Regression_bert_1500

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

Train Loss: 0.2224
Train Mae: 0.4296
Train Mse: 0.2717
Train R2-score: 0.8508
Validation Loss: 0.1846
Validation Mae: 0.4542
Validation Mse: 0.2649
Validation R2-score: 0.7458
Epoch: 39

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:

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

Training results

Train Loss	Train Mae	Train Mse	Train R2-score	Validation Loss	Validation Mae	Validation Mse	Validation R2-score	Epoch
0.2245	0.4557	0.2970	0.6980	0.1019	0.3869	0.1805	0.6789	0
0.1102	0.3741	0.1848	0.8480	0.0839	0.2912	0.1209	0.8311	1
0.0845	0.3575	0.1689	0.2888	0.0620	0.3294	0.1463	0.8421	2
0.0554	0.3231	0.1366	0.7118	0.0613	0.2950	0.1305	0.7960	3
0.0481	0.3071	0.1276	0.8265	0.0516	0.3112	0.1357	0.8145	4
0.0461	0.2876	0.1168	0.8131	0.0524	0.2770	0.1097	0.8434	5
0.0454	0.2809	0.1140	0.7029	0.0502	0.3041	0.1340	0.8063	6
0.0448	0.2930	0.1212	0.6336	0.0514	0.2761	0.1143	0.8154	7
0.0446	0.2728	0.1079	0.8086	0.0532	0.2696	0.1145	0.8033	8
0.0450	0.2733	0.1086	0.6564	0.0504	0.2590	0.1033	0.8335	9
0.0792	0.3267	0.1471	0.5533	0.0848	0.3585	0.1709	0.7364	10
0.0673	0.3235	0.1407	0.6299	0.0621	0.3478	0.1549	0.7915	11
0.0571	0.3151	0.1347	0.6762	0.0729	0.2784	0.1203	0.8483	12
0.0528	0.2797	0.1104	0.7393	0.0642	0.2901	0.1291	0.7939	13
0.0490	0.2930	0.1187	0.6853	0.0821	0.2995	0.1446	0.7879	14
0.0566	0.2789	0.1120	0.7938	0.0878	0.3242	0.1589	0.8498	15
0.1926	0.4228	0.2638	0.6635	0.2201	0.4528	0.2705	0.6836	16
0.1921	0.4231	0.2458	0.7452	0.1018	0.3940	0.1900	0.7454	17
0.1288	0.4126	0.2154	0.6279	0.1035	0.3943	0.1991	0.7491	18
0.1897	0.4375	0.2617	0.8354	0.2664	0.4703	0.3447	0.7632	19
0.2095	0.4337	0.2676	0.8187	0.2730	0.4652	0.3425	0.8093	20
0.2682	0.4555	0.3358	0.7789	0.2851	0.4590	0.3518	0.8013	21
0.2416	0.4616	0.3213	0.7117	0.2466	0.4999	0.3667	0.6966	22
0.1798	0.4569	0.2737	0.5086	0.0945	0.4097	0.2023	0.7475	23
0.1361	0.4186	0.2140	0.5820	0.1181	0.4135	0.2075	0.7951	24
0.1660	0.4334	0.2322	0.4041	0.2522	0.4329	0.3019	0.7785	25
0.3046	0.4603	0.3555	0.7796	0.2886	0.4894	0.3587	0.7229	26
0.2843	0.4723	0.3491	0.7267	0.2937	0.4712	0.3616	0.7308	27
0.2793	0.4531	0.3379	0.7893	0.2952	0.4762	0.3738	0.7540	28
0.2410	0.4286	0.2899	0.8189	0.2358	0.4379	0.2932	0.7727	29
0.2270	0.4203	0.2691	0.8224	0.2361	0.4466	0.2992	0.7742	30
0.2260	0.4341	0.2776	0.8716	0.2290	0.4345	0.2885	0.7809	31
0.2198	0.4225	0.2629	0.7666	0.2303	0.4392	0.2895	0.7775	32
0.2247	0.4245	0.2709	0.8193	0.2291	0.4424	0.2921	0.7813	33
0.2241	0.4276	0.2704	0.6859	0.2280	0.4430	0.2960	0.7879	34
0.2239	0.4292	0.2709	0.8308	0.2314	0.4445	0.2893	0.7641	35
0.2174	0.4251	0.2637	0.7179	0.2354	0.4565	0.3017	0.7566	36
0.2211	0.4257	0.2686	0.7896	0.2409	0.4467	0.3014	0.7683	37
0.2321	0.4218	0.2745	0.8021	0.2413	0.4525	0.3062	0.7663	38
0.2224	0.4296	0.2717	0.8508	0.1846	0.4542	0.2649	0.7458	39

Framework versions

Transformers 4.28.1
TensorFlow 2.12.0
Datasets 2.11.0
Tokenizers 0.13.3

Regression_bert_1500

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

NSDT 3DConvert

UnrealSynth

DreamTexture.js