Regression_albert_aug_CustomLoss_3

This model is a fine-tuned version of 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:

optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': 1e-04, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
training_precision: float32

Train Loss	Train Mae	Train Mse	Train R2-score	Validation Loss	Validation Mae	Validation Mse	Validation R2-score	Epoch
0.2614	0.5480	0.4524	0.7369	0.2408	0.5194	0.4609	0.7578	0
0.2442	0.5374	0.4362	0.7109	0.2334	0.5376	0.4391	0.7399	1
0.2431	0.5349	0.4356	0.7503	0.2432	0.5234	0.4657	0.7591	2
0.2386	0.5250	0.4264	0.7926	0.2348	0.5525	0.4316	0.7203	3
0.2409	0.5342	0.4325	0.7166	0.2431	0.5233	0.4656	0.7591	4
0.2400	0.5298	0.4310	0.7553	0.2358	0.5250	0.4490	0.7513	5
0.2384	0.5274	0.4299	0.7791	0.2341	0.5491	0.4329	0.7253	6
0.2413	0.5306	0.4335	0.7593	0.2365	0.5583	0.4299	0.7109	7
0.2381	0.5299	0.4298	0.7784	0.2335	0.5452	0.4347	0.7306	8
0.2379	0.5280	0.4297	0.7575	0.2335	0.5448	0.4349	0.7312	9
0.2374	0.5306	0.4309	0.8098	0.2334	0.5441	0.4352	0.7321	10
0.2381	0.5302	0.4303	0.7428	0.2337	0.5466	0.4340	0.7288	11
0.2376	0.5323	0.4275	0.7806	0.2333	0.5411	0.4369	0.7358	12
0.2339	0.5277	0.4217	0.7986	0.2363	0.5232	0.4506	0.7525	13
0.2368	0.5301	0.4296	0.7669	0.2410	0.5680	0.4286	0.6930	14