Upload README.md

d5b06a4 verified about 2 months ago

No virus

7.7 kB


	---
	language:
	- eng
	license: wtfpl
	tags:
	- multilabel-image-classification
	- multilabel
	- generated_from_trainer
	base_model: facebook/dinov2-large
	model-index:
	- name: drone-DinoVdeau-large-2024_07_31-batch-size8_epochs100_freeze
	results: []
	---

	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:

	- Explained variance: 0.3552
	- Loss: 0.3286
	- MAE: 0.1261
	- MSE: 0.0374
	- R2: 0.3545
	- RMSE: 0.1933

	---

	# Model description
	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.

	The source code for training the model can be found in this [Git repository](https://github.com/SeatizenDOI/DinoVdeau).

	- Developed by: [lombardata](https://huggingface.co/lombardata), credits to [César Leblanc](https://huggingface.co/CesarLeblanc) and [Victor Illien](https://huggingface.co/groderg)

	---

	# Intended uses & limitations
	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.

	---

	# Training and evaluation data
	Details on the number of images for each class are given in the following table:
	\| Class \| train \| val \| test \| Total \|
	\|:------------------------\|--------:\|------:\|-------:\|--------:\|
	\| Acropore_branched \| 2371 \| 782 \| 785 \| 3938 \|
	\| Acropore_digitised \| 1693 \| 580 \| 579 \| 2852 \|
	\| Acropore_sub_massive \| 353 \| 99 \| 97 \| 549 \|
	\| Acropore_tabular \| 1112 \| 420 \| 410 \| 1942 \|
	\| Algae \| 13150 \| 4386 \| 4405 \| 21941 \|
	\| Dead_coral \| 6824 \| 2242 \| 2250 \| 11316 \|
	\| Millepore \| 1543 \| 611 \| 631 \| 2785 \|
	\| No_acropore_encrusting \| 2799 \| 1044 \| 1041 \| 4884 \|
	\| No_acropore_massive \| 6578 \| 2216 \| 2170 \| 10964 \|
	\| No_acropore_sub_massive \| 5252 \| 1802 \| 1793 \| 8847 \|
	\| Rock \| 13532 \| 4529 \| 4529 \| 22590 \|
	\| Rubble \| 12641 \| 4222 \| 4231 \| 21094 \|
	\| Sand \| 13315 \| 4438 \| 4438 \| 22191 \|

	---

	# Training procedure

	## Training hyperparameters

	The following hyperparameters were used during training:

	- Number of Epochs: 100
	- Learning Rate: 0.001
	- Train Batch Size: 8
	- Eval Batch Size: 8
	- Optimizer: Adam
	- LR Scheduler Type: ReduceLROnPlateau with a patience of 5 epochs and a factor of 0.1
	- Freeze Encoder: Yes
	- Data Augmentation: Yes


	## Data Augmentation
	Data were augmented using the following transformations :

	Train Transforms
	- PreProcess: No additional parameters
	- Resize: probability=1.00
	- RandomHorizontalFlip: probability=0.25
	- RandomVerticalFlip: probability=0.25
	- ColorJiggle: probability=0.25
	- RandomPerspective: probability=0.25
	- Normalize: probability=1.00

	Val Transforms
	- PreProcess: No additional parameters
	- Resize: probability=1.00
	- Normalize: probability=1.00



