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	---
	language:
	- eng
	license: cc0-1.0
	tags:
	- multilabel-image-classification
	- multilabel
	- generated_from_trainer
	base_model: drone-DinoVdeau-from-probs-large-2024_11_15-batch-size64_freeze_probs
	model-index:
	- name: drone-DinoVdeau-from-probs-large-2024_11_15-batch-size64_freeze_probs
	results: []
	---

	drone-DinoVdeau-from-probs is a fine-tuned version of [drone-DinoVdeau-from-probs-large-2024_11_15-batch-size64_freeze_probs](https://huggingface.co/drone-DinoVdeau-from-probs-large-2024_11_15-batch-size64_freeze_probs). It achieves the following results on the test set:


	- Loss: 0.4672
	- RMSE: 0.1553
	- MAE: 0.1147
	- KL Divergence: 0.3577

	---

	# Model description
	drone-DinoVdeau-from-probs is a model built on top of drone-DinoVdeau-from-probs-large-2024_11_15-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 \| 1220 \| 363 \| 362 \| 1945 \|
	\| Acropore_digitised \| 586 \| 195 \| 189 \| 970 \|
	\| Acropore_tabular \| 308 \| 133 \| 119 \| 560 \|
	\| Algae \| 4777 \| 1372 \| 1384 \| 7533 \|
	\| Dead_coral \| 2513 \| 671 \| 693 \| 3877 \|
	\| Millepore \| 136 \| 55 \| 59 \| 250 \|
	\| No_acropore_encrusting \| 252 \| 88 \| 93 \| 433 \|
	\| No_acropore_massive \| 2158 \| 725 \| 726 \| 3609 \|
	\| No_acropore_sub_massive \| 2036 \| 582 \| 612 \| 3230 \|
	\| Rock \| 5976 \| 1941 \| 1928 \| 9845 \|
	\| Rubble \| 4851 \| 1486 \| 1474 \| 7811 \|
	\| Sand \| 6155 \| 2019 \| 1990 \| 10164 \|

	---

	# Training procedure

	## Training hyperparameters

	The following hyperparameters were used during training:

