ASAP_nosemantic_FineTuningBERT_AugV12_k1_task1_organization_k1_k1_fold0

genki10/ASAP_nosemantic_FineTuningBERT_AugV12_k1_task1_organization_k1_k1_fold0

ASAP_nosemantic_FineTuningBERT_AugV12_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. --

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At a glance

Task: Text Classification
Library: transformers
License: apache-2.0
Model type: bert
Access: Public
Created: Mar 6, 2025
Updated: Mar 7, 2025
SHA: a0576108

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Base models

Model card

From the Hugging Face model README

ASAP_nosemantic_FineTuningBERT_AugV12_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: 0.8426
Qwk: 0.4688
Mse: 0.8426
Rmse: 0.9179

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	13.1546	0.0	13.1546	3.6269
No log	2.0	2	11.2459	0.0122	11.2459	3.3535
No log	3.0	3	9.5394	0.0069	9.5394	3.0886
No log	4.0	4	7.9336	0.0054	7.9336	2.8167
No log	5.0	5	7.6540	0.0068	7.6540	2.7666
No log	6.0	6	6.7519	0.0256	6.7519	2.5984
No log	7.0	7	5.2670	0.0430	5.2670	2.2950
No log	8.0	8	4.4305	0.0183	4.4305	2.1049
No log	9.0	9	3.8629	0.0160	3.8629	1.9654
No log	10.0	10	3.9649	0.0160	3.9649	1.9912
No log	11.0	11	3.4687	0.0153	3.4687	1.8625
No log	12.0	12	2.7598	0.0625	2.7598	1.6613
No log	13.0	13	2.5907	0.0652	2.5907	1.6096
No log	14.0	14	2.3715	0.0648	2.3715	1.5400
No log	15.0	15	2.0918	0.1410	2.0918	1.4463
No log	16.0	16	2.2416	0.1341	2.2416	1.4972
No log	17.0	17	2.2142	0.1213	2.2142	1.4880
No log	18.0	18	1.8659	0.1021	1.8659	1.3660
No log	19.0	19	1.4170	0.0472	1.4170	1.1904
No log	20.0	20	1.3619	0.0435	1.3619	1.1670
No log	21.0	21	1.3312	0.0408	1.3312	1.1538
No log	22.0	22	1.1288	0.0418	1.1288	1.0624
No log	23.0	23	1.0077	0.0344	1.0077	1.0038
No log	24.0	24	1.1787	0.0344	1.1787	1.0857
No log	25.0	25	1.2361	0.0344	1.2361	1.1118
No log	26.0	26	1.1148	0.0390	1.1148	1.0559
No log	27.0	27	0.9120	0.1587	0.9120	0.9550
No log	28.0	28	0.7638	0.3324	0.7638	0.8740
No log	29.0	29	0.8498	0.1161	0.8498	0.9218
No log	30.0	30	0.9402	0.0272	0.9402	0.9696
No log	31.0	31	0.9277	0.0379	0.9277	0.9632
No log	32.0	32	0.8361	0.1282	0.8361	0.9144
No log	33.0	33	0.7364	0.3463	0.7364	0.8582
No log	34.0	34	0.7281	0.3559	0.7281	0.8533
No log	35.0	35	0.8657	0.3091	0.8657	0.9304
No log	36.0	36	0.9333	0.2779	0.9333	0.9661
No log	37.0	37	0.8773	0.3455	0.8773	0.9366
No log	38.0	38	0.7682	0.3442	0.7682	0.8765
No log	39.0	39	0.7117	0.3219	0.7117	0.8436
No log	40.0	40	0.7237	0.3216	0.7237	0.8507
No log	41.0	41	0.7407	0.3505	0.7407	0.8606
No log	42.0	42	0.7568	0.3546	0.7568	0.8700
