electra-base-finetuned-xe_ey_fae

This model is a fine-tuned version of google/electra-base-generator on the datasets/all_binary_and_xe_ey_fae_counterfactual dataset. It achieves the following results on the evaluation set:

Loss: 1.7211
Accuracy: 0.6673

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: 1e-05
train_batch_size: 8
eval_batch_size: 8
seed: 100
gradient_accumulation_steps: 2
total_train_batch_size: 16
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 3.0
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
2.5359	0.06	500	2.0696	0.6228
2.1807	0.13	1000	1.9677	0.6352
2.1028	0.19	1500	1.9192	0.6415
2.0658	0.26	2000	1.8923	0.6451
2.0426	0.32	2500	1.8699	0.6478
2.0133	0.39	3000	1.8580	0.6490
1.9978	0.45	3500	1.8411	0.6507
1.9862	0.52	4000	1.8297	0.6524
1.9745	0.58	4500	1.8154	0.6545
1.9606	0.64	5000	1.8056	0.6557
1.9486	0.71	5500	1.8033	0.6560
1.9416	0.77	6000	1.7894	0.6581
1.9279	0.84	6500	1.7848	0.6582
1.9196	0.9	7000	1.7786	0.6593
1.9168	0.97	7500	1.7762	0.6592
1.9123	1.03	8000	1.7744	0.6597
1.8942	1.1	8500	1.7625	0.6611
1.9053	1.16	9000	1.7576	0.6623
1.898	1.22	9500	1.7588	0.6620
1.8896	1.29	10000	1.7518	0.6625
1.8796	1.35	10500	1.7557	0.6619
1.8838	1.42	11000	1.7511	0.6628
1.8869	1.48	11500	1.7437	0.6640
1.8756	1.55	12000	1.7425	0.6641
1.8775	1.61	12500	1.7409	0.6641
1.8757	1.68	13000	1.7372	0.6649
1.8616	1.74	13500	1.7387	0.6646
1.8675	1.8	14000	1.7335	0.6648
1.8725	1.87	14500	1.7288	0.6660
1.8678	1.93	15000	1.7305	0.6659
1.8611	2.0	15500	1.7256	0.6666
1.853	2.06	16000	1.7286	0.6661
1.8487	2.13	16500	1.7285	0.6659
1.8543	2.19	17000	1.7229	0.6668
1.8519	2.26	17500	1.7240	0.6670
1.851	2.32	18000	1.7275	0.6662
1.8547	2.38	18500	1.7197	0.6673
1.8476	2.45	19000	1.7164	0.6675
1.8444	2.51	19500	1.7214	0.6676
1.8544	2.58	20000	1.7217	0.6668
1.8491	2.64	20500	1.7175	0.6678

Framework versions

Transformers 4.36.2
Pytorch 2.2.0+cu121
Datasets 2.17.0
Tokenizers 0.15.2

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Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 1e-05

train_batch_size: 8

eval_batch_size: 8

seed: 100

gradient_accumulation_steps: 2

total_train_batch_size: 16

optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08

lr_scheduler_type: linear

num_epochs: 3.0

mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
2.5359	0.06	500	2.0696	0.6228
2.1807	0.13	1000	1.9677	0.6352
2.1028	0.19	1500	1.9192	0.6415
2.0658	0.26	2000	1.8923	0.6451
2.0426	0.32	2500	1.8699	0.6478
2.0133	0.39	3000	1.8580	0.6490
1.9978	0.45	3500	1.8411	0.6507
1.9862	0.52	4000	1.8297	0.6524
1.9745	0.58	4500	1.8154	0.6545
1.9606	0.64	5000	1.8056	0.6557
1.9486	0.71	5500	1.8033	0.6560
1.9416	0.77	6000	1.7894	0.6581
1.9279	0.84	6500	1.7848	0.6582
1.9196	0.9	7000	1.7786	0.6593
1.9168	0.97	7500	1.7762	0.6592
1.9123	1.03	8000	1.7744	0.6597
1.8942	1.1	8500	1.7625	0.6611
1.9053	1.16	9000	1.7576	0.6623
1.898	1.22	9500	1.7588	0.6620
1.8896	1.29	10000	1.7518	0.6625
1.8796	1.35	10500	1.7557	0.6619
1.8838	1.42	11000	1.7511	0.6628
1.8869	1.48	11500	1.7437	0.6640
1.8756	1.55	12000	1.7425	0.6641
1.8775	1.61	12500	1.7409	0.6641
1.8757	1.68	13000	1.7372	0.6649
1.8616	1.74	13500	1.7387	0.6646
1.8675	1.8	14000	1.7335	0.6648
1.8725	1.87	14500	1.7288	0.6660
1.8678	1.93	15000	1.7305	0.6659
1.8611	2.0	15500	1.7256	0.6666
1.853	2.06	16000	1.7286	0.6661
1.8487	2.13	16500	1.7285	0.6659
1.8543	2.19	17000	1.7229	0.6668
1.8519	2.26	17500	1.7240	0.6670
1.851	2.32	18000	1.7275	0.6662
1.8547	2.38	18500	1.7197	0.6673
1.8476	2.45	19000	1.7164	0.6675
1.8444	2.51	19500	1.7214	0.6676
1.8544	2.58	20000	1.7217	0.6668
1.8491	2.64	20500	1.7175	0.6678

Framework versions

Transformers 4.36.2

Pytorch 2.2.0+cu121

Datasets 2.17.0

Tokenizers 0.15.2

ucla-nb-project/electra-finetuned

electra-base-finetuned-xe_ey_fae

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

electra-base-finetuned-xe_ey_fae

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions