DREsS_New_FineTuningBERT_noAug_organization_fold0

MayBashendy/DREsS_New_FineTuningBERT_noAug_organization_fold0

DREsS_New_FineTuningBERT_noAug_organization_fold0 is a text classification model from MayBashendy. 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: Nov 3, 2024
Updated: Nov 3, 2024
SHA: c4faf338

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Base models

Model card

From the Hugging Face model README

DREsS_New_FineTuningBERT_noAug_organization_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: 0.5253
Qwk: 0.5229
Mse: 0.7020
Rmse: 0.8379

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: 32
eval_batch_size: 32
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 10

Training results

Training Loss	Epoch	Step	Validation Loss	Qwk	Mse	Rmse
No log	0.08	2	7.1228	0.0	6.8535	2.6179
No log	0.16	4	5.7691	0.0	6.8535	2.6179
No log	0.24	6	4.7582	0.0128	4.3232	2.0792
No log	0.32	8	3.7184	0.0	2.9343	1.7130
No log	0.4	10	2.8850	0.0	2.9343	1.7130
No log	0.48	12	2.2066	0.0	2.9343	1.7130
No log	0.56	14	1.6950	0.1108	1.2475	1.1169
No log	0.64	16	1.3221	0.0	1.0152	1.0075
No log	0.72	18	1.0213	0.0	1.0152	1.0075
No log	0.8	20	0.8472	0.0	1.0152	1.0075
No log	0.88	22	0.7850	0.0	1.0152	1.0075
No log	0.96	24	0.7322	0.0785	0.9444	0.9718
No log	1.04	26	0.7033	0.3068	0.7374	0.8587
No log	1.12	28	0.6734	0.2478	0.8081	0.8989
No log	1.2	30	0.6344	0.2859	0.7424	0.8616
No log	1.28	32	0.6063	0.2771	0.7778	0.8819
No log	1.3600	34	0.6820	0.2459	0.8182	0.9045
No log	1.44	36	0.5497	0.3495	0.7020	0.8379
No log	1.52	38	0.5406	0.4722	0.6313	0.7946
No log	1.6	40	0.5204	0.5372	0.5556	0.7454
No log	1.6800	42	0.5119	0.5319	0.5556	0.7454
No log	1.76	44	0.6096	0.3517	0.7172	0.8469
No log	1.8400	46	0.5802	0.4292	0.6515	0.8072
No log	1.92	48	0.5488	0.5362	0.6212	0.7882
No log	2.0	50	0.5550	0.5257	0.6263	0.7914
No log	2.08	52	0.5085	0.5545	0.5606	0.7487
No log	2.16	54	0.4904	0.5814	0.5101	0.7142
No log	2.24	56	0.5165	0.5308	0.6061	0.7785
No log	2.32	58	0.4739	0.5885	0.5	0.7071
No log	2.4	60	0.4835	0.5761	0.5202	0.7213
No log	2.48	62	0.5309	0.5148	0.6162	0.7850
No log	2.56	64	0.4710	0.5255	0.5505	0.7420
No log	2.64	66	0.5348	0.4330	0.6364	0.7977
No log	2.7200	68	0.4796	0.5017	0.5657	0.7521
No log	2.8	70	0.4743	0.5331	0.5505	0.7420
No log	2.88	72	0.4658	0.5275	0.5303	0.7282
No log	2.96	74	0.4627	0.5321	0.5253	0.7247
No log	3.04	76	0.4636	0.5334	0.5505	0.7420
No log	3.12	78	0.4533	0.5610	0.5051	0.7107
No log	3.2	80	0.5542	0.4235	0.6364	0.7977
No log	3.2800	82	0.5056	0.5156	0.5556	0.7454
No log	3.36	84	0.4606	0.6050	0.4848	0.6963
No log	3.44	86	0.4628	0.5990	0.5051	0.7107
No log	3.52	88	0.5460	0.5163	0.6162	0.7850
No log	3.6	90	0.7683	0.3226	0.8737	0.9347
No log	3.68	92	0.6491	0.3972	0.7677	0.8762
No log	3.76	94	0.4486	0.5865	0.5354	0.7317
No log	3.84	96	0.5108	0.5534	0.5758	0.7588
No log	3.92	98	0.4498	0.5999	0.5	0.7071
No log	4.0	100	0.5292	0.5000	0.5909	0.7687
No log	4.08	102	0.5414	0.4738	0.6061	0.7785
No log	4.16	104	0.4510	0.5291	0.5606	0.7487
No log	4.24	106	0.4483	0.5770	0.5253	0.7247
No log	4.32	108	0.4597	0.5373	0.5606	0.7487
No log	4.4	110	0.5673	0.4527	0.6364	0.7977
No log	4.48	112	0.5088	0.5281	0.5556	0.7454
No log	4.5600	114	0.4562	0.5516	0.5556	0.7454
No log	4.64	116	0.4528	0.5513	0.5556	0.7454
No log	4.72	118	0.4503	0.5580	0.5505	0.7420
No log	4.8	120	0.4653	0.5462	0.5758	0.7588
No log	4.88	122	0.4611	0.5437	0.5606	0.7487
No log	4.96	124	0.5554	0.4913	0.5909	0.7687
No log	5.04	126	0.6130	0.4287	0.6667	0.8165
No log	5.12	128	0.5080	0.5500	0.5253	0.7247
No log	5.2	130	0.4833	0.5472	0.5960	0.7720
No log	5.28	132	0.5145	0.5431	0.6212	0.7882
No log	5.36	134	0.4652	0.5603	0.5556	0.7454
No log	5.44	136	0.5268	0.5167	0.5606	0.7487
No log	5.52	138	0.5336	0.4909	0.5909	0.7687
No log	5.6	140	0.4775	0.5856	0.4899	0.6999
No log	5.68	142	0.5173	0.5288	0.6414	0.8009
No log	5.76	144	0.5711	0.5211	0.6717	0.8196
No log	5.84	146	0.4912	0.5490	0.6061	0.7785
No log	5.92	148	0.4728	0.5925	0.4899	0.6999
No log	6.0	150	0.4856	0.5688	0.5101	0.7142
No log	6.08	152	0.4628	0.5981	0.5051	0.7107
No log	6.16	154	0.4849	0.5714	0.5808	0.7621
No log	6.24	156	0.4693	0.5568	0.5758	0.7588
No log	6.32	158	0.4905	0.5995	0.4899	0.6999
No log	6.4	160	0.5298	0.5478	0.5556	0.7454
No log	6.48	162	0.4983	0.5867	0.5101	0.7142
No log	6.5600	164	0.4745	0.5881	0.5303	0.7282
No log	6.64	166	0.4762	0.5765	0.5455	0.7385
No log	6.72	168	0.4782	0.5897	0.5253	0.7247
No log	6.8	170	0.4752	0.5838	0.5404	0.7351
No log	6.88	172	0.4850	0.5612	0.5758	0.7588
No log	6.96	174	0.5342	0.5534	0.5859	0.7654
No log	7.04	176	0.5496	0.5235	0.6212	0.7882
No log	7.12	178	0.5053	0.5819	0.5556	0.7454
No log	7.2	180	0.4900	0.5946	0.5505	0.7420
No log	7.28	182	0.4964	0.5942	0.5556	0.7454
No log	7.36	184	0.5000	0.5653	0.5960	0.7720
No log	7.44	186	0.5141	0.5330	0.6465	0.8040
No log	7.52	188	0.5015	0.5608	0.6111	0.7817
No log	7.6	190	0.4869	0.5462	0.6414	0.8009
No log	7.68	192	0.4892	0.5258	0.6667	0.8165
No log	7.76	194	0.4983	0.5459	0.6515	0.8072
No log	7.84	196	0.5166	0.5475	0.6616	0.8134
No log	7.92	198	0.5111	0.5458	0.6667	0.8165
No log	8.0	200	0.5124	0.5568	0.6364	0.7977
No log	8.08	202	0.5320	0.5385	0.6414	0.8009
No log	8.16	204	0.5563	0.5337	0.6364	0.7977
No log	8.24	206	0.5353	0.5557	0.5909	0.7687
No log	8.32	208	0.5062	0.5514	0.6061	0.7785
No log	8.4	210	0.5013	0.5678	0.5960	0.7720
No log	8.48	212	0.5017	0.5678	0.5960	0.7720
No log	8.56	214	0.5078	0.5359	0.6465	0.8040
No log	8.64	216	0.5094	0.5395	0.6465	0.8040
No log	8.72	218	0.5064	0.5469	0.6263	0.7914
No log	8.8	220	0.5025	0.5481	0.6313	0.7946
No log	8.88	222	0.5034	0.5481	0.6313	0.7946
No log	8.96	224	0.5070	0.5574	0.6162	0.7850
No log	9.04	226	0.5148	0.5434	0.6515	0.8072
No log	9.12	228	0.5183	0.5332	0.6667	0.8165
No log	9.2	230	0.5144	0.5434	0.6515	0.8072
No log	9.28	232	0.5134	0.5360	0.6667	0.8165
No log	9.36	234	0.5095	0.5574	0.6263	0.7914
No log	9.44	236	0.5092	0.5497	0.6414	0.8009
No log	9.52	238	0.5128	0.5464	0.6515	0.8072
No log	9.6	240	0.5191	0.5188	0.7071	0.8409
No log	9.68	242	0.5235	0.5262	0.6970	0.8348
No log	9.76	244	0.5257	0.5229	0.7020	0.8379
No log	9.84	246	0.5258	0.5229	0.7020	0.8379
No log	9.92	248	0.5257	0.5229	0.7020	0.8379
No log	10.0	250	0.5253	0.5229	0.7020	0.8379

Framework versions

Transformers 4.44.2
Pytorch 2.4.0+cu118
Datasets 2.21.0
Tokenizers 0.19.1

Task: Text Classification
Library: transformers
Type: bert
License: apache-2.0
Created: Nov 3, 2024
Updated: Nov 3, 2024