No log	43.0	43	0.7650	0.3509	0.7650	0.8746
No log	44.0	44	0.7598	0.3780	0.7598	0.8716
No log	45.0	45	0.7672	0.3777	0.7672	0.8759
No log	46.0	46	0.8158	0.3820	0.8158	0.9032
No log	47.0	47	0.8206	0.3692	0.8206	0.9059
No log	48.0	48	0.7931	0.3732	0.7931	0.8906
No log	49.0	49	0.7378	0.3935	0.7378	0.8589
No log	50.0	50	0.6969	0.4402	0.6969	0.8348
No log	51.0	51	0.6803	0.4108	0.6803	0.8248
No log	52.0	52	0.6731	0.4238	0.6731	0.8205
No log	53.0	53	0.6782	0.4126	0.6782	0.8235
No log	54.0	54	0.6963	0.4103	0.6963	0.8345
No log	55.0	55	0.7159	0.4250	0.7159	0.8461
No log	56.0	56	0.7400	0.4346	0.7400	0.8603
No log	57.0	57	0.7603	0.4295	0.7603	0.8720
No log	58.0	58	0.7873	0.4230	0.7873	0.8873
No log	59.0	59	0.8218	0.4387	0.8218	0.9065
No log	60.0	60	0.8354	0.4311	0.8354	0.9140
No log	61.0	61	0.8320	0.4115	0.8320	0.9122
No log	62.0	62	0.8371	0.4212	0.8371	0.9149
No log	63.0	63	0.8547	0.4183	0.8547	0.9245
No log	64.0	64	0.8720	0.4275	0.8720	0.9338
No log	65.0	65	0.8465	0.4268	0.8465	0.9201
No log	66.0	66	0.8232	0.4479	0.8232	0.9073
No log	67.0	67	0.7820	0.4398	0.7820	0.8843
No log	68.0	68	0.7685	0.4428	0.7685	0.8766
No log	69.0	69	0.7732	0.4486	0.7732	0.8793
No log	70.0	70	0.7895	0.4481	0.7895	0.8885
No log	71.0	71	0.7731	0.4341	0.7731	0.8793
No log	72.0	72	0.7611	0.4285	0.7611	0.8724
No log	73.0	73	0.7777	0.4586	0.7777	0.8819
No log	74.0	74	0.7784	0.4535	0.7784	0.8822
No log	75.0	75	0.7652	0.4544	0.7652	0.8747
No log	76.0	76	0.7650	0.4418	0.7650	0.8746
No log	77.0	77	0.7741	0.4251	0.7741	0.8798
No log	78.0	78	0.8004	0.4462	0.8004	0.8947
No log	79.0	79	0.8034	0.4243	0.8034	0.8963
No log	80.0	80	0.8213	0.4507	0.8213	0.9062
No log	81.0	81	0.8282	0.4507	0.8282	0.9101
No log	82.0	82	0.8090	0.4301	0.8090	0.8995
No log	83.0	83	0.7930	0.4278	0.7930	0.8905
No log	84.0	84	0.7839	0.4382	0.7839	0.8854
No log	85.0	85	0.7884	0.4421	0.7884	0.8879
No log	86.0	86	0.7976	0.4430	0.7976	0.8931
No log	87.0	87	0.8225	0.4622	0.8225	0.9069
No log	88.0	88	0.8303	0.4639	0.8303	0.9112
No log	89.0	89	0.8194	0.4713	0.8194	0.9052
No log	90.0	90	0.8016	0.4659	0.8016	0.8953
No log	91.0	91	0.7853	0.4457	0.7853	0.8862
No log	92.0	92	0.7824	0.4389	0.7824	0.8845
No log	93.0	93	0.7849	0.4412	0.7849	0.8860
No log	94.0	94	0.7903	0.4464	0.7903	0.8890
No log	95.0	95	0.7993	0.4592	0.7993	0.8941
No log	96.0	96	0.8119	0.4549	0.8119	0.9011
No log	97.0	97	0.8258	0.4685	0.8258	0.9087
No log	98.0	98	0.8366	0.4663	0.8366	0.9147
No log	99.0	99	0.8417	0.4691	0.8417	0.9174
No log	100.0	100	0.8426	0.4688	0.8426	0.9179

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 6, 2025
Updated: Mar 7, 2025