ASAP_nosemantic_FineTuningBERT_AugV12_k3_task1_organization_k3_k3_fold1

genki10/ASAP_nosemantic_FineTuningBERT_AugV12_k3_task1_organization_k3_k3_fold1

ASAP_nosemantic_FineTuningBERT_AugV12_k3_task1_organization_k3_k3_fold1 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 7, 2025
Updated: Mar 7, 2025
SHA: 4b7785e7

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Base models

Model card

From the Hugging Face model README

ASAP_nosemantic_FineTuningBERT_AugV12_k3_task1_organization_k3_k3_fold1

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: 0.5689
Qwk: 0.6068
Mse: 0.5678
Rmse: 0.7536

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	8.7079	-0.0002	8.7054	2.9505
No log	2.0	4	6.6334	0.0	6.6311	2.5751
No log	3.0	6	5.6318	-0.0174	5.6296	2.3727
No log	4.0	8	4.5695	0.0	4.5673	2.1371
No log	5.0	10	3.5587	0.0	3.5567	1.8859
No log	6.0	12	2.8353	0.0	2.8334	1.6833
No log	7.0	14	2.2997	-0.0048	2.2979	1.5159
No log	8.0	16	1.7900	0.0583	1.7884	1.3373
No log	9.0	18	1.4261	0.0	1.4245	1.1935
No log	10.0	20	1.1842	0.0	1.1827	1.0875
No log	11.0	22	1.0009	0.0315	0.9996	0.9998
No log	12.0	24	0.8553	0.0901	0.8540	0.9241
No log	13.0	26	0.8179	0.2930	0.8167	0.9037
No log	14.0	28	0.7211	0.4715	0.7200	0.8485
No log	15.0	30	0.7083	0.5416	0.7072	0.8409
No log	16.0	32	0.6312	0.6028	0.6301	0.7938
No log	17.0	34	0.5877	0.6125	0.5866	0.7659
No log	18.0	36	0.5545	0.5776	0.5533	0.7439
No log	19.0	38	0.8021	0.4951	0.8011	0.8950
No log	20.0	40	0.5824	0.5213	0.5812	0.7623
No log	21.0	42	0.5565	0.5400	0.5553	0.7452
No log	22.0	44	0.9822	0.4211	0.9809	0.9904
No log	23.0	46	0.5661	0.5510	0.5648	0.7515
No log	24.0	48	0.5510	0.5252	0.5497	0.7414
No log	25.0	50	0.5836	0.5421	0.5824	0.7631
No log	26.0	52	0.5580	0.5231	0.5568	0.7462
No log	27.0	54	0.5456	0.5647	0.5445	0.7379
No log	28.0	56	0.5855	0.5328	0.5843	0.7644
No log	29.0	58	0.5648	0.5965	0.5636	0.7507
No log	30.0	60	0.5590	0.5948	0.5578	0.7469
No log	31.0	62	0.5727	0.6043	0.5715	0.7560
No log	32.0	64	0.5754	0.6320	0.5743	0.7578
No log	33.0	66	0.6079	0.6299	0.6069	0.7790
No log	34.0	68	0.6543	0.6060	0.6533	0.8083
No log	35.0	70	0.6192	0.6168	0.6182	0.7863
No log	36.0	72	0.5974	0.6211	0.5964	0.7722
No log	37.0	74	0.6100	0.5922	0.6090	0.7804
No log	38.0	76	0.5713	0.6111	0.5702	0.7551
No log	39.0	78	0.5684	0.6358	0.5672	0.7532
No log	40.0	80	0.7083	0.5693	0.7071	0.8409
No log	41.0	82	0.5909	0.6014	0.5897	0.7679
No log	42.0	84	0.6064	0.5927	0.6052	0.7780
No log	43.0	86	0.5884	0.6215	0.5873	0.7663
No log	44.0	88	0.5705	0.6124	0.5693	0.7545
No log	45.0	90	0.5870	0.6324	0.5859	0.7654
No log	46.0	92	0.6587	0.5977	0.6576	0.8109
No log	47.0	94	0.5952	0.5908	0.5941	0.7708
No log	48.0	96	0.5555	0.6029	0.5544	0.7446
No log	49.0	98	0.6215	0.5830	0.6203	0.7876
No log	50.0	100	0.5372	0.6185	0.5361	0.7322
No log	51.0	102	0.5473	0.6206	0.5462	0.7390
No log	52.0	104	0.5861	0.6127	0.5850	0.7648
No log	53.0	106	0.5666	0.6326	0.5655	0.7520
No log	54.0	108	0.5991	0.6364	0.5980	0.7733
No log	55.0	110	0.8231	0.5675	0.8219	0.9066
No log	56.0	112	0.6251	0.6218	0.6240	0.7900
No log	57.0	114	0.6119	0.6183	0.6108	0.7815
No log	58.0	116	0.6099	0.5966	0.6088	0.7803
No log	59.0	118	0.5372	0.6349	0.5361	0.7322
No log	60.0	120	0.5358	0.6371	0.5347	0.7312
No log	61.0	122	0.5870	0.5991	0.5859	0.7654
No log	62.0	124	0.5552	0.6286	0.5541	0.7444
No log	63.0	126	0.5886	0.6087	0.5875	0.7665
No log	64.0	128	0.5814	0.6240	0.5803	0.7618
No log	65.0	130	0.6902	0.5803	0.6891	0.8301
No log	66.0	132	0.6394	0.5906	0.6383	0.7989
No log	67.0	134	0.5714	0.6214	0.5703	0.7552
No log	68.0	136	0.6069	0.6002	0.6058	0.7783
No log	69.0	138	0.7724	0.5456	0.7714	0.8783
No log	70.0	140	0.6393	0.5728	0.6383	0.7989
No log	71.0	142	0.5411	0.6286	0.5401	0.7349
No log	72.0	144	0.5599	0.6179	0.5589	0.7476
No log	73.0	146	0.6592	0.5746	0.6581	0.8112
No log	74.0	148	0.5841	0.6021	0.5830	0.7636
No log	75.0	150	0.5430	0.6160	0.5420	0.7362
No log	76.0	152	0.5411	0.6313	0.5400	0.7348
No log	77.0	154	0.5925	0.5958	0.5914	0.7690
No log	78.0	156	0.5614	0.6080	0.5603	0.7485
No log	79.0	158	0.5460	0.6304	0.5449	0.7382
No log	80.0	160	0.5689	0.6068	0.5678	0.7536

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 7, 2025
Updated: Mar 7, 2025