	- Number of Epochs: 79.0
	- 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 \| KL div \| Learning Rate
	--- \| --- \| --- \| --- \| --- \| ---
	1 \| 0.5005590319633484 \| 0.1552 \| 0.1904 \| 0.1025 \| 0.001
	2 \| 0.47547808289527893 \| 0.1245 \| 0.1681 \| 0.5180 \| 0.001
	3 \| 0.47452571988105774 \| 0.1227 \| 0.1675 \| 0.6862 \| 0.001
	4 \| 0.47420722246170044 \| 0.1255 \| 0.1672 \| 0.3212 \| 0.001
	5 \| 0.47245556116104126 \| 0.1224 \| 0.1653 \| 0.5072 \| 0.001
	6 \| 0.4725925624370575 \| 0.1216 \| 0.1657 \| 0.6710 \| 0.001
	7 \| 0.4731809198856354 \| 0.1255 \| 0.1655 \| 0.3162 \| 0.001
	8 \| 0.47284314036369324 \| 0.1260 \| 0.1651 \| 0.2719 \| 0.001
	9 \| 0.4707973003387451 \| 0.1206 \| 0.1639 \| 0.6393 \| 0.001
	10 \| 0.4732784628868103 \| 0.1230 \| 0.1654 \| 0.5359 \| 0.001
	11 \| 0.47162503004074097 \| 0.1253 \| 0.1647 \| 0.2479 \| 0.001
	12 \| 0.47083696722984314 \| 0.1244 \| 0.1631 \| 0.3119 \| 0.001
	13 \| 0.47152063250541687 \| 0.1230 \| 0.1635 \| 0.3694 \| 0.001
	14 \| 0.47212228178977966 \| 0.1216 \| 0.1653 \| 0.5592 \| 0.001
	15 \| 0.47012239694595337 \| 0.1213 \| 0.1628 \| 0.4936 \| 0.001
	16 \| 0.4718552827835083 \| 0.1229 \| 0.1646 \| 0.2820 \| 0.001
	17 \| 0.46933484077453613 \| 0.1200 \| 0.1621 \| 0.5294 \| 0.001
	18 \| 0.4710436165332794 \| 0.1216 \| 0.1635 \| 0.4093 \| 0.001
	19 \| 0.4698491394519806 \| 0.1219 \| 0.1622 \| 0.2918 \| 0.001
	20 \| 0.4691685736179352 \| 0.1190 \| 0.1617 \| 0.4772 \| 0.001
	21 \| 0.46830564737319946 \| 0.1204 \| 0.1606 \| 0.4336 \| 0.001
	22 \| 0.47239789366722107 \| 0.1183 \| 0.1650 \| 0.7962 \| 0.001
	23 \| 0.47136834263801575 \| 0.1223 \| 0.1641 \| 0.2854 \| 0.001
	24 \| 0.4706868529319763 \| 0.1207 \| 0.1633 \| 0.4206 \| 0.001
	25 \| 0.46786901354789734 \| 0.1185 \| 0.1606 \| 0.5436 \| 0.001
	26 \| 0.47084224224090576 \| 0.1192 \| 0.1634 \| 0.4964 \| 0.001
	27 \| 0.4695045053958893 \| 0.1185 \| 0.1625 \| 0.6399 \| 0.001
	28 \| 0.4700873792171478 \| 0.1184 \| 0.1624 \| 0.5737 \| 0.001
	29 \| 0.4698559045791626 \| 0.1200 \| 0.1624 \| 0.4459 \| 0.001
	30 \| 0.4722815454006195 \| 0.1254 \| 0.1643 \| 0.2726 \| 0.001
	31 \| 0.46958214044570923 \| 0.1184 \| 0.1622 \| 0.5308 \| 0.001
	32 \| 0.46677276492118835 \| 0.1175 \| 0.1593 \| 0.4200 \| 0.0001
	33 \| 0.46626824140548706 \| 0.1177 \| 0.1587 \| 0.3529 \| 0.0001
	34 \| 0.46665358543395996 \| 0.1181 \| 0.1592 \| 0.3588 \| 0.0001
	35 \| 0.46587392687797546 \| 0.1160 \| 0.1584 \| 0.4813 \| 0.0001
	36 \| 0.46578526496887207 \| 0.1173 \| 0.1581 \| 0.3504 \| 0.0001
	37 \| 0.4654408395290375 \| 0.1158 \| 0.1578 \| 0.3919 \| 0.0001
	38 \| 0.46546319127082825 \| 0.1166 \| 0.1580 \| 0.4058 \| 0.0001
	39 \| 0.465843141078949 \| 0.1174 \| 0.1585 \| 0.4118 \| 0.0001
	40 \| 0.46561121940612793 \| 0.1170 \| 0.1579 \| 0.3564 \| 0.0001
