Version19ASAP_FineTuningBERT_AugV19_k3_task1_organization_k3_k3_fold3

genki10/Version19ASAP_FineTuningBERT_AugV19_k3_task1_organization_k3_k3_fold3

Version19ASAP_FineTuningBERT_AugV19_k3_task1_organization_k3_k3_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 9, 2025
Updated: Mar 9, 2025
SHA: 67c16606

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Base models

Model card

From the Hugging Face model README

Version19ASAP_FineTuningBERT_AugV19_k3_task1_organization_k3_k3_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: 0.6505
Qwk: 0.6694
Mse: 0.6500
Rmse: 0.8062

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	15.0144	0.0	15.0119	3.8745
No log	2.0	2	13.8261	0.0	13.8234	3.7180
No log	3.0	3	12.1672	0.0000	12.1651	3.4879
No log	4.0	4	11.0417	0.0107	11.0398	3.3226
No log	5.0	5	10.3854	0.0	10.3836	3.2224
No log	6.0	6	9.3993	0.0	9.3975	3.0655
No log	7.0	7	8.0982	0.0	8.0968	2.8455
No log	8.0	8	7.0494	0.0	7.0482	2.6548
No log	9.0	9	6.0057	0.0481	6.0047	2.4504
No log	10.0	10	5.1561	0.0271	5.1551	2.2705
No log	11.0	11	4.3634	0.0175	4.3623	2.0886
No log	12.0	12	4.0312	0.0062	4.0304	2.0076
No log	13.0	13	3.6123	0.0	3.6115	1.9004
No log	14.0	14	3.0863	0.0	3.0854	1.7565
No log	15.0	15	2.7467	0.0423	2.7460	1.6571
No log	16.0	16	2.4703	0.0791	2.4697	1.5715
No log	17.0	17	2.1218	0.1270	2.1213	1.4565
No log	18.0	18	1.9593	0.1149	1.9588	1.3996
No log	19.0	19	1.6971	0.0658	1.6967	1.3026
No log	20.0	20	1.4368	0.0463	1.4364	1.1985
No log	21.0	21	1.3072	0.0302	1.3067	1.1431
No log	22.0	22	1.1482	0.0302	1.1478	1.0714
No log	23.0	23	1.0434	0.0102	1.0432	1.0214
No log	24.0	24	0.9848	0.0102	0.9847	0.9923
No log	25.0	25	0.8959	0.2592	0.8957	0.9464
No log	26.0	26	0.8341	0.3642	0.8339	0.9132
No log	27.0	27	0.7936	0.3447	0.7934	0.8907
No log	28.0	28	0.7538	0.3392	0.7537	0.8682
No log	29.0	29	0.7255	0.3394	0.7254	0.8517
No log	30.0	30	0.7077	0.3358	0.7076	0.8412
No log	31.0	31	0.7092	0.2678	0.7092	0.8421
No log	32.0	32	0.7270	0.2445	0.7270	0.8527
No log	33.0	33	0.7284	0.2258	0.7284	0.8535
No log	34.0	34	0.7139	0.2258	0.7138	0.8449
No log	35.0	35	0.7057	0.2258	0.7055	0.8400
No log	36.0	36	0.7365	0.2070	0.7364	0.8581
No log	37.0	37	0.7834	0.2030	0.7833	0.8850
No log	38.0	38	0.7898	0.3157	0.7898	0.8887
No log	39.0	39	0.7496	0.4336	0.7495	0.8657
No log	40.0	40	0.7154	0.4437	0.7152	0.8457
No log	41.0	41	0.6839	0.4586	0.6836	0.8268
No log	42.0	42	0.6213	0.4869	0.6211	0.7881
No log	43.0	43	0.6217	0.4975	0.6214	0.7883
No log	44.0	44	0.7062	0.4927	0.7059	0.8402
No log	45.0	45	0.7093	0.5056	0.7089	0.8420
No log	46.0	46	0.6339	0.5474	0.6335	0.7959
No log	47.0	47	0.5720	0.5870	0.5716	0.7560
No log	48.0	48	0.5708	0.6008	0.5704	0.7552
No log	49.0	49	0.5844	0.6074	0.5839	0.7642
No log	50.0	50	0.6282	0.6088	0.6277	0.7923
No log	51.0	51	0.5935	0.6372	0.5930	0.7701
No log	52.0	52	0.5454	0.6564	0.5450	0.7383
No log	53.0	53	0.5648	0.6479	0.5644	0.7512
No log	54.0	54	0.6446	0.6482	0.6440	0.8025
No log	55.0	55	0.6897	0.6437	0.6890	0.8301
No log	56.0	56	0.6640	0.6443	0.6633	0.8145
No log	57.0	57	0.6229	0.6499	0.6224	0.7889
No log	58.0	58	0.5893	0.6557	0.5888	0.7673
No log	59.0	59	0.5870	0.6592	0.5865	0.7659
No log	60.0	60	0.6184	0.6467	0.6179	0.7860
No log	61.0	61	0.6630	0.6388	0.6624	0.8139
No log	62.0	62	0.6378	0.6650	0.6373	0.7983
No log	63.0	63	0.6008	0.6686	0.6003	0.7748
No log	64.0	64	0.6154	0.6710	0.6149	0.7842
No log	65.0	65	0.6654	0.6560	0.6649	0.8154
No log	66.0	66	0.6557	0.6522	0.6551	0.8094
No log	67.0	67	0.6330	0.6513	0.6325	0.7953
No log	68.0	68	0.5881	0.6634	0.5877	0.7666
No log	69.0	69	0.5841	0.6656	0.5837	0.7640
No log	70.0	70	0.6053	0.6708	0.6048	0.7777
No log	71.0	71	0.6692	0.6497	0.6687	0.8177
No log	72.0	72	0.6789	0.6541	0.6784	0.8236
No log	73.0	73	0.6316	0.6725	0.6311	0.7944
No log	74.0	74	0.6164	0.6730	0.6160	0.7848
No log	75.0	75	0.5982	0.6832	0.5978	0.7732
No log	76.0	76	0.6081	0.6734	0.6077	0.7795
No log	77.0	77	0.5995	0.6785	0.5991	0.7740
No log	78.0	78	0.6010	0.6824	0.6006	0.7750
No log	79.0	79	0.6189	0.6729	0.6185	0.7864
No log	80.0	80	0.6612	0.6590	0.6607	0.8129
No log	81.0	81	0.6730	0.6501	0.6724	0.8200
No log	82.0	82	0.6530	0.6660	0.6525	0.8078
No log	83.0	83	0.6307	0.6590	0.6303	0.7939
No log	84.0	84	0.6278	0.6660	0.6274	0.7921
No log	85.0	85	0.6412	0.6668	0.6407	0.8004
No log	86.0	86	0.6577	0.6694	0.6572	0.8107
No log	87.0	87	0.6830	0.6638	0.6825	0.8261
No log	88.0	88	0.6879	0.6609	0.6873	0.8291
No log	89.0	89	0.6755	0.6586	0.6750	0.8216
No log	90.0	90	0.6729	0.6624	0.6724	0.8200
No log	91.0	91	0.6646	0.6705	0.6641	0.8149
No log	92.0	92	0.6581	0.6691	0.6576	0.8109
No log	93.0	93	0.6461	0.6735	0.6456	0.8035
No log	94.0	94	0.6451	0.6735	0.6446	0.8029
No log	95.0	95	0.6505	0.6694	0.6500	0.8062

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 9, 2025
Updated: Mar 9, 2025