Version22NewTestASAP_FineTuningBERT_AugV22_k3_task1_organization_k3_k3_fold2

genki10/Version22NewTestASAP_FineTuningBERT_AugV22_k3_task1_organization_k3_k3_fold2

Version22NewTestASAP_FineTuningBERT_AugV22_k3_task1_organization_k3_k3_fold2 is a text classification model from genki10. Use it when you need a label for a piece of text. It is set up for transformers. The card lists the license as apache-2.0.

should probably proofread and complete it, then remove this comment. --

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At a glance

Task: Text Classification
Library: transformers
License: apache-2.0
Model type: bert
Access: Public
Created: Mar 15, 2025
Updated: Mar 15, 2025
SHA: 1398c5ba

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Base models

Model card

From the Hugging Face model README

Version22NewTestASAP_FineTuningBERT_AugV22_k3_task1_organization_k3_k3_fold2

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

Loss: 1.0437
Qwk: 0.4345
Mse: 1.0434
Rmse: 1.0215

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: 64
eval_batch_size: 64
seed: 42
optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: linear
num_epochs: 100

Training results

Training Loss	Epoch	Step	Validation Loss	Qwk	Mse	Rmse
No log	1.0	2	9.6680	0.0	9.6683	3.1094
No log	2.0	4	9.2364	0.0	9.2366	3.0392
No log	3.0	6	8.8281	0.0	8.8282	2.9712
No log	4.0	8	8.0620	0.0	8.0622	2.8394
No log	5.0	10	6.5375	0.0	6.5376	2.5569
No log	6.0	12	5.6194	0.0101	5.6196	2.3706
No log	7.0	14	4.7110	0.0039	4.7113	2.1705
No log	8.0	16	3.8303	0.0	3.8307	1.9572
No log	9.0	18	3.2611	0.0	3.2616	1.8060
No log	10.0	20	2.8310	0.0	2.8314	1.6827
No log	11.0	22	2.4688	0.0594	2.4692	1.5714
No log	12.0	24	2.1799	0.1044	2.1803	1.4766
No log	13.0	26	1.9262	0.0539	1.9265	1.3880
No log	14.0	28	1.7336	0.0628	1.7340	1.3168
No log	15.0	30	2.0743	0.1795	2.0746	1.4404
No log	16.0	32	2.2165	0.1242	2.2169	1.4889
No log	17.0	34	1.6535	0.1460	1.6537	1.2860
No log	18.0	36	1.8070	0.1296	1.8071	1.3443
No log	19.0	38	1.9003	0.1198	1.9006	1.3786
No log	20.0	40	1.3501	0.1256	1.3503	1.1620
No log	21.0	42	1.5066	0.1801	1.5068	1.2275
No log	22.0	44	1.8145	0.1397	1.8147	1.3471
No log	23.0	46	1.3592	0.2050	1.3591	1.1658
No log	24.0	48	1.6871	0.1501	1.6869	1.2988
No log	25.0	50	2.4862	0.1099	2.4861	1.5767
No log	26.0	52	1.5497	0.1970	1.5492	1.2447
No log	27.0	54	1.5201	0.2062	1.5198	1.2328
No log	28.0	56	2.1195	0.1380	2.1194	1.4558
No log	29.0	58	1.5253	0.1932	1.5250	1.2349
No log	30.0	60	1.7736	0.1588	1.7732	1.3316
No log	31.0	62	2.5759	0.1059	2.5756	1.6049
No log	32.0	64	1.6078	0.2171	1.6070	1.2677
No log	33.0	66	1.1516	0.3616	1.1509	1.0728
