You can deploy this model and start earning money today!

alex2awesome/
source-role-model

Name: source-role-model
Availability: InStock

transformers

pytorch

roberta

generated_from_trainer

mit

endpoints_compatible

Views

No views yet

source-role-model

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

Loss: 2.5543
F1: 0.5814

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: 5e-05
train_batch_size: 5
eval_batch_size: 5
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 10.0

Training results

Training Loss	Epoch	Step	Validation Loss	F1
No log	0.12	100	1.0000	0.3391
No log	0.25	200	0.8371	0.5055
No log	0.37	300	0.8684	0.5019
No log	0.49	400	0.8668	0.5208
0.9644	0.62	500	0.8473	0.5422
0.9644	0.74	600	0.8852	0.4956
0.9644	0.86	700	0.8368	0.5124
0.9644	0.99	800	0.7913	0.5848
0.9644	1.11	900	1.0570	0.4950
0.8375	1.23	1000	0.9402	0.5280
0.8375	1.35	1100	0.8023	0.5084
0.8375	1.48	1200	0.9299	0.4807
0.8375	1.6	1300	0.9661	0.5194
0.8375	1.72	1400	0.8014	0.6016
0.8149	1.85	1500	0.8608	0.6105
0.8149	1.97	1600	0.9195	0.5741
0.8149	2.09	1700	1.2378	0.5964
0.8149	2.22	1800	1.0415	0.5902
0.8149	2.34	1900	1.0499	0.5526
0.6932	2.46	2000	1.0600	0.5832
0.6932	2.59	2100	0.9368	0.6074
0.6932	2.71	2200	1.0872	0.6270
0.6932	2.83	2300	1.0912	0.5707
0.6932	2.96	2400	0.8815	0.5602
0.6214	3.08	2500	1.1650	0.5993
0.6214	3.2	2600	1.4485	0.5821
0.6214	3.33	2700	1.5382	0.5775
0.6214	3.45	2800	1.3999	0.5696
0.6214	3.57	2900	1.3702	0.6114
0.5686	3.69	3000	1.3840	0.5635
0.5686	3.82	3100	1.3547	0.5403
0.5686	3.94	3200	1.0283	0.5723
0.5686	4.06	3300	1.3593	0.6242
0.5686	4.19	3400	1.5985	0.6004
0.4807	4.31	3500	1.5351	0.6177
0.4807	4.43	3600	1.4109	0.5779
0.4807	4.56	3700	1.6972	0.5637
0.4807	4.68	3800	1.5336	0.6047
0.4807	4.8	3900	1.7811	0.5909
0.4387	4.93	4000	1.5862	0.5869
0.4387	5.05	4100	1.7106	0.5637
0.4387	5.17	4200	1.5251	0.5624
0.4387	5.3	4300	1.5519	0.5944
0.4387	5.42	4400	1.7315	0.5908
0.3219	5.54	4500	1.7588	0.6015
0.3219	5.67	4600	1.9277	0.5635
0.3219	5.79	4700	1.7663	0.5891
0.3219	5.91	4800	1.8401	0.5917
0.3219	6.03	4900	2.0516	0.5845
0.2311	6.16	5000	2.0510	0.6166
0.2311	6.28	5100	2.1673	0.5732
0.2311	6.4	5200	2.0931	0.5819
0.2311	6.53	5300	2.2803	0.5961
0.2311	6.65	5400	1.9985	0.6010
0.1669	6.77	5500	2.1742	0.5664
0.1669	6.9	5600	2.1021	0.5732
0.1669	7.02	5700	2.2043	0.5641
0.1669	7.14	5800	2.2018	0.5837
0.1669	7.27	5900	2.3575	0.5721
0.1698	7.39	6000	2.4663	0.5662
0.1698	7.51	6100	2.2658	0.5851
0.1698	7.64	6200	2.1585	0.5676
0.1698	7.76	6300	2.1755	0.5774
0.1698	7.88	6400	2.2680	0.5696
0.1378	8.0	6500	2.3505	0.5615
0.1378	8.13	6600	2.2773	0.5705
0.1378	8.25	6700	2.3112	0.5662
0.1378	8.37	6800	2.4572	0.5679
0.1378	8.5	6900	2.4642	0.5766
0.0756	8.62	7000	2.4643	0.5885
0.0756	8.74	7100	2.5096	0.5779
0.0756	8.87	7200	2.4261	0.5789
0.0756	8.99	7300	2.3973	0.5757
0.0756	9.11	7400	2.4137	0.5906
0.0842	9.24	7500	2.4577	0.5844
0.0842	9.36	7600	2.5034	0.5840
0.0842	9.48	7700	2.5176	0.5810
0.0842	9.61	7800	2.5240	0.5852
0.0842	9.73	7900	2.5141	0.5824
0.0634	9.85	8000	2.5482	0.5814
0.0634	9.98	8100	2.5543	0.5814

Framework versions

Transformers 4.30.2
Pytorch 2.0.1+cu117
Datasets 2.13.1
Tokenizers 0.13.3

alex2awesome/source-role-model

source-role-model

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

alex2awesome/
source-role-model