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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-size32_freeze_probs
	model-index:
	- name: drone-DinoVdeau-from-probs-large-2024_11_15-batch-size32_freeze_probs
	results: []
	---

	drone-DinoVdeau-from-probs is a fine-tuned version of [drone-DinoVdeau-from-probs-large-2024_11_15-batch-size32_freeze_probs](https://huggingface.co/drone-DinoVdeau-from-probs-large-2024_11_15-batch-size32_freeze_probs). It achieves the following results on the test set:


	- Loss: 0.4668
	- RMSE: 0.1546
	- MAE: 0.1143
	- KL Divergence: 0.3931

	---

	# Model description
	drone-DinoVdeau-from-probs is a model built on top of drone-DinoVdeau-from-probs-large-2024_11_15-batch-size32_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: 83.0
	- 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 \| MAE \| RMSE \| KL div \| Learning Rate
	--- \| --- \| --- \| --- \| --- \| ---
	1 \| 0.4855400025844574 \| 0.1364 \| 0.1771 \| 0.3101 \| 0.001
	2 \| 0.47601452469825745 \| 0.1247 \| 0.1688 \| 0.5077 \| 0.001
	3 \| 0.4776814579963684 \| 0.1230 \| 0.1707 \| 0.7896 \| 0.001
	4 \| 0.47429159283638 \| 0.1238 \| 0.1672 \| 0.4932 \| 0.001
	5 \| 0.47457176446914673 \| 0.1277 \| 0.1669 \| 0.2901 \| 0.001
	6 \| 0.4749792814254761 \| 0.1253 \| 0.1674 \| 0.4399 \| 0.001
	7 \| 0.4744807779788971 \| 0.1259 \| 0.1671 \| 0.4868 \| 0.001
	8 \| 0.47424906492233276 \| 0.1257 \| 0.1672 \| 0.3241 \| 0.001
	9 \| 0.4729686379432678 \| 0.1236 \| 0.1658 \| 0.4560 \| 0.001
	10 \| 0.4750550389289856 \| 0.1269 \| 0.1679 \| 0.2141 \| 0.001
	11 \| 0.4733181595802307 \| 0.1265 \| 0.1663 \| 0.2530 \| 0.001
	12 \| 0.4758349061012268 \| 0.1264 \| 0.1684 \| 0.3966 \| 0.001
	13 \| 0.4722050428390503 \| 0.1223 \| 0.1650 \| 0.6055 \| 0.001
	14 \| 0.4747372567653656 \| 0.1250 \| 0.1666 \| 0.4203 \| 0.001
	15 \| 0.47325292229652405 \| 0.1227 \| 0.1662 \| 0.6553 \| 0.001
	16 \| 0.4734710156917572 \| 0.1241 \| 0.1656 \| 0.3576 \| 0.001
	17 \| 0.4721581041812897 \| 0.1221 \| 0.1643 \| 0.4545 \| 0.001
	18 \| 0.4723944365978241 \| 0.1225 \| 0.1647 \| 0.4902 \| 0.001
	19 \| 0.47289156913757324 \| 0.1261 \| 0.1650 \| 0.3158 \| 0.001
	20 \| 0.4697262644767761 \| 0.1203 \| 0.1623 \| 0.4574 \| 0.0001
