google_electra-small-discriminator_freeze_negsam

This model is a fine-tuned version of google/electra-small-discriminator on the None dataset. It achieves the following results on the evaluation set:

Loss: 0.6004
Accuracy: 0.8810
F1 Macro: 0.4523
Precision Destination: 0.9256
Recall Destination: 0.9463
Precision Origin: 0.6111
Recall Origin: 0.75
Precision Other: 0.3
Recall Other: 0.15
Precision Transit: 0.0
Recall Transit: 0.0
Super Metric: 1.6963
Raw Super Metric: 1.6963

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 2e-05
train_batch_size: 16
eval_batch_size: 16
seed: 42
optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: linear
num_epochs: 10
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1 Macro	Precision Destination	Recall Destination	Precision Origin	Recall Origin	Precision Other	Recall Other	Super Metric	Raw Super Metric
1.1254	1.0	269	0.9680	0.8637	0.2798	0.8674	0.9955	0.625	0.1136	0.0	0.0	1.0936	1.1092
0.7562	2.0	538	0.6568	0.8541	0.3775	0.9196	0.9217	0.4853	0.75	0.0	0.0	1.6717	1.6717
0.556	3.0	807	0.5947	0.8656	0.3922	0.9138	0.9485	0.5532	0.5909	0.1	0.05	1.5395	1.5395
0.4718	4.0	1076	0.4209	0.8503	0.4168	0.9458	0.8971	0.4396	0.9091	0.3333	0.1	1.8062	1.8062
0.4991	5.0	1345	0.5285	0.8733	0.4032	0.9237	0.9485	0.5357	0.6818	0.1667	0.05	1.6304	1.6304
0.3546	6.0	1614	0.5450	0.8772	0.4269	0.9297	0.9463	0.5424	0.7273	0.2857	0.1	1.6736	1.6736
0.3032	7.0	1883	0.6399	0.8772	0.4660	0.9158	0.9485	0.6222	0.6364	0.3846	0.25	1.5849	1.5849
0.2039	8.0	2152	0.5867	0.8791	0.4473	0.9256	0.9463	0.5818	0.7273	0.3333	0.15	1.6736	1.6736
0.3003	9.0	2421	0.5695	0.8791	0.4508	0.9294	0.9418	0.6071	0.7727	0.25	0.15	1.7146	1.7146
0.2917	10.0	2690	0.6004	0.8810	0.4523	0.9256	0.9463	0.6111	0.75	0.3	0.15	1.6963	1.6963

Framework versions

Transformers 4.51.3
Pytorch 2.6.0+cu124
Datasets 3.5.0
Tokenizers 0.21.1

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google_electra-small-discriminator_freeze_negsam

This model is a fine-tuned version of google/electra-small-discriminator on the None dataset. It achieves the following results on the evaluation set:

Loss: 0.6004

Accuracy: 0.8810

F1 Macro: 0.4523

Precision Destination: 0.9256

Recall Destination: 0.9463

Precision Origin: 0.6111

Recall Origin: 0.75

Precision Other: 0.3

Recall Other: 0.15

Precision Transit: 0.0

Recall Transit: 0.0

Super Metric: 1.6963

Raw Super Metric: 1.6963

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 2e-05

train_batch_size: 16

eval_batch_size: 16

seed: 42

optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments

lr_scheduler_type: linear

num_epochs: 10

mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1 Macro	Precision Destination	Recall Destination	Precision Origin	Recall Origin	Precision Other	Recall Other	Super Metric	Raw Super Metric
1.1254	1.0	269	0.9680	0.8637	0.2798	0.8674	0.9955	0.625	0.1136	0.0	0.0	1.0936	1.1092
0.7562	2.0	538	0.6568	0.8541	0.3775	0.9196	0.9217	0.4853	0.75	0.0	0.0	1.6717	1.6717
0.556	3.0	807	0.5947	0.8656	0.3922	0.9138	0.9485	0.5532	0.5909	0.1	0.05	1.5395	1.5395
0.4718	4.0	1076	0.4209	0.8503	0.4168	0.9458	0.8971	0.4396	0.9091	0.3333	0.1	1.8062	1.8062
0.4991	5.0	1345	0.5285	0.8733	0.4032	0.9237	0.9485	0.5357	0.6818	0.1667	0.05	1.6304	1.6304
0.3546	6.0	1614	0.5450	0.8772	0.4269	0.9297	0.9463	0.5424	0.7273	0.2857	0.1	1.6736	1.6736
0.3032	7.0	1883	0.6399	0.8772	0.4660	0.9158	0.9485	0.6222	0.6364	0.3846	0.25	1.5849	1.5849
0.2039	8.0	2152	0.5867	0.8791	0.4473	0.9256	0.9463	0.5818	0.7273	0.3333	0.15	1.6736	1.6736
0.3003	9.0	2421	0.5695	0.8791	0.4508	0.9294	0.9418	0.6071	0.7727	0.25	0.15	1.7146	1.7146
0.2917	10.0	2690	0.6004	0.8810	0.4523	0.9256	0.9463	0.6111	0.75	0.3	0.15	1.6963	1.6963

Framework versions

Transformers 4.51.3

Pytorch 2.6.0+cu124

Datasets 3.5.0

Tokenizers 0.21.1

Fariman/google_electra-small-discriminator_freeze_negsam

google_electra-small-discriminator_freeze_negsam

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

google_electra-small-discriminator_freeze_negsam

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions