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sickcell/
bert-base-spanish-wwm-uncased-finetuned-github_cybersecurity_READMEs

Name: bert-base-spanish-wwm-uncased-finetuned-github_cybersecurity_READMEs
Availability: InStock

generated_from_trainer

dccuchile/bert-base-spanish-wwm-uncased

finetune

autotrain_compatible

endpoints_compatible

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bert-base-spanish-wwm-uncased-finetuned-github_cybersecurity_READMEs

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

Loss: 1.3626
Accuracy: 0.7721

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: 3e-05
train_batch_size: 32
eval_batch_size: 32
seed: 42
gradient_accumulation_steps: 4
total_train_batch_size: 128
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 1000
num_epochs: 200

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
No log	0.97	14	6.6700	0.2868
No log	2.0	29	6.1329	0.3035
No log	2.97	43	5.3933	0.3612
No log	4.0	58	5.0109	0.3777
No log	4.97	72	4.8244	0.3982
No log	6.0	87	4.3103	0.4191
No log	6.97	101	3.9390	0.4472
No log	8.0	116	3.7105	0.4643
No log	8.97	130	3.6200	0.4682
No log	10.0	145	3.3792	0.4746
No log	10.97	159	3.2035	0.5035
No log	12.0	174	3.0204	0.5292
No log	12.97	188	2.9428	0.5446
No log	14.0	203	2.8275	0.5586
No log	14.97	217	2.8530	0.5389
No log	16.0	232	2.7320	0.5552
No log	16.97	246	2.6976	0.5569
No log	18.0	261	2.6423	0.5672
No log	18.97	275	2.5589	0.5768
No log	20.0	290	2.5393	0.5725
No log	20.97	304	2.4149	0.5883
No log	22.0	319	2.3377	0.6106
No log	22.97	333	2.3686	0.6006
No log	24.0	348	2.3694	0.5896
No log	24.97	362	2.3411	0.6006
No log	26.0	377	2.1990	0.6192
No log	26.97	391	2.1937	0.6187
No log	28.0	406	2.1599	0.6263
No log	28.97	420	2.1169	0.6288
No log	30.0	435	2.1136	0.6363
No log	30.97	449	2.1705	0.6269
No log	32.0	464	1.9909	0.6551
No log	32.97	478	1.9930	0.6452
No log	34.0	493	1.9380	0.6622
3.3393	34.97	507	2.0509	0.6429
3.3393	36.0	522	1.9449	0.6556
3.3393	36.97	536	1.9595	0.6500
3.3393	38.0	551	1.8646	0.6703
3.3393	38.97	565	1.9297	0.6553
3.3393	40.0	580	1.8071	0.6820
3.3393	40.97	594	1.9239	0.6564
3.3393	42.0	609	1.7737	0.6769
3.3393	42.97	623	1.7695	0.6889
3.3393	44.0	638	1.7444	0.6842
3.3393	44.97	652	1.7503	0.6839
3.3393	46.0	667	1.7654	0.6932
3.3393	46.97	681	1.7225	0.6862
3.3393	48.0	696	1.8165	0.6815
3.3393	48.97	710	1.7971	0.6840
3.3393	50.0	725	1.7177	0.6942
3.3393	50.97	739	1.6890	0.6982
3.3393	52.0	754	1.7212	0.6990
3.3393	52.97	768	1.7562	0.6892
3.3393	54.0	783	1.7142	0.6971
3.3393	54.97	797	1.6899	0.6955
3.3393	56.0	812	1.7568	0.6898
3.3393	56.97	826	1.6427	0.7137
3.3393	58.0	841	1.5932	0.7183
3.3393	58.97	855	1.6001	0.7193
3.3393	60.0	870	1.6482	0.7109
3.3393	60.97	884	1.5384	0.7211
3.3393	62.0	899	1.6092	0.7085
3.3393	62.97	913	1.6621	0.7068
3.3393	64.0	928	1.5781	0.7108
3.3393	64.97	942	1.5365	0.7297
3.3393	66.0	957	1.5426	0.7155
3.3393	66.97	971	1.6601	0.7051
3.3393	68.0	986	1.5874	0.7218
1.654	68.97	1000	1.6337	0.7148
1.654	70.0	1015	1.5324	0.7244
1.654	70.97	1029	1.5848	0.7245
1.654	72.0	1044	1.4755	0.7301
1.654	72.97	1058	1.5183	0.7323
1.654	74.0	1073	1.4930	0.7307
1.654	74.97	1087	1.4618	0.7350
1.654	76.0	1102	1.5082	0.7381
1.654	76.97	1116	1.4550	0.7402
1.654	78.0	1131	1.4609	0.7350
1.654	78.97	1145	1.5692	0.7258
1.654	80.0	1160	1.4066	0.7524
1.654	80.97	1174	1.5256	0.7283
1.654	82.0	1189	1.4466	0.7396
1.654	82.97	1203	1.4642	0.7357
1.654	84.0	1218	1.4985	0.7364
1.654	84.97	1232	1.4829	0.7421
1.654	86.0	1247	1.4528	0.7423
1.654	86.97	1261	1.3744	0.7470
1.654	88.0	1276	1.4098	0.7534
1.654	88.97	1290	1.4666	0.7439
1.654	90.0	1305	1.3889	0.7606
1.654	90.97	1319	1.4525	0.7436
1.654	92.0	1334	1.3673	0.7547
