Version2ASAP_FineTuningBERT_AugV8_k1_task1_organization_k1_k1_fold3

genki10/Version2ASAP_FineTuningBERT_AugV8_k1_task1_organization_k1_k1_fold3

Version2ASAP_FineTuningBERT_AugV8_k1_task1_organization_k1_k1_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. --

Downloads · 30 days

41% of all-time downloads

All-time downloads

Public

Repo size

21 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 1, 2025
Updated: Mar 1, 2025
SHA: 1c64f830

Try a prompt

Base models

Model card

From the Hugging Face model README

Version2ASAP_FineTuningBERT_AugV8_k1_task1_organization_k1_k1_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: 0.7803
Qwk: 0.5559
Mse: 0.7803
Rmse: 0.8833

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	2	11.8592	0.0149	11.8578	3.4435
No log	2.0	4	9.8257	0.0018	9.8239	3.1343
No log	3.0	6	7.7489	0.0	7.7474	2.7834
No log	4.0	8	6.0569	-0.0093	6.0554	2.4608
No log	5.0	10	4.5880	0.0185	4.5869	2.1417
No log	6.0	12	3.8196	0.0076	3.8184	1.9541
No log	7.0	14	2.8437	0.0038	2.8428	1.6861
No log	8.0	16	2.2347	0.0653	2.2339	1.4946
No log	9.0	18	1.7242	0.0463	1.7235	1.3128
No log	10.0	20	1.4937	0.0365	1.4930	1.2219
No log	11.0	22	1.1631	0.0202	1.1625	1.0782
No log	12.0	24	0.9807	0.0202	0.9802	0.9901
No log	13.0	26	1.1508	0.0472	1.1503	1.0725
No log	14.0	28	0.9339	0.1744	0.9335	0.9662
No log	15.0	30	0.7733	0.3845	0.7729	0.8792
No log	16.0	32	1.0015	0.1126	1.0008	1.0004
No log	17.0	34	0.7846	0.3270	0.7841	0.8855
No log	18.0	36	0.8874	0.3368	0.8872	0.9419
No log	19.0	38	1.1059	0.2195	1.1056	1.0515
No log	20.0	40	0.7118	0.4657	0.7118	0.8437
No log	21.0	42	0.7259	0.4836	0.7258	0.8519
No log	22.0	44	0.9306	0.4005	0.9306	0.9647
No log	23.0	46	0.7733	0.4583	0.7734	0.8794
No log	24.0	48	0.7487	0.4694	0.7487	0.8653
No log	25.0	50	0.8163	0.4451	0.8164	0.9035
No log	26.0	52	0.6254	0.4898	0.6252	0.7907
No log	27.0	54	0.6712	0.4736	0.6712	0.8193
No log	28.0	56	0.9432	0.4223	0.9435	0.9713
No log	29.0	58	0.6924	0.5028	0.6925	0.8322
No log	30.0	60	0.6964	0.4976	0.6966	0.8346
No log	31.0	62	1.0120	0.4152	1.0123	1.0061
No log	32.0	64	0.6909	0.4980	0.6910	0.8313
No log	33.0	66	0.6609	0.4844	0.6610	0.8130
No log	34.0	68	0.6925	0.5137	0.6926	0.8322
No log	35.0	70	1.0236	0.4273	1.0238	1.0118
No log	36.0	72	0.6843	0.4879	0.6843	0.8272
No log	37.0	74	0.6537	0.5068	0.6537	0.8085
No log	38.0	76	0.6567	0.5409	0.6567	0.8104
No log	39.0	78	1.1777	0.4110	1.1779	1.0853
No log	40.0	80	0.9109	0.5164	0.9109	0.9544
No log	41.0	82	0.7411	0.5575	0.7410	0.8608
No log	42.0	84	0.7602	0.5375	0.7601	0.8718
No log	43.0	86	0.8923	0.5197	0.8925	0.9447
No log	44.0	88	0.7274	0.5227	0.7274	0.8529
No log	45.0	90	0.6512	0.5485	0.6512	0.8070
No log	46.0	92	0.6972	0.5486	0.6972	0.8350
No log	47.0	94	0.9044	0.5009	0.9045	0.9510
No log	48.0	96	0.8431	0.5205	0.8431	0.9182
No log	49.0	98	0.7829	0.5373	0.7829	0.8848
No log	50.0	100	0.8056	0.5411	0.8055	0.8975
No log	51.0	102	0.9978	0.4813	0.9980	0.9990
No log	52.0	104	0.9792	0.4745	0.9794	0.9896
No log	53.0	106	0.8163	0.5414	0.8163	0.9035
No log	54.0	108	0.7789	0.5299	0.7789	0.8826
No log	55.0	110	0.9101	0.4904	0.9103	0.9541
No log	56.0	112	0.8299	0.5151	0.8301	0.9111
No log	57.0	114	0.7486	0.5425	0.7487	0.8652
No log	58.0	116	0.7730	0.5391	0.7731	0.8793
No log	59.0	118	0.9069	0.4877	0.9072	0.9525
No log	60.0	120	0.8199	0.5096	0.8200	0.9056
No log	61.0	122	0.7803	0.5559	0.7803	0.8833

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 1, 2025
Updated: Mar 1, 2025