Version27NewTestASAP_FineTuningBERT_AugV27_k3_task1_organization_k3_k3_fold4

genki10/Version27NewTestASAP_FineTuningBERT_AugV27_k3_task1_organization_k3_k3_fold4

Version27NewTestASAP_FineTuningBERT_AugV27_k3_task1_organization_k3_k3_fold4 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 18, 2025
Updated: Mar 18, 2025
SHA: 0d2b89af

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Base models

Model card

From the Hugging Face model README

Version27NewTestASAP_FineTuningBERT_AugV27_k3_task1_organization_k3_k3_fold4

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: 0.6529
Qwk: 0.5648
Mse: 0.6529
Rmse: 0.8080

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	14.6314	0.0	14.6314	3.8251
No log	2.0	4	12.2692	0.0	12.2692	3.5027
No log	3.0	6	9.9737	0.0066	9.9737	3.1581
No log	4.0	8	7.7158	0.0018	7.7158	2.7777
No log	5.0	10	5.5404	0.0686	5.5404	2.3538
No log	6.0	12	3.7990	0.0079	3.7990	1.9491
No log	7.0	14	2.6051	0.0040	2.6051	1.6140
No log	8.0	16	2.0976	0.1616	2.0976	1.4483
No log	9.0	18	1.4779	0.0420	1.4779	1.2157
No log	10.0	20	1.1374	0.0316	1.1374	1.0665
No log	11.0	22	0.9740	0.0316	0.9740	0.9869
No log	12.0	24	0.8095	0.3480	0.8095	0.8997
No log	13.0	26	0.7346	0.4054	0.7346	0.8571
No log	14.0	28	0.6515	0.3743	0.6515	0.8071
No log	15.0	30	0.6759	0.5030	0.6759	0.8221
No log	16.0	32	0.5668	0.5061	0.5668	0.7529
No log	17.0	34	0.6652	0.5390	0.6652	0.8156
No log	18.0	36	0.5753	0.5364	0.5753	0.7585
No log	19.0	38	0.7934	0.5319	0.7934	0.8907
No log	20.0	40	0.5839	0.5388	0.5839	0.7641
No log	21.0	42	0.6019	0.5666	0.6019	0.7758
No log	22.0	44	0.6716	0.5794	0.6716	0.8195
No log	23.0	46	0.6197	0.5773	0.6197	0.7872
No log	24.0	48	0.6649	0.5924	0.6649	0.8154
No log	25.0	50	0.6998	0.5919	0.6998	0.8365
No log	26.0	52	0.7202	0.5988	0.7202	0.8486
No log	27.0	54	0.6556	0.6124	0.6556	0.8097
No log	28.0	56	0.6501	0.6360	0.6501	0.8063
No log	29.0	58	0.6210	0.6175	0.6210	0.7880
No log	30.0	60	0.6323	0.6200	0.6323	0.7952
No log	31.0	62	0.7047	0.6289	0.7047	0.8395
No log	32.0	64	0.7298	0.5938	0.7298	0.8543
No log	33.0	66	0.6589	0.6181	0.6589	0.8117
No log	34.0	68	0.9631	0.4715	0.9631	0.9814
No log	35.0	70	0.6021	0.6107	0.6021	0.7759
No log	36.0	72	1.0091	0.4968	1.0091	1.0045
No log	37.0	74	0.7669	0.5514	0.7669	0.8757
No log	38.0	76	0.7919	0.4929	0.7919	0.8899
No log	39.0	78	1.1351	0.4040	1.1351	1.0654
No log	40.0	80	0.6510	0.5434	0.6510	0.8068
No log	41.0	82	0.7800	0.5383	0.7800	0.8832
No log	42.0	84	0.6956	0.5422	0.6956	0.8340
No log	43.0	86	0.6248	0.5949	0.6248	0.7905
No log	44.0	88	0.6024	0.6077	0.6024	0.7761
No log	45.0	90	0.7641	0.5473	0.7641	0.8741
No log	46.0	92	0.6522	0.6054	0.6522	0.8076
No log	47.0	94	0.6301	0.6252	0.6301	0.7938
No log	48.0	96	0.6111	0.6011	0.6111	0.7818
No log	49.0	98	0.6311	0.6391	0.6311	0.7944
No log	50.0	100	0.6264	0.6378	0.6264	0.7915
No log	51.0	102	0.7371	0.5658	0.7371	0.8586
No log	52.0	104	0.6154	0.6375	0.6154	0.7845
No log	53.0	106	0.5999	0.6386	0.5999	0.7745
No log	54.0	108	0.6246	0.5881	0.6246	0.7903
No log	55.0	110	0.6091	0.6443	0.6091	0.7805
No log	56.0	112	0.6166	0.6469	0.6166	0.7853
No log	57.0	114	0.6502	0.5956	0.6502	0.8063
No log	58.0	116	0.6416	0.6296	0.6416	0.8010
No log	59.0	118	0.6357	0.6364	0.6357	0.7973
No log	60.0	120	0.6444	0.5893	0.6444	0.8027
No log	61.0	122	0.6188	0.6114	0.6188	0.7866
No log	62.0	124	0.6086	0.6270	0.6086	0.7801
No log	63.0	126	0.6231	0.5971	0.6231	0.7894
No log	64.0	128	0.6033	0.6395	0.6033	0.7767
No log	65.0	130	0.6151	0.5909	0.6151	0.7843
No log	66.0	132	0.8197	0.5413	0.8197	0.9054
No log	67.0	134	0.6625	0.5574	0.6625	0.8140
No log	68.0	136	0.5979	0.6156	0.5979	0.7733
No log	69.0	138	0.5935	0.6142	0.5935	0.7704
No log	70.0	140	0.7617	0.5431	0.7617	0.8727
No log	71.0	142	0.6538	0.5874	0.6538	0.8086
No log	72.0	144	0.6414	0.6287	0.6414	0.8009
No log	73.0	146	0.6242	0.6249	0.6242	0.7900
No log	74.0	148	0.7411	0.5817	0.7411	0.8609
No log	75.0	150	0.6433	0.6032	0.6433	0.8021
No log	76.0	152	0.6520	0.6034	0.6520	0.8075
No log	77.0	154	0.6063	0.6243	0.6063	0.7787
No log	78.0	156	0.6537	0.5777	0.6537	0.8085
No log	79.0	158	0.6023	0.6062	0.6023	0.7761
No log	80.0	160	0.6262	0.6339	0.6262	0.7913
No log	81.0	162	0.6174	0.6231	0.6174	0.7857
No log	82.0	164	0.7510	0.5747	0.7510	0.8666
No log	83.0	166	0.6830	0.5980	0.6830	0.8264
No log	84.0	168	0.6389	0.6301	0.6389	0.7993
No log	85.0	170	0.6124	0.6231	0.6124	0.7826
No log	86.0	172	0.6529	0.5648	0.6529	0.8080

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 18, 2025
Updated: Mar 18, 2025