Regression_bert_1500

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
0.3911	0.5811	0.4875	0.5636	0.3808	0.6393	0.4778	0.4775	0
0.3669	0.5644	0.4527	0.6196	0.3524	0.5673	0.4286	0.6944	1
0.3652	0.5606	0.4457	0.6645	0.3711	0.6253	0.4600	0.5315	2
0.3669	0.5642	0.4490	0.5194	0.3525	0.5695	0.4286	0.6901	3
0.3693	0.5693	0.4580	0.6646	0.3558	0.5904	0.4329	0.6414	4
0.3682	0.5633	0.4540	0.7464	0.3602	0.5255	0.4485	0.7509	5
0.3712	0.5632	0.4527	0.6645	0.3650	0.6145	0.4489	0.5693	6
0.3781	0.5720	0.4661	0.5801	0.3545	0.5849	0.4309	0.6553	7
0.3659	0.5673	0.4564	0.1693	0.3723	0.6271	0.4621	0.5247	8
0.3693	0.5642	0.4487	0.7048	0.3524	0.5641	0.4289	0.7006	9
0.3656	0.5655	0.4495	0.6565	0.3575	0.5328	0.4425	0.7448	10
0.3685	0.5632	0.4540	0.7202	0.3551	0.5878	0.4319	0.6482	11
0.3702	0.5646	0.4543	0.7295	0.3528	0.5557	0.4306	0.7152	12
0.3661	0.5615	0.4450	0.6631	0.3683	0.5240	0.4664	0.7592	13
0.3835	0.5742	0.4757	0.7335	0.3531	0.5523	0.4316	0.7206	14
0.3641	0.5628	0.4472	0.7325	0.3559	0.5909	0.4331	0.6399	15
0.3764	0.5633	0.4566	0.7291	0.3549	0.5867	0.4315	0.6508	16
0.3625	0.5594	0.4443	0.5555	0.3648	0.6141	0.4486	0.5707	17
0.3816	0.5743	0.4693	0.6649	0.3559	0.5385	0.4385	0.7389	18
0.3721	0.5721	0.4618	0.6791	0.3529	0.5745	0.4288	0.6795	19
0.3711	0.5659	0.4586	0.2709	0.3610	0.5234	0.4505	0.7525	20
0.3693	0.5641	0.4501	0.7400	0.3525	0.5607	0.4294	0.7068	21
0.3665	0.5651	0.4539	0.5632	0.3640	0.6123	0.4470	0.5765	22