1.654	92.97	1348	1.4549	0.7430
1.654	94.0	1363	1.4008	0.7417
1.654	94.97	1377	1.3820	0.7472
1.654	96.0	1392	1.3900	0.7592
1.654	96.97	1406	1.4227	0.7458
1.654	98.0	1421	1.4179	0.7546
1.654	98.97	1435	1.4474	0.7476
1.654	100.0	1450	1.4092	0.7485
1.654	100.97	1464	1.3163	0.7678
1.654	102.0	1479	1.3801	0.7631
1.654	102.97	1493	1.4153	0.7496
1.1613	104.0	1508	1.3168	0.7616
1.1613	104.97	1522	1.3385	0.7607
1.1613	106.0	1537	1.4633	0.7406
1.1613	106.97	1551	1.4509	0.7473
1.1613	108.0	1566	1.3938	0.7577
1.1613	108.97	1580	1.4659	0.7451
1.1613	110.0	1595	1.4536	0.7403
1.1613	110.97	1609	1.4069	0.7529
1.1613	112.0	1624	1.2818	0.7721
1.1613	112.97	1638	1.3530	0.7618
1.1613	114.0	1653	1.3854	0.7555
1.1613	114.97	1667	1.3213	0.7589
1.1613	116.0	1682	1.3547	0.7578
1.1613	116.97	1696	1.4230	0.7544
1.1613	118.0	1711	1.3296	0.7650
1.1613	118.97	1725	1.3777	0.7616
1.1613	120.0	1740	1.3832	0.7639
1.1613	120.97	1754	1.4333	0.7524
1.1613	122.0	1769	1.3613	0.7655
1.1613	122.97	1783	1.4481	0.7533
1.1613	124.0	1798	1.4398	0.7550
1.1613	124.97	1812	1.3509	0.7678
1.1613	126.0	1827	1.3034	0.7705
1.1613	126.97	1841	1.4733	0.7468
1.1613	128.0	1856	1.4400	0.7557
1.1613	128.97	1870	1.3901	0.7599
1.1613	130.0	1885	1.3529	0.7683
1.1613	130.97	1899	1.3677	0.7568
1.1613	132.0	1914	1.4481	0.7561
1.1613	132.97	1928	1.2518	0.7826
1.1613	134.0	1943	1.4324	0.7527
1.1613	134.97	1957	1.3740	0.7591
1.1613	136.0	1972	1.3782	0.7628
1.1613	136.97	1986	1.2933	0.7735
0.9181	138.0	2001	1.3451	0.7709
0.9181	138.97	2015	1.4064	0.7646
0.9181	140.0	2030	1.3908	0.7661
0.9181	140.97	2044	1.3139	0.7692
0.9181	142.0	2059	1.3602	0.7698
0.9181	142.97	2073	1.3171	0.7763
0.9181	144.0	2088	1.3736	0.7627
0.9181	144.97	2102	1.3348	0.7670
0.9181	146.0	2117	1.3745	0.7672
0.9181	146.97	2131	1.3725	0.7657
0.9181	148.0	2146	1.3939	0.7662
0.9181	148.97	2160	1.3793	0.7654
0.9181	150.0	2175	1.3246	0.7713
0.9181	150.97	2189	1.2930	0.7767
0.9181	152.0	2204	1.2810	0.7786
0.9181	152.97	2218	1.3552	0.7677
0.9181	154.0	2233	1.4365	0.7662
0.9181	154.97	2247	1.3108	0.7701
0.9181	156.0	2262	1.2976	0.7802
0.9181	156.97	2276	1.3652	0.7743
0.9181	158.0	2291	1.3912	0.7628
0.9181	158.97	2305	1.3401	0.7689
0.9181	160.0	2320	1.2996	0.7723
0.9181	160.97	2334	1.3340	0.7764
0.9181	162.0	2349	1.2927	0.7751
0.9181	162.97	2363	1.3123	0.7766
0.9181	164.0	2378	1.3185	0.7712
0.9181	164.97	2392	1.3288	0.7737
0.9181	166.0	2407	1.3510	0.7685
0.9181	166.97	2421	1.3598	0.7699
0.9181	168.0	2436	1.3490	0.7638
0.9181	168.97	2450	1.3381	0.7643
0.9181	170.0	2465	1.3074	0.7761
0.9181	170.97	2479	1.3886	0.7631
0.9181	172.0	2494	1.3931	0.7634
0.7949	172.97	2508	1.3627	0.7662
0.7949	174.0	2523	1.4032	0.7653
0.7949	174.97	2537	1.3016	0.7740
0.7949	176.0	2552	1.3341	0.7710
0.7949	176.97	2566	1.3820	0.7624
0.7949	178.0	2581	1.3502	0.7761
0.7949	178.97	2595	1.3273	0.7752
0.7949	180.0	2610	1.3915	0.7623
0.7949	180.97	2624	1.4012	0.7616
0.7949	182.0	2639	1.3881	0.7692
0.7949	182.97	2653	1.2757	0.7807
0.7949	184.0	2668	1.3941	0.7629
0.7949	184.97	2682	1.3301	0.7800
0.7949	186.0	2697	1.3781	0.7735
0.7949	186.97	2711	1.3267	0.7782
0.7949	188.0	2726	1.3695	0.7688
0.7949	188.97	2740	1.3516	0.7752
0.7949	190.0	2755	1.3627	0.7733
0.7949	190.97	2769	1.3846	0.7713
0.7949	192.0	2784	1.3710	0.7662
0.7949	192.97	2798	1.3902	0.7660
0.7949	193.1	2800	1.4705	0.7550

Framework versions

Transformers 4.40.0.dev0
Pytorch 2.2.1+cu121
Datasets 2.18.0
Tokenizers 0.15.2

sickcell/bert-base-spanish-wwm-uncased-finetuned-github_cybersecurity_READMEs

bert-base-spanish-wwm-uncased-finetuned-github_cybersecurity_READMEs

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

sickcell/
bert-base-spanish-wwm-uncased-finetuned-github_cybersecurity_READMEs