	41 \| 0.4657152593135834 \| 0.1171 \| 0.1582 \| 0.3573 \| 0.0001
	42 \| 0.4651602804660797 \| 0.1155 \| 0.1579 \| 0.5042 \| 0.0001
	43 \| 0.4651065468788147 \| 0.1157 \| 0.1575 \| 0.4462 \| 0.0001
	44 \| 0.46537330746650696 \| 0.1166 \| 0.1579 \| 0.4236 \| 0.0001
	45 \| 0.46489208936691284 \| 0.1151 \| 0.1574 \| 0.4510 \| 0.0001
	46 \| 0.46484702825546265 \| 0.1157 \| 0.1575 \| 0.4490 \| 0.0001
	47 \| 0.4648602306842804 \| 0.1152 \| 0.1574 \| 0.4751 \| 0.0001
	48 \| 0.4647873342037201 \| 0.1151 \| 0.1575 \| 0.5305 \| 0.0001
	49 \| 0.4647849500179291 \| 0.1154 \| 0.1574 \| 0.4799 \| 0.0001
	50 \| N/A \| 0.0000 \| 0.0000 \| 0.0000 \| 0.0001
	51 \| 0.465638667345047 \| 0.1151 \| 0.1582 \| 0.4879 \| 0.0001
	52 \| 0.46429532766342163 \| 0.1155 \| 0.1566 \| 0.4199 \| 0.0001
	53 \| 0.46441230177879333 \| 0.1156 \| 0.1569 \| 0.3880 \| 0.0001
	54 \| 0.4646008610725403 \| 0.1148 \| 0.1569 \| 0.4229 \| 0.0001
	55 \| 0.4644174873828888 \| 0.1159 \| 0.1569 \| 0.4009 \| 0.0001
	56 \| 0.464743047952652 \| 0.1164 \| 0.1572 \| 0.3405 \| 0.0001
	57 \| 0.4645179808139801 \| 0.1152 \| 0.1569 \| 0.4188 \| 0.0001
	58 \| 0.465102881193161 \| 0.1164 \| 0.1576 \| 0.3079 \| 0.0001
	59 \| 0.4644688367843628 \| 0.1150 \| 0.1570 \| 0.4339 \| 1e-05
	60 \| 0.46417686343193054 \| 0.1150 \| 0.1566 \| 0.3894 \| 1e-05
	61 \| 0.4639436900615692 \| 0.1146 \| 0.1563 \| 0.4145 \| 1e-05
	62 \| 0.4641311764717102 \| 0.1148 \| 0.1565 \| 0.4064 \| 1e-05
	63 \| 0.4643491506576538 \| 0.1149 \| 0.1565 \| 0.3542 \| 1e-05
	64 \| 0.46402981877326965 \| 0.1150 \| 0.1564 \| 0.3718 \| 1e-05
	65 \| 0.4640822410583496 \| 0.1152 \| 0.1565 \| 0.4128 \| 1e-05
	66 \| 0.46441909670829773 \| 0.1145 \| 0.1570 \| 0.4988 \| 1e-05
	67 \| 0.46383005380630493 \| 0.1151 \| 0.1562 \| 0.4122 \| 1e-05
	68 \| 0.4639807641506195 \| 0.1144 \| 0.1565 \| 0.4579 \| 1e-05
	69 \| 0.4637599587440491 \| 0.1143 \| 0.1561 \| 0.4197 \| 1e-05
	70 \| 0.46392253041267395 \| 0.1145 \| 0.1563 \| 0.4286 \| 1e-05
	71 \| 0.46406444907188416 \| 0.1153 \| 0.1563 \| 0.3542 \| 1e-05
	72 \| 0.46417826414108276 \| 0.1147 \| 0.1566 \| 0.4250 \| 1e-05
	73 \| 0.4637835919857025 \| 0.1140 \| 0.1561 \| 0.4397 \| 1e-05
	74 \| 0.463798850774765 \| 0.1145 \| 0.1563 \| 0.4437 \| 1e-05
	75 \| 0.46379053592681885 \| 0.1145 \| 0.1561 \| 0.4049 \| 1e-05
	76 \| 0.4639701247215271 \| 0.1141 \| 0.1565 \| 0.4926 \| 1.0000000000000002e-06
	77 \| 0.463869571685791 \| 0.1142 \| 0.1562 \| 0.4427 \| 1.0000000000000002e-06
	78 \| 0.46388140320777893 \| 0.1145 \| 0.1563 \| 0.4293 \| 1.0000000000000002e-06
	79 \| 0.46412238478660583 \| 0.1147 \| 0.1564 \| 0.3765 \| 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-from-probs-large-2024_11_15-batch-size64_freeze_probs
	model-index:
	- name: drone-DinoVdeau-from-probs-large-2024_11_15-batch-size64_freeze_probs
	results: []
	---

