DREsS_New_FineTuningBERT_noAug_language_fold1

MayBashendy/DREsS_New_FineTuningBERT_noAug_language_fold1

DREsS_New_FineTuningBERT_noAug_language_fold1 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: d232e720

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Base models

Model card

From the Hugging Face model README

DREsS_New_FineTuningBERT_noAug_language_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: 0.3764
Qwk: 0.4462
Mse: 0.5758
Rmse: 0.7588

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	8.2483	-0.0009	6.6566	2.5800
No log	0.16	4	5.6486	0.0	6.6212	2.5732
No log	0.24	6	4.2709	0.0094	4.7828	2.1870
No log	0.32	8	3.0709	0.0	2.7626	1.6621
No log	0.4	10	2.1932	0.0	2.7626	1.6621
No log	0.48	12	1.5896	0.0032	2.6414	1.6252
No log	0.56	14	1.1685	0.0184	0.9242	0.9614
No log	0.64	16	0.9146	0.0158	0.8990	0.9482
No log	0.72	18	0.6741	0.0	0.9040	0.9508
No log	0.8	20	0.6502	0.0	0.9040	0.9508
No log	0.88	22	0.5599	0.0867	0.8889	0.9428
No log	0.96	24	0.6138	0.0265	1.0101	1.0050
No log	1.04	26	0.5005	0.2232	0.7222	0.8498
No log	1.12	28	0.5258	0.0127	0.8939	0.9455
No log	1.2	30	0.5074	0.2852	0.7172	0.8469
No log	1.28	32	0.4850	0.2314	0.7273	0.8528
No log	1.3600	34	0.5309	0.0191	0.8889	0.9428
No log	1.44	36	0.4687	0.3981	0.5859	0.7654
No log	1.52	38	0.5156	0.2938	0.7121	0.8439
No log	1.6	40	0.4130	0.3366	0.6364	0.7977
No log	1.6800	42	0.4324	0.2885	0.6970	0.8348
No log	1.76	44	0.3752	0.4455	0.5505	0.7420
No log	1.8400	46	0.4516	0.4006	0.6010	0.7752
No log	1.92	48	0.3608	0.4651	0.5354	0.7317
No log	2.0	50	0.3671	0.3895	0.6010	0.7752
No log	2.08	52	0.3769	0.4383	0.5556	0.7454
No log	2.16	54	0.5742	0.2888	0.7222	0.8498
No log	2.24	56	0.4107	0.4115	0.5808	0.7621
No log	2.32	58	0.4366	0.2936	0.6869	0.8288
No log	2.4	60	0.4247	0.2889	0.6919	0.8318
No log	2.48	62	0.4044	0.4179	0.5758	0.7588
No log	2.56	64	0.4976	0.3429	0.6616	0.8134
No log	2.64	66	0.3489	0.4762	0.5152	0.7177
No log	2.7200	68	0.3686	0.3390	0.6465	0.8040
No log	2.8	70	0.3493	0.3848	0.6061	0.7785
No log	2.88	72	0.4477	0.4035	0.5960	0.7720
No log	2.96	74	0.5255	0.3429	0.6616	0.8134
No log	3.04	76	0.3362	0.4673	0.5354	0.7317
No log	3.12	78	0.4810	0.3833	0.7475	0.8646
No log	3.2	80	0.4501	0.4166	0.6869	0.8288
No log	3.2800	82	0.3298	0.5058	0.4899	0.6999
No log	3.36	84	0.5681	0.3475	0.6616	0.8134
No log	3.44	86	0.5562	0.3711	0.6364	0.7977
No log	3.52	88	0.3689	0.4429	0.5455	0.7385
No log	3.6	90	0.4067	0.3626	0.6667	0.8165
No log	3.68	92	0.4273	0.3566	0.6869	0.8288
No log	3.76	94	0.3448	0.4120	0.5758	0.7588
No log	3.84	96	0.3761	0.4485	0.5455	0.7385
No log	3.92	98	0.3475	0.4540	0.5354	0.7317
No log	4.0	100	0.3299	0.4517	0.5404	0.7351
No log	4.08	102	0.3185	0.4810	0.5101	0.7142
No log	4.16	104	0.3396	0.4532	0.5354	0.7317
No log	4.24	106	0.3780	0.4454	0.5505	0.7420
No log	4.32	108	0.3752	0.4454	0.5505	0.7420
No log	4.4	110	0.3852	0.4454	0.5505	0.7420
No log	4.48	112	0.3408	0.4565	0.5455	0.7385
No log	4.5600	114	0.3407	0.4565	0.5455	0.7385
No log	4.64	116	0.3564	0.4504	0.5556	0.7454
No log	4.72	118	0.4222	0.3994	0.6061	0.7785
No log	4.8	120	0.3605	0.4611	0.5455	0.7385
No log	4.88	122	0.3356	0.4922	0.5101	0.7142
No log	4.96	124	0.3356	0.4922	0.5101	0.7142
No log	5.04	126	0.3400	0.4836	0.5202	0.7213
No log	5.12	128	0.3906	0.4549	0.5505	0.7420
No log	5.2	130	0.3903	0.4595	0.5455	0.7385
No log	5.28	132	0.3418	0.4976	0.5152	0.7177
No log	5.36	134	0.3425	0.4994	0.5101	0.7142
No log	5.44	136	0.3515	0.4770	0.5404	0.7351
No log	5.52	138	0.3499	0.4822	0.5354	0.7317
No log	5.6	140	0.3567	0.4996	0.5152	0.7177
No log	5.68	142	0.3495	0.5038	0.5152	0.7177
No log	5.76	144	0.3404	0.5556	0.4646	0.6816
No log	5.84	146	0.3430	0.5552	0.4596	0.6779
No log	5.92	148	0.3592	0.5220	0.4949	0.7035
No log	6.0	150	0.3588	0.4975	0.5152	0.7177
No log	6.08	152	0.3651	0.4780	0.5303	0.7282
No log	6.16	154	0.3961	0.4542	0.5556	0.7454
No log	6.24	156	0.3858	0.4511	0.5556	0.7454
No log	6.32	158	0.3599	0.4404	0.5657	0.7521
No log	6.4	160	0.3559	0.4213	0.5960	0.7720
No log	6.48	162	0.3689	0.4428	0.5657	0.7521
No log	6.5600	164	0.4552	0.4136	0.5960	0.7720
No log	6.64	166	0.4900	0.4145	0.5960	0.7720
No log	6.72	168	0.4007	0.4535	0.5606	0.7487
No log	6.8	170	0.3523	0.4911	0.5303	0.7282
No log	6.88	172	0.3519	0.4920	0.5253	0.7247
No log	6.96	174	0.3860	0.4465	0.5606	0.7487
No log	7.04	176	0.3995	0.4534	0.5505	0.7420
No log	7.12	178	0.3773	0.4604	0.5505	0.7420
No log	7.2	180	0.3521	0.5073	0.5152	0.7177
No log	7.28	182	0.3520	0.5073	0.5152	0.7177
No log	7.36	184	0.3809	0.4315	0.5808	0.7621
No log	7.44	186	0.4365	0.4170	0.5960	0.7720
No log	7.52	188	0.4171	0.4060	0.6111	0.7817
No log	7.6	190	0.3709	0.4456	0.5808	0.7621
No log	7.68	192	0.3605	0.4889	0.5455	0.7385
No log	7.76	194	0.3705	0.4655	0.5707	0.7555
No log	7.84	196	0.4188	0.4153	0.5960	0.7720
No log	7.92	198	0.4371	0.4285	0.5859	0.7654
No log	8.0	200	0.3910	0.4626	0.5505	0.7420
No log	8.08	202	0.3490	0.5593	0.4747	0.6890
No log	8.16	204	0.3464	0.5374	0.5152	0.7177
No log	8.24	206	0.3448	0.5294	0.5152	0.7177
No log	8.32	208	0.3507	0.5463	0.4848	0.6963
No log	8.4	210	0.3966	0.4399	0.5758	0.7588
No log	8.48	212	0.4335	0.4091	0.6010	0.7752
No log	8.56	214	0.4352	0.4091	0.6010	0.7752
No log	8.64	216	0.3986	0.4356	0.5859	0.7654
No log	8.72	218	0.3580	0.4861	0.5455	0.7385
No log	8.8	220	0.3502	0.5256	0.5101	0.7142
No log	8.88	222	0.3527	0.5256	0.5101	0.7142
No log	8.96	224	0.3679	0.4552	0.5657	0.7521
No log	9.04	226	0.3930	0.4299	0.5960	0.7720
No log	9.12	228	0.4250	0.4029	0.6061	0.7785
No log	9.2	230	0.4253	0.4029	0.6061	0.7785
No log	9.28	232	0.4071	0.4461	0.5758	0.7588
No log	9.36	234	0.3842	0.4194	0.6061	0.7785
No log	9.44	236	0.3664	0.4634	0.5606	0.7487
No log	9.52	238	0.3606	0.5137	0.5253	0.7247
No log	9.6	240	0.3602	0.5087	0.5303	0.7282
No log	9.68	242	0.3638	0.5187	0.5202	0.7213
No log	9.76	244	0.3699	0.4537	0.5657	0.7521
No log	9.84	246	0.3735	0.4499	0.5707	0.7555
No log	9.92	248	0.3758	0.4604	0.5606	0.7487
No log	10.0	250	0.3764	0.4462	0.5758	0.7588

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