	21 \| 0.46890661120414734 \| 0.1197 \| 0.1613 \| 0.4569 \| 0.0001
	22 \| 0.46905258297920227 \| 0.1202 \| 0.1617 \| 0.4535 \| 0.0001
	23 \| 0.4691086411476135 \| 0.1210 \| 0.1614 \| 0.2971 \| 0.0001
	24 \| 0.46915334463119507 \| 0.1196 \| 0.1616 \| 0.3916 \| 0.0001
	25 \| 0.4676876664161682 \| 0.1181 \| 0.1601 \| 0.4516 \| 0.0001
	26 \| 0.4679708480834961 \| 0.1171 \| 0.1605 \| 0.6089 \| 0.0001
	27 \| 0.4674595892429352 \| 0.1182 \| 0.1600 \| 0.4741 \| 0.0001
	28 \| 0.46810340881347656 \| 0.1200 \| 0.1606 \| 0.3356 \| 0.0001
	29 \| 0.4678303897380829 \| 0.1181 \| 0.1603 \| 0.4330 \| 0.0001
	30 \| 0.46800243854522705 \| 0.1194 \| 0.1602 \| 0.3160 \| 0.0001
	31 \| 0.4676785469055176 \| 0.1179 \| 0.1600 \| 0.4190 \| 0.0001
	32 \| 0.46752873063087463 \| 0.1188 \| 0.1598 \| 0.3706 \| 0.0001
	33 \| 0.46710190176963806 \| 0.1181 \| 0.1593 \| 0.3504 \| 0.0001
	34 \| 0.4670344293117523 \| 0.1180 \| 0.1594 \| 0.3881 \| 0.0001
	35 \| 0.4662601053714752 \| 0.1166 \| 0.1587 \| 0.4398 \| 0.0001
	36 \| 0.46657058596611023 \| 0.1170 \| 0.1587 \| 0.4382 \| 0.0001
	37 \| 0.4657588005065918 \| 0.1163 \| 0.1581 \| 0.4330 \| 0.0001
	38 \| 0.4659184217453003 \| 0.1162 \| 0.1583 \| 0.4878 \| 0.0001
	39 \| 0.46703553199768066 \| 0.1178 \| 0.1595 \| 0.3791 \| 0.0001
	40 \| 0.4664987027645111 \| 0.1178 \| 0.1588 \| 0.3889 \| 0.0001
	41 \| 0.46659526228904724 \| 0.1184 \| 0.1589 \| 0.3222 \| 0.0001
	42 \| 0.4655005633831024 \| 0.1164 \| 0.1579 \| 0.4262 \| 0.0001
	43 \| 0.4656265676021576 \| 0.1162 \| 0.1579 \| 0.4611 \| 0.0001
	44 \| 0.4655725955963135 \| 0.1164 \| 0.1580 \| 0.4586 \| 0.0001
	45 \| 0.46600833535194397 \| 0.1158 \| 0.1583 \| 0.4368 \| 0.0001
	46 \| 0.4660418927669525 \| 0.1164 \| 0.1582 \| 0.4118 \| 0.0001
	47 \| 0.46521857380867004 \| 0.1154 \| 0.1577 \| 0.5424 \| 0.0001
	48 \| 0.46598610281944275 \| 0.1160 \| 0.1586 \| 0.5251 \| 0.0001
	49 \| 0.46604350209236145 \| 0.1161 \| 0.1585 \| 0.5007 \| 0.0001
	50 \| 0.46660009026527405 \| 0.1185 \| 0.1586 \| 0.2424 \| 0.0001
	51 \| 0.4660661220550537 \| 0.1162 \| 0.1584 \| 0.4171 \| 0.0001
	52 \| 0.4649689793586731 \| 0.1155 \| 0.1575 \| 0.4912 \| 0.0001
	53 \| 0.4653578996658325 \| 0.1169 \| 0.1578 \| 0.4030 \| 0.0001
