Upload README.md

3995206 verified 4 months ago

10.7 kB


	---
	language:
	- eng
	license: wtfpl
	tags:
	- multilabel-image-classification
	- multilabel
	- generated_from_trainer
	base_model: facebook/dinov2-base
	model-index:
	- name: dinov2-base-2024_09_09-batch-size32_epochs150_freeze
	results: []
	---

	DinoVd'eau is a fine-tuned version of [facebook/dinov2-base](https://huggingface.co/facebook/dinov2-base). It achieves the following results on the test set:

	- Loss: 0.1321
	- F1 Micro: 0.8069
	- F1 Macro: 0.7121
	- Roc Auc: 0.8742
	- Accuracy: 0.2869

	---

	# 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 \| 1469 \| 464 \| 475 \| 2408 \|
	\| Acropore_digitised \| 568 \| 160 \| 160 \| 888 \|
	\| Acropore_sub_massive \| 150 \| 50 \| 43 \| 243 \|
	\| Acropore_tabular \| 999 \| 297 \| 293 \| 1589 \|
	\| Algae_assembly \| 2546 \| 847 \| 845 \| 4238 \|
	\| Algae_drawn_up \| 367 \| 126 \| 127 \| 620 \|
	\| Algae_limestone \| 1652 \| 557 \| 563 \| 2772 \|
	\| Algae_sodding \| 3148 \| 984 \| 985 \| 5117 \|
	\| Atra/Leucospilota \| 1084 \| 348 \| 360 \| 1792 \|
	\| Bleached_coral \| 219 \| 71 \| 70 \| 360 \|
	\| Blurred \| 191 \| 67 \| 62 \| 320 \|
	\| Dead_coral \| 1979 \| 642 \| 643 \| 3264 \|
	\| Fish \| 2018 \| 656 \| 647 \| 3321 \|
	\| Homo_sapiens \| 161 \| 62 \| 59 \| 282 \|
	\| Human_object \| 157 \| 58 \| 55 \| 270 \|
	\| Living_coral \| 406 \| 154 \| 141 \| 701 \|
	\| Millepore \| 385 \| 127 \| 125 \| 637 \|
	\| No_acropore_encrusting \| 441 \| 130 \| 154 \| 725 \|
	\| No_acropore_foliaceous \| 204 \| 36 \| 46 \| 286 \|
	\| No_acropore_massive \| 1031 \| 336 \| 338 \| 1705 \|
	\| No_acropore_solitary \| 202 \| 53 \| 48 \| 303 \|
	\| No_acropore_sub_massive \| 1401 \| 433 \| 422 \| 2256 \|
	\| Rock \| 4489 \| 1495 \| 1473 \| 7457 \|
	\| Rubble \| 3092 \| 1030 \| 1001 \| 5123 \|
	\| Sand \| 5842 \| 1939 \| 1938 \| 9719 \|
	\| Sea_cucumber \| 1408 \| 439 \| 447 \| 2294 \|
	\| Sea_urchins \| 327 \| 107 \| 111 \| 545 \|
	\| Sponge \| 269 \| 96 \| 105 \| 470 \|
	\| Syringodium_isoetifolium \| 1212 \| 392 \| 391 \| 1995 \|
	\| Thalassodendron_ciliatum \| 782 \| 261 \| 260 \| 1303 \|
	\| Useless \| 579 \| 193 \| 193 \| 965 \|

	---

	# Training procedure

	## Training hyperparameters

	The following hyperparameters were used during training:

