Version23NewTestASAP_FineTuningBERT_AugV23_k1_task1_organization_k1_k1_fold1

genki10/Version23NewTestASAP_FineTuningBERT_AugV23_k1_task1_organization_k1_k1_fold1

Version23NewTestASAP_FineTuningBERT_AugV23_k1_task1_organization_k1_k1_fold1 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. --

Downloads · 30 days

35% of all-time downloads

All-time downloads

Public

Repo size

43.4 GB

Likes

Public

Hugging Face

Repo makeup

Click a slice to open those files.

.bin876 MB · 100%

At a glance

Task: Text Classification
Library: transformers
License: apache-2.0
Model type: bert
Access: Public
Created: Mar 16, 2025
Updated: Mar 16, 2025
SHA: 13d86e14

Try a prompt

Base models

Model card

From the Hugging Face model README

Version23NewTestASAP_FineTuningBERT_AugV23_k1_task1_organization_k1_k1_fold1

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: 0.5425
Qwk: 0.6207
Mse: 0.5417
Rmse: 0.7360

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: 150

Training results

Training Loss	Epoch	Step	Validation Loss	Qwk	Mse	Rmse
No log	1.0	1	11.4151	-0.0066	11.4122	3.3782
No log	2.0	2	9.2482	0.0018	9.2455	3.0406
No log	3.0	3	8.4991	0.0012	8.4969	2.9149
No log	4.0	4	7.4722	0.0	7.4699	2.7331
No log	5.0	5	7.1108	0.0	7.1084	2.6662
No log	6.0	6	6.6101	0.0	6.6078	2.5706
No log	7.0	7	5.9278	-0.0008	5.9255	2.4342
No log	8.0	8	5.3594	0.0244	5.3572	2.3146
No log	9.0	9	4.7808	0.0	4.7787	2.1860
No log	10.0	10	4.1873	0.0	4.1851	2.0458
No log	11.0	11	3.7283	0.0	3.7262	1.9303
No log	12.0	12	3.1724	0.0	3.1705	1.7806
No log	13.0	13	2.7732	0.0	2.7714	1.6648
No log	14.0	14	2.4277	0.0077	2.4261	1.5576
No log	15.0	15	2.3291	0.1291	2.3273	1.5256
No log	16.0	16	1.9602	0.1711	1.9585	1.3995
No log	17.0	17	1.6883	0.0521	1.6868	1.2988
No log	18.0	18	1.5252	0.0418	1.5237	1.2344
No log	19.0	19	1.3534	0.0315	1.3518	1.1627
No log	20.0	20	1.2578	0.0315	1.2562	1.1208
No log	21.0	21	1.1165	0.0315	1.1151	1.0560
No log	22.0	22	1.0801	0.0106	1.0788	1.0386
No log	23.0	23	0.9400	0.0315	0.9385	0.9688
No log	24.0	24	0.8603	0.1325	0.8589	0.9268
No log	25.0	25	0.8095	0.2576	0.8083	0.8990
No log	26.0	26	0.7237	0.4923	0.7223	0.8499
No log	27.0	27	0.8626	0.2665	0.8611	0.9280
No log	28.0	28	0.8159	0.3463	0.8145	0.9025
No log	29.0	29	0.6065	0.5241	0.6053	0.7780
No log	30.0	30	0.6677	0.4364	0.6667	0.8165
No log	31.0	31	0.6084	0.4674	0.6073	0.7793
No log	32.0	32	0.5571	0.5659	0.5558	0.7455
No log	33.0	33	0.5578	0.5673	0.5566	0.7460
No log	34.0	34	0.5132	0.5112	0.5121	0.7156
No log	35.0	35	0.4981	0.5669	0.4969	0.7049
No log	36.0	36	0.4828	0.5849	0.4817	0.6940
No log	37.0	37	0.4921	0.5629	0.4910	0.7007
No log	38.0	38	0.4888	0.5969	0.4876	0.6983
No log	39.0	39	0.6501	0.5635	0.6487	0.8054
No log	40.0	40	0.6466	0.5751	0.6451	0.8032
No log	41.0	41	0.4952	0.5784	0.4940	0.7028
No log	42.0	42	0.6481	0.4994	0.6471	0.8044
No log	43.0	43	0.7327	0.4498	0.7316	0.8553
No log	44.0	44	0.6327	0.4869	0.6316	0.7948
No log	45.0	45	0.5107	0.5676	0.5094	0.7137
No log	46.0	46	0.5794	0.5830	0.5779	0.7602
No log	47.0	47	0.5426	0.5880	0.5412	0.7357
No log	48.0	48	0.5680	0.5619	0.5669	0.7529
No log	49.0	49	0.5953	0.5501	0.5943	0.7709
No log	50.0	50	0.5479	0.5980	0.5467	0.7394
No log	51.0	51	0.5787	0.6000	0.5774	0.7599
No log	52.0	52	0.5570	0.6294	0.5558	0.7455
No log	53.0	53	0.5482	0.6340	0.5470	0.7396
No log	54.0	54	0.5384	0.6319	0.5372	0.7329
No log	55.0	55	0.6240	0.6106	0.6229	0.7892
No log	56.0	56	0.6078	0.6138	0.6066	0.7789
No log	57.0	57	0.5392	0.5910	0.5379	0.7334
No log	58.0	58	0.5498	0.5961	0.5485	0.7406
No log	59.0	59	0.6376	0.5894	0.6364	0.7977
No log	60.0	60	0.6189	0.5888	0.6177	0.7859