	## Training results
	Epoch \| Explained Variance \| Validation Loss \| MAE \| MSE \| R2 \| RMSE \| Learning Rate
	--- \| --- \| --- \| --- \| --- \| --- \| --- \| ---
	0 \| 0 \| 0 \| 0 \| 0 \| 0 \| 0 \| 0.001
	1 \| 0.233 \| 0.353 \| 0.135 \| 0.045 \| 0.228 \| 0.212 \| 0.001
	2 \| 0.242 \| 0.354 \| 0.145 \| 0.046 \| 0.232 \| 0.213 \| 0.001
	3 \| 0.264 \| 0.35 \| 0.141 \| 0.044 \| 0.26 \| 0.209 \| 0.001
	4 \| 0.273 \| 0.347 \| 0.14 \| 0.043 \| 0.271 \| 0.208 \| 0.001
	5 \| 0.257 \| 0.365 \| 0.139 \| 0.043 \| 0.256 \| 0.208 \| 0.001
	6 \| 0.228 \| 0.37 \| 0.14 \| 0.044 \| 0.226 \| 0.209 \| 0.001
	7 \| 0.28 \| 0.357 \| 0.137 \| 0.042 \| 0.278 \| 0.206 \| 0.001
	8 \| 0.287 \| 0.345 \| 0.134 \| 0.042 \| 0.282 \| 0.205 \| 0.001
	9 \| 0.28 \| 0.345 \| 0.139 \| 0.043 \| 0.278 \| 0.206 \| 0.001
	10 \| 0.282 \| 0.386 \| 0.133 \| 0.043 \| 0.279 \| 0.206 \| 0.001
	11 \| 0.28 \| 0.347 \| 0.137 \| 0.043 \| 0.276 \| 0.207 \| 0.001
	12 \| 0.285 \| 0.351 \| 0.137 \| 0.042 \| 0.28 \| 0.206 \| 0.001
	13 \| 0.277 \| 0.397 \| 0.136 \| 0.043 \| 0.275 \| 0.207 \| 0.001
	14 \| 0.276 \| 0.359 \| 0.137 \| 0.043 \| 0.271 \| 0.208 \| 0.001
	15 \| 0.276 \| 0.349 \| 0.136 \| 0.042 \| 0.274 \| 0.206 \| 0.001
	16 \| 0.303 \| 0.339 \| 0.133 \| 0.041 \| 0.303 \| 0.202 \| 0.0001
	17 \| 0.306 \| 0.338 \| 0.134 \| 0.04 \| 0.306 \| 0.201 \| 0.0001
	18 \| 0.31 \| 0.337 \| 0.132 \| 0.04 \| 0.308 \| 0.2 \| 0.0001
	19 \| 0.307 \| 0.338 \| 0.131 \| 0.04 \| 0.305 \| 0.201 \| 0.0001
	20 \| 0.312 \| 0.336 \| 0.131 \| 0.04 \| 0.311 \| 0.2 \| 0.0001
	21 \| 0.312 \| 0.337 \| 0.129 \| 0.04 \| 0.308 \| 0.2 \| 0.0001
	22 \| 0.316 \| 0.336 \| 0.132 \| 0.04 \| 0.315 \| 0.199 \| 0.0001
	23 \| 0.32 \| 0.335 \| 0.131 \| 0.039 \| 0.319 \| 0.199 \| 0.0001
	24 \| 0.319 \| 0.335 \| 0.13 \| 0.04 \| 0.318 \| 0.199 \| 0.0001
	25 \| 0.324 \| 0.334 \| 0.13 \| 0.039 \| 0.323 \| 0.198 \| 0.0001
	26 \| 0.32 \| 0.335 \| 0.13 \| 0.04 \| 0.318 \| 0.199 \| 0.0001
	27 \| 0.321 \| 0.335 \| 0.13 \| 0.039 \| 0.32 \| 0.198 \| 0.0001
	28 \| 0.326 \| 0.334 \| 0.127 \| 0.039 \| 0.321 \| 0.198 \| 0.0001
	29 \| 0.33 \| 0.333 \| 0.129 \| 0.039 \| 0.328 \| 0.197 \| 0.0001
	30 \| 0.33 \| 0.333 \| 0.13 \| 0.039 \| 0.33 \| 0.197 \| 0.0001
	31 \| 0.328 \| 0.333 \| 0.13 \| 0.039 \| 0.325 \| 0.198 \| 0.0001