	- Number of Epochs: 150
	- Learning Rate: 0.001
	- Train Batch Size: 32
	- Eval Batch Size: 32
	- 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 \| Validation Loss \| Accuracy \| F1 Macro \| F1 Micro \| Learning Rate
	--- \| --- \| --- \| --- \| --- \| ---
	1 \| 0.16006726026535034 \| 0.23284823284823286 \| 0.7633800438966739 \| 0.6250897780499145 \| 0.001
	2 \| 0.150440976023674 \| 0.24982674982674982 \| 0.7780064686856808 \| 0.646165211379598 \| 0.001
	3 \| 0.14829224348068237 \| 0.2564102564102564 \| 0.7816936696175046 \| 0.6644318154557648 \| 0.001
	4 \| 0.14641565084457397 \| 0.2553707553707554 \| 0.7862639635912287 \| 0.680888104485521 \| 0.001
	5 \| 0.14226503670215607 \| 0.2681912681912682 \| 0.7891243298442687 \| 0.6919100708566497 \| 0.001
	6 \| 0.1439608633518219 \| 0.26507276507276506 \| 0.7901946045268521 \| 0.6987715680115144 \| 0.001
	7 \| 0.1425073742866516 \| 0.2681912681912682 \| 0.7937821236053655 \| 0.6849790066180481 \| 0.001
	8 \| 0.14294348657131195 \| 0.2636867636867637 \| 0.793083667950504 \| 0.6880365824342907 \| 0.001
	9 \| 0.14630228281021118 \| 0.25571725571725573 \| 0.7926595005517636 \| 0.6884565577441364 \| 0.001
	10 \| 0.13922064006328583 \| 0.27442827442827444 \| 0.8009224940284985 \| 0.7049759390767861 \| 0.001
	11 \| 0.14429208636283875 \| 0.26992376992376993 \| 0.785345272946444 \| 0.6892328865834217 \| 0.001
	12 \| 0.14520499110221863 \| 0.2713097713097713 \| 0.7888341543513957 \| 0.6976448599197044 \| 0.001
	13 \| 0.13695523142814636 \| 0.2765072765072765 \| 0.8007200870802982 \| 0.7032121010324246 \| 0.001
	14 \| 0.14012356102466583 \| 0.273042273042273 \| 0.7983576642335767 \| 0.6875097222118577 \| 0.001
	15 \| 0.13785772025585175 \| 0.2817047817047817 \| 0.8048810652595126 \| 0.7001361694791496 \| 0.001
	16 \| 0.1429404616355896 \| 0.2681912681912682 \| 0.7968854097268487 \| 0.7063273106998997 \| 0.001
	17 \| 0.1451471894979477 \| 0.26126126126126126 \| 0.7956287718153646 \| 0.6860743816280108 \| 0.001
	18 \| 0.141770601272583 \| 0.2713097713097713 \| 0.7906203368151778 \| 0.6849355289660601 \| 0.001
	19 \| 0.14384245872497559 \| 0.2654192654192654 \| 0.7899699957136733 \| 0.6794374521554336 \| 0.001
	20 \| 0.13193023204803467 \| 0.28655578655578656 \| 0.8068363147728227 \| 0.7201978132992005 \| 0.0001
	21 \| 0.13121400773525238 \| 0.2875952875952876 \| 0.8080536912751679 \| 0.7236910659256566 \| 0.0001
	22 \| 0.1310088187456131 \| 0.2934857934857935 \| 0.810120343368793 \| 0.7222147145142929 \| 0.0001
	23 \| 0.1304517388343811 \| 0.2934857934857935 \| 0.8120394137616957 \| 0.7226400439644629 \| 0.0001
	24 \| 0.13093852996826172 \| 0.29521829521829523 \| 0.8096162584162916 \| 0.7237916982943077 \| 0.0001
	25 \| 0.13081994652748108 \| 0.2948717948717949 \| 0.8093388464269307 \| 0.7170657451815683 \| 0.0001
	26 \| 0.13007444143295288 \| 0.2910602910602911 \| 0.8099862459884133 \| 0.7200172245050901 \| 0.0001
	27 \| 0.13034380972385406 \| 0.29244629244629244 \| 0.8082065853250877 \| 0.7207907434740295 \| 0.0001
	28 \| 0.13018907606601715 \| 0.29695079695079696 \| 0.810349848163401 \| 0.7217805682073449 \| 0.0001
	29 \| 0.13019531965255737 \| 0.29625779625779625 \| 0.8104190823256585 \| 0.723719101965087 \| 0.0001
	30 \| 0.13030356168746948 \| 0.2955647955647956 \| 0.8096606287736832 \| 0.718144679800513 \| 0.0001
	31 \| 0.1301266849040985 \| 0.2959112959112959 \| 0.8092418049879057 \| 0.7189603352791966 \| 0.0001
	32 \| 0.1301257312297821 \| 0.2927927927927928 \| 0.8097980303789017 \| 0.7210148516296496 \| 0.0001
	33 \| 0.12959885597229004 \| 0.29625779625779625 \| 0.8099594769603543 \| 0.7204264964359948 \| 1e-05
	34 \| 0.12959957122802734 \| 0.2955647955647956 \| 0.8100854344655136 \| 0.722168676552786 \| 1e-05
	35 \| 0.12954092025756836 \| 0.2955647955647956 \| 0.8108894430590192 \| 0.7220033007887567 \| 1e-05
	36 \| 0.12953610718250275 \| 0.29313929313929316 \| 0.8104569713142095 \| 0.7211650841899886 \| 1e-05
	37 \| 0.1295497566461563 \| 0.29625779625779625 \| 0.8118778893007372 \| 0.7239071903959954 \| 1e-05
	38 \| 0.12949061393737793 \| 0.2959112959112959 \| 0.8104318798247445 \| 0.7212977755433345 \| 1e-05
	39 \| 0.12945865094661713 \| 0.2966042966042966 \| 0.8106218263547823 \| 0.7221707642640621 \| 1e-05
	40 \| 0.12946291267871857 \| 0.2955647955647956 \| 0.8113418729013804 \| 0.7232749192333074 \| 1e-05
	41 \| 0.1294611394405365 \| 0.2945252945252945 \| 0.8100071001962995 \| 0.722313917509489 \| 1e-05
	42 \| 0.12951640784740448 \| 0.2972972972972973 \| 0.8111398315684148 \| 0.7219276596712088 \| 1e-05
	43 \| 0.12940654158592224 \| 0.29313929313929316 \| 0.8097862391449566 \| 0.7212066160587719 \| 1e-05
	44 \| 0.12948854267597198 \| 0.29695079695079696 \| 0.8108311081441923 \| 0.7211905265653523 \| 1e-05
	45 \| 0.12943118810653687 \| 0.2945252945252945 \| 0.8103943697164036 \| 0.7217673828508766 \| 1e-05
	46 \| 0.12941767275333405 \| 0.29764379764379767 \| 0.8113435070065285 \| 0.7232663108413819 \| 1e-05
	47 \| 0.12936843931674957 \| 0.2945252945252945 \| 0.8107185952648442 \| 0.7229077354567445 \| 1e-05
	48 \| 0.12944123148918152 \| 0.2955647955647956 \| 0.8102512730611904 \| 0.7208766406208041 \| 1e-05
	49 \| 0.12932655215263367 \| 0.2959112959112959 \| 0.8111032502392942 \| 0.7215165769975259 \| 1e-05
	50 \| 0.1294257938861847 \| 0.2966042966042966 \| 0.8106959890041235 \| 0.7210862927892402 \| 1e-05
	51 \| 0.12937645614147186 \| 0.29244629244629244 \| 0.8098573930447837 \| 0.7224236625273444 \| 1e-05
	52 \| 0.12941104173660278 \| 0.2972972972972973 \| 0.8110019973368842 \| 0.7223932851056244 \| 1e-05
	53 \| 0.12947481870651245 \| 0.29799029799029797 \| 0.8110783049860689 \| 0.7225026360610024 \| 1e-05
	54 \| 0.12942463159561157 \| 0.29625779625779625 \| 0.8104531646623112 \| 0.7221711249170111 \| 1e-05
	55 \| 0.12934881448745728 \| 0.2955647955647956 \| 0.8107163657542226 \| 0.7231230527181782 \| 1e-05
	56 \| 0.12935101985931396 \| 0.2959112959112959 \| 0.810738813735692 \| 0.7226955143721722 \| 1.0000000000000002e-06
	57 \| 0.12934598326683044 \| 0.2955647955647956 \| 0.8110560712650376 \| 0.7230703100391168 \| 1.0000000000000002e-06
	58 \| 0.1293543428182602 \| 0.2966042966042966 \| 0.8112406328059951 \| 0.7230017316798248 \| 1.0000000000000002e-06
	59 \| 0.12936049699783325 \| 0.2966042966042966 \| 0.8110088687179914 \| 0.7227156089091311 \| 1.0000000000000002e-06


