Version25NewTestASAP_FineTuningBERT_AugV25_k1_task1_organization_k1_k1_fold0

genki10/Version25NewTestASAP_FineTuningBERT_AugV25_k1_task1_organization_k1_k1_fold0

Version25NewTestASAP_FineTuningBERT_AugV25_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

39% of all-time downloads

All-time downloads

Public

Repo size

20.6 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: ca0f101b

Try a prompt

Base models

Model card

From the Hugging Face model README

Version25NewTestASAP_FineTuningBERT_AugV25_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: 0.9772
Qwk: 0.4953
Mse: 0.9772
Rmse: 0.9885

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	9.7540	0.0036	9.7540	3.1231
No log	2.0	4	7.7317	0.0	7.7317	2.7806
No log	3.0	6	6.2882	0.0185	6.2882	2.5076
No log	4.0	8	5.1103	0.0112	5.1103	2.2606
No log	5.0	10	4.6565	0.0102	4.6565	2.1579
No log	6.0	12	3.8755	0.0036	3.8755	1.9686
No log	7.0	14	2.9825	0.0	2.9825	1.7270
No log	8.0	16	2.4891	0.0798	2.4891	1.5777
No log	9.0	18	2.0521	0.0484	2.0521	1.4325
No log	10.0	20	2.0404	0.0315	2.0404	1.4284
No log	11.0	22	2.0618	0.0322	2.0618	1.4359
No log	12.0	24	1.5678	0.0316	1.5678	1.2521
No log	13.0	26	1.2719	0.0316	1.2719	1.1278
No log	14.0	28	1.1392	0.0106	1.1392	1.0673
No log	15.0	30	0.9941	0.0	0.9941	0.9970
No log	16.0	32	0.9370	0.0110	0.9370	0.9680
No log	17.0	34	0.8825	0.1937	0.8825	0.9394
No log	18.0	36	0.9090	0.2517	0.9090	0.9534
No log	19.0	38	1.5142	0.1678	1.5142	1.2305
No log	20.0	40	1.1942	0.2680	1.1942	1.0928
No log	21.0	42	0.7449	0.3075	0.7449	0.8631
No log	22.0	44	0.7899	0.1876	0.7899	0.8887
No log	23.0	46	0.6854	0.2636	0.6854	0.8279
No log	24.0	48	0.6504	0.4662	0.6504	0.8065
No log	25.0	50	0.9294	0.3689	0.9294	0.9640
No log	26.0	52	0.8968	0.4342	0.8968	0.9470
No log	27.0	54	0.6955	0.4945	0.6955	0.8339
No log	28.0	56	0.8371	0.4594	0.8371	0.9149
No log	29.0	58	1.4260	0.3368	1.4260	1.1942
No log	30.0	60	2.0227	0.2515	2.0227	1.4222
No log	31.0	62	1.6883	0.2909	1.6883	1.2994
No log	32.0	64	0.7691	0.4363	0.7691	0.8770
No log	33.0	66	0.6549	0.4374	0.6549	0.8092
No log	34.0	68	0.6445	0.4446	0.6445	0.8028
No log	35.0	70	0.7569	0.4351	0.7569	0.8700
No log	36.0	72	1.1689	0.3536	1.1689	1.0811
No log	37.0	74	1.2248	0.3427	1.2248	1.1067
No log	38.0	76	0.7570	0.4274	0.7570	0.8701
No log	39.0	78	0.6604	0.4230	0.6604	0.8126
No log	40.0	80	0.7565	0.3481	0.7565	0.8698
No log	41.0	82	0.7591	0.4113	0.7591	0.8713
No log	42.0	84	0.6244	0.5149	0.6244	0.7902
No log	43.0	86	0.6806	0.4359	0.6806	0.8250
No log	44.0	88	0.7466	0.4214	0.7466	0.8641
No log	45.0	90	0.6125	0.5645	0.6125	0.7826
No log	46.0	92	0.6367	0.5589	0.6367	0.7979
No log	47.0	94	0.6838	0.5436	0.6838	0.8270
No log	48.0	96	0.8303	0.4718	0.8303	0.9112
No log	49.0	98	0.7040	0.5265	0.7040	0.8391
No log	50.0	100	0.6440	0.5460	0.6440	0.8025
No log	51.0	102	0.6403	0.5595	0.6403	0.8002
No log	52.0	104	0.9723	0.4027	0.9723	0.9861
No log	53.0	106	1.1411	0.3226	1.1411	1.0682
No log	54.0	108	0.8195	0.4296	0.8195	0.9053
No log	55.0	110	0.6567	0.5520	0.6567	0.8104
No log	56.0	112	0.7052	0.5334	0.7052	0.8397
No log	57.0	114	0.6851	0.5435	0.6851	0.8277
No log	58.0	116	0.6642	0.5328	0.6642	0.8150
No log	59.0	118	0.6887	0.5215	0.6887	0.8299
No log	60.0	120	0.6605	0.5418	0.6605	0.8127
No log	61.0	122	0.6565	0.5391	0.6565	0.8102
No log	62.0	124	0.6976	0.5391	0.6976	0.8352
No log	63.0	126	0.7110	0.5317	0.7110	0.8432
No log	64.0	128	0.7499	0.5388	0.7499	0.8660
No log	65.0	130	0.8344	0.5206	0.8344	0.9135
No log	66.0	132	0.7434	0.5316	0.7434	0.8622
No log	67.0	134	0.7238	0.5065	0.7238	0.8508
No log	68.0	136	0.7211	0.5132	0.7211	0.8492
No log	69.0	138	0.7999	0.5422	0.7999	0.8943
No log	70.0	140	0.8049	0.5192	0.8049	0.8972
No log	71.0	142	0.6817	0.5358	0.6817	0.8256
No log	72.0	144	0.7164	0.5084	0.7164	0.8464
No log	73.0	146	0.7214	0.5165	0.7214	0.8493
No log	74.0	148	0.8377	0.5383	0.8377	0.9153
No log	75.0	150	0.9772	0.4953	0.9772	0.9885

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