Albert-Base

This model is a fine-tuned version of albert/albert-base-v2 on an unknown dataset. It achieves the following results on the evaluation set:

Model description

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The following hyperparameters were used during training:

learning_rate: 5e-05
train_batch_size: 128
eval_batch_size: 512
seed: 42
optimizer: Use 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: 40

Training Loss	Epoch	Step	Validation Loss	Accuracy
0.4359	1.0	158	0.3263	0.9016
0.2711	2.0	316	0.3180	0.9063
0.1583	3.0	474	0.3126	0.9117
0.1463	4.0	632	0.3113	0.9132
0.0722	5.0	790	0.2950	0.9234
0.0627	6.0	948	0.3461	0.9178
0.0528	7.0	1106	0.3541	0.9177
0.0336	8.0	1264	0.3469	0.9251
0.0416	9.0	1422	0.4367	0.9130
0.0356	10.0	1580	0.3814	0.9226
0.0092	11.0	1738	0.4591	0.9206
0.0127	12.0	1896	0.4355	0.9277
0.0055	13.0	2054	0.4419	0.9264
0.0019	14.0	2212	0.4578	0.9297
0.0009	15.0	2370	0.4947	0.9284
0.0017	16.0	2528	0.4893	0.9318
0.0003	17.0	2686	0.5132	0.9278
0.0013	18.0	2844	0.4901	0.9307
0.0029	19.0	3002	0.5176	0.9301
0.0064	20.0	3160	0.5325	0.9321
0.0001	21.0	3318	0.5367	0.9294
0.0001	22.0	3476	0.5389	0.9309
0.008	23.0	3634	0.5888	0.9274
0.0001	24.0	3792	0.5542	0.9315
0.0003	25.0	3950	0.5350	0.9323
0.0	26.0	4108	0.5480	0.9312
0.0	27.0	4266	0.5337	0.9343
0.0	28.0	4424	0.5453	0.9331
0.0	29.0	4582	0.5434	0.9333
0.0	30.0	4740	0.5443	0.9335
0.0	31.0	4898	0.5457	0.9338
0.0	32.0	5056	0.5468	0.9339
0.0	33.0	5214	0.5481	0.9339
0.0	34.0	5372	0.5492	0.9339
0.0	35.0	5530	0.5503	0.9343
0.0	36.0	5688	0.5511	0.9345
0.0	37.0	5846	0.5519	0.9343
0.0	38.0	6004	0.5525	0.9343
0.0	39.0	6162	0.5529	0.9343
0.0	40.0	6320	0.5530	0.9343