	---

	# CO2 Emissions

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

	- Emissions: 0.7291228651023076 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-base
	model-index:
	- name: dinov2-base-2024_09_09-batch-size32_epochs150_freeze
	results: []
	---

	DinoVd'eau is a fine-tuned version of [facebook/dinov2-base](https://huggingface.co/facebook/dinov2-base). It achieves the following results on the test set:

	- Loss: 0.1321
	- F1 Micro: 0.8069
	- F1 Macro: 0.7121
	- Roc Auc: 0.8742
	- Accuracy: 0.2869

	---

	# 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 \| 1469 \| 464 \| 475 \| 2408 \|
	\| Acropore_digitised \| 568 \| 160 \| 160 \| 888 \|
	\| Acropore_sub_massive \| 150 \| 50 \| 43 \| 243 \|
	\| Acropore_tabular \| 999 \| 297 \| 293 \| 1589 \|
	\| Algae_assembly \| 2546 \| 847 \| 845 \| 4238 \|
	\| Algae_drawn_up \| 367 \| 126 \| 127 \| 620 \|
	\| Algae_limestone \| 1652 \| 557 \| 563 \| 2772 \|
	\| Algae_sodding \| 3148 \| 984 \| 985 \| 5117 \|
	\| Atra/Leucospilota \| 1084 \| 348 \| 360 \| 1792 \|
	\| Bleached_coral \| 219 \| 71 \| 70 \| 360 \|
	\| Blurred \| 191 \| 67 \| 62 \| 320 \|
	\| Dead_coral \| 1979 \| 642 \| 643 \| 3264 \|
	\| Fish \| 2018 \| 656 \| 647 \| 3321 \|
	\| Homo_sapiens \| 161 \| 62 \| 59 \| 282 \|
	\| Human_object \| 157 \| 58 \| 55 \| 270 \|
	\| Living_coral \| 406 \| 154 \| 141 \| 701 \|
	\| Millepore \| 385 \| 127 \| 125 \| 637 \|
	\| No_acropore_encrusting \| 441 \| 130 \| 154 \| 725 \|
	\| No_acropore_foliaceous \| 204 \| 36 \| 46 \| 286 \|
	\| No_acropore_massive \| 1031 \| 336 \| 338 \| 1705 \|
	\| No_acropore_solitary \| 202 \| 53 \| 48 \| 303 \|
	\| No_acropore_sub_massive \| 1401 \| 433 \| 422 \| 2256 \|
	\| Rock \| 4489 \| 1495 \| 1473 \| 7457 \|
	\| Rubble \| 3092 \| 1030 \| 1001 \| 5123 \|
	\| Sand \| 5842 \| 1939 \| 1938 \| 9719 \|
	\| Sea_cucumber \| 1408 \| 439 \| 447 \| 2294 \|
	\| Sea_urchins \| 327 \| 107 \| 111 \| 545 \|
	\| Sponge \| 269 \| 96 \| 105 \| 470 \|
	\| Syringodium_isoetifolium \| 1212 \| 392 \| 391 \| 1995 \|
	\| Thalassodendron_ciliatum \| 782 \| 261 \| 260 \| 1303 \|
	\| Useless \| 579 \| 193 \| 193 \| 965 \|

	---

	# Training procedure

	## Training hyperparameters

	The following hyperparameters were used during training:

