0.196 | 0.0001 |
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39 | 0.397 | 0.358 | 0.131 | 0.038 | 0.396 | 0.195 | 0.0001 |
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40 | 0.4 | 0.357 | 0.133 | 0.038 | 0.398 | 0.195 | 0.0001 |
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41 | 0.399 | 0.358 | 0.132 | 0.038 | 0.396 | 0.195 | 0.0001 |
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42 | 0.399 | 0.357 | 0.133 | 0.038 | 0.397 | 0.195 | 0.0001 |
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43 | 0.402 | 0.357 | 0.133 | 0.038 | 0.401 | 0.194 | 0.0001 |
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44 | 0.403 | 0.357 | 0.131 | 0.038 | 0.401 | 0.194 | 0.0001 |
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45 | 0.403 | 0.357 | 0.132 | 0.038 | 0.402 | 0.194 | 0.0001 |
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46 | 0.401 | 0.357 | 0.13 | 0.038 | 0.4 | 0.194 | 0.0001 |
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47 | 0.4 | 0.357 | 0.129 | 0.038 | 0.397 | 0.195 | 0.0001 |
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48 | 0.404 | 0.356 | 0.13 | 0.038 | 0.402 | 0.194 | 0.0001 |
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49 | 0.402 | 0.357 | 0.131 | 0.038 | 0.401 | 0.194 | 0.0001 |
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50 | 0.401 | 0.357 | 0.132 | 0.038 | 0.4 | 0.194 | 0.0001 |
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51 | 0.402 | 0.358 | 0.134 | 0.038 | 0.396 | 0.195 | 0.0001 |
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52 | 0.405 | 0.356 | 0.131 | 0.037 | 0.404 | 0.194 | 0.0001 |
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53 | 0.405 | 0.357 | 0.131 | 0.038 | 0.403 | 0.194 | 0.0001 |
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54 | 0.402 | 0.357 | 0.132 | 0.038 | 0.401 | 0.194 | 0.0001 |
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55 | 0.405 | 0.356 | 0.129 | 0.038 | 0.403 | 0.194 | 0.0001 |
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56 | 0.405 | 0.357 | 0.128 | 0.038 | 0.402 | 0.194 | 0.0001 |
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57 | 0.405 | 0.356 | 0.129 | 0.038 | 0.403 | 0.194 | 0.0001 |
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58 | 0.406 | 0.356 | 0.13 | 0.038 | 0.404 | 0.194 | 0.0001 |
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59 | 0.406 | 0.356 | 0.129 | 0.037 | 0.405 | 0.194 | 1e-05 |
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60 | 0.408 | 0.356 | 0.13 | 0.037 | 0.406 | 0.193 | 1e-05 |
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61 | 0.407 | 0.355 | 0.13 | 0.037 | 0.407 | 0.193 | 1e-05 |
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62 | 0.406 | 0.356 | 0.132 | 0.038 | 0.404 | 0.194 | 1e-05 |
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63 | 0.409 | 0.356 | 0.129 | 0.037 | 0.408 | 0.193 | 1e-05 |
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64 | 0.409 | 0.355 | 0.13 | 0.037 | 0.408 | 0.193 | 1e-05 |
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65 | 0.406 | 0.356 | 0.131 | 0.038 | 0.405 | 0.194 | 1e-05 |
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66 | 0.409 | 0.355 | 0.13 | 0.037 | 0.408 | 0.193 | 1e-05 |
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67 | 0.408 | 0.355 | 0.13 | 0.037 | 0.408 | 0.193 | 1e-05 |
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68 | 0.407 | 0.356 | 0.13 | 0.037 | 0.406 | 0.193 | 1e-05 |
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69 | 0.409 | 0.355 | 0.13 | 0.037 | 0.408 | 0.193 | 1e-05 |
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70 | 0.409 | 0.356 | 0.131 | 0.037 | 0.407 | 0.193 | 1e-05 |
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71 | 0.407 | 0.356 | 0.13 | 0.037 | 0.407 | 0.193 | 1e-05 |
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72 | 0.408 | 0.356 | 0.13 | 0.037 | 0.407 | 0.193 | 1e-05 |
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73 | 0.409 | 0.355 | 0.13 | 0.037 | 0.408 | 0.193 | 1.0000000000000002e-06 |
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74 | 0.409 | 0.355 | 0.128 | 0.037 | 0.409 | 0.193 | 1.0000000000000002e-06 |
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75 | 0.406 | 0.356 | 0.13 | 0.037 | 0.405 | 0.194 | 1.0000000000000002e-06 |
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76 | 0.408 | 0.356 | 0.128 | 0.037 | 0.406 | 0.193 | 1.0000000000000002e-06 |
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77 | 0.405 | 0.356 | 0.132 | 0.038 | 0.404 | 0.194 | 1.0000000000000002e-06 |
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78 | 0.409 | 0.355 | 0.131 | 0.037 | 0.409 | 0.193 | 1.0000000000000002e-06 |
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79 | 0.402 | 0.357 | 0.131 | 0.038 | 0.4 | 0.195 | 1.0000000000000002e-06 |
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80 | 0.406 | 0.356 | 0.131 | 0.037 | 0.405 | 0.194 | 1.0000000000000002e-06 |
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81 | 0.409 | 0.356 | 0.131 | 0.037 | 0.408 | 0.193 | 1.0000000000000002e-07 |
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82 | 0.409 | 0.356 | 0.131 | 0.037 | 0.407 | 0.193 | 1.0000000000000002e-07 |
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83 | 0.41 | 0.356 | 0.13 | 0.037 | 0.407 | 0.193 | 1.0000000000000002e-07 |
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84 | 0.408 | 0.356 | 0.131 | 0.037 | 0.406 | 0.193 | 1.0000000000000002e-07 |
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# CO2 Emissions |
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The estimated CO2 emissions for training this model are documented below: |
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- **Emissions**: 0.22861184690098074 grams of CO2 |
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- **Source**: Code Carbon |
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- **Training Type**: fine-tuning |
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- **Geographical Location**: Brest, France |
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- **Hardware Used**: NVIDIA Tesla V100 PCIe 32 Go |
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# Framework Versions |
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- **Transformers**: 4.41.1 |
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- **Pytorch**: 2.3.0+cu121 |
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- **Datasets**: 2.19.1 |
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- **Tokenizers**: 0.19.1 |
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