Version12AGAINNNASAP_FineTuningBERT_AugV12_k1_task1_organization_k1_k1_fold3

genki10/Version12AGAINNNASAP_FineTuningBERT_AugV12_k1_task1_organization_k1_k1_fold3

Version12AGAINNNASAP_FineTuningBERT_AugV12_k1_task1_organization_k1_k1_fold3 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 8, 2025
Updated: Mar 8, 2025
SHA: a6622961

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Base models

Model card

From the Hugging Face model README

Version12AGAINNNASAP_FineTuningBERT_AugV12_k1_task1_organization_k1_k1_fold3

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: 0.6546
Qwk: 0.6326
Mse: 0.6542
Rmse: 0.8088

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	1	11.8326	0.0007	11.8312	3.4396
No log	2.0	2	10.8763	0.0105	10.8742	3.2976
No log	3.0	3	10.6153	0.0	10.6132	3.2578
No log	4.0	4	10.2296	0.0	10.2276	3.1981
No log	5.0	5	9.7601	0.0	9.7582	3.1238
No log	6.0	6	9.2283	0.0	9.2265	3.0375
No log	7.0	7	8.6525	0.0	8.6507	2.9412
No log	8.0	8	8.0476	0.0	8.0459	2.8365
No log	9.0	9	7.4389	0.0	7.4373	2.7271
No log	10.0	10	6.9004	0.0	6.8989	2.6266
No log	11.0	11	6.3692	0.0006	6.3678	2.5234
No log	12.0	12	5.8999	0.0567	5.8985	2.4287
No log	13.0	13	5.4415	0.0297	5.4402	2.3324
No log	14.0	14	4.9559	0.0185	4.9547	2.2259
No log	15.0	15	4.5045	0.0076	4.5034	2.1221
No log	16.0	16	4.0040	0.0038	4.0030	2.0007
No log	17.0	17	3.6449	0.0038	3.6440	1.9089
No log	18.0	18	3.3254	0.0	3.3244	1.8233
No log	19.0	19	2.9659	0.0	2.9650	1.7219
No log	20.0	20	2.5994	0.0820	2.5985	1.6120
No log	21.0	21	2.3294	0.1617	2.3286	1.5260
No log	22.0	22	2.1241	0.1137	2.1233	1.4572
No log	23.0	23	1.8541	0.0528	1.8533	1.3614
No log	24.0	24	1.6805	0.0514	1.6798	1.2961
No log	25.0	25	1.6218	0.0514	1.6212	1.2733
No log	26.0	26	1.4308	0.0417	1.4301	1.1959
No log	27.0	27	1.2481	0.0391	1.2474	1.1169
No log	28.0	28	1.1481	0.0391	1.1475	1.0712
No log	29.0	29	1.1564	0.0302	1.1559	1.0751
No log	30.0	30	1.0155	0.0302	1.0150	1.0075
No log	31.0	31	0.9008	0.2263	0.9003	0.9489
No log	32.0	32	0.8470	0.3513	0.8466	0.9201
No log	33.0	33	0.7990	0.3883	0.7987	0.8937
No log	34.0	34	0.7692	0.3658	0.7689	0.8768
No log	35.0	35	0.7400	0.3350	0.7397	0.8601
No log	36.0	36	0.7625	0.2919	0.7623	0.8731
No log	37.0	37	0.7437	0.2605	0.7435	0.8623
No log	38.0	38	0.6994	0.2803	0.6992	0.8362
No log	39.0	39	0.6705	0.3150	0.6704	0.8188
No log	40.0	40	0.6669	0.3240	0.6667	0.8165
No log	41.0	41	0.7153	0.3386	0.7152	0.8457
No log	42.0	42	0.7100	0.3368	0.7099	0.8426
No log	43.0	43	0.6488	0.3329	0.6488	0.8055
No log	44.0	44	0.6434	0.3331	0.6434	0.8021