No log	34.0	68	1.7834	0.1827	1.7830	1.3353
No log	35.0	70	1.7487	0.1967	1.7482	1.3222
No log	36.0	72	0.9598	0.4173	0.9590	0.9793
No log	37.0	74	0.9653	0.4050	0.9644	0.9820
No log	38.0	76	1.5153	0.2593	1.5143	1.2306
No log	39.0	78	1.7433	0.2086	1.7427	1.3201
No log	40.0	80	1.0526	0.3822	1.0520	1.0257
No log	41.0	82	0.8829	0.4269	0.8823	0.9393
No log	42.0	84	1.2520	0.3520	1.2515	1.1187
No log	43.0	86	1.2826	0.3450	1.2819	1.1322
No log	44.0	88	1.1870	0.3720	1.1861	1.0891
No log	45.0	90	1.3240	0.3168	1.3233	1.1503
No log	46.0	92	1.2945	0.2993	1.2941	1.1376
No log	47.0	94	0.9508	0.4158	0.9504	0.9749
No log	48.0	96	0.9781	0.4112	0.9778	0.9888
No log	49.0	98	1.2343	0.3385	1.2340	1.1109
No log	50.0	100	1.1453	0.3765	1.1448	1.0700
No log	51.0	102	1.2122	0.3500	1.2117	1.1008
No log	52.0	104	1.1370	0.3741	1.1365	1.0661
No log	53.0	106	1.0232	0.4358	1.0226	1.0112
No log	54.0	108	1.0181	0.4407	1.0175	1.0087
No log	55.0	110	1.1119	0.3980	1.1114	1.0542
No log	56.0	112	1.0891	0.3792	1.0887	1.0434
No log	57.0	114	0.9176	0.4624	0.9173	0.9577
No log	58.0	116	0.8672	0.4870	0.8668	0.9310
No log	59.0	118	0.9909	0.4206	0.9906	0.9953
No log	60.0	120	1.1705	0.3391	1.1701	1.0817
No log	61.0	122	1.0572	0.4348	1.0567	1.0279
No log	62.0	124	1.0971	0.4268	1.0965	1.0472
No log	63.0	126	1.3067	0.3279	1.3063	1.1430
No log	64.0	128	1.2751	0.3332	1.2749	1.1291
No log	65.0	130	1.1126	0.3939	1.1122	1.0546
No log	66.0	132	1.1624	0.4066	1.1620	1.0780
No log	67.0	134	1.1389	0.4224	1.1385	1.0670
No log	68.0	136	1.1609	0.3875	1.1606	1.0773
No log	69.0	138	1.1404	0.3814	1.1402	1.0678
No log	70.0	140	0.9861	0.4273	0.9859	0.9929
No log	71.0	142	0.9966	0.4196	0.9964	0.9982
No log	72.0	144	1.1508	0.3789	1.1506	1.0727
No log	73.0	146	1.1641	0.3757	1.1637	1.0788
No log	74.0	148	1.2263	0.375	1.2258	1.1072
No log	75.0	150	1.1622	0.3864	1.1618	1.0779
No log	76.0	152	1.1054	0.4065	1.1050	1.0512
No log	77.0	154	1.0307	0.4232	1.0303	1.0150
No log	78.0	156	1.0147	0.4136	1.0145	1.0072
No log	79.0	158	1.0481	0.3983	1.0479	1.0237
No log	80.0	160	0.9955	0.4156	0.9953	0.9976
No log	81.0	162	0.9972	0.4352	0.9969	0.9985
No log	82.0	164	1.0834	0.4080	1.0830	1.0407
No log	83.0	166	1.1377	0.3949	1.1373	1.0664
No log	84.0	168	1.1267	0.3996	1.1263	1.0613
No log	85.0	170	1.0859	0.4024	1.0856	1.0419
No log	86.0	172	1.0912	0.3965	1.0908	1.0444
No log	87.0	174	1.0921	0.3938	1.0918	1.0449
No log	88.0	176	1.0437	0.4345	1.0434	1.0215

Framework versions

Transformers 4.47.0
Pytorch 2.5.1+cu121
Datasets 3.3.1
Tokenizers 0.21.0

Task: Text Classification
Library: transformers
Type: bert
License: apache-2.0
Created: Mar 15, 2025
Updated: Mar 15, 2025