	32 \| 0.331 \| 0.332 \| 0.128 \| 0.039 \| 0.33 \| 0.197 \| 0.0001
	33 \| 0.334 \| 0.333 \| 0.13 \| 0.039 \| 0.331 \| 0.196 \| 0.0001
	34 \| 0.33 \| 0.333 \| 0.129 \| 0.039 \| 0.328 \| 0.197 \| 0.0001
	35 \| 0.325 \| 0.334 \| 0.131 \| 0.039 \| 0.324 \| 0.198 \| 0.0001
	36 \| 0.337 \| 0.332 \| 0.13 \| 0.038 \| 0.337 \| 0.196 \| 0.0001
	37 \| 0.328 \| 0.334 \| 0.13 \| 0.039 \| 0.327 \| 0.197 \| 0.0001
	38 \| 0.338 \| 0.332 \| 0.129 \| 0.038 \| 0.336 \| 0.196 \| 0.0001
	39 \| 0.338 \| 0.332 \| 0.128 \| 0.038 \| 0.338 \| 0.196 \| 0.0001
	40 \| 0.337 \| 0.332 \| 0.129 \| 0.038 \| 0.336 \| 0.196 \| 0.0001
	41 \| 0.335 \| 0.333 \| 0.131 \| 0.039 \| 0.333 \| 0.196 \| 0.0001
	42 \| 0.338 \| 0.332 \| 0.129 \| 0.038 \| 0.337 \| 0.196 \| 0.0001
	43 \| 0.338 \| 0.331 \| 0.129 \| 0.038 \| 0.338 \| 0.196 \| 0.0001
	44 \| 0.336 \| 0.333 \| 0.128 \| 0.039 \| 0.335 \| 0.196 \| 0.0001
	45 \| 0.339 \| 0.331 \| 0.128 \| 0.038 \| 0.338 \| 0.196 \| 0.0001
	46 \| 0.341 \| 0.332 \| 0.129 \| 0.038 \| 0.339 \| 0.195 \| 0.0001
	47 \| 0.34 \| 0.331 \| 0.127 \| 0.038 \| 0.339 \| 0.196 \| 0.0001
	48 \| 0.299 \| 0.339 \| 0.131 \| 0.039 \| 0.295 \| 0.199 \| 0.0001
	49 \| 0.338 \| 0.331 \| 0.128 \| 0.038 \| 0.337 \| 0.196 \| 0.0001
	50 \| 0.342 \| 0.332 \| 0.127 \| 0.038 \| 0.339 \| 0.196 \| 0.0001
	51 \| 0.341 \| 0.331 \| 0.127 \| 0.038 \| 0.341 \| 0.195 \| 0.0001
	52 \| 0.345 \| 0.33 \| 0.127 \| 0.038 \| 0.344 \| 0.195 \| 0.0001
	53 \| 0.34 \| 0.331 \| 0.128 \| 0.038 \| 0.339 \| 0.196 \| 0.0001
	54 \| 0.341 \| 0.331 \| 0.129 \| 0.038 \| 0.34 \| 0.196 \| 0.0001
	55 \| 0.349 \| 0.329 \| 0.127 \| 0.038 \| 0.349 \| 0.194 \| 0.0001
	56 \| 0.344 \| 0.33 \| 0.126 \| 0.038 \| 0.343 \| 0.195 \| 0.0001
	57 \| 0.341 \| 0.331 \| 0.126 \| 0.038 \| 0.339 \| 0.196 \| 0.0001
	58 \| 0.348 \| 0.33 \| 0.126 \| 0.038 \| 0.347 \| 0.194 \| 0.0001
	59 \| 0.343 \| 0.332 \| 0.128 \| 0.038 \| 0.341 \| 0.195 \| 0.0001
	60 \| 0.346 \| 0.331 \| 0.128 \| 0.038 \| 0.345 \| 0.195 \| 0.0001
	61 \| 0.346 \| 0.33 \| 0.125 \| 0.038 \| 0.344 \| 0.195 \| 0.0001
	62 \| 0.347 \| 0.329 \| 0.126 \| 0.038 \| 0.346 \| 0.194 \| 1e-05
	63 \| 0.35 \| 0.33 \| 0.128 \| 0.038 \| 0.348 \| 0.194 \| 1e-05
	64 \| 0.345 \| 0.33 \| 0.126 \| 0.038 \| 0.344 \| 0.195 \| 1e-05
	65 \| 0.349 \| 0.33 \| 0.128 \| 0.038 \| 0.347 \| 0.195 \| 1e-05