	54 \| 0.4660585820674896 \| 0.1153 \| 0.1585 \| 0.4811 \| 0.0001
	55 \| 0.46527624130249023 \| 0.1167 \| 0.1576 \| 0.3774 \| 0.0001
	56 \| 0.4654240906238556 \| 0.1176 \| 0.1575 \| 0.3254 \| 0.0001
	57 \| 0.4654492139816284 \| 0.1162 \| 0.1575 \| 0.3649 \| 0.0001
	58 \| 0.46654412150382996 \| 0.1166 \| 0.1584 \| 0.4075 \| 0.0001
	59 \| 0.465238481760025 \| 0.1157 \| 0.1575 \| 0.4202 \| 1e-05
	60 \| 0.46530231833457947 \| 0.1157 \| 0.1571 \| 0.4084 \| 1e-05
	61 \| 0.4653523564338684 \| 0.1153 \| 0.1573 \| 0.4497 \| 1e-05
	62 \| 0.46477487683296204 \| 0.1153 \| 0.1568 \| 0.4112 \| 1e-05
	63 \| 0.46481335163116455 \| 0.1152 \| 0.1567 \| 0.3748 \| 1e-05
	64 \| 0.46523070335388184 \| 0.1162 \| 0.1571 \| 0.3044 \| 1e-05
	65 \| 0.46484872698783875 \| 0.1153 \| 0.1569 \| 0.4685 \| 1e-05
	66 \| 0.46500927209854126 \| 0.1148 \| 0.1573 \| 0.5087 \| 1e-05
	67 \| 0.4645930230617523 \| 0.1155 \| 0.1568 \| 0.4274 \| 1e-05
	68 \| 0.46456360816955566 \| 0.1144 \| 0.1566 \| 0.4969 \| 1e-05
	69 \| 0.464430034160614 \| 0.1145 \| 0.1564 \| 0.4480 \| 1e-05
	70 \| 0.4648461937904358 \| 0.1150 \| 0.1567 \| 0.4291 \| 1e-05
	71 \| 0.4645022749900818 \| 0.1156 \| 0.1565 \| 0.3797 \| 1e-05
	72 \| 0.46473589539527893 \| 0.1150 \| 0.1569 \| 0.4280 \| 1e-05
	73 \| 0.46414923667907715 \| 0.1142 \| 0.1563 \| 0.4592 \| 1e-05
	74 \| 0.4641610085964203 \| 0.1151 \| 0.1564 \| 0.4321 \| 1e-05
	75 \| 0.4644509255886078 \| 0.1152 \| 0.1565 \| 0.3843 \| 1e-05
	76 \| 0.4646488130092621 \| 0.1147 \| 0.1569 \| 0.5216 \| 1e-05
	77 \| 0.46475714445114136 \| 0.1152 \| 0.1569 \| 0.4094 \| 1e-05
	78 \| 0.46428272128105164 \| 0.1149 \| 0.1564 \| 0.4399 \| 1e-05
	79 \| 0.4645934998989105 \| 0.1147 \| 0.1567 \| 0.4178 \| 1e-05
	80 \| 0.46436014771461487 \| 0.1150 \| 0.1564 \| 0.4373 \| 1.0000000000000002e-06
	81 \| 0.46448636054992676 \| 0.1151 \| 0.1567 \| 0.4701 \| 1.0000000000000002e-06
	82 \| 0.4644375145435333 \| 0.1146 \| 0.1565 \| 0.4601 \| 1.0000000000000002e-06
	83 \| 0.46457409858703613 \| 0.1147 \| 0.1567 \| 0.4511 \| 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-size32_freeze_probs
	model-index:
	- name: drone-DinoVdeau-from-probs-large-2024_11_15-batch-size32_freeze_probs
	results: []
	---

