Version25NewTestASAP_FineTuningBERT_AugV25_k1_task1_organization_k1_k1_fold3

genki10/Version25NewTestASAP_FineTuningBERT_AugV25_k1_task1_organization_k1_k1_fold3

Version25NewTestASAP_FineTuningBERT_AugV25_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

46% of all-time downloads

All-time downloads

Public

Repo size

27.6 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: 82f7a8c9

Try a prompt

Base models

Model card

From the Hugging Face model README

Version25NewTestASAP_FineTuningBERT_AugV25_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: 0.6613
Qwk: 0.5630
Mse: 0.6616
Rmse: 0.8134

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	14.2457	-0.0061	14.2421	3.7739
No log	2.0	4	9.4488	0.0008	9.4470	3.0736
No log	3.0	6	8.3202	0.0	8.3186	2.8842
No log	4.0	8	6.9758	0.0	6.9744	2.6409
No log	5.0	10	5.3031	0.0237	5.3020	2.3026
No log	6.0	12	4.3579	0.0048	4.3566	2.0872
No log	7.0	14	3.0447	-0.0003	3.0439	1.7447
No log	8.0	16	2.4563	0.1034	2.4556	1.5670
No log	9.0	18	1.7641	0.0947	1.7635	1.3280
No log	10.0	20	1.2426	0.0229	1.2421	1.1145
No log	11.0	22	0.9986	0.0202	0.9982	0.9991
No log	12.0	24	0.8996	0.2619	0.8993	0.9483
No log	13.0	26	0.8599	0.2440	0.8597	0.9272
No log	14.0	28	0.7975	0.3411	0.7974	0.8929
No log	15.0	30	0.8562	0.2899	0.8560	0.9252
No log	16.0	32	1.2280	0.1351	1.2280	1.1081
No log	17.0	34	1.5894	0.2042	1.5895	1.2607
No log	18.0	36	1.2336	0.2394	1.2339	1.1108
No log	19.0	38	0.6149	0.4862	0.6151	0.7843
No log	20.0	40	0.6046	0.4702	0.6049	0.7778
No log	21.0	42	0.6729	0.4453	0.6732	0.8205
No log	22.0	44	0.7523	0.4210	0.7526	0.8675
No log	23.0	46	0.6131	0.4795	0.6135	0.7833
No log	24.0	48	0.5617	0.4961	0.5620	0.7497
No log	25.0	50	0.5604	0.4869	0.5607	0.7488
No log	26.0	52	0.6262	0.4711	0.6265	0.7915
No log	27.0	54	0.5878	0.4423	0.5881	0.7669
No log	28.0	56	0.7083	0.4694	0.7088	0.8419
No log	29.0	58	0.8497	0.4080	0.8503	0.9221
No log	30.0	60	0.7092	0.4901	0.7098	0.8425
No log	31.0	62	0.7600	0.3896	0.7605	0.8721
No log	32.0	64	0.7256	0.4029	0.7261	0.8521
No log	33.0	66	0.6840	0.5314	0.6845	0.8273
No log	34.0	68	0.7647	0.4875	0.7652	0.8747
No log	35.0	70	0.6318	0.5187	0.6321	0.7951
No log	36.0	72	0.7494	0.4451	0.7497	0.8658
No log	37.0	74	1.1878	0.3321	1.1881	1.0900
No log	38.0	76	1.2956	0.2917	1.2958	1.1383
No log	39.0	78	0.8744	0.4025	0.8747	0.9352
No log	40.0	80	0.6327	0.5386	0.6331	0.7957
No log	41.0	82	0.8056	0.4939	0.8060	0.8978
No log	42.0	84	0.7760	0.5033	0.7766	0.8812
No log	43.0	86	0.6983	0.4928	0.6989	0.8360
No log	44.0	88	0.6798	0.4723	0.6804	0.8249
No log	45.0	90	0.6598	0.4622	0.6605	0.8127
No log	46.0	92	0.6665	0.4971	0.6672	0.8168
No log	47.0	94	0.6520	0.5321	0.6526	0.8079
No log	48.0	96	0.6366	0.5447	0.6372	0.7983
No log	49.0	98	0.6198	0.5612	0.6204	0.7876
No log	50.0	100	0.6721	0.5031	0.6726	0.8201
No log	51.0	102	0.6745	0.5227	0.6751	0.8216
No log	52.0	104	0.6601	0.5591	0.6606	0.8128
No log	53.0	106	0.7580	0.5634	0.7585	0.8709
No log	54.0	108	0.7314	0.5443	0.7320	0.8556
No log	55.0	110	0.7770	0.4962	0.7777	0.8818
No log	56.0	112	0.7580	0.5110	0.7586	0.8710
No log	57.0	114	0.6969	0.5336	0.6974	0.8351
No log	58.0	116	0.6765	0.5604	0.6769	0.8227
No log	59.0	118	0.6510	0.5633	0.6512	0.8070
No log	60.0	120	0.6189	0.5626	0.6192	0.7869
No log	61.0	122	0.6849	0.5076	0.6852	0.8278
No log	62.0	124	0.6319	0.5487	0.6322	0.7951
No log	63.0	126	0.7016	0.5414	0.7019	0.8378
No log	64.0	128	0.8154	0.4987	0.8157	0.9032
No log	65.0	130	0.7535	0.5317	0.7539	0.8683
No log	66.0	132	0.6904	0.5349	0.6909	0.8312
No log	67.0	134	0.6865	0.5340	0.6871	0.8289
No log	68.0	136	0.6821	0.5245	0.6827	0.8263
No log	69.0	138	0.7178	0.5476	0.7184	0.8476
No log	70.0	140	0.7227	0.5494	0.7231	0.8504
No log	71.0	142	0.6722	0.5467	0.6727	0.8202
No log	72.0	144	0.6356	0.5466	0.6361	0.7975
No log	73.0	146	0.6308	0.5717	0.6312	0.7945
No log	74.0	148	0.6184	0.5779	0.6188	0.7866
No log	75.0	150	0.6381	0.5898	0.6386	0.7991
No log	76.0	152	0.6582	0.5718	0.6587	0.8116
No log	77.0	154	0.7134	0.5683	0.7139	0.8449
No log	78.0	156	0.7255	0.5615	0.7260	0.8521
No log	79.0	158	0.6940	0.5670	0.6946	0.8334
No log	80.0	160	0.6660	0.5713	0.6666	0.8165
No log	81.0	162	0.6546	0.5622	0.6552	0.8094
No log	82.0	164	0.6286	0.5692	0.6292	0.7932
No log	83.0	166	0.6676	0.5573	0.6681	0.8174
No log	84.0	168	0.7245	0.5471	0.7249	0.8514
No log	85.0	170	0.6997	0.5462	0.7001	0.8367
No log	86.0	172	0.6533	0.5546	0.6538	0.8086
No log	87.0	174	0.7289	0.5408	0.7295	0.8541
No log	88.0	176	0.8647	0.4458	0.8653	0.9302
No log	89.0	178	0.8591	0.4529	0.8597	0.9272
No log	90.0	180	0.7146	0.5275	0.7151	0.8457
No log	91.0	182	0.6184	0.5503	0.6188	0.7867
No log	92.0	184	0.7634	0.5189	0.7637	0.8739
No log	93.0	186	0.9434	0.4641	0.9437	0.9714
No log	94.0	188	0.9234	0.4755	0.9237	0.9611
No log	95.0	190	0.8183	0.4745	0.8186	0.9048
No log	96.0	192	0.7114	0.5005	0.7117	0.8437
No log	97.0	194	0.6375	0.5299	0.6378	0.7986
No log	98.0	196	0.6032	0.5481	0.6036	0.7769
No log	99.0	198	0.6315	0.5371	0.6320	0.7950
No log	100.0	200	0.6514	0.5305	0.6519	0.8074
No log	101.0	202	0.6458	0.5325	0.6463	0.8039
No log	102.0	204	0.6423	0.5351	0.6428	0.8017
No log	103.0	206	0.6406	0.5622	0.6411	0.8007
No log	104.0	208	0.6645	0.5620	0.6649	0.8154
No log	105.0	210	0.6613	0.5630	0.6616	0.8134

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