Version27NewTestASAP_FineTuningBERT_AugV27_k1_task1_organization_k1_k1_fold0

genki10/Version27NewTestASAP_FineTuningBERT_AugV27_k1_task1_organization_k1_k1_fold0

Version27NewTestASAP_FineTuningBERT_AugV27_k1_task1_organization_k1_k1_fold0 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

28% of all-time downloads

All-time downloads

Public

Repo size

18 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: 39006f54

Try a prompt

Base models

Model card

From the Hugging Face model README

Version27NewTestASAP_FineTuningBERT_AugV27_k1_task1_organization_k1_k1_fold0

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: 0.7563
Qwk: 0.5593
Mse: 0.7563
Rmse: 0.8697

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	1	11.4339	-0.0029	11.4339	3.3814
No log	2.0	2	9.8095	0.0017	9.8095	3.1320
No log	3.0	3	8.8314	0.0	8.8314	2.9718
No log	4.0	4	7.8490	0.0	7.8490	2.8016
No log	5.0	5	6.7665	0.0	6.7665	2.6013
No log	6.0	6	5.5564	0.0167	5.5564	2.3572
No log	7.0	7	4.4538	0.0115	4.4538	2.1104
No log	8.0	8	3.8037	0.0115	3.8037	1.9503
No log	9.0	9	3.5055	0.0115	3.5055	1.8723
No log	10.0	10	3.0648	0.0115	3.0648	1.7506
No log	11.0	11	2.5788	0.0339	2.5788	1.6058
No log	12.0	12	2.2430	0.1097	2.2430	1.4977
No log	13.0	13	1.9833	0.0678	1.9833	1.4083
No log	14.0	14	1.7654	0.0627	1.7654	1.3287
No log	15.0	15	1.5612	0.0564	1.5612	1.2495
No log	16.0	16	1.3490	0.0409	1.3490	1.1615
No log	17.0	17	1.1482	0.0382	1.1482	1.0715
No log	18.0	18	1.0029	0.0316	1.0029	1.0014
No log	19.0	19	0.9051	0.0316	0.9051	0.9513
No log	20.0	20	0.8456	0.2241	0.8456	0.9196
No log	21.0	21	0.7926	0.3707	0.7926	0.8903
No log	22.0	22	0.7305	0.4098	0.7305	0.8547
No log	23.0	23	0.6850	0.4208	0.6850	0.8277
No log	24.0	24	0.6794	0.4905	0.6794	0.8242
No log	25.0	25	0.6388	0.5019	0.6388	0.7993
No log	26.0	26	0.5927	0.5030	0.5927	0.7699
No log	27.0	27	0.6368	0.5126	0.6368	0.7980
No log	28.0	28	0.5906	0.5392	0.5906	0.7685
No log	29.0	29	0.6124	0.5247	0.6124	0.7826
No log	30.0	30	0.6181	0.5310	0.6181	0.7862
No log	31.0	31	0.5317	0.5598	0.5317	0.7292
No log	32.0	32	0.5022	0.5531	0.5022	0.7086
No log	33.0	33	0.5009	0.5519	0.5009	0.7078
No log	34.0	34	0.6004	0.5466	0.6004	0.7749
No log	35.0	35	0.6090	0.5386	0.6090	0.7804
No log	36.0	36	0.4961	0.5885	0.4961	0.7043
No log	37.0	37	0.5161	0.5845	0.5161	0.7184
No log	38.0	38	0.5502	0.5917	0.5502	0.7418
No log	39.0	39	0.5239	0.5957	0.5239	0.7238
No log	40.0	40	0.5422	0.5977	0.5422	0.7363
No log	41.0	41	0.5584	0.5837	0.5584	0.7473
No log	42.0	42	0.6245	0.5455	0.6245	0.7902
No log	43.0	43	0.7790	0.4574	0.7790	0.8826
No log	44.0	44	0.8038	0.4461	0.8038	0.8966
No log	45.0	45	0.7015	0.5071	0.7015	0.8376
No log	46.0	46	0.5926	0.5796	0.5926	0.7698
No log	47.0	47	0.6056	0.5408	0.6056	0.7782
No log	48.0	48	0.6015	0.5729	0.6015	0.7756
No log	49.0	49	0.6496	0.5683	0.6496	0.8060
No log	50.0	50	0.7315	0.5085	0.7315	0.8553
No log	51.0	51	0.7059	0.5350	0.7059	0.8401
No log	52.0	52	0.6421	0.5833	0.6421	0.8013
No log	53.0	53	0.6378	0.5908	0.6378	0.7986
No log	54.0	54	0.6883	0.5517	0.6883	0.8296
No log	55.0	55	0.7120	0.5459	0.7120	0.8438
No log	56.0	56	0.6615	0.5776	0.6615	0.8133
No log	57.0	57	0.6669	0.5761	0.6669	0.8166
No log	58.0	58	0.7198	0.5661	0.7198	0.8484
No log	59.0	59	0.7756	0.5473	0.7756	0.8807
No log	60.0	60	0.7070	0.5614	0.7070	0.8408
No log	61.0	61	0.6287	0.5774	0.6287	0.7929
No log	62.0	62	0.6296	0.5819	0.6296	0.7935
No log	63.0	63	0.6661	0.5630	0.6661	0.8161
No log	64.0	64	0.8277	0.5406	0.8277	0.9098
No log	65.0	65	0.8802	0.5288	0.8802	0.9382
No log	66.0	66	0.7861	0.5526	0.7861	0.8866
No log	67.0	67	0.6557	0.5660	0.6557	0.8098
No log	68.0	68	0.6359	0.5882	0.6359	0.7975
No log	69.0	69	0.6551	0.5711	0.6551	0.8094
No log	70.0	70	0.7563	0.5593	0.7563	0.8697

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