No log	61.0	61	0.5663	0.5861	0.5649	0.7516
No log	62.0	62	0.5696	0.5914	0.5682	0.7538
No log	63.0	63	0.5795	0.6107	0.5783	0.7604
No log	64.0	64	0.6768	0.6053	0.6758	0.8221
No log	65.0	65	0.6412	0.6066	0.6402	0.8001
No log	66.0	66	0.5258	0.6282	0.5247	0.7244
No log	67.0	67	0.5553	0.5937	0.5540	0.7443
No log	68.0	68	0.5720	0.5889	0.5707	0.7554
No log	69.0	69	0.5063	0.6305	0.5051	0.7107
No log	70.0	70	0.5088	0.6061	0.5077	0.7125
No log	71.0	71	0.5661	0.6232	0.5651	0.7517
No log	72.0	72	0.5385	0.6120	0.5375	0.7331
No log	73.0	73	0.5035	0.6229	0.5024	0.7088
No log	74.0	74	0.5214	0.6162	0.5202	0.7213
No log	75.0	75	0.5430	0.6167	0.5419	0.7361
No log	76.0	76	0.5497	0.6094	0.5486	0.7407
No log	77.0	77	0.5764	0.6229	0.5754	0.7586
No log	78.0	78	0.5448	0.6167	0.5438	0.7374
No log	79.0	79	0.5660	0.6176	0.5650	0.7517
No log	80.0	80	0.5185	0.6044	0.5175	0.7194
No log	81.0	81	0.4852	0.6397	0.4841	0.6958
No log	82.0	82	0.4912	0.6316	0.4903	0.7002
No log	83.0	83	0.4858	0.6447	0.4848	0.6963
No log	84.0	84	0.5082	0.6553	0.5073	0.7123
No log	85.0	85	0.5910	0.6373	0.5902	0.7683
No log	86.0	86	0.5957	0.6391	0.5949	0.7713
No log	87.0	87	0.5354	0.6545	0.5346	0.7312
No log	88.0	88	0.5169	0.6581	0.5160	0.7183
No log	89.0	89	0.5277	0.6600	0.5268	0.7258
No log	90.0	90	0.5761	0.6202	0.5752	0.7584
No log	91.0	91	0.5714	0.6107	0.5704	0.7553
No log	92.0	92	0.6147	0.6097	0.6138	0.7835
No log	93.0	93	0.5840	0.6182	0.5831	0.7636
No log	94.0	94	0.6024	0.6177	0.6015	0.7755
No log	95.0	95	0.5778	0.6225	0.5768	0.7595
No log	96.0	96	0.5633	0.6231	0.5622	0.7498
No log	97.0	97	0.5664	0.6242	0.5655	0.7520
No log	98.0	98	0.6084	0.6243	0.6075	0.7794
No log	99.0	99	0.5940	0.6313	0.5931	0.7702
No log	100.0	100	0.5338	0.6298	0.5329	0.7300
No log	101.0	101	0.4856	0.6411	0.4846	0.6961
No log	102.0	102	0.4819	0.6396	0.4808	0.6934
No log	103.0	103	0.4716	0.6625	0.4705	0.6860
No log	104.0	104	0.4817	0.6471	0.4808	0.6934
No log	105.0	105	0.5669	0.6415	0.5661	0.7524
No log	106.0	106	0.6198	0.6348	0.6190	0.7868
No log	107.0	107	0.5991	0.6414	0.5983	0.7735
No log	108.0	108	0.5290	0.6425	0.5282	0.7268
No log	109.0	109	0.4898	0.6745	0.4889	0.6992
No log	110.0	110	0.4955	0.6512	0.4945	0.7032
No log	111.0	111	0.5026	0.6376	0.5016	0.7082
No log	112.0	112	0.5081	0.6512	0.5071	0.7121
No log	113.0	113	0.5551	0.6323	0.5542	0.7445
No log	114.0	114	0.6039	0.6405	0.6031	0.7766
No log	115.0	115	0.6093	0.6334	0.6085	0.7800
No log	116.0	116	0.5699	0.6361	0.5691	0.7544
No log	117.0	117	0.5220	0.6111	0.5211	0.7219
No log	118.0	118	0.5111	0.6208	0.5101	0.7142
No log	119.0	119	0.5105	0.6199	0.5095	0.7138
No log	120.0	120	0.5294	0.6164	0.5285	0.7270
No log	121.0	121	0.5386	0.6188	0.5377	0.7333
No log	122.0	122	0.5775	0.6368	0.5766	0.7593
No log	123.0	123	0.5822	0.6493	0.5813	0.7625
No log	124.0	124	0.5549	0.6280	0.5540	0.7443
No log	125.0	125	0.5138	0.6266	0.5129	0.7162
No log	126.0	126	0.5032	0.6235	0.5023	0.7088
No log	127.0	127	0.5060	0.6290	0.5051	0.7107
No log	128.0	128	0.5252	0.6256	0.5244	0.7241
No log	129.0	129	0.5457	0.6342	0.5449	0.7381
No log	130.0	130	0.5476	0.6316	0.5468	0.7394
No log	131.0	131	0.5313	0.6268	0.5305	0.7284
No log	132.0	132	0.5246	0.6272	0.5237	0.7237
No log	133.0	133	0.5285	0.6309	0.5276	0.7264
No log	134.0	134	0.5190	0.6304	0.5181	0.7198
No log	135.0	135	0.5229	0.6263	0.5220	0.7225
No log	136.0	136	0.5157	0.6278	0.5148	0.7175
No log	137.0	137	0.5170	0.6273	0.5161	0.7184
No log	138.0	138	0.5251	0.6272	0.5242	0.7240
No log	139.0	139	0.5425	0.6207	0.5417	0.7360

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 16, 2025
Updated: Mar 16, 2025