ASAP_nosemantic_FineTuningBERT_AugV12_k1_task1_organization_k1_k1_fold4

genki10/ASAP_nosemantic_FineTuningBERT_AugV12_k1_task1_organization_k1_k1_fold4

ASAP_nosemantic_FineTuningBERT_AugV12_k1_task1_organization_k1_k1_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. --

Downloads · 30 days

5% of all-time downloads

All-time downloads

Public

Repo size

47.7 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 7, 2025
Updated: Mar 7, 2025
SHA: 2962d146

Try a prompt

Base models

Model card

From the Hugging Face model README

ASAP_nosemantic_FineTuningBERT_AugV12_k1_task1_organization_k1_k1_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: 0.7894
Qwk: 0.5829
Mse: 0.7894
Rmse: 0.8885

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	10.9216	0.0104	10.9216	3.3048
No log	2.0	2	10.0522	0.0037	10.0522	3.1705
No log	3.0	3	9.7415	0.0018	9.7415	3.1211
No log	4.0	4	9.4758	0.0	9.4758	3.0783
No log	5.0	5	9.1147	0.0	9.1147	3.0191
No log	6.0	6	8.4517	0.0	8.4517	2.9072
No log	7.0	7	7.3526	0.0	7.3526	2.7116
No log	8.0	8	6.8928	0.0	6.8928	2.6254
No log	9.0	9	6.6995	0.0	6.6995	2.5883
No log	10.0	10	6.1033	-0.0013	6.1033	2.4705
No log	11.0	11	5.3756	0.0079	5.3756	2.3185
No log	12.0	12	4.8142	0.0079	4.8142	2.1941
No log	13.0	13	4.4509	0.0079	4.4509	2.1097
No log	14.0	14	3.9980	0.0079	3.9980	1.9995
No log	15.0	15	3.5755	0.0040	3.5755	1.8909
No log	16.0	16	3.2771	0.0040	3.2771	1.8103
No log	17.0	17	3.0176	0.0040	3.0176	1.7371
No log	18.0	18	2.7291	0.0040	2.7291	1.6520
No log	19.0	19	2.4477	0.0044	2.4477	1.5645
No log	20.0	20	2.2195	0.0715	2.2195	1.4898
No log	21.0	21	2.0156	0.0522	2.0156	1.4197
No log	22.0	22	1.8377	0.0317	1.8377	1.3556
No log	23.0	23	1.6917	0.0213	1.6917	1.3007
No log	24.0	24	1.5495	0.0213	1.5495	1.2448
No log	25.0	25	1.4119	0.0213	1.4119	1.1882
No log	26.0	26	1.3002	0.0213	1.3002	1.1403
No log	27.0	27	1.1991	0.0213	1.1991	1.0951
No log	28.0	28	1.1096	0.0213	1.1096	1.0534
No log	29.0	29	1.0249	0.0344	1.0249	1.0124
No log	30.0	30	0.9922	0.0713	0.9922	0.9961
No log	31.0	31	0.8998	0.0713	0.8998	0.9486
No log	32.0	32	0.8362	0.1078	0.8362	0.9145
No log	33.0	33	0.7859	0.3208	0.7859	0.8865
No log	34.0	34	0.7429	0.4819	0.7429	0.8619
No log	35.0	35	0.8084	0.4610	0.8084	0.8991
No log	36.0	36	0.7004	0.5341	0.7004	0.8369
No log	37.0	37	0.6340	0.5021	0.6340	0.7962
No log	38.0	38	0.6050	0.5336	0.6050	0.7778
No log	39.0	39	0.6399	0.5674	0.6399	0.7999
No log	40.0	40	0.6897	0.5569	0.6897	0.8305
No log	41.0	41	0.5870	0.5769	0.5870	0.7661
No log	42.0	42	0.5628	0.5875	0.5628	0.7502
No log	43.0	43	0.6623	0.5560	0.6623	0.8138
No log	44.0	44	0.5949	0.5716	0.5949	0.7713
No log	45.0	45	0.5188	0.5750	0.5188	0.7203
No log	46.0	46	0.5162	0.5765	0.5162	0.7185
No log	47.0	47	0.6120	0.5615	0.6120	0.7823
No log	48.0	48	0.6154	0.5599	0.6154	0.7845
No log	49.0	49	0.5225	0.5791	0.5225	0.7228
No log	50.0	50	0.5220	0.5601	0.5220	0.7225
No log	51.0	51	0.5254	0.5645	0.5254	0.7249
No log	52.0	52	0.5649	0.5590	0.5649	0.7516
No log	53.0	53	0.5601	0.5892	0.5601	0.7484
No log	54.0	54	0.5694	0.5791	0.5694	0.7546
No log	55.0	55	0.5777	0.5642	0.5777	0.7601
No log	56.0	56	0.5693	0.6022	0.5693	0.7545
No log	57.0	57	0.5720	0.6051	0.5720	0.7563
No log	58.0	58	0.6077	0.5833	0.6077	0.7796
No log	59.0	59	0.6119	0.5852	0.6119	0.7823
No log	60.0	60	0.5850	0.6066	0.5850	0.7649
No log	61.0	61	0.5921	0.6104	0.5921	0.7695
No log	62.0	62	0.6434	0.5953	0.6434	0.8021
No log	63.0	63	0.6790	0.5824	0.6790	0.8240
No log	64.0	64	0.6392	0.6065	0.6392	0.7995
No log	65.0	65	0.6503	0.6032	0.6503	0.8064
No log	66.0	66	0.7062	0.5794	0.7062	0.8404
No log	67.0	67	0.6820	0.6070	0.6820	0.8258
No log	68.0	68	0.6523	0.6275	0.6523	0.8076
No log	69.0	69	0.6769	0.6107	0.6769	0.8227
No log	70.0	70	0.7669	0.5643	0.7669	0.8757
No log	71.0	71	0.7732	0.5650	0.7732	0.8793
No log	72.0	72	0.7033	0.5946	0.7033	0.8387
No log	73.0	73	0.6669	0.6327	0.6669	0.8166
No log	74.0	74	0.6765	0.6096	0.6765	0.8225
No log	75.0	75	0.7323	0.5783	0.7323	0.8557
No log	76.0	76	0.7372	0.5744	0.7372	0.8586
No log	77.0	77	0.6865	0.5974	0.6865	0.8286
No log	78.0	78	0.6870	0.5969	0.6870	0.8289
No log	79.0	79	0.7350	0.5815	0.7350	0.8573
No log	80.0	80	0.7471	0.5751	0.7471	0.8644
No log	81.0	81	0.7103	0.5864	0.7103	0.8428
No log	82.0	82	0.6934	0.6123	0.6934	0.8327
No log	83.0	83	0.7107	0.6007	0.7107	0.8430
No log	84.0	84	0.7602	0.5807	0.7602	0.8719
No log	85.0	85	0.7674	0.5798	0.7674	0.8760
No log	86.0	86	0.7453	0.5861	0.7453	0.8633
No log	87.0	87	0.7064	0.6224	0.7064	0.8405
No log	88.0	88	0.6987	0.6289	0.6987	0.8359
No log	89.0	89	0.7076	0.6224	0.7076	0.8412
No log	90.0	90	0.7347	0.5907	0.7347	0.8571
No log	91.0	91	0.7787	0.5811	0.7787	0.8824
No log	92.0	92	0.7964	0.5824	0.7964	0.8924
No log	93.0	93	0.7894	0.5829	0.7894	0.8885

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 7, 2025
Updated: Mar 7, 2025