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	---
	language:
	- eng
	license: cc0-1.0
	tags:
	- multilabel-image-classification
	- multilabel
	- generated_from_trainer
	base_model: drone-DinoVdeau-produttoria-probabilities-large-2024_11_04-batch-size64_freeze_probs
	model-index:
	- name: drone-DinoVdeau-produttoria-probabilities-large-2024_11_04-batch-size64_freeze_probs
	results: []
	---

	drone-DinoVdeau-produttoria-probabilities is a fine-tuned version of [drone-DinoVdeau-produttoria-probabilities-large-2024_11_04-batch-size64_freeze_probs](https://huggingface.co/drone-DinoVdeau-produttoria-probabilities-large-2024_11_04-batch-size64_freeze_probs). It achieves the following results on the test set:


	- Loss: 0.3194
	- F1 Micro: 0.8663
	- F1 Macro: 0.8311
	- Accuracy: 0.1799
	- RMSE: 0.2404
	- MAE: 0.1536
	- R2: 0.4282

	\| Class \| F1 per class \|
	\|----------\|-------\|
	\| Acropore_branched \| 0.8010 \|
	\| Acropore_digitised \| 0.7454 \|
	\| Acropore_tabular \| 0.6426 \|
	\| Algae \| 0.9852 \|
	\| Dead_coral \| 0.8448 \|
	\| Fish \| 0.7497 \|
	\| Millepore \| 0.6641 \|
	\| No_acropore_encrusting \| 0.7391 \|
	\| No_acropore_massive \| 0.8688 \|
	\| No_acropore_sub_massive \| 0.8137 \|
	\| Rock \| 0.9924 \|
	\| Rubble \| 0.9691 \|
	\| Sand \| 0.9888 \|


	---

	# Model description
	drone-DinoVdeau-produttoria-probabilities is a model built on top of drone-DinoVdeau-produttoria-probabilities-large-2024_11_04-batch-size64_freeze_probs 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 estimated number of images for each class are given in the following table:
	\| Class \| train \| test \| val \| Total \|
	\|:------------------------\|--------:\|-------:\|------:\|--------:\|
	\| Acropore_branched \| 2028 \| 684 \| 686 \| 3398 \|
	\| Acropore_digitised \| 2006 \| 735 \| 717 \| 3458 \|
	\| Acropore_tabular \| 1237 \| 461 \| 451 \| 2149 \|
	\| Algae \| 11086 \| 3671 \| 3675 \| 18432 \|
	\| Dead_coral \| 6354 \| 2161 \| 2147 \| 10662 \|
	\| Fish \| 4032 \| 1430 \| 1430 \| 6892 \|
	\| Millepore \| 1943 \| 783 \| 772 \| 3498 \|
	\| No_acropore_encrusting \| 2663 \| 986 \| 957 \| 4606 \|
	\| No_acropore_massive \| 6897 \| 2375 \| 2375 \| 11647 \|
	\| No_acropore_sub_massive \| 5416 \| 1988 \| 1958 \| 9362 \|
	\| Rock \| 11164 \| 3726 \| 3725 \| 18615 \|
	\| Rubble \| 10687 \| 3570 \| 3572 \| 17829 \|
	\| Sand \| 11151 \| 3726 \| 3723 \| 18600 \|

	---

	# Training procedure

	## Training hyperparameters

	The following hyperparameters were used during training:

	- Number of Epochs: 69.0
	- Learning Rate: 0.001
	- Train Batch Size: 64
	- Eval Batch Size: 64
	- 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 \| MAE \| RMSE \| R2 \| Learning Rate
	--- \| --- \| --- \| --- \| --- \| ---
	1 \| 0.36101797223091125 \| 0.1878 \| 0.2645 \| 0.2818 \| 0.001
	2 \| 0.3464529514312744 \| 0.1778 \| 0.2566 \| 0.3349 \| 0.001
	3 \| 0.34133487939834595 \| 0.1731 \| 0.2532 \| 0.3536 \| 0.001
	4 \| 0.3406243324279785 \| 0.1743 \| 0.2532 \| 0.3519 \| 0.001
	5 \| 0.3341675400733948 \| 0.1661 \| 0.2496 \| 0.3702 \| 0.001
	6 \| 0.33848145604133606 \| 0.1739 \| 0.2512 \| 0.3651 \| 0.001
	7 \| 0.3320676386356354 \| 0.1650 \| 0.2476 \| 0.3836 \| 0.001
	8 \| 0.3332081437110901 \| 0.1629 \| 0.2484 \| 0.3802 \| 0.001
	9 \| 0.3305376172065735 \| 0.1652 \| 0.2468 \| 0.3859 \| 0.001
	10 \| 0.33136793971061707 \| 0.1655 \| 0.2476 \| 0.3827 \| 0.001
	11 \| 0.3319685757160187 \| 0.1602 \| 0.2474 \| 0.3840 \| 0.001
	12 \| 0.3341500759124756 \| 0.1683 \| 0.2494 \| 0.3761 \| 0.001
	13 \| 0.33248215913772583 \| 0.1649 \| 0.2480 \| 0.3821 \| 0.001
	14 \| 0.33228376507759094 \| 0.1700 \| 0.2472 \| 0.3878 \| 0.001
	15 \| 0.334873229265213 \| 0.1703 \| 0.2493 \| 0.3749 \| 0.001
	16 \| 0.3279329538345337 \| 0.1649 \| 0.2448 \| 0.3983 \| 0.0001
	17 \| 0.3279244005680084 \| 0.1648 \| 0.2448 \| 0.3984 \| 0.0001
	18 \| 0.3262367248535156 \| 0.1622 \| 0.2440 \| 0.4025 \| 0.0001
	19 \| 0.3247373402118683 \| 0.1588 \| 0.2432 \| 0.4046 \| 0.0001
	20 \| 0.32612329721450806 \| 0.1625 \| 0.2433 \| 0.4059 \| 0.0001
	21 \| 0.3241129517555237 \| 0.1606 \| 0.2424 \| 0.4095 \| 0.0001
	22 \| 0.32355180382728577 \| 0.1587 \| 0.2422 \| 0.4111 \| 0.0001
	23 \| 0.3242079019546509 \| 0.1601 \| 0.2423 \| 0.4107 \| 0.0001
	24 \| 0.3227241337299347 \| 0.1586 \| 0.2414 \| 0.4150 \| 0.0001
	25 \| 0.3223778307437897 \| 0.1587 \| 0.2413 \| 0.4148 \| 0.0001
	26 \| 0.3217927813529968 \| 0.1557 \| 0.2413 \| 0.4143 \| 0.0001
	27 \| 0.3227355182170868 \| 0.1603 \| 0.2416 \| 0.4138 \| 0.0001
	28 \| 0.32067713141441345 \| 0.1562 \| 0.2405 \| 0.4186 \| 0.0001
	29 \| 0.32205939292907715 \| 0.1597 \| 0.2411 \| 0.4163 \| 0.0001
	30 \| 0.32246074080467224 \| 0.1608 \| 0.2413 \| 0.4164 \| 0.0001
	31 \| 0.3223503530025482 \| 0.1535 \| 0.2416 \| 0.4134 \| 0.0001
	32 \| 0.3212696313858032 \| 0.1553 \| 0.2408 \| 0.4180 \| 0.0001
	33 \| 0.32156360149383545 \| 0.1583 \| 0.2414 \| 0.4123 \| 0.0001
	34 \| 0.3205103278160095 \| 0.1562 \| 0.2406 \| 0.4172 \| 0.0001
	35 \| 0.3197581171989441 \| 0.1535 \| 0.2399 \| 0.4215 \| 0.0001
	36 \| 0.3211075961589813 \| 0.1577 \| 0.2406 \| 0.4187 \| 0.0001
	37 \| 0.3203599154949188 \| 0.1520 \| 0.2403 \| 0.4188 \| 0.0001
	38 \| 0.32143038511276245 \| 0.1560 \| 0.2409 \| 0.4170 \| 0.0001
	39 \| 0.3195198178291321 \| 0.1520 \| 0.2397 \| 0.4226 \| 0.0001
	40 \| 0.3207896649837494 \| 0.1577 \| 0.2404 \| 0.4204 \| 0.0001
	41 \| 0.3197501003742218 \| 0.1547 \| 0.2398 \| 0.4217 \| 0.0001
	42 \| 0.32175716757774353 \| 0.1589 \| 0.2410 \| 0.4174 \| 0.0001
	43 \| 0.3189575970172882 \| 0.1544 \| 0.2396 \| 0.4235 \| 0.0001
	44 \| 0.31898385286331177 \| 0.1534 \| 0.2396 \| 0.4230 \| 0.0001
	45 \| 0.31977778673171997 \| 0.1566 \| 0.2397 \| 0.4239 \| 0.0001
	46 \| 0.3193351626396179 \| 0.1556 \| 0.2398 \| 0.4213 \| 0.0001
	47 \| 0.31895366311073303 \| 0.1524 \| 0.2393 \| 0.4245 \| 0.0001
	48 \| 0.3192996680736542 \| 0.1525 \| 0.2398 \| 0.4215 \| 0.0001
	49 \| 0.32073548436164856 \| 0.1558 \| 0.2405 \| 0.4187 \| 0.0001
	50 \| 0.3198453485965729 \| 0.1572 \| 0.2400 \| 0.4218 \| 1e-05
	51 \| 0.32436585426330566 \| 0.1602 \| 0.2426 \| 0.4092 \| 1e-05
	52 \| 0.31899821758270264 \| 0.1550 \| 0.2396 \| 0.4227 \| 1e-05
	53 \| 0.31892043352127075 \| 0.1552 \| 0.2394 \| 0.4249 \| 1e-05
	54 \| 0.3194037675857544 \| 0.1540 \| 0.2396 \| 0.4227 \| 1e-05
	55 \| 0.3184601366519928 \| 0.1539 \| 0.2391 \| 0.4250 \| 1e-05
	56 \| 0.318115234375 \| 0.1527 \| 0.2388 \| 0.4273 \| 1e-05
	57 \| 0.31871330738067627 \| 0.1532 \| 0.2392 \| 0.4259 \| 1e-05
	58 \| 0.32010164856910706 \| 0.1567 \| 0.2401 \| 0.4217 \| 1e-05
	59 \| 0.31807705760002136 \| 0.1528 \| 0.2388 \| 0.4270 \| 1e-05
	60 \| 0.3181913495063782 \| 0.1534 \| 0.2389 \| 0.4256 \| 1e-05
	61 \| 0.3185857832431793 \| 0.1510 \| 0.2391 \| 0.4255 \| 1e-05
	62 \| 0.32031872868537903 \| 0.1596 \| 0.2398 \| 0.4240 \| 1e-05
	63 \| 0.31964218616485596 \| 0.1570 \| 0.2397 \| 0.4242 \| 1e-05
	64 \| 0.31808170676231384 \| 0.1527 \| 0.2391 \| 0.4244 \| 1e-05
	65 \| 0.31850185990333557 \| 0.1550 \| 0.2390 \| 0.4259 \| 1e-05
	66 \| 0.3186076879501343 \| 0.1562 \| 0.2389 \| 0.4278 \| 1.0000000000000002e-06
	67 \| 0.3181016743183136 \| 0.1526 \| 0.2387 \| 0.4270 \| 1.0000000000000002e-06
	68 \| 0.3194774389266968 \| 0.1549 \| 0.2397 \| 0.4221 \| 1.0000000000000002e-06
	69 \| 0.3183264136314392 \| 0.1540 \| 0.2390 \| 0.4259 \| 1.0000000000000002e-06


	---

	# Framework Versions

	- Transformers: 4.41.0
	- Pytorch: 2.5.0+cu124
	- Datasets: 3.0.2
	- Tokenizers: 0.19.1


	---
	language:
	- eng
	license: cc0-1.0
	tags:
	- multilabel-image-classification
	- multilabel
	- generated_from_trainer
	base_model: drone-DinoVdeau-produttoria-probabilities-large-2024_11_04-batch-size64_freeze_probs
	model-index:
	- name: drone-DinoVdeau-produttoria-probabilities-large-2024_11_04-batch-size64_freeze_probs
	results: []
	---

	drone-DinoVdeau-produttoria-probabilities is a fine-tuned version of [drone-DinoVdeau-produttoria-probabilities-large-2024_11_04-batch-size64_freeze_probs](https://huggingface.co/drone-DinoVdeau-produttoria-probabilities-large-2024_11_04-batch-size64_freeze_probs). It achieves the following results on the test set:


	- Loss: 0.3194
	- F1 Micro: 0.8663
	- F1 Macro: 0.8311
	- Accuracy: 0.1799
	- RMSE: 0.2404
	- MAE: 0.1536
	- R2: 0.4282

	\| Class \| F1 per class \|
	\|----------\|-------\|
	\| Acropore_branched \| 0.8010 \|
	\| Acropore_digitised \| 0.7454 \|
	\| Acropore_tabular \| 0.6426 \|
	\| Algae \| 0.9852 \|
	\| Dead_coral \| 0.8448 \|
	\| Fish \| 0.7497 \|
	\| Millepore \| 0.6641 \|
	\| No_acropore_encrusting \| 0.7391 \|
	\| No_acropore_massive \| 0.8688 \|
	\| No_acropore_sub_massive \| 0.8137 \|
	\| Rock \| 0.9924 \|
	\| Rubble \| 0.9691 \|
	\| Sand \| 0.9888 \|


	---

	# Model description
	drone-DinoVdeau-produttoria-probabilities is a model built on top of drone-DinoVdeau-produttoria-probabilities-large-2024_11_04-batch-size64_freeze_probs 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 estimated number of images for each class are given in the following table:
	\| Class \| train \| test \| val \| Total \|
	\|:------------------------\|--------:\|-------:\|------:\|--------:\|
	\| Acropore_branched \| 2028 \| 684 \| 686 \| 3398 \|
	\| Acropore_digitised \| 2006 \| 735 \| 717 \| 3458 \|
	\| Acropore_tabular \| 1237 \| 461 \| 451 \| 2149 \|
	\| Algae \| 11086 \| 3671 \| 3675 \| 18432 \|
	\| Dead_coral \| 6354 \| 2161 \| 2147 \| 10662 \|
	\| Fish \| 4032 \| 1430 \| 1430 \| 6892 \|
	\| Millepore \| 1943 \| 783 \| 772 \| 3498 \|
	\| No_acropore_encrusting \| 2663 \| 986 \| 957 \| 4606 \|
	\| No_acropore_massive \| 6897 \| 2375 \| 2375 \| 11647 \|
	\| No_acropore_sub_massive \| 5416 \| 1988 \| 1958 \| 9362 \|
	\| Rock \| 11164 \| 3726 \| 3725 \| 18615 \|
	\| Rubble \| 10687 \| 3570 \| 3572 \| 17829 \|
	\| Sand \| 11151 \| 3726 \| 3723 \| 18600 \|

	---

	# Training procedure

	## Training hyperparameters

	The following hyperparameters were used during training:

	- Number of Epochs: 69.0
	- Learning Rate: 0.001
	- Train Batch Size: 64
	- Eval Batch Size: 64
	- 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 \| MAE \| RMSE \| R2 \| Learning Rate
	--- \| --- \| --- \| --- \| --- \| ---
	1 \| 0.36101797223091125 \| 0.1878 \| 0.2645 \| 0.2818 \| 0.001
	2 \| 0.3464529514312744 \| 0.1778 \| 0.2566 \| 0.3349 \| 0.001
	3 \| 0.34133487939834595 \| 0.1731 \| 0.2532 \| 0.3536 \| 0.001
	4 \| 0.3406243324279785 \| 0.1743 \| 0.2532 \| 0.3519 \| 0.001
	5 \| 0.3341675400733948 \| 0.1661 \| 0.2496 \| 0.3702 \| 0.001
	6 \| 0.33848145604133606 \| 0.1739 \| 0.2512 \| 0.3651 \| 0.001
	7 \| 0.3320676386356354 \| 0.1650 \| 0.2476 \| 0.3836 \| 0.001
	8 \| 0.3332081437110901 \| 0.1629 \| 0.2484 \| 0.3802 \| 0.001
	9 \| 0.3305376172065735 \| 0.1652 \| 0.2468 \| 0.3859 \| 0.001
	10 \| 0.33136793971061707 \| 0.1655 \| 0.2476 \| 0.3827 \| 0.001
	11 \| 0.3319685757160187 \| 0.1602 \| 0.2474 \| 0.3840 \| 0.001
	12 \| 0.3341500759124756 \| 0.1683 \| 0.2494 \| 0.3761 \| 0.001
	13 \| 0.33248215913772583 \| 0.1649 \| 0.2480 \| 0.3821 \| 0.001
	14 \| 0.33228376507759094 \| 0.1700 \| 0.2472 \| 0.3878 \| 0.001
	15 \| 0.334873229265213 \| 0.1703 \| 0.2493 \| 0.3749 \| 0.001
	16 \| 0.3279329538345337 \| 0.1649 \| 0.2448 \| 0.3983 \| 0.0001
	17 \| 0.3279244005680084 \| 0.1648 \| 0.2448 \| 0.3984 \| 0.0001
	18 \| 0.3262367248535156 \| 0.1622 \| 0.2440 \| 0.4025 \| 0.0001
	19 \| 0.3247373402118683 \| 0.1588 \| 0.2432 \| 0.4046 \| 0.0001
	20 \| 0.32612329721450806 \| 0.1625 \| 0.2433 \| 0.4059 \| 0.0001
	21 \| 0.3241129517555237 \| 0.1606 \| 0.2424 \| 0.4095 \| 0.0001
	22 \| 0.32355180382728577 \| 0.1587 \| 0.2422 \| 0.4111 \| 0.0001
	23 \| 0.3242079019546509 \| 0.1601 \| 0.2423 \| 0.4107 \| 0.0001
	24 \| 0.3227241337299347 \| 0.1586 \| 0.2414 \| 0.4150 \| 0.0001
	25 \| 0.3223778307437897 \| 0.1587 \| 0.2413 \| 0.4148 \| 0.0001
	26 \| 0.3217927813529968 \| 0.1557 \| 0.2413 \| 0.4143 \| 0.0001
	27 \| 0.3227355182170868 \| 0.1603 \| 0.2416 \| 0.4138 \| 0.0001
	28 \| 0.32067713141441345 \| 0.1562 \| 0.2405 \| 0.4186 \| 0.0001
	29 \| 0.32205939292907715 \| 0.1597 \| 0.2411 \| 0.4163 \| 0.0001
	30 \| 0.32246074080467224 \| 0.1608 \| 0.2413 \| 0.4164 \| 0.0001
	31 \| 0.3223503530025482 \| 0.1535 \| 0.2416 \| 0.4134 \| 0.0001
	32 \| 0.3212696313858032 \| 0.1553 \| 0.2408 \| 0.4180 \| 0.0001
	33 \| 0.32156360149383545 \| 0.1583 \| 0.2414 \| 0.4123 \| 0.0001
	34 \| 0.3205103278160095 \| 0.1562 \| 0.2406 \| 0.4172 \| 0.0001
	35 \| 0.3197581171989441 \| 0.1535 \| 0.2399 \| 0.4215 \| 0.0001
	36 \| 0.3211075961589813 \| 0.1577 \| 0.2406 \| 0.4187 \| 0.0001
	37 \| 0.3203599154949188 \| 0.1520 \| 0.2403 \| 0.4188 \| 0.0001
	38 \| 0.32143038511276245 \| 0.1560 \| 0.2409 \| 0.4170 \| 0.0001
	39 \| 0.3195198178291321 \| 0.1520 \| 0.2397 \| 0.4226 \| 0.0001
	40 \| 0.3207896649837494 \| 0.1577 \| 0.2404 \| 0.4204 \| 0.0001
	41 \| 0.3197501003742218 \| 0.1547 \| 0.2398 \| 0.4217 \| 0.0001
	42 \| 0.32175716757774353 \| 0.1589 \| 0.2410 \| 0.4174 \| 0.0001
	43 \| 0.3189575970172882 \| 0.1544 \| 0.2396 \| 0.4235 \| 0.0001
	44 \| 0.31898385286331177 \| 0.1534 \| 0.2396 \| 0.4230 \| 0.0001
	45 \| 0.31977778673171997 \| 0.1566 \| 0.2397 \| 0.4239 \| 0.0001
	46 \| 0.3193351626396179 \| 0.1556 \| 0.2398 \| 0.4213 \| 0.0001
	47 \| 0.31895366311073303 \| 0.1524 \| 0.2393 \| 0.4245 \| 0.0001
	48 \| 0.3192996680736542 \| 0.1525 \| 0.2398 \| 0.4215 \| 0.0001
	49 \| 0.32073548436164856 \| 0.1558 \| 0.2405 \| 0.4187 \| 0.0001
	50 \| 0.3198453485965729 \| 0.1572 \| 0.2400 \| 0.4218 \| 1e-05
	51 \| 0.32436585426330566 \| 0.1602 \| 0.2426 \| 0.4092 \| 1e-05
	52 \| 0.31899821758270264 \| 0.1550 \| 0.2396 \| 0.4227 \| 1e-05
	53 \| 0.31892043352127075 \| 0.1552 \| 0.2394 \| 0.4249 \| 1e-05
	54 \| 0.3194037675857544 \| 0.1540 \| 0.2396 \| 0.4227 \| 1e-05
	55 \| 0.3184601366519928 \| 0.1539 \| 0.2391 \| 0.4250 \| 1e-05
	56 \| 0.318115234375 \| 0.1527 \| 0.2388 \| 0.4273 \| 1e-05
	57 \| 0.31871330738067627 \| 0.1532 \| 0.2392 \| 0.4259 \| 1e-05
	58 \| 0.32010164856910706 \| 0.1567 \| 0.2401 \| 0.4217 \| 1e-05
	59 \| 0.31807705760002136 \| 0.1528 \| 0.2388 \| 0.4270 \| 1e-05
	60 \| 0.3181913495063782 \| 0.1534 \| 0.2389 \| 0.4256 \| 1e-05
	61 \| 0.3185857832431793 \| 0.1510 \| 0.2391 \| 0.4255 \| 1e-05
	62 \| 0.32031872868537903 \| 0.1596 \| 0.2398 \| 0.4240 \| 1e-05
	63 \| 0.31964218616485596 \| 0.1570 \| 0.2397 \| 0.4242 \| 1e-05
	64 \| 0.31808170676231384 \| 0.1527 \| 0.2391 \| 0.4244 \| 1e-05
	65 \| 0.31850185990333557 \| 0.1550 \| 0.2390 \| 0.4259 \| 1e-05
	66 \| 0.3186076879501343 \| 0.1562 \| 0.2389 \| 0.4278 \| 1.0000000000000002e-06
	67 \| 0.3181016743183136 \| 0.1526 \| 0.2387 \| 0.4270 \| 1.0000000000000002e-06
	68 \| 0.3194774389266968 \| 0.1549 \| 0.2397 \| 0.4221 \| 1.0000000000000002e-06
	69 \| 0.3183264136314392 \| 0.1540 \| 0.2390 \| 0.4259 \| 1.0000000000000002e-06


	---

	# Framework Versions

	- Transformers: 4.41.0
	- Pytorch: 2.5.0+cu124
	- Datasets: 3.0.2
	- Tokenizers: 0.19.1