Anwaarma/NewMergedSpamModelFinal - AI Model Zoo

NewMergedSpamModelFinal

This model is a fine-tuned version of aubmindlab/bert-base-arabert on the None dataset. It achieves the following results on the evaluation set:

Loss: 0.0000
Accuracy: 1.0

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: 2e-05
train_batch_size: 16
eval_batch_size: 16
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 10

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
0.3172	0.05	50	0.2393	0.91
0.1727	0.11	100	0.1355	0.95
0.1024	0.16	150	0.0967	0.97
0.1318	0.21	200	0.0372	0.99
0.1189	0.27	250	0.0906	0.97
0.1043	0.32	300	0.0070	1.0
0.0734	0.37	350	0.0127	1.0
0.077	0.43	400	0.0021	1.0
0.0955	0.48	450	0.0583	0.98
0.0836	0.53	500	0.0227	0.99
0.0428	0.58	550	0.0027	1.0
0.0348	0.64	600	0.0838	0.97
0.0752	0.69	650	0.0082	1.0
0.0525	0.74	700	0.0177	0.99
0.0758	0.8	750	0.0350	0.98
0.0711	0.85	800	0.0089	1.0
0.0429	0.9	850	0.1653	0.96
0.0787	0.96	900	0.0020	1.0
0.0717	1.01	950	0.0051	1.0
0.0283	1.06	1000	0.0014	1.0
0.0163	1.12	1050	0.0551	0.99
0.0635	1.17	1100	0.0009	1.0
0.0554	1.22	1150	0.0039	1.0
0.0242	1.28	1200	0.0021	1.0
0.0419	1.33	1250	0.0068	1.0
0.0138	1.38	1300	0.0004	1.0
0.0284	1.43	1350	0.0004	1.0
0.0334	1.49	1400	0.0006	1.0
0.0335	1.54	1450	0.0589	0.99
0.0294	1.59	1500	0.0178	0.99
0.0224	1.65	1550	0.0481	0.99
0.0454	1.7	1600	0.0018	1.0
0.0197	1.75	1650	0.0271	0.99
0.0375	1.81	1700	0.0650	0.99
0.0567	1.86	1750	0.0016	1.0
0.0312	1.91	1800	0.0589	0.98
0.0232	1.97	1850	0.0816	0.98
0.0285	2.02	1900	0.0014	1.0
0.0242	2.07	1950	0.0005	1.0
0.03	2.13	2000	0.0012	1.0
0.0142	2.18	2050	0.0007	1.0
0.0238	2.23	2100	0.0005	1.0
0.0069	2.28	2150	0.0001	1.0
0.0169	2.34	2200	0.0002	1.0
0.0136	2.39	2250	0.0055	1.0
0.0075	2.44	2300	0.0363	0.99
0.0132	2.5	2350	0.0005	1.0
0.0026	2.55	2400	0.0002	1.0
0.0001	2.6	2450	0.0001	1.0
0.0356	2.66	2500	0.0002	1.0
0.0238	2.71	2550	0.0006	1.0
0.0077	2.76	2600	0.0002	1.0
0.0153	2.82	2650	0.0011	1.0
0.0106	2.87	2700	0.0009	1.0
0.0216	2.92	2750	0.0001	1.0
0.0411	2.98	2800	0.0001	1.0
0.0186	3.03	2850	0.0001	1.0
0.0084	3.08	2900	0.0002	1.0
0.0051	3.13	2950	0.0003	1.0
0.0079	3.19	3000	0.0030	1.0
0.0065	3.24	3050	0.0002	1.0
0.0005	3.29	3100	0.0002	1.0
0.0036	3.35	3150	0.0000	1.0
0.014	3.4	3200	0.0001	1.0
0.0197	3.45	3250	0.0004	1.0
0.0316	3.51	3300	0.0000	1.0
0.015	3.56	3350	0.0001	1.0
0.0082	3.61	3400	0.0001	1.0
0.0237	3.67	3450	0.0003	1.0
0.0106	3.72	3500	0.0598	0.99
0.0006	3.77	3550	0.0698	0.99
0.0272	3.83	3600	0.0002	1.0
0.0261	3.88	3650	0.0002	1.0
0.0204	3.93	3700	0.0003	1.0
0.0002	3.99	3750	0.0001	1.0
0.0167	4.04	3800	0.0001	1.0
0.0001	4.09	3850	0.0001	1.0
0.0153	4.14	3900	0.0002	1.0
0.0002	4.2	3950	0.0000	1.0
0.0116	4.25	4000	0.0001	1.0
0.0001	4.3	4050	0.0000	1.0
0.0001	4.36	4100	0.0000	1.0
0.0002	4.41	4150	0.0000	1.0
0.0	4.46	4200	0.0000	1.0
0.0058	4.52	4250	0.0000	1.0
0.0003	4.57	4300	0.0000	1.0