	drone-DinoVdeau-from-probs is a fine-tuned version of [drone-DinoVdeau-from-probs-large-2024_11_15-batch-size32_freeze_probs](https://huggingface.co/drone-DinoVdeau-from-probs-large-2024_11_15-batch-size32_freeze_probs). It achieves the following results on the test set:


	- Loss: 0.4668
	- RMSE: 0.1546
	- MAE: 0.1143
	- KL Divergence: 0.3931

	---

	# Model description
	drone-DinoVdeau-from-probs is a model built on top of drone-DinoVdeau-from-probs-large-2024_11_15-batch-size32_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: 83.0
	- 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 \| MAE \| RMSE \| KL div \| Learning Rate
	--- \| --- \| --- \| --- \| --- \| ---
	1 \| 0.4855400025844574 \| 0.1364 \| 0.1771 \| 0.3101 \| 0.001
	2 \| 0.47601452469825745 \| 0.1247 \| 0.1688 \| 0.5077 \| 0.001
	3 \| 0.4776814579963684 \| 0.1230 \| 0.1707 \| 0.7896 \| 0.001
	4 \| 0.47429159283638 \| 0.1238 \| 0.1672 \| 0.4932 \| 0.001
	5 \| 0.47457176446914673 \| 0.1277 \| 0.1669 \| 0.2901 \| 0.001
	6 \| 0.4749792814254761 \| 0.1253 \| 0.1674 \| 0.4399 \| 0.001
	7 \| 0.4744807779788971 \| 0.1259 \| 0.1671 \| 0.4868 \| 0.001
	8 \| 0.47424906492233276 \| 0.1257 \| 0.1672 \| 0.3241 \| 0.001
	9 \| 0.4729686379432678 \| 0.1236 \| 0.1658 \| 0.4560 \| 0.001
	10 \| 0.4750550389289856 \| 0.1269 \| 0.1679 \| 0.2141 \| 0.001
	11 \| 0.4733181595802307 \| 0.1265 \| 0.1663 \| 0.2530 \| 0.001
	12 \| 0.4758349061012268 \| 0.1264 \| 0.1684 \| 0.3966 \| 0.001
	13 \| 0.4722050428390503 \| 0.1223 \| 0.1650 \| 0.6055 \| 0.001
	14 \| 0.4747372567653656 \| 0.1250 \| 0.1666 \| 0.4203 \| 0.001
	15 \| 0.47325292229652405 \| 0.1227 \| 0.1662 \| 0.6553 \| 0.001
	16 \| 0.4734710156917572 \| 0.1241 \| 0.1656 \| 0.3576 \| 0.001
	17 \| 0.4721581041812897 \| 0.1221 \| 0.1643 \| 0.4545 \| 0.001
	18 \| 0.4723944365978241 \| 0.1225 \| 0.1647 \| 0.4902 \| 0.001
	19 \| 0.47289156913757324 \| 0.1261 \| 0.1650 \| 0.3158 \| 0.001
	20 \| 0.4697262644767761 \| 0.1203 \| 0.1623 \| 0.4574 \| 0.0001
	21 \| 0.46890661120414734 \| 0.1197 \| 0.1613 \| 0.4569 \| 0.0001
	22 \| 0.46905258297920227 \| 0.1202 \| 0.1617 \| 0.4535 \| 0.0001
	23 \| 0.4691086411476135 \| 0.1210 \| 0.1614 \| 0.2971 \| 0.0001
	24 \| 0.46915334463119507 \| 0.1196 \| 0.1616 \| 0.3916 \| 0.0001
	25 \| 0.4676876664161682 \| 0.1181 \| 0.1601 \| 0.4516 \| 0.0001
	26 \| 0.4679708480834961 \| 0.1171 \| 0.1605 \| 0.6089 \| 0.0001
	27 \| 0.4674595892429352 \| 0.1182 \| 0.1600 \| 0.4741 \| 0.0001
	28 \| 0.46810340881347656 \| 0.1200 \| 0.1606 \| 0.3356 \| 0.0001
	29 \| 0.4678303897380829 \| 0.1181 \| 0.1603 \| 0.4330 \| 0.0001
	30 \| 0.46800243854522705 \| 0.1194 \| 0.1602 \| 0.3160 \| 0.0001
	31 \| 0.4676785469055176 \| 0.1179 \| 0.1600 \| 0.4190 \| 0.0001
	32 \| 0.46752873063087463 \| 0.1188 \| 0.1598 \| 0.3706 \| 0.0001
	33 \| 0.46710190176963806 \| 0.1181 \| 0.1593 \| 0.3504 \| 0.0001
	34 \| 0.4670344293117523 \| 0.1180 \| 0.1594 \| 0.3881 \| 0.0001
	35 \| 0.4662601053714752 \| 0.1166 \| 0.1587 \| 0.4398 \| 0.0001
	36 \| 0.46657058596611023 \| 0.1170 \| 0.1587 \| 0.4382 \| 0.0001
	37 \| 0.4657588005065918 \| 0.1163 \| 0.1581 \| 0.4330 \| 0.0001
	38 \| 0.4659184217453003 \| 0.1162 \| 0.1583 \| 0.4878 \| 0.0001