	---

	# CO2 Emissions

	The estimated CO2 emissions for training this model are documented below:

	- Emissions: 0.19095786836275294 grams of CO2
	- Source: Code Carbon
	- Training Type: fine-tuning
	- Geographical Location: Brest, France
	- Hardware Used: NVIDIA Tesla V100 PCIe 32 Go


	---

	# Framework Versions

	- Transformers: 4.41.1
	- Pytorch: 2.3.0+cu121
	- Datasets: 2.19.1
	- Tokenizers: 0.19.1


	---
	language:
	- eng
	license: wtfpl
	tags:
	- multilabel-image-classification
	- multilabel
	- generated_from_trainer
	base_model: facebook/dinov2-large
	model-index:
	- name: drone-DinoVdeau-large-2024_07_31-batch-size8_epochs100_freeze
	results: []
	---

	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:

	- Explained variance: 0.3552
	- Loss: 0.3286
	- MAE: 0.1261
	- MSE: 0.0374
	- R2: 0.3545
	- RMSE: 0.1933

	---

	# Model description
	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.

	The source code for training the model can be found in this [Git repository](https://github.com/SeatizenDOI/DinoVdeau).

	- Developed by: [lombardata](https://huggingface.co/lombardata), credits to [César Leblanc](https://huggingface.co/CesarLeblanc) and [Victor Illien](https://huggingface.co/groderg)

	---

	# Intended uses & limitations
	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.

	---

	# Training and evaluation data
	Details on the number of images for each class are given in the following table:
	\| Class \| train \| val \| test \| Total \|
	\|:------------------------\|--------:\|------:\|-------:\|--------:\|
	\| Acropore_branched \| 2371 \| 782 \| 785 \| 3938 \|
	\| Acropore_digitised \| 1693 \| 580 \| 579 \| 2852 \|
	\| Acropore_sub_massive \| 353 \| 99 \| 97 \| 549 \|
	\| Acropore_tabular \| 1112 \| 420 \| 410 \| 1942 \|
	\| Algae \| 13150 \| 4386 \| 4405 \| 21941 \|
	\| Dead_coral \| 6824 \| 2242 \| 2250 \| 11316 \|
	\| Millepore \| 1543 \| 611 \| 631 \| 2785 \|
	\| No_acropore_encrusting \| 2799 \| 1044 \| 1041 \| 4884 \|
	\| No_acropore_massive \| 6578 \| 2216 \| 2170 \| 10964 \|
	\| No_acropore_sub_massive \| 5252 \| 1802 \| 1793 \| 8847 \|
	\| Rock \| 13532 \| 4529 \| 4529 \| 22590 \|
	\| Rubble \| 12641 \| 4222 \| 4231 \| 21094 \|
	\| Sand \| 13315 \| 4438 \| 4438 \| 22191 \|

	---

	# Training procedure

	## Training hyperparameters

	The following hyperparameters were used during training:

	- Number of Epochs: 100
	- Learning Rate: 0.001
	- Train Batch Size: 8
	- Eval Batch Size: 8
	- Optimizer: Adam
	- LR Scheduler Type: ReduceLROnPlateau with a patience of 5 epochs and a factor of 0.1
	- Freeze Encoder: Yes
	- Data Augmentation: Yes


	## Data Augmentation
	Data were augmented using the following transformations :

	Train Transforms
	- PreProcess: No additional parameters
	- Resize: probability=1.00
	- RandomHorizontalFlip: probability=0.25
	- RandomVerticalFlip: probability=0.25
	- ColorJiggle: probability=0.25
	- RandomPerspective: probability=0.25
	- Normalize: probability=1.00

	Val Transforms
	- PreProcess: No additional parameters
	- Resize: probability=1.00
	- Normalize: probability=1.00