	drone-DinoVdeau-from-probs is a fine-tuned version of [drone-DinoVdeau-from-probs-large-2024_11_15-batch-size64_freeze_probs](https://huggingface.co/drone-DinoVdeau-from-probs-large-2024_11_15-batch-size64_freeze_probs). It achieves the following results on the test set:


	- Loss: 0.4672
	- RMSE: 0.1553
	- MAE: 0.1147
	- KL Divergence: 0.3577

	---

	# Model description
	drone-DinoVdeau-from-probs is a model built on top of drone-DinoVdeau-from-probs-large-2024_11_15-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 \| 1220 \| 363 \| 362 \| 1945 \|
	\| Acropore_digitised \| 586 \| 195 \| 189 \| 970 \|
	\| Acropore_tabular \| 308 \| 133 \| 119 \| 560 \|
	\| Algae \| 4777 \| 1372 \| 1384 \| 7533 \|
	\| Dead_coral \| 2513 \| 671 \| 693 \| 3877 \|
	\| Millepore \| 136 \| 55 \| 59 \| 250 \|
	\| No_acropore_encrusting \| 252 \| 88 \| 93 \| 433 \|
	\| No_acropore_massive \| 2158 \| 725 \| 726 \| 3609 \|
	\| No_acropore_sub_massive \| 2036 \| 582 \| 612 \| 3230 \|
	\| Rock \| 5976 \| 1941 \| 1928 \| 9845 \|
	\| Rubble \| 4851 \| 1486 \| 1474 \| 7811 \|
	\| Sand \| 6155 \| 2019 \| 1990 \| 10164 \|

	---

	# Training procedure

	## Training hyperparameters

	The following hyperparameters were used during training:

	- Number of Epochs: 79.0
	- 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 \| KL div \| Learning Rate
	--- \| --- \| --- \| --- \| --- \| ---
	1 \| 0.5005590319633484 \| 0.1552 \| 0.1904 \| 0.1025 \| 0.001
	2 \| 0.47547808289527893 \| 0.1245 \| 0.1681 \| 0.5180 \| 0.001
	3 \| 0.47452571988105774 \| 0.1227 \| 0.1675 \| 0.6862 \| 0.001
	4 \| 0.47420722246170044 \| 0.1255 \| 0.1672 \| 0.3212 \| 0.001
	5 \| 0.47245556116104126 \| 0.1224 \| 0.1653 \| 0.5072 \| 0.001
	6 \| 0.4725925624370575 \| 0.1216 \| 0.1657 \| 0.6710 \| 0.001
	7 \| 0.4731809198856354 \| 0.1255 \| 0.1655 \| 0.3162 \| 0.001
	8 \| 0.47284314036369324 \| 0.1260 \| 0.1651 \| 0.2719 \| 0.001
	9 \| 0.4707973003387451 \| 0.1206 \| 0.1639 \| 0.6393 \| 0.001
	10 \| 0.4732784628868103 \| 0.1230 \| 0.1654 \| 0.5359 \| 0.001
	11 \| 0.47162503004074097 \| 0.1253 \| 0.1647 \| 0.2479 \| 0.001
	12 \| 0.47083696722984314 \| 0.1244 \| 0.1631 \| 0.3119 \| 0.001
	13 \| 0.47152063250541687 \| 0.1230 \| 0.1635 \| 0.3694 \| 0.001
	14 \| 0.47212228178977966 \| 0.1216 \| 0.1653 \| 0.5592 \| 0.001
	15 \| 0.47012239694595337 \| 0.1213 \| 0.1628 \| 0.4936 \| 0.001
	16 \| 0.4718552827835083 \| 0.1229 \| 0.1646 \| 0.2820 \| 0.001
	17 \| 0.46933484077453613 \| 0.1200 \| 0.1621 \| 0.5294 \| 0.001
	18 \| 0.4710436165332794 \| 0.1216 \| 0.1635 \| 0.4093 \| 0.001
	19 \| 0.4698491394519806 \| 0.1219 \| 0.1622 \| 0.2918 \| 0.001
	20 \| 0.4691685736179352 \| 0.1190 \| 0.1617 \| 0.4772 \| 0.001
	21 \| 0.46830564737319946 \| 0.1204 \| 0.1606 \| 0.4336 \| 0.001
	22 \| 0.47239789366722107 \| 0.1183 \| 0.1650 \| 0.7962 \| 0.001
	23 \| 0.47136834263801575 \| 0.1223 \| 0.1641 \| 0.2854 \| 0.001
	24 \| 0.4706868529319763 \| 0.1207 \| 0.1633 \| 0.4206 \| 0.001
	25 \| 0.46786901354789734 \| 0.1185 \| 0.1606 \| 0.5436 \| 0.001
	26 \| 0.47084224224090576 \| 0.1192 \| 0.1634 \| 0.4964 \| 0.001
	27 \| 0.4695045053958893 \| 0.1185 \| 0.1625 \| 0.6399 \| 0.001
	28 \| 0.4700873792171478 \| 0.1184 \| 0.1624 \| 0.5737 \| 0.001
	29 \| 0.4698559045791626 \| 0.1200 \| 0.1624 \| 0.4459 \| 0.001
	30 \| 0.4722815454006195 \| 0.1254 \| 0.1643 \| 0.2726 \| 0.001
	31 \| 0.46958214044570923 \| 0.1184 \| 0.1622 \| 0.5308 \| 0.001
	32 \| 0.46677276492118835 \| 0.1175 \| 0.1593 \| 0.4200 \| 0.0001
	33 \| 0.46626824140548706 \| 0.1177 \| 0.1587 \| 0.3529 \| 0.0001
	34 \| 0.46665358543395996 \| 0.1181 \| 0.1592 \| 0.3588 \| 0.0001
	35 \| 0.46587392687797546 \| 0.1160 \| 0.1584 \| 0.4813 \| 0.0001
	36 \| 0.46578526496887207 \| 0.1173 \| 0.1581 \| 0.3504 \| 0.0001
	37 \| 0.4654408395290375 \| 0.1158 \| 0.1578 \| 0.3919 \| 0.0001
	38 \| 0.46546319127082825 \| 0.1166 \| 0.1580 \| 0.4058 \| 0.0001
	39 \| 0.465843141078949 \| 0.1174 \| 0.1585 \| 0.4118 \| 0.0001
	40 \| 0.46561121940612793 \| 0.1170 \| 0.1579 \| 0.3564 \| 0.0001
	41 \| 0.4657152593135834 \| 0.1171 \| 0.1582 \| 0.3573 \| 0.0001
	42 \| 0.4651602804660797 \| 0.1155 \| 0.1579 \| 0.5042 \| 0.0001
	43 \| 0.4651065468788147 \| 0.1157 \| 0.1575 \| 0.4462 \| 0.0001
	44 \| 0.46537330746650696 \| 0.1166 \| 0.1579 \| 0.4236 \| 0.0001
	45 \| 0.46489208936691284 \| 0.1151 \| 0.1574 \| 0.4510 \| 0.0001
	46 \| 0.46484702825546265 \| 0.1157 \| 0.1575 \| 0.4490 \| 0.0001
	47 \| 0.4648602306842804 \| 0.1152 \| 0.1574 \| 0.4751 \| 0.0001
	48 \| 0.4647873342037201 \| 0.1151 \| 0.1575 \| 0.5305 \| 0.0001
	49 \| 0.4647849500179291 \| 0.1154 \| 0.1574 \| 0.4799 \| 0.0001
	50 \| N/A \| 0.0000 \| 0.0000 \| 0.0000 \| 0.0001
	51 \| 0.465638667345047 \| 0.1151 \| 0.1582 \| 0.4879 \| 0.0001
	52 \| 0.46429532766342163 \| 0.1155 \| 0.1566 \| 0.4199 \| 0.0001
	53 \| 0.46441230177879333 \| 0.1156 \| 0.1569 \| 0.3880 \| 0.0001
	54 \| 0.4646008610725403 \| 0.1148 \| 0.1569 \| 0.4229 \| 0.0001
	55 \| 0.4644174873828888 \| 0.1159 \| 0.1569 \| 0.4009 \| 0.0001
	56 \| 0.464743047952652 \| 0.1164 \| 0.1572 \| 0.3405 \| 0.0001
	57 \| 0.4645179808139801 \| 0.1152 \| 0.1569 \| 0.4188 \| 0.0001
	58 \| 0.465102881193161 \| 0.1164 \| 0.1576 \| 0.3079 \| 0.0001
	59 \| 0.4644688367843628 \| 0.1150 \| 0.1570 \| 0.4339 \| 1e-05
	60 \| 0.46417686343193054 \| 0.1150 \| 0.1566 \| 0.3894 \| 1e-05
	61 \| 0.4639436900615692 \| 0.1146 \| 0.1563 \| 0.4145 \| 1e-05
	62 \| 0.4641311764717102 \| 0.1148 \| 0.1565 \| 0.4064 \| 1e-05
	63 \| 0.4643491506576538 \| 0.1149 \| 0.1565 \| 0.3542 \| 1e-05
	64 \| 0.46402981877326965 \| 0.1150 \| 0.1564 \| 0.3718 \| 1e-05
	65 \| 0.4640822410583496 \| 0.1152 \| 0.1565 \| 0.4128 \| 1e-05
	66 \| 0.46441909670829773 \| 0.1145 \| 0.1570 \| 0.4988 \| 1e-05
	67 \| 0.46383005380630493 \| 0.1151 \| 0.1562 \| 0.4122 \| 1e-05
	68 \| 0.4639807641506195 \| 0.1144 \| 0.1565 \| 0.4579 \| 1e-05
	69 \| 0.4637599587440491 \| 0.1143 \| 0.1561 \| 0.4197 \| 1e-05
	70 \| 0.46392253041267395 \| 0.1145 \| 0.1563 \| 0.4286 \| 1e-05
	71 \| 0.46406444907188416 \| 0.1153 \| 0.1563 \| 0.3542 \| 1e-05
	72 \| 0.46417826414108276 \| 0.1147 \| 0.1566 \| 0.4250 \| 1e-05
	73 \| 0.4637835919857025 \| 0.1140 \| 0.1561 \| 0.4397 \| 1e-05
	74 \| 0.463798850774765 \| 0.1145 \| 0.1563 \| 0.4437 \| 1e-05
	75 \| 0.46379053592681885 \| 0.1145 \| 0.1561 \| 0.4049 \| 1e-05
	76 \| 0.4639701247215271 \| 0.1141 \| 0.1565 \| 0.4926 \| 1.0000000000000002e-06
	77 \| 0.463869571685791 \| 0.1142 \| 0.1562 \| 0.4427 \| 1.0000000000000002e-06
	78 \| 0.46388140320777893 \| 0.1145 \| 0.1563 \| 0.4293 \| 1.0000000000000002e-06
	79 \| 0.46412238478660583 \| 0.1147 \| 0.1564 \| 0.3765 \| 1.0000000000000002e-06


	---

	# Framework Versions

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