ASAP_FineTuningBERT_AugV17_k1_task1_organization_k1_k1_fold1

genki10/ASAP_FineTuningBERT_AugV17_k1_task1_organization_k1_k1_fold1

ASAP_FineTuningBERT_AugV17_k1_task1_organization_k1_k1_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 6, 2025
Updated: Mar 6, 2025
SHA: 761d9540

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Base models

Model card

From the Hugging Face model README

ASAP_FineTuningBERT_AugV17_k1_task1_organization_k1_k1_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: 0.7292
Qwk: 0.5569
Mse: 0.7284
Rmse: 0.8535

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	10.8211	0.0093	10.8186	3.2892
No log	2.0	4	9.6705	0.0	9.6682	3.1094
No log	3.0	6	8.3981	0.0	8.3956	2.8975
No log	4.0	8	7.0587	0.0	7.0563	2.6564
No log	5.0	10	5.3208	0.0416	5.3185	2.3062
No log	6.0	12	3.6484	0.0118	3.6464	1.9095
No log	7.0	14	2.8218	0.0	2.8202	1.6793
No log	8.0	16	1.9821	0.1088	1.9805	1.4073
No log	9.0	18	1.5371	0.0730	1.5355	1.2392
No log	10.0	20	1.1820	0.0443	1.1806	1.0865
No log	11.0	22	1.1687	0.1031	1.1673	1.0804
No log	12.0	24	0.9529	0.0888	0.9517	0.9755
No log	13.0	26	0.7743	0.3899	0.7731	0.8793
No log	14.0	28	0.7722	0.3509	0.7712	0.8782
No log	15.0	30	1.0052	0.2617	1.0042	1.0021
No log	16.0	32	1.4944	0.2490	1.4933	1.2220
No log	17.0	34	1.1447	0.2256	1.1435	1.0694
No log	18.0	36	0.8388	0.3802	0.8377	0.9153
No log	19.0	38	1.0181	0.2810	1.0170	1.0085
No log	20.0	40	1.0487	0.2636	1.0474	1.0234
No log	21.0	42	0.7900	0.4207	0.7887	0.8881
No log	22.0	44	0.7349	0.4491	0.7336	0.8565
No log	23.0	46	1.0520	0.3295	1.0504	1.0249
No log	24.0	48	0.9989	0.3726	0.9973	0.9986
No log	25.0	50	0.7439	0.4650	0.7425	0.8617
No log	26.0	52	0.7936	0.4137	0.7922	0.8900
No log	27.0	54	0.8887	0.4036	0.8871	0.9419
No log	28.0	56	0.7973	0.4472	0.7958	0.8921
No log	29.0	58	0.7324	0.4806	0.7310	0.8550
No log	30.0	60	0.7657	0.4877	0.7643	0.8742
No log	31.0	62	0.8324	0.4577	0.8310	0.9116
No log	32.0	64	0.7949	0.4941	0.7937	0.8909
No log	33.0	66	1.1225	0.3548	1.1210	1.0588
No log	34.0	68	1.0444	0.3973	1.0429	1.0212
No log	35.0	70	0.8563	0.5076	0.8551	0.9247
No log	36.0	72	1.0416	0.4209	1.0401	1.0199
No log	37.0	74	0.9480	0.4644	0.9466	0.9729
No log	38.0	76	0.8406	0.4636	0.8395	0.9162
No log	39.0	78	0.8328	0.4991	0.8315	0.9119
No log	40.0	80	0.8898	0.4771	0.8884	0.9426
No log	41.0	82	0.8086	0.5055	0.8075	0.8986
No log	42.0	84	0.7942	0.5206	0.7931	0.8906
No log	43.0	86	0.8868	0.4529	0.8854	0.9409
No log	44.0	88	0.8692	0.4386	0.8678	0.9316