	39 \| 0.46703553199768066 \| 0.1178 \| 0.1595 \| 0.3791 \| 0.0001
	40 \| 0.4664987027645111 \| 0.1178 \| 0.1588 \| 0.3889 \| 0.0001
	41 \| 0.46659526228904724 \| 0.1184 \| 0.1589 \| 0.3222 \| 0.0001
	42 \| 0.4655005633831024 \| 0.1164 \| 0.1579 \| 0.4262 \| 0.0001
	43 \| 0.4656265676021576 \| 0.1162 \| 0.1579 \| 0.4611 \| 0.0001
	44 \| 0.4655725955963135 \| 0.1164 \| 0.1580 \| 0.4586 \| 0.0001
	45 \| 0.46600833535194397 \| 0.1158 \| 0.1583 \| 0.4368 \| 0.0001
	46 \| 0.4660418927669525 \| 0.1164 \| 0.1582 \| 0.4118 \| 0.0001
	47 \| 0.46521857380867004 \| 0.1154 \| 0.1577 \| 0.5424 \| 0.0001
	48 \| 0.46598610281944275 \| 0.1160 \| 0.1586 \| 0.5251 \| 0.0001
	49 \| 0.46604350209236145 \| 0.1161 \| 0.1585 \| 0.5007 \| 0.0001
	50 \| 0.46660009026527405 \| 0.1185 \| 0.1586 \| 0.2424 \| 0.0001
	51 \| 0.4660661220550537 \| 0.1162 \| 0.1584 \| 0.4171 \| 0.0001
	52 \| 0.4649689793586731 \| 0.1155 \| 0.1575 \| 0.4912 \| 0.0001
	53 \| 0.4653578996658325 \| 0.1169 \| 0.1578 \| 0.4030 \| 0.0001
	54 \| 0.4660585820674896 \| 0.1153 \| 0.1585 \| 0.4811 \| 0.0001
	55 \| 0.46527624130249023 \| 0.1167 \| 0.1576 \| 0.3774 \| 0.0001
	56 \| 0.4654240906238556 \| 0.1176 \| 0.1575 \| 0.3254 \| 0.0001
	57 \| 0.4654492139816284 \| 0.1162 \| 0.1575 \| 0.3649 \| 0.0001
	58 \| 0.46654412150382996 \| 0.1166 \| 0.1584 \| 0.4075 \| 0.0001
	59 \| 0.465238481760025 \| 0.1157 \| 0.1575 \| 0.4202 \| 1e-05
	60 \| 0.46530231833457947 \| 0.1157 \| 0.1571 \| 0.4084 \| 1e-05
	61 \| 0.4653523564338684 \| 0.1153 \| 0.1573 \| 0.4497 \| 1e-05
	62 \| 0.46477487683296204 \| 0.1153 \| 0.1568 \| 0.4112 \| 1e-05
	63 \| 0.46481335163116455 \| 0.1152 \| 0.1567 \| 0.3748 \| 1e-05
	64 \| 0.46523070335388184 \| 0.1162 \| 0.1571 \| 0.3044 \| 1e-05
	65 \| 0.46484872698783875 \| 0.1153 \| 0.1569 \| 0.4685 \| 1e-05
	66 \| 0.46500927209854126 \| 0.1148 \| 0.1573 \| 0.5087 \| 1e-05
	67 \| 0.4645930230617523 \| 0.1155 \| 0.1568 \| 0.4274 \| 1e-05
	68 \| 0.46456360816955566 \| 0.1144 \| 0.1566 \| 0.4969 \| 1e-05
	69 \| 0.464430034160614 \| 0.1145 \| 0.1564 \| 0.4480 \| 1e-05
	70 \| 0.4648461937904358 \| 0.1150 \| 0.1567 \| 0.4291 \| 1e-05
	71 \| 0.4645022749900818 \| 0.1156 \| 0.1565 \| 0.3797 \| 1e-05
	72 \| 0.46473589539527893 \| 0.1150 \| 0.1569 \| 0.4280 \| 1e-05
	73 \| 0.46414923667907715 \| 0.1142 \| 0.1563 \| 0.4592 \| 1e-05
	74 \| 0.4641610085964203 \| 0.1151 \| 0.1564 \| 0.4321 \| 1e-05
	75 \| 0.4644509255886078 \| 0.1152 \| 0.1565 \| 0.3843 \| 1e-05
	76 \| 0.4646488130092621 \| 0.1147 \| 0.1569 \| 0.5216 \| 1e-05
	77 \| 0.46475714445114136 \| 0.1152 \| 0.1569 \| 0.4094 \| 1e-05
	78 \| 0.46428272128105164 \| 0.1149 \| 0.1564 \| 0.4399 \| 1e-05
	79 \| 0.4645934998989105 \| 0.1147 \| 0.1567 \| 0.4178 \| 1e-05
	80 \| 0.46436014771461487 \| 0.1150 \| 0.1564 \| 0.4373 \| 1.0000000000000002e-06
	81 \| 0.46448636054992676 \| 0.1151 \| 0.1567 \| 0.4701 \| 1.0000000000000002e-06
	82 \| 0.4644375145435333 \| 0.1146 \| 0.1565 \| 0.4601 \| 1.0000000000000002e-06
	83 \| 0.46457409858703613 \| 0.1147 \| 0.1567 \| 0.4511 \| 1.0000000000000002e-06


	---

	# Framework Versions

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