0.0057	4.62	4350	0.0000	1.0
0.0139	4.68	4400	0.0298	0.99
0.0147	4.73	4450	0.0000	1.0
0.0074	4.78	4500	0.0000	1.0
0.0057	4.84	4550	0.0001	1.0
0.0111	4.89	4600	0.0000	1.0
0.0035	4.94	4650	0.0000	1.0
0.0003	4.99	4700	0.0000	1.0
0.0017	5.05	4750	0.0000	1.0
0.0227	5.1	4800	0.0000	1.0
0.0103	5.15	4850	0.0007	1.0
0.0123	5.21	4900	0.0000	1.0
0.0137	5.26	4950	0.0000	1.0
0.0104	5.31	5000	0.0001	1.0
0.0005	5.37	5050	0.0000	1.0
0.0248	5.42	5100	0.0288	0.99
0.0066	5.47	5150	0.0000	1.0
0.0082	5.53	5200	0.0000	1.0
0.0002	5.58	5250	0.0000	1.0
0.0128	5.63	5300	0.0000	1.0
0.0029	5.69	5350	0.0000	1.0
0.0119	5.74	5400	0.0000	1.0
0.0035	5.79	5450	0.0000	1.0
0.0002	5.84	5500	0.0000	1.0
0.0002	5.9	5550	0.0000	1.0
0.0	5.95	5600	0.0000	1.0
0.0036	6.0	5650	0.0000	1.0
0.0	6.06	5700	0.0000	1.0
0.0018	6.11	5750	0.0000	1.0
0.0082	6.16	5800	0.0000	1.0
0.0095	6.22	5850	0.0000	1.0
0.0135	6.27	5900	0.0749	0.99
0.001	6.32	5950	0.0000	1.0
0.0004	6.38	6000	0.0000	1.0
0.0	6.43	6050	0.0000	1.0
0.0046	6.48	6100	0.0000	1.0
0.0039	6.54	6150	0.0000	1.0
0.0028	6.59	6200	0.0000	1.0
0.0	6.64	6250	0.0000	1.0
0.0115	6.7	6300	0.0000	1.0
0.0047	6.75	6350	0.0000	1.0
0.0	6.8	6400	0.0000	1.0
0.0001	6.85	6450	0.0000	1.0
0.009	6.91	6500	0.0000	1.0
0.0005	6.96	6550	0.0001	1.0
0.0089	7.01	6600	0.0000	1.0
0.0002	7.07	6650	0.0003	1.0
0.0066	7.12	6700	0.0000	1.0
0.0002	7.17	6750	0.0000	1.0
0.0001	7.23	6800	0.0000	1.0
0.0001	7.28	6850	0.0000	1.0
0.0105	7.33	6900	0.0000	1.0
0.0056	7.39	6950	0.0000	1.0
0.0	7.44	7000	0.0000	1.0
0.0119	7.49	7050	0.0000	1.0
0.0	7.55	7100	0.0000	1.0
0.0051	7.6	7150	0.0000	1.0
0.0001	7.65	7200	0.0000	1.0
0.0	7.7	7250	0.0000	1.0
0.0	7.76	7300	0.0000	1.0
0.0034	7.81	7350	0.0000	1.0
0.0015	7.86	7400	0.0000	1.0
0.0	7.92	7450	0.0000	1.0
0.0018	7.97	7500	0.0000	1.0
0.0004	8.02	7550	0.0000	1.0
0.0004	8.08	7600	0.0000	1.0
0.0004	8.13	7650	0.0000	1.0
0.0002	8.18	7700	0.0000	1.0
0.0012	8.24	7750	0.0000	1.0
0.0009	8.29	7800	0.0000	1.0
0.0003	8.34	7850	0.0000	1.0
0.0044	8.4	7900	0.0000	1.0
0.0	8.45	7950	0.0000	1.0
0.0	8.5	8000	0.0000	1.0
0.0	8.55	8050	0.0000	1.0
0.0	8.61	8100	0.0000	1.0
0.0023	8.66	8150	0.0000	1.0
0.0	8.71	8200	0.0000	1.0
0.0006	8.77	8250	0.0000	1.0
0.0049	8.82	8300	0.0000	1.0
0.0012	8.87	8350	0.0000	1.0
0.0036	8.93	8400	0.0000	1.0
0.0004	8.98	8450	0.0000	1.0
0.0002	9.03	8500	0.0000	1.0
0.0003	9.09	8550	0.0000	1.0
0.0	9.14	8600	0.0000	1.0
0.0	9.19	8650	0.0000	1.0
0.0038	9.25	8700	0.0000	1.0
0.0001	9.3	8750	0.0000	1.0
0.0002	9.35	8800	0.0000	1.0
0.0	9.4	8850	0.0000	1.0
0.0	9.46	8900	0.0000	1.0
0.0035	9.51	8950	0.0000	1.0
0.0031	9.56	9000	0.0000	1.0
0.0002	9.62	9050	0.0000	1.0
0.0	9.67	9100	0.0000	1.0
0.0	9.72	9150	0.0000	1.0
0.0006	9.78	9200	0.0000	1.0
0.0013	9.83	9250	0.0000	1.0
0.0026	9.88	9300	0.0000	1.0
0.0001	9.94	9350	0.0000	1.0
0.0	9.99	9400	0.0000	1.0

Framework versions

Transformers 4.30.2
Pytorch 2.0.1+cu118
Datasets 2.13.1
Tokenizers 0.13.3