	## Training results
	Epoch \| Explained Variance \| Validation Loss \| MAE \| MSE \| R2 \| RMSE \| Learning Rate
	--- \| --- \| --- \| --- \| --- \| --- \| --- \| ---
	0 \| 0 \| 0 \| 0 \| 0 \| 0 \| 0 \| 0.001
	1 \| 0.233 \| 0.353 \| 0.135 \| 0.045 \| 0.228 \| 0.212 \| 0.001
	2 \| 0.242 \| 0.354 \| 0.145 \| 0.046 \| 0.232 \| 0.213 \| 0.001
	3 \| 0.264 \| 0.35 \| 0.141 \| 0.044 \| 0.26 \| 0.209 \| 0.001
	4 \| 0.273 \| 0.347 \| 0.14 \| 0.043 \| 0.271 \| 0.208 \| 0.001
	5 \| 0.257 \| 0.365 \| 0.139 \| 0.043 \| 0.256 \| 0.208 \| 0.001
	6 \| 0.228 \| 0.37 \| 0.14 \| 0.044 \| 0.226 \| 0.209 \| 0.001
	7 \| 0.28 \| 0.357 \| 0.137 \| 0.042 \| 0.278 \| 0.206 \| 0.001
	8 \| 0.287 \| 0.345 \| 0.134 \| 0.042 \| 0.282 \| 0.205 \| 0.001
	9 \| 0.28 \| 0.345 \| 0.139 \| 0.043 \| 0.278 \| 0.206 \| 0.001
	10 \| 0.282 \| 0.386 \| 0.133 \| 0.043 \| 0.279 \| 0.206 \| 0.001
	11 \| 0.28 \| 0.347 \| 0.137 \| 0.043 \| 0.276 \| 0.207 \| 0.001
	12 \| 0.285 \| 0.351 \| 0.137 \| 0.042 \| 0.28 \| 0.206 \| 0.001
	13 \| 0.277 \| 0.397 \| 0.136 \| 0.043 \| 0.275 \| 0.207 \| 0.001
	14 \| 0.276 \| 0.359 \| 0.137 \| 0.043 \| 0.271 \| 0.208 \| 0.001
	15 \| 0.276 \| 0.349 \| 0.136 \| 0.042 \| 0.274 \| 0.206 \| 0.001
	16 \| 0.303 \| 0.339 \| 0.133 \| 0.041 \| 0.303 \| 0.202 \| 0.0001
	17 \| 0.306 \| 0.338 \| 0.134 \| 0.04 \| 0.306 \| 0.201 \| 0.0001
	18 \| 0.31 \| 0.337 \| 0.132 \| 0.04 \| 0.308 \| 0.2 \| 0.0001
	19 \| 0.307 \| 0.338 \| 0.131 \| 0.04 \| 0.305 \| 0.201 \| 0.0001
	20 \| 0.312 \| 0.336 \| 0.131 \| 0.04 \| 0.311 \| 0.2 \| 0.0001
	21 \| 0.312 \| 0.337 \| 0.129 \| 0.04 \| 0.308 \| 0.2 \| 0.0001
	22 \| 0.316 \| 0.336 \| 0.132 \| 0.04 \| 0.315 \| 0.199 \| 0.0001
	23 \| 0.32 \| 0.335 \| 0.131 \| 0.039 \| 0.319 \| 0.199 \| 0.0001
	24 \| 0.319 \| 0.335 \| 0.13 \| 0.04 \| 0.318 \| 0.199 \| 0.0001
	25 \| 0.324 \| 0.334 \| 0.13 \| 0.039 \| 0.323 \| 0.198 \| 0.0001
	26 \| 0.32 \| 0.335 \| 0.13 \| 0.04 \| 0.318 \| 0.199 \| 0.0001
	27 \| 0.321 \| 0.335 \| 0.13 \| 0.039 \| 0.32 \| 0.198 \| 0.0001
	28 \| 0.326 \| 0.334 \| 0.127 \| 0.039 \| 0.321 \| 0.198 \| 0.0001
	29 \| 0.33 \| 0.333 \| 0.129 \| 0.039 \| 0.328 \| 0.197 \| 0.0001
	30 \| 0.33 \| 0.333 \| 0.13 \| 0.039 \| 0.33 \| 0.197 \| 0.0001
	31 \| 0.328 \| 0.333 \| 0.13 \| 0.039 \| 0.325 \| 0.198 \| 0.0001
	32 \| 0.331 \| 0.332 \| 0.128 \| 0.039 \| 0.33 \| 0.197 \| 0.0001
	33 \| 0.334 \| 0.333 \| 0.13 \| 0.039 \| 0.331 \| 0.196 \| 0.0001
	34 \| 0.33 \| 0.333 \| 0.129 \| 0.039 \| 0.328 \| 0.197 \| 0.0001
