Version19ASAP_FineTuningBERT_AugV19_k15_task1_organization_k15_k15_fold2

genki10/Version19ASAP_FineTuningBERT_AugV19_k15_task1_organization_k15_k15_fold2

Version19ASAP_FineTuningBERT_AugV19_k15_task1_organization_k15_k15_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 9, 2025
Updated: Mar 9, 2025
SHA: 1c396042

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Base models

Model card

From the Hugging Face model README

Version19ASAP_FineTuningBERT_AugV19_k15_task1_organization_k15_k15_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: 0.6212
Qwk: 0.5934
Mse: 0.6206
Rmse: 0.7878

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	3	10.6730	0.0059	10.6732	3.2670
No log	2.0	6	9.4947	0.0	9.4949	3.0814
No log	3.0	9	7.5952	0.0	7.5955	2.7560
No log	4.0	12	6.5938	0.0	6.5941	2.5679
No log	5.0	15	5.0594	0.0	5.0598	2.2494
No log	6.0	18	3.5876	0.0	3.5880	1.8942
No log	7.0	21	2.5798	0.0	2.5802	1.6063
No log	8.0	24	1.9150	0.0399	1.9154	1.3840
No log	9.0	27	1.3916	0.0	1.3920	1.1798
No log	10.0	30	1.0039	0.0	1.0043	1.0022
No log	11.0	33	0.8413	0.1965	0.8417	0.9174
No log	12.0	36	0.7840	0.1388	0.7843	0.8856
No log	13.0	39	0.8694	0.0371	0.8697	0.9326
No log	14.0	42	0.9975	0.0371	0.9976	0.9988
No log	15.0	45	1.0550	0.2985	1.0548	1.0270
No log	16.0	48	0.8746	0.4013	0.8739	0.9349
No log	17.0	51	0.7452	0.4958	0.7442	0.8626
No log	18.0	54	1.0236	0.3905	1.0235	1.0117
No log	19.0	57	0.8065	0.5322	0.8056	0.8976
No log	20.0	60	0.6906	0.5605	0.6902	0.8308
No log	21.0	63	0.6581	0.5873	0.6578	0.8110
No log	22.0	66	0.7252	0.6001	0.7248	0.8513
No log	23.0	69	0.9425	0.4883	0.9425	0.9708
No log	24.0	72	0.9903	0.5517	0.9895	0.9948
No log	25.0	75	0.6581	0.6013	0.6577	0.8110
No log	26.0	78	0.6810	0.5903	0.6804	0.8249
No log	27.0	81	0.7664	0.5622	0.7657	0.8750
No log	28.0	84	0.6160	0.5836	0.6156	0.7846
No log	29.0	87	0.7949	0.5492	0.7942	0.8912
No log	30.0	90	0.6744	0.5789	0.6740	0.8210
No log	31.0	93	0.8142	0.5460	0.8134	0.9019
No log	32.0	96	0.6569	0.5843	0.6565	0.8102
No log	33.0	99	0.7748	0.5664	0.7741	0.8798
No log	34.0	102	0.6693	0.5893	0.6687	0.8178
No log	35.0	105	0.7609	0.5654	0.7602	0.8719
No log	36.0	108	0.6779	0.6001	0.6773	0.8230
No log	37.0	111	0.8728	0.5479	0.8719	0.9337
No log	38.0	114	0.6761	0.6036	0.6755	0.8219
No log	39.0	117	0.7160	0.5854	0.7152	0.8457
No log	40.0	120	0.6726	0.5896	0.6719	0.8197
No log	41.0	123	0.6961	0.5966	0.6953	0.8338
No log	42.0	126	0.6733	0.5954	0.6726	0.8201
No log	43.0	129	0.7007	0.5896	0.6999	0.8366
No log	44.0	132	0.7881	0.5566	0.7873	0.8873
No log	45.0	135	0.6456	0.5887	0.6450	0.8031
No log	46.0	138	0.8537	0.5436	0.8529	0.9235
No log	47.0	141	0.6534	0.6025	0.6528	0.8080
No log	48.0	144	0.7167	0.5827	0.7160	0.8462
No log	49.0	147	0.9268	0.5403	0.9259	0.9622
No log	50.0	150	0.6522	0.6003	0.6517	0.8073
No log	51.0	153	0.6489	0.5962	0.6483	0.8052
No log	52.0	156	0.8580	0.5298	0.8572	0.9258
No log	53.0	159	0.6394	0.6051	0.6388	0.7993
No log	54.0	162	0.8001	0.5512	0.7993	0.8940
No log	55.0	165	0.7429	0.5777	0.7421	0.8615
No log	56.0	168	0.6413	0.5960	0.6407	0.8005
No log	57.0	171	0.8283	0.5321	0.8275	0.9097
No log	58.0	174	0.6833	0.5802	0.6826	0.8262
No log	59.0	177	0.7258	0.5851	0.7250	0.8515
No log	60.0	180	0.8250	0.5506	0.8241	0.9078
No log	61.0	183	0.6969	0.5999	0.6963	0.8344
No log	62.0	186	0.8287	0.5637	0.8279	0.9099
No log	63.0	189	0.8388	0.5515	0.8380	0.9154
No log	64.0	192	0.7096	0.5753	0.7089	0.8420
No log	65.0	195	0.8823	0.5267	0.8814	0.9388
No log	66.0	198	0.7399	0.5533	0.7392	0.8597
No log	67.0	201	0.6665	0.5830	0.6659	0.8160
No log	68.0	204	0.6870	0.5636	0.6863	0.8284
No log	69.0	207	0.6931	0.5599	0.6925	0.8322
No log	70.0	210	0.6696	0.5736	0.6690	0.8179
No log	71.0	213	0.6805	0.5725	0.6798	0.8245
No log	72.0	216	0.7031	0.5490	0.7024	0.8381
No log	73.0	219	0.6212	0.5934	0.6206	0.7878

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