No log	45.0	90	0.7457	0.4962	0.7447	0.8630
No log	46.0	92	0.7920	0.5016	0.7911	0.8894
No log	47.0	94	0.9657	0.4229	0.9645	0.9821
No log	48.0	96	0.9932	0.4164	0.9919	0.9959
No log	49.0	98	0.8780	0.4587	0.8771	0.9365
No log	50.0	100	0.9600	0.4398	0.9593	0.9794
No log	51.0	102	0.8512	0.4463	0.8501	0.9220
No log	52.0	104	0.8813	0.4393	0.8801	0.9381
No log	53.0	106	0.8053	0.5176	0.8043	0.8968
No log	54.0	108	0.8034	0.5339	0.8025	0.8958
No log	55.0	110	0.7705	0.5399	0.7695	0.8772
No log	56.0	112	0.8074	0.4939	0.8062	0.8979
No log	57.0	114	0.7758	0.5444	0.7747	0.8802
No log	58.0	116	0.7913	0.5245	0.7902	0.8889
No log	59.0	118	0.8811	0.4831	0.8798	0.9380
No log	60.0	120	0.8430	0.4839	0.8418	0.9175
No log	61.0	122	0.8126	0.4916	0.8116	0.9009
No log	62.0	124	0.8483	0.4738	0.8474	0.9205
No log	63.0	126	0.8312	0.4757	0.8302	0.9111
No log	64.0	128	0.8224	0.4876	0.8214	0.9063
No log	65.0	130	0.7897	0.4969	0.7887	0.8881
No log	66.0	132	0.7557	0.5183	0.7547	0.8687
No log	67.0	134	0.7436	0.5044	0.7425	0.8617
No log	68.0	136	0.7508	0.4938	0.7497	0.8659
No log	69.0	138	0.7249	0.5220	0.7239	0.8508
No log	70.0	140	0.7985	0.5461	0.7978	0.8932
No log	71.0	142	0.8430	0.5485	0.8424	0.9178
No log	72.0	144	0.7731	0.5662	0.7723	0.8788
No log	73.0	146	0.7819	0.5206	0.7809	0.8837
No log	74.0	148	0.7686	0.5352	0.7676	0.8762
No log	75.0	150	0.7346	0.5562	0.7338	0.8566
No log	76.0	152	0.7256	0.5632	0.7247	0.8513
No log	77.0	154	0.7238	0.5599	0.7229	0.8503
No log	78.0	156	0.7522	0.5611	0.7514	0.8668
No log	79.0	158	0.7173	0.5737	0.7164	0.8464
No log	80.0	160	0.7137	0.5178	0.7127	0.8442
No log	81.0	162	0.7105	0.5212	0.7096	0.8424
No log	82.0	164	0.7213	0.5694	0.7204	0.8488
No log	83.0	166	0.7496	0.5549	0.7488	0.8653
No log	84.0	168	0.7314	0.5526	0.7306	0.8547
No log	85.0	170	0.7200	0.5549	0.7192	0.8480
No log	86.0	172	0.7253	0.5569	0.7244	0.8511
No log	87.0	174	0.7253	0.5496	0.7244	0.8511
No log	88.0	176	0.7369	0.5633	0.7361	0.8579
No log	89.0	178	0.7548	0.5571	0.7540	0.8683
No log	90.0	180	0.7456	0.5540	0.7448	0.8630
No log	91.0	182	0.7319	0.5704	0.7310	0.8550
No log	92.0	184	0.7207	0.5657	0.7198	0.8484
No log	93.0	186	0.7160	0.5698	0.7151	0.8456
No log	94.0	188	0.7165	0.5700	0.7156	0.8459
No log	95.0	190	0.7256	0.5601	0.7247	0.8513
No log	96.0	192	0.7302	0.5537	0.7293	0.8540
No log	97.0	194	0.7292	0.5565	0.7283	0.8534
No log	98.0	196	0.7309	0.5560	0.7300	0.8544
No log	99.0	198	0.7292	0.5569	0.7284	0.8535

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 6, 2025