No log	45.0	45	0.6579	0.3331	0.6579	0.8111
No log	46.0	46	0.6515	0.4180	0.6515	0.8071
No log	47.0	47	0.5868	0.4960	0.5867	0.7660
No log	48.0	48	0.5653	0.5240	0.5652	0.7518
No log	49.0	49	0.5841	0.5507	0.5841	0.7642
No log	50.0	50	0.5849	0.5436	0.5849	0.7648
No log	51.0	51	0.5481	0.5587	0.5480	0.7402
No log	52.0	52	0.5373	0.5788	0.5372	0.7329
No log	53.0	53	0.5698	0.5800	0.5697	0.7548
No log	54.0	54	0.5704	0.5816	0.5703	0.7552
No log	55.0	55	0.5375	0.6154	0.5373	0.7330
No log	56.0	56	0.5337	0.6264	0.5334	0.7304
No log	57.0	57	0.5636	0.6158	0.5634	0.7506
No log	58.0	58	0.6343	0.6057	0.6342	0.7963
No log	59.0	59	0.6637	0.5993	0.6635	0.8145
No log	60.0	60	0.6348	0.6149	0.6346	0.7966
No log	61.0	61	0.5942	0.6338	0.5939	0.7706
No log	62.0	62	0.5872	0.6280	0.5869	0.7661
No log	63.0	63	0.6078	0.6365	0.6075	0.7794
No log	64.0	64	0.6587	0.6280	0.6584	0.8114
No log	65.0	65	0.7184	0.6123	0.7181	0.8474
No log	66.0	66	0.7236	0.6183	0.7233	0.8505
No log	67.0	67	0.6781	0.6377	0.6778	0.8233
No log	68.0	68	0.6321	0.6289	0.6317	0.7948
No log	69.0	69	0.6312	0.6359	0.6308	0.7942
No log	70.0	70	0.6327	0.6323	0.6323	0.7952
No log	71.0	71	0.6349	0.6150	0.6345	0.7965
No log	72.0	72	0.6684	0.6307	0.6680	0.8173
No log	73.0	73	0.7606	0.5993	0.7602	0.8719
No log	74.0	74	0.8122	0.5668	0.8119	0.9010
No log	75.0	75	0.8166	0.5686	0.8162	0.9035
No log	76.0	76	0.7707	0.5869	0.7703	0.8777
No log	77.0	77	0.6971	0.6188	0.6967	0.8347
No log	78.0	78	0.6559	0.6311	0.6555	0.8096
No log	79.0	79	0.6405	0.6460	0.6401	0.8001
No log	80.0	80	0.6432	0.6575	0.6428	0.8017
No log	81.0	81	0.6443	0.6558	0.6439	0.8024
No log	82.0	82	0.6509	0.6479	0.6505	0.8065
No log	83.0	83	0.6684	0.6371	0.6680	0.8173
No log	84.0	84	0.6694	0.6356	0.6691	0.8180
No log	85.0	85	0.6644	0.6292	0.6640	0.8149
No log	86.0	86	0.6554	0.6356	0.6550	0.8093
No log	87.0	87	0.6556	0.6269	0.6552	0.8095
No log	88.0	88	0.6608	0.6293	0.6604	0.8126
No log	89.0	89	0.6641	0.6257	0.6637	0.8147
No log	90.0	90	0.6682	0.6311	0.6678	0.8172
No log	91.0	91	0.6784	0.6226	0.6780	0.8234
No log	92.0	92	0.6796	0.6089	0.6792	0.8241
No log	93.0	93	0.6793	0.6209	0.6789	0.8240
No log	94.0	94	0.6745	0.6278	0.6741	0.8210
No log	95.0	95	0.6660	0.6284	0.6656	0.8158
No log	96.0	96	0.6630	0.6296	0.6625	0.8140
No log	97.0	97	0.6604	0.6331	0.6600	0.8124
No log	98.0	98	0.6576	0.6331	0.6572	0.8107
No log	99.0	99	0.6554	0.6337	0.6549	0.8093
No log	100.0	100	0.6546	0.6326	0.6542	0.8088

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 8, 2025
Updated: Mar 8, 2025