Version27NewTestASAP_FineTuningBERT_AugV27_k3_task1_organization_k3_k3_fold1

genki10/Version27NewTestASAP_FineTuningBERT_AugV27_k3_task1_organization_k3_k3_fold1

Version27NewTestASAP_FineTuningBERT_AugV27_k3_task1_organization_k3_k3_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

27% of all-time downloads

All-time downloads

Public

Repo size

41.6 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 17, 2025
Updated: Mar 17, 2025
SHA: d05d6346

Try a prompt

Base models

Model card

From the Hugging Face model README

Version27NewTestASAP_FineTuningBERT_AugV27_k3_task1_organization_k3_k3_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: 0.6094
Qwk: 0.5813
Mse: 0.6093
Rmse: 0.7806

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	2	9.6178	0.0037	9.6151	3.1008
No log	2.0	4	7.1481	0.0	7.1457	2.6731
No log	3.0	6	5.5481	0.0664	5.5459	2.3550
No log	4.0	8	4.4128	0.0215	4.4107	2.1002
No log	5.0	10	3.4918	0.0	3.4897	1.8681
No log	6.0	12	2.7932	0.0	2.7914	1.6707
No log	7.0	14	2.1173	0.1711	2.1156	1.4545
No log	8.0	16	1.6283	0.0545	1.6267	1.2754
No log	9.0	18	1.3013	0.0	1.2998	1.1401
No log	10.0	20	1.0254	0.0	1.0241	1.0120
No log	11.0	22	0.8896	0.1202	0.8883	0.9425
No log	12.0	24	0.7839	0.3258	0.7827	0.8847
No log	13.0	26	0.6986	0.5222	0.6975	0.8352
No log	14.0	28	0.7605	0.3804	0.7595	0.8715
No log	15.0	30	0.5577	0.5076	0.5568	0.7462
No log	16.0	32	0.7155	0.4582	0.7146	0.8453
No log	17.0	34	0.6854	0.5193	0.6846	0.8274
No log	18.0	36	0.4660	0.5858	0.4654	0.6822
No log	19.0	38	0.4696	0.5843	0.4692	0.6850
No log	20.0	40	0.8539	0.4823	0.8535	0.9238
No log	21.0	42	0.5500	0.5523	0.5497	0.7414
No log	22.0	44	0.5815	0.5791	0.5813	0.7624
No log	23.0	46	0.6415	0.4940	0.6414	0.8008
No log	24.0	48	0.6223	0.5947	0.6220	0.7887
No log	25.0	50	0.6499	0.5848	0.6497	0.8061
No log	26.0	52	0.6527	0.5473	0.6526	0.8078
No log	27.0	54	0.6759	0.5781	0.6757	0.8220
No log	28.0	56	0.6447	0.5961	0.6445	0.8028
No log	29.0	58	0.6411	0.5873	0.6411	0.8007
No log	30.0	60	0.6868	0.5708	0.6866	0.8286
No log	31.0	62	0.6310	0.5885	0.6309	0.7943
No log	32.0	64	0.6166	0.5809	0.6165	0.7852
No log	33.0	66	0.6781	0.5824	0.6781	0.8235
No log	34.0	68	0.6434	0.5970	0.6433	0.8021
No log	35.0	70	0.5922	0.5710	0.5921	0.7695
No log	36.0	72	0.7697	0.5253	0.7697	0.8773
No log	37.0	74	0.7347	0.5427	0.7347	0.8571
No log	38.0	76	0.6191	0.5620	0.6190	0.7868
No log	39.0	78	0.6645	0.5745	0.6645	0.8152
No log	40.0	80	0.6294	0.6046	0.6295	0.7934
No log	41.0	82	0.6357	0.5431	0.6357	0.7973
No log	42.0	84	0.6209	0.5798	0.6209	0.7880
No log	43.0	86	0.8206	0.5009	0.8206	0.9059
No log	44.0	88	0.6068	0.5912	0.6068	0.7790
No log	45.0	90	0.6311	0.5909	0.6312	0.7945
No log	46.0	92	0.8113	0.5113	0.8115	0.9009
No log	47.0	94	0.6313	0.5829	0.6315	0.7947
No log	48.0	96	0.6293	0.5975	0.6295	0.7934
No log	49.0	98	0.7054	0.5525	0.7055	0.8399
No log	50.0	100	0.5931	0.5547	0.5930	0.7701
No log	51.0	102	0.6022	0.5544	0.6022	0.7760
No log	52.0	104	0.6822	0.5726	0.6822	0.8260
No log	53.0	106	0.6220	0.5808	0.6221	0.7887
No log	54.0	108	0.6423	0.5833	0.6424	0.8015
No log	55.0	110	0.7235	0.5382	0.7236	0.8507
No log	56.0	112	0.6421	0.5675	0.6421	0.8013
No log	57.0	114	0.6715	0.5632	0.6714	0.8194
No log	58.0	116	0.6326	0.5904	0.6326	0.7954
No log	59.0	118	0.6067	0.5950	0.6067	0.7789
No log	60.0	120	0.6829	0.5825	0.6830	0.8264
No log	61.0	122	0.5733	0.6186	0.5733	0.7572
No log	62.0	124	0.6243	0.6010	0.6243	0.7901
No log	63.0	126	0.7012	0.5699	0.7013	0.8374
No log	64.0	128	0.5974	0.5937	0.5975	0.7730
No log	65.0	130	0.6103	0.5757	0.6104	0.7813
No log	66.0	132	0.7307	0.5422	0.7308	0.8549
No log	67.0	134	0.7870	0.5283	0.7871	0.8872
No log	68.0	136	0.6104	0.5833	0.6104	0.7813
No log	69.0	138	0.6012	0.5779	0.6011	0.7753
No log	70.0	140	0.7370	0.5416	0.7370	0.8585
No log	71.0	142	0.7227	0.5469	0.7227	0.8501
No log	72.0	144	0.6079	0.5926	0.6079	0.7796
No log	73.0	146	0.6619	0.5836	0.6619	0.8136
No log	74.0	148	0.6296	0.5978	0.6296	0.7935
No log	75.0	150	0.6014	0.5885	0.6014	0.7755
No log	76.0	152	0.6589	0.5753	0.6590	0.8118
No log	77.0	154	0.6361	0.5965	0.6361	0.7976
No log	78.0	156	0.6696	0.5707	0.6697	0.8183
No log	79.0	158	0.6077	0.5957	0.6077	0.7795
No log	80.0	160	0.6884	0.5569	0.6885	0.8297
No log	81.0	162	0.6634	0.5658	0.6635	0.8145
No log	82.0	164	0.6121	0.5876	0.6121	0.7824
No log	83.0	166	0.6568	0.5843	0.6570	0.8105
No log	84.0	168	0.6403	0.5878	0.6404	0.8003
No log	85.0	170	0.6185	0.6159	0.6186	0.7865
No log	86.0	172	0.6397	0.5901	0.6398	0.7999
No log	87.0	174	0.6039	0.6085	0.6039	0.7771
No log	88.0	176	0.6378	0.5893	0.6378	0.7986
No log	89.0	178	0.5903	0.5920	0.5903	0.7683
No log	90.0	180	0.5860	0.5990	0.5858	0.7654
No log	91.0	182	0.6094	0.5813	0.6093	0.7806

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 17, 2025
Updated: Mar 17, 2025