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: 0.0001
train_batch_size: 16
eval_batch_size: 16
seed: 17
gradient_accumulation_steps: 6
total_train_batch_size: 96
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: cosine
num_epochs: 15
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
0.5453	0.27	100	0.4824	0.7776	0.7726	0.7751	0.7696
0.4324	0.54	200	0.4796	0.8033	0.7279	0.7637	0.7686
0.4042	0.82	300	0.3790	0.7697	0.9608	0.8547	0.8321
0.3849	1.09	400	0.4100	0.8125	0.8198	0.8161	0.8102
0.3621	1.36	500	0.3689	0.8099	0.8967	0.8511	0.8387
0.3457	1.63	600	0.3313	0.7896	0.9543	0.8642	0.8459
0.3443	1.9	700	0.3424	0.7836	0.9528	0.8600	0.8405
0.3287	2.18	800	0.3308	0.8206	0.8947	0.8561	0.8454
0.3224	2.45	900	0.4546	0.8481	0.6624	0.7438	0.7655
0.3096	2.72	1000	0.3402	0.8300	0.8754	0.8521	0.8438
0.3095	2.99	1100	0.3691	0.8035	0.9076	0.8524	0.8385
0.2901	3.27	1200	0.3643	0.8008	0.8982	0.8467	0.8329
0.2939	3.54	1300	0.3021	0.8047	0.9613	0.8760	0.8602
0.2946	3.81	1400	0.3617	0.8363	0.8322	0.8342	0.8301
0.2856	4.08	1500	0.4884	0.8401	0.7850	0.8116	0.8127
0.2683	4.35	1600	0.3540	0.841	0.8352	0.8381	0.8341
0.2724	4.63	1700	0.3078	0.8391	0.8957	0.8665	0.8581
0.2685	4.9	1800	0.2913	0.8455	0.8967	0.8704	0.8627
0.2449	5.17	1900	0.3866	0.8465	0.8515	0.8490	0.8443
0.2468	5.44	2000	0.3072	0.8406	0.8952	0.8670	0.8589
0.2557	5.71	2100	0.3735	0.8595	0.7900	0.8233	0.8257
0.25	5.99	2200	0.3117	0.8755	0.8376	0.8561	0.8553
0.2256	6.26	2300	0.3264	0.8407	0.8913	0.8653	0.8574
0.234	6.53	2400	0.3617	0.8950	0.7572	0.8203	0.8295
0.2259	6.8	2500	0.3284	0.8476	0.8893	0.8679	0.8609
0.2261	7.07	2600	0.3486	0.9034	0.7805	0.8375	0.8443
0.2087	7.35	2700	0.3971	0.8628	0.8118	0.8365	0.8369
0.2035	7.62	2800	0.3106	0.8722	0.8942	0.8831	0.8783
0.2116	7.89	2900	0.3734	0.8805	0.8083	0.8429	0.8451
0.1956	8.16	3000	0.3443	0.8612	0.8654	0.8633	0.8591
0.1826	8.44	3100	0.3795	0.8908	0.7900	0.8374	0.8423
0.1918	8.71	3200	0.3362	0.8894	0.8267	0.8569	0.8581
0.1886	8.98	3300	0.3259	0.8589	0.8798	0.8693	0.8640
0.1716	9.25	3400	0.4269	0.8621	0.8347	0.8481	0.8464
0.1654	9.52	3500	0.4066	0.8881	0.8317	0.8590	0.8597
0.1625	9.8	3600	0.3927	0.8882	0.8128	0.8488	0.8512
0.1659	10.07	3700	0.3797	0.8895	0.8193	0.8529	0.8548
0.1519	10.34	3800	0.4089	0.8808	0.8217	0.8502	0.8512
0.1484	10.61	3900	0.3865	0.8853	0.8237	0.8534	0.8546
0.1427	10.88	4000	0.4347	0.8892	0.8009	0.8427	0.8464
0.1375	11.16	4100	0.4688	0.8878	0.8213	0.8532	0.8548
0.1276	11.43	4200	0.4687	0.8932	0.7974	0.8426	0.8469
0.1275	11.7	4300	0.4493	0.8936	0.8009	0.8447	0.8487
0.1349	11.97	4400	0.4618	0.8975	0.7825	0.8361	0.8423
0.1217	12.24	4500	0.4636	0.8987	0.7974	0.8450	0.8497
0.1211	12.52	4600	0.4527	0.8815	0.8307	0.8553	0.8556
0.1164	12.79	4700	0.4669	0.8950	0.8123	0.8516	0.8546
0.1119	13.06	4800	0.4617	0.8875	0.8148	0.8496	0.8517
0.11	13.33	4900	0.4718	0.8894	0.8103	0.8480	0.8507
0.1138	13.61	5000	0.4892	0.8939	0.7989	0.8437	0.8479
0.1058	13.88	5100	0.4725	0.8875	0.8108	0.8474	0.8500
0.1042	14.15	5200	0.4788	0.8908	0.8064	0.8465	0.8497
0.107	14.42	5300	0.4759	0.8900	0.8073	0.8467	0.8497
0.1047	14.69	5400	0.4767	0.8894	0.8064	0.8458	0.8489
0.1085	14.97	5500	0.4769	0.8894	0.8064	0.8458	0.8489

Framework versions

Transformers 4.36.2
Pytorch 2.1.2
Datasets 2.16.1
Tokenizers 0.15.0

Coelhomatias/deit-cvc-drop-aug

deit-cvc-drop-aug

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

Coelhomatias/
deit-cvc-drop-aug