	- Number of Epochs: 150
	- Learning Rate: 0.001
	- Train Batch Size: 32
	- Eval Batch Size: 32
	- 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 \| Validation Loss \| Accuracy \| F1 Macro \| F1 Micro \| Learning Rate
	--- \| --- \| --- \| --- \| --- \| ---
	1 \| 0.16006726026535034 \| 0.23284823284823286 \| 0.7633800438966739 \| 0.6250897780499145 \| 0.001
	2 \| 0.150440976023674 \| 0.24982674982674982 \| 0.7780064686856808 \| 0.646165211379598 \| 0.001
	3 \| 0.14829224348068237 \| 0.2564102564102564 \| 0.7816936696175046 \| 0.6644318154557648 \| 0.001
	4 \| 0.14641565084457397 \| 0.2553707553707554 \| 0.7862639635912287 \| 0.680888104485521 \| 0.001
	5 \| 0.14226503670215607 \| 0.2681912681912682 \| 0.7891243298442687 \| 0.6919100708566497 \| 0.001
	6 \| 0.1439608633518219 \| 0.26507276507276506 \| 0.7901946045268521 \| 0.6987715680115144 \| 0.001
	7 \| 0.1425073742866516 \| 0.2681912681912682 \| 0.7937821236053655 \| 0.6849790066180481 \| 0.001
	8 \| 0.14294348657131195 \| 0.2636867636867637 \| 0.793083667950504 \| 0.6880365824342907 \| 0.001
	9 \| 0.14630228281021118 \| 0.25571725571725573 \| 0.7926595005517636 \| 0.6884565577441364 \| 0.001
	10 \| 0.13922064006328583 \| 0.27442827442827444 \| 0.8009224940284985 \| 0.7049759390767861 \| 0.001
	11 \| 0.14429208636283875 \| 0.26992376992376993 \| 0.785345272946444 \| 0.6892328865834217 \| 0.001
	12 \| 0.14520499110221863 \| 0.2713097713097713 \| 0.7888341543513957 \| 0.6976448599197044 \| 0.001
	13 \| 0.13695523142814636 \| 0.2765072765072765 \| 0.8007200870802982 \| 0.7032121010324246 \| 0.001
	14 \| 0.14012356102466583 \| 0.273042273042273 \| 0.7983576642335767 \| 0.6875097222118577 \| 0.001
	15 \| 0.13785772025585175 \| 0.2817047817047817 \| 0.8048810652595126 \| 0.7001361694791496 \| 0.001
	16 \| 0.1429404616355896 \| 0.2681912681912682 \| 0.7968854097268487 \| 0.7063273106998997 \| 0.001
	17 \| 0.1451471894979477 \| 0.26126126126126126 \| 0.7956287718153646 \| 0.6860743816280108 \| 0.001
	18 \| 0.141770601272583 \| 0.2713097713097713 \| 0.7906203368151778 \| 0.6849355289660601 \| 0.001
	19 \| 0.14384245872497559 \| 0.2654192654192654 \| 0.7899699957136733 \| 0.6794374521554336 \| 0.001
	20 \| 0.13193023204803467 \| 0.28655578655578656 \| 0.8068363147728227 \| 0.7201978132992005 \| 0.0001
	21 \| 0.13121400773525238 \| 0.2875952875952876 \| 0.8080536912751679 \| 0.7236910659256566 \| 0.0001
	22 \| 0.1310088187456131 \| 0.2934857934857935 \| 0.810120343368793 \| 0.7222147145142929 \| 0.0001
	23 \| 0.1304517388343811 \| 0.2934857934857935 \| 0.8120394137616957 \| 0.7226400439644629 \| 0.0001
	24 \| 0.13093852996826172 \| 0.29521829521829523 \| 0.8096162584162916 \| 0.7237916982943077 \| 0.0001
	25 \| 0.13081994652748108 \| 0.2948717948717949 \| 0.8093388464269307 \| 0.7170657451815683 \| 0.0001
	26 \| 0.13007444143295288 \| 0.2910602910602911 \| 0.8099862459884133 \| 0.7200172245050901 \| 0.0001
	27 \| 0.13034380972385406 \| 0.29244629244629244 \| 0.8082065853250877 \| 0.7207907434740295 \| 0.0001
	28 \| 0.13018907606601715 \| 0.29695079695079696 \| 0.810349848163401 \| 0.7217805682073449 \| 0.0001
	29 \| 0.13019531965255737 \| 0.29625779625779625 \| 0.8104190823256585 \| 0.723719101965087 \| 0.0001
