Version19ASAP_FineTuningBERT_AugV19_k3_task1_organization_k3_k3_fold2

genki10/Version19ASAP_FineTuningBERT_AugV19_k3_task1_organization_k3_k3_fold2

Version19ASAP_FineTuningBERT_AugV19_k3_task1_organization_k3_k3_fold2 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 9, 2025
Updated: Mar 9, 2025
SHA: 8dea89a2

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Base models

Model card

From the Hugging Face model README

Version19ASAP_FineTuningBERT_AugV19_k3_task1_organization_k3_k3_fold2

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: 0.6490
Qwk: 0.5666
Mse: 0.6486
Rmse: 0.8054

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	13.5050	0.0	13.5052	3.6749
No log	2.0	2	11.5637	-0.0008	11.5641	3.4006
No log	3.0	3	9.8983	0.0066	9.8989	3.1462
No log	4.0	4	9.1109	0.0	9.1116	3.0185
No log	5.0	5	7.7460	0.0	7.7465	2.7832
No log	6.0	6	7.4989	0.0	7.4992	2.7385
No log	7.0	7	6.9785	0.0012	6.9789	2.6418
No log	8.0	8	5.8114	0.0348	5.8118	2.4108
No log	9.0	9	4.7188	0.0246	4.7193	2.1724
No log	10.0	10	4.1510	0.0117	4.1514	2.0375
No log	11.0	11	3.7349	0.0078	3.7353	1.9327
No log	12.0	12	3.3987	0.0039	3.3992	1.8437
No log	13.0	13	2.7531	0.0050	2.7536	1.6594
No log	14.0	14	2.3576	0.1027	2.3581	1.5356
No log	15.0	15	2.0313	0.1127	2.0318	1.4254
No log	16.0	16	1.8495	0.1161	1.8501	1.3602
No log	17.0	17	1.6576	0.0844	1.6581	1.2877
No log	18.0	18	1.3280	0.0372	1.3284	1.1526
No log	19.0	19	1.2753	0.0345	1.2757	1.1295
No log	20.0	20	1.1213	0.0280	1.1217	1.0591
No log	21.0	21	1.0098	0.0345	1.0102	1.0051
No log	22.0	22	1.0711	0.0557	1.0715	1.0351
No log	23.0	23	0.9404	0.1447	0.9408	0.9699
No log	24.0	24	0.7685	0.3570	0.7688	0.8768
No log	25.0	25	0.7339	0.3557	0.7341	0.8568
No log	26.0	26	0.7044	0.3325	0.7047	0.8394
No log	27.0	27	0.6950	0.3496	0.6952	0.8338
No log	28.0	28	0.6681	0.3402	0.6682	0.8175
No log	29.0	29	0.6925	0.2644	0.6926	0.8322
No log	30.0	30	0.6615	0.2835	0.6614	0.8133
No log	31.0	31	0.5934	0.3651	0.5934	0.7703
No log	32.0	32	0.5838	0.3687	0.5837	0.7640
No log	33.0	33	0.6327	0.3058	0.6325	0.7953
No log	34.0	34	0.6656	0.3004	0.6653	0.8157
No log	35.0	35	0.6137	0.3878	0.6134	0.7832
No log	36.0	36	0.5510	0.4655	0.5509	0.7422
No log	37.0	37	0.5408	0.4856	0.5406	0.7352
No log	38.0	38	0.5734	0.5117	0.5729	0.7569
No log	39.0	39	0.6217	0.5105	0.6210	0.7880
No log	40.0	40	0.6008	0.5556	0.6001	0.7746
No log	41.0	41	0.5415	0.5581	0.5410	0.7355
No log	42.0	42	0.5450	0.5695	0.5444	0.7379
No log	43.0	43	0.5895	0.5782	0.5887	0.7673
No log	44.0	44	0.5937	0.5775	0.5929	0.7700
No log	45.0	45	0.5810	0.5930	0.5804	0.7619
No log	46.0	46	0.6049	0.6010	0.6043	0.7773
No log	47.0	47	0.6624	0.5693	0.6614	0.8133
No log	48.0	48	0.6771	0.5722	0.6761	0.8222
No log	49.0	49	0.6359	0.5985	0.6351	0.7969
No log	50.0	50	0.6374	0.5931	0.6367	0.7980
No log	51.0	51	0.6486	0.5966	0.6478	0.8049
No log	52.0	52	0.6985	0.5745	0.6976	0.8352
No log	53.0	53	0.7386	0.5580	0.7376	0.8588
No log	54.0	54	0.7130	0.5697	0.7122	0.8439
No log	55.0	55	0.7334	0.5660	0.7329	0.8561
No log	56.0	56	0.7161	0.5565	0.7157	0.8460
No log	57.0	57	0.6601	0.5770	0.6596	0.8121
No log	58.0	58	0.7107	0.5713	0.7099	0.8425
No log	59.0	59	0.8449	0.5333	0.8437	0.9185
No log	60.0	60	0.8551	0.5341	0.8539	0.9241
No log	61.0	61	0.7541	0.5513	0.7533	0.8679
No log	62.0	62	0.6697	0.5619	0.6693	0.8181
No log	63.0	63	0.6730	0.5656	0.6727	0.8202
No log	64.0	64	0.6709	0.5660	0.6705	0.8188
No log	65.0	65	0.6706	0.5645	0.6701	0.8186
No log	66.0	66	0.6490	0.5666	0.6486	0.8054

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 9, 2025
Updated: Mar 9, 2025