Version19ASAP_FineTuningBERT_AugV19_k15_task1_organization_k15_k15_fold0

genki10/Version19ASAP_FineTuningBERT_AugV19_k15_task1_organization_k15_k15_fold0

Version19ASAP_FineTuningBERT_AugV19_k15_task1_organization_k15_k15_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 9, 2025
Updated: Mar 9, 2025
SHA: d1a5602b

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Base models

Model card

From the Hugging Face model README

Version19ASAP_FineTuningBERT_AugV19_k15_task1_organization_k15_k15_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: 0.4992
Qwk: 0.6379
Mse: 0.4992
Rmse: 0.7065

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	3	8.1164	0.0	8.1164	2.8489
No log	2.0	6	6.7911	0.0	6.7911	2.6060
No log	3.0	9	5.4629	0.0115	5.4629	2.3373
No log	4.0	12	4.2330	0.0039	4.2330	2.0574
No log	5.0	15	3.0179	0.0	3.0179	1.7372
No log	6.0	18	2.0213	0.0511	2.0213	1.4217
No log	7.0	21	1.4026	0.0316	1.4026	1.1843
No log	8.0	24	0.9895	0.0212	0.9895	0.9947
No log	9.0	27	0.8074	0.1669	0.8074	0.8986
No log	10.0	30	0.7633	0.0968	0.7633	0.8737
No log	11.0	33	0.8627	0.0521	0.8627	0.9288
No log	12.0	36	1.0489	0.0521	1.0489	1.0242
No log	13.0	39	1.0357	0.3032	1.0357	1.0177
No log	14.0	42	1.5208	0.1330	1.5208	1.2332
No log	15.0	45	0.8064	0.4101	0.8064	0.8980
No log	16.0	48	1.1644	0.3893	1.1644	1.0791
No log	17.0	51	0.5504	0.5548	0.5504	0.7419
No log	18.0	54	0.7121	0.5855	0.7121	0.8439
No log	19.0	57	0.7293	0.5065	0.7293	0.8540
No log	20.0	60	0.5277	0.6545	0.5277	0.7264
No log	21.0	63	0.6577	0.5788	0.6577	0.8110
No log	22.0	66	0.4528	0.6511	0.4528	0.6729
No log	23.0	69	0.4280	0.6475	0.4280	0.6542
No log	24.0	72	0.7482	0.6029	0.7482	0.8650
No log	25.0	75	0.4439	0.6335	0.4439	0.6663
No log	26.0	78	0.7796	0.5967	0.7796	0.8830
No log	27.0	81	0.4598	0.6501	0.4598	0.6781
No log	28.0	84	0.6317	0.6265	0.6317	0.7948
No log	29.0	87	0.5528	0.6604	0.5528	0.7435
No log	30.0	90	0.4667	0.6491	0.4667	0.6832
No log	31.0	93	0.5166	0.6357	0.5166	0.7187
No log	32.0	96	0.5189	0.6265	0.5189	0.7204
No log	33.0	99	0.5355	0.6632	0.5355	0.7318
No log	34.0	102	0.4682	0.6525	0.4682	0.6843
No log	35.0	105	0.8180	0.5548	0.8180	0.9045
No log	36.0	108	0.4957	0.6108	0.4957	0.7040
No log	37.0	111	0.4899	0.6424	0.4899	0.6999
No log	38.0	114	0.6058	0.6223	0.6058	0.7783
No log	39.0	117	0.4413	0.6200	0.4413	0.6643
No log	40.0	120	0.5476	0.6510	0.5476	0.7400
No log	41.0	123	0.5235	0.6448	0.5235	0.7235
No log	42.0	126	0.4345	0.6267	0.4345	0.6592
No log	43.0	129	0.5780	0.6305	0.5780	0.7602
No log	44.0	132	0.4642	0.6571	0.4642	0.6813
No log	45.0	135	0.5316	0.6412	0.5316	0.7291
No log	46.0	138	0.5957	0.6232	0.5957	0.7718
No log	47.0	141	0.4680	0.6439	0.4680	0.6841
No log	48.0	144	0.6008	0.6307	0.6008	0.7751
No log	49.0	147	0.4771	0.6522	0.4771	0.6907
No log	50.0	150	0.5122	0.6431	0.5122	0.7157
No log	51.0	153	0.6812	0.5866	0.6812	0.8254
No log	52.0	156	0.4658	0.6450	0.4658	0.6825
No log	53.0	159	0.5009	0.6685	0.5009	0.7077
No log	54.0	162	0.5708	0.6336	0.5708	0.7555
No log	55.0	165	0.4406	0.6384	0.4406	0.6638
No log	56.0	168	0.5306	0.6433	0.5306	0.7284
No log	57.0	171	0.7476	0.5514	0.7476	0.8647
No log	58.0	174	0.5185	0.6438	0.5185	0.7201
No log	59.0	177	0.4951	0.6569	0.4951	0.7036
No log	60.0	180	0.5622	0.6515	0.5622	0.7498
No log	61.0	183	0.5003	0.6616	0.5003	0.7073
No log	62.0	186	0.5502	0.6299	0.5502	0.7417
No log	63.0	189	0.6296	0.6112	0.6296	0.7934
No log	64.0	192	0.5143	0.6609	0.5143	0.7171
No log	65.0	195	0.5315	0.6683	0.5315	0.7290
No log	66.0	198	0.6434	0.6060	0.6434	0.8021
No log	67.0	201	0.6076	0.6101	0.6076	0.7795
No log	68.0	204	0.5508	0.6446	0.5508	0.7422
No log	69.0	207	0.5353	0.6502	0.5353	0.7317
No log	70.0	210	0.4599	0.6657	0.4599	0.6782
No log	71.0	213	0.4941	0.6513	0.4941	0.7029
No log	72.0	216	0.5427	0.6445	0.5427	0.7367
No log	73.0	219	0.4992	0.6379	0.4992	0.7065

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 9, 2025
Updated: Mar 9, 2025