	30 \| 0.13030356168746948 \| 0.2955647955647956 \| 0.8096606287736832 \| 0.718144679800513 \| 0.0001
	31 \| 0.1301266849040985 \| 0.2959112959112959 \| 0.8092418049879057 \| 0.7189603352791966 \| 0.0001
	32 \| 0.1301257312297821 \| 0.2927927927927928 \| 0.8097980303789017 \| 0.7210148516296496 \| 0.0001
	33 \| 0.12959885597229004 \| 0.29625779625779625 \| 0.8099594769603543 \| 0.7204264964359948 \| 1e-05
	34 \| 0.12959957122802734 \| 0.2955647955647956 \| 0.8100854344655136 \| 0.722168676552786 \| 1e-05
	35 \| 0.12954092025756836 \| 0.2955647955647956 \| 0.8108894430590192 \| 0.7220033007887567 \| 1e-05
	36 \| 0.12953610718250275 \| 0.29313929313929316 \| 0.8104569713142095 \| 0.7211650841899886 \| 1e-05
	37 \| 0.1295497566461563 \| 0.29625779625779625 \| 0.8118778893007372 \| 0.7239071903959954 \| 1e-05
	38 \| 0.12949061393737793 \| 0.2959112959112959 \| 0.8104318798247445 \| 0.7212977755433345 \| 1e-05
	39 \| 0.12945865094661713 \| 0.2966042966042966 \| 0.8106218263547823 \| 0.7221707642640621 \| 1e-05
	40 \| 0.12946291267871857 \| 0.2955647955647956 \| 0.8113418729013804 \| 0.7232749192333074 \| 1e-05
	41 \| 0.1294611394405365 \| 0.2945252945252945 \| 0.8100071001962995 \| 0.722313917509489 \| 1e-05
	42 \| 0.12951640784740448 \| 0.2972972972972973 \| 0.8111398315684148 \| 0.7219276596712088 \| 1e-05
	43 \| 0.12940654158592224 \| 0.29313929313929316 \| 0.8097862391449566 \| 0.7212066160587719 \| 1e-05
	44 \| 0.12948854267597198 \| 0.29695079695079696 \| 0.8108311081441923 \| 0.7211905265653523 \| 1e-05
	45 \| 0.12943118810653687 \| 0.2945252945252945 \| 0.8103943697164036 \| 0.7217673828508766 \| 1e-05
	46 \| 0.12941767275333405 \| 0.29764379764379767 \| 0.8113435070065285 \| 0.7232663108413819 \| 1e-05
	47 \| 0.12936843931674957 \| 0.2945252945252945 \| 0.8107185952648442 \| 0.7229077354567445 \| 1e-05
	48 \| 0.12944123148918152 \| 0.2955647955647956 \| 0.8102512730611904 \| 0.7208766406208041 \| 1e-05
	49 \| 0.12932655215263367 \| 0.2959112959112959 \| 0.8111032502392942 \| 0.7215165769975259 \| 1e-05
	50 \| 0.1294257938861847 \| 0.2966042966042966 \| 0.8106959890041235 \| 0.7210862927892402 \| 1e-05
	51 \| 0.12937645614147186 \| 0.29244629244629244 \| 0.8098573930447837 \| 0.7224236625273444 \| 1e-05
	52 \| 0.12941104173660278 \| 0.2972972972972973 \| 0.8110019973368842 \| 0.7223932851056244 \| 1e-05
	53 \| 0.12947481870651245 \| 0.29799029799029797 \| 0.8110783049860689 \| 0.7225026360610024 \| 1e-05
	54 \| 0.12942463159561157 \| 0.29625779625779625 \| 0.8104531646623112 \| 0.7221711249170111 \| 1e-05
	55 \| 0.12934881448745728 \| 0.2955647955647956 \| 0.8107163657542226 \| 0.7231230527181782 \| 1e-05
	56 \| 0.12935101985931396 \| 0.2959112959112959 \| 0.810738813735692 \| 0.7226955143721722 \| 1.0000000000000002e-06
	57 \| 0.12934598326683044 \| 0.2955647955647956 \| 0.8110560712650376 \| 0.7230703100391168 \| 1.0000000000000002e-06
	58 \| 0.1293543428182602 \| 0.2966042966042966 \| 0.8112406328059951 \| 0.7230017316798248 \| 1.0000000000000002e-06
	59 \| 0.12936049699783325 \| 0.2966042966042966 \| 0.8110088687179914 \| 0.7227156089091311 \| 1.0000000000000002e-06


	---

	# CO2 Emissions

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

	- Emissions: 0.7291228651023076 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