	35 \| 0.325 \| 0.334 \| 0.131 \| 0.039 \| 0.324 \| 0.198 \| 0.0001
	36 \| 0.337 \| 0.332 \| 0.13 \| 0.038 \| 0.337 \| 0.196 \| 0.0001
	37 \| 0.328 \| 0.334 \| 0.13 \| 0.039 \| 0.327 \| 0.197 \| 0.0001
	38 \| 0.338 \| 0.332 \| 0.129 \| 0.038 \| 0.336 \| 0.196 \| 0.0001
	39 \| 0.338 \| 0.332 \| 0.128 \| 0.038 \| 0.338 \| 0.196 \| 0.0001
	40 \| 0.337 \| 0.332 \| 0.129 \| 0.038 \| 0.336 \| 0.196 \| 0.0001
	41 \| 0.335 \| 0.333 \| 0.131 \| 0.039 \| 0.333 \| 0.196 \| 0.0001
	42 \| 0.338 \| 0.332 \| 0.129 \| 0.038 \| 0.337 \| 0.196 \| 0.0001
	43 \| 0.338 \| 0.331 \| 0.129 \| 0.038 \| 0.338 \| 0.196 \| 0.0001
	44 \| 0.336 \| 0.333 \| 0.128 \| 0.039 \| 0.335 \| 0.196 \| 0.0001
	45 \| 0.339 \| 0.331 \| 0.128 \| 0.038 \| 0.338 \| 0.196 \| 0.0001
	46 \| 0.341 \| 0.332 \| 0.129 \| 0.038 \| 0.339 \| 0.195 \| 0.0001
	47 \| 0.34 \| 0.331 \| 0.127 \| 0.038 \| 0.339 \| 0.196 \| 0.0001
	48 \| 0.299 \| 0.339 \| 0.131 \| 0.039 \| 0.295 \| 0.199 \| 0.0001
	49 \| 0.338 \| 0.331 \| 0.128 \| 0.038 \| 0.337 \| 0.196 \| 0.0001
	50 \| 0.342 \| 0.332 \| 0.127 \| 0.038 \| 0.339 \| 0.196 \| 0.0001
	51 \| 0.341 \| 0.331 \| 0.127 \| 0.038 \| 0.341 \| 0.195 \| 0.0001
	52 \| 0.345 \| 0.33 \| 0.127 \| 0.038 \| 0.344 \| 0.195 \| 0.0001
	53 \| 0.34 \| 0.331 \| 0.128 \| 0.038 \| 0.339 \| 0.196 \| 0.0001
	54 \| 0.341 \| 0.331 \| 0.129 \| 0.038 \| 0.34 \| 0.196 \| 0.0001
	55 \| 0.349 \| 0.329 \| 0.127 \| 0.038 \| 0.349 \| 0.194 \| 0.0001
	56 \| 0.344 \| 0.33 \| 0.126 \| 0.038 \| 0.343 \| 0.195 \| 0.0001
	57 \| 0.341 \| 0.331 \| 0.126 \| 0.038 \| 0.339 \| 0.196 \| 0.0001
	58 \| 0.348 \| 0.33 \| 0.126 \| 0.038 \| 0.347 \| 0.194 \| 0.0001
	59 \| 0.343 \| 0.332 \| 0.128 \| 0.038 \| 0.341 \| 0.195 \| 0.0001
	60 \| 0.346 \| 0.331 \| 0.128 \| 0.038 \| 0.345 \| 0.195 \| 0.0001
	61 \| 0.346 \| 0.33 \| 0.125 \| 0.038 \| 0.344 \| 0.195 \| 0.0001
	62 \| 0.347 \| 0.329 \| 0.126 \| 0.038 \| 0.346 \| 0.194 \| 1e-05
	63 \| 0.35 \| 0.33 \| 0.128 \| 0.038 \| 0.348 \| 0.194 \| 1e-05
	64 \| 0.345 \| 0.33 \| 0.126 \| 0.038 \| 0.344 \| 0.195 \| 1e-05
	65 \| 0.349 \| 0.33 \| 0.128 \| 0.038 \| 0.347 \| 0.195 \| 1e-05


	---

	# CO2 Emissions

	The estimated CO2 emissions for training this model are documented below:

	- Emissions: 0.19095786836275294 grams of CO2
	- Source: Code Carbon
	- Training Type: fine-tuning
	- Geographical Location: Brest, France
	- Hardware Used: NVIDIA Tesla V100 PCIe 32 Go


	---

	# Framework Versions

	- Transformers: 4.41.1
	- Pytorch: 2.3.0+cu121
	- Datasets: 2.19.1
